How to spot heritable breast cancer: A primary care physician’s guide

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How to spot heritable breast cancer: A primary care physician’s guide

PATIENT 1: A PERSONAL AND FAMILY HISTORY OF BREAST CANCER

A 55-year-old Ashkenazi Jewish woman presents to your clinic for her annual physical. She reports that she had been diagnosed with breast cancer 10 years ago and that it had been treated with lumpectomy. You recall that Ashkenazi Jewish ethnicity and a diagnosis of breast cancer before age 50 are red flags for a hereditary cancer syndrome, and you ask about her family history of cancer. She reports that her mother was diagnosed with breast cancer in her 60s. The patient wants to know if her daughter should start breast cancer screening.

What do you do next?

Facing increasing demands and a plethora of information to be discussed in a short time, primary care physicians may find it challenging to inform patients about the possibility of a hereditary cancer syndrome, to assess the risk, to organize genetic testing if appropriate, and to counsel patients about their management options. As our knowledge of the genetics of breast cancer continues to expand, this information will become more detailed and complex.

Nevertheless, primary care physicians can help identify patients who may have a syndrome of inherited cancer predisposition or whose family history raises concern for familial breast cancer. Patients in both groups may be candidates for genetic risk assessment, for special management options for women at high risk, or for both.

This article provides an overview of inherited conditions associated with higher breast cancer risk, and guidelines to help physicians recognize patients in their own practice for whom a genetics referral may be appropriate.

BREAST CANCER IS COMPLEX AND HETEROGENEOUS

Breast cancer is the second-leading cause of cancer deaths in women. According to the American Cancer Society, an estimated 234,340 new cases of breast cancer are expected to be diagnosed in women in the United States in 2013, and about 2,240 new cases are expected in men; 39,620 women and 410 men are expected to die of it.1

Breast cancer is a complex and heterogeneous disease, influenced by many factors, of which female sex and increasing age are the most significant. Modifiable risk factors include obesity, use of combined hormone replacement therapy, and physical inactivity. Other risk factors include dense breast tissue, having had a breast biopsy in the past, the finding of atypical hyperplasia on biopsy, a history of high-dose chest radiation, and reproductive factors that include early menarche, late menopause, nulliparity, and birth of first child after age 30.

After female sex and age, family history of the disease is the most significant risk factor for breast cancer.2 If a woman has a first-degree relative (mother, sister, daughter) with breast cancer, her risk is 1.8 times higher, and if she has a second-degree relative (aunt, grandmother) with breast cancer, her risk is 1.3 times higher.3

Hereditary cancer predisposition syndromes account for 5% to 10% of cases of breast cancer. These are caused by a germline mutation in a highly penetrant gene that considerably increases the risk of malignancies of the breast and other tissues. These conditions are inherited in an autosomal-dominant fashion, with age of onset tending to be significantly—several decades—younger than the median age of onset in the general population. The most common of these is hereditary breast and ovarian cancer syndrome, caused by germline mutations of the BRCA1 or BRCA2 gene.

Familial breast cancers account for 15% to 20% of cases. Here, the women who develop breast cancer have multiple family members who are also affected but without an obvious inheritance pattern, and the age of onset is similar to that in the general population.4

Sporadic forms of breast cancer account for the remaining 70% to 80% of cases. Their development can be attributed mainly to nonhereditary causes, such as the environmental and personal risk factors listed above. In general, sporadic forms of breast cancer occur at older ages, with no particular inheritance pattern and with frequency of occurrence in a family comparable to that in the general population.

IS A GENETICS CONSULTATION NEEDED?

In the case described above, the primary care physician gathered basic information about the patient’s cancer-related personal and family history. Asking a few key questions (Table  1)5,6 can help physicians understand two important things: whether a more detailed assessment of genetic risk and counseling by a genetics professional are indicated, and whether the patient would benefit from additional cancer screening and prevention.

Table 2 summarizes the National Comprehensive Cancer Network’s recommendations for cancer genetics consultation.5 These red flags for a hereditary breast cancer syndrome can help primary care providers identify patients for whom a cancer genetics referral is appropriate. Of note: the maternal and paternal family histories are equally important.

Because our patient was diagnosed with breast cancer before age 50 and is of Ashkenazi Jewish ethnicity, she meets these criteria and warrants a cancer genetics consultation.

 

 

What is a cancer-focused genetic counseling session?

The tenets of genetic counseling, described previously in this series,7 are relevant to hereditary cancer syndromes. Cancer risk assessment and genetic counseling constitute the process of identifying and counseling individuals at risk of familial or hereditary cancer.8

As in other genetic counseling scenarios, a detailed pedigree (family tree) is taken, and this information, along with the patient’s personal medical history, allows a genetics specialist to determine if the presentation is most suggestive of sporadic, familial, or hereditary cancer.

A common misconception among patients is that there is a single genetic test for hereditary breast cancer, when in fact many highly penetrant predisposition genes have been linked to heightened risk (see below). The syndromes summarized in Table 35,9–18 are part of the differential diagnosis for every patient presenting with a personal or family history of breast cancer, and the detailed information from the personal and family history, ascertained during the assessment, ensures the right syndrome is explored within a family.

Cancer-focused genetic counseling may also help a patient or family process the psychological and emotional responses that can occur when cancer risk is discussed: eg, fear of cancer and death; guilt a parent may feel for passing on a genetic predisposition; and survivor guilt experienced by family members who test negative.

Genetic counselors are trained to recognize patients who may benefit from additional counseling. Not all patients pursuing cancer-focused genetic testing need a thorough evaluation by a psychologist, unlike those with adult-onset neurodegenerative conditions such as Huntington disease. Rather, the genetic counselor discusses the psychological implications of cancer-focused genetic testing and can refer the patient to a psychologist, therapist, social worker, or others if he or she feels the patient may benefit.8

Some patients come to a genetic counseling session with concerns about whether their insurance will pay for testing, and about whether they will face discrimination because of the testing results. In most situations, genetic testing is deemed medically necessary and is covered by the patient’s insurance. When testing is necessary, genetic counselors are skilled at preauthorizing it and writing letters of medical necessity. They are also familiar with laws and regulations that protect patients, such as the Genetic Information Nondiscrimination Act, which protects patients from insurance and employment discrimination.

Because a cancer-focused genetic counseling session typically lasts 1 hour, the counselor has enough time to address these and any other concerns that might prevent a patient who is otherwise interested in genetic testing from pursuing it.

HOW CAN GENETIC TESTING HELP?

Genetic testing for hereditary cancer syndromes can have personal benefit for the patient and at-risk family members.

Note that the syndromes in Table 3 all increase the risk of more than one type of cancer. Patients with these syndromes frequently receive care from multiple subspecialists to mitigate those risks. Guidelines exist for each of these syndromes and, if followed, may prevent the morbidity and possibly death from the genotype-specific cancers that would otherwise be in the patient’s future. For patients found to have a hereditary cancer syndrome, medical management options include more-frequent cancer screening or surveillance, prophylactic surgery, and preventive medical treatment, which will be reviewed in a future article in this series.

Identifying the specific mutation in one family member allows at-risk relatives, both female and male, to then take advantage of predictive testing, with genetic counseling. If they test positive for the risk-increasing mutation, they too can take advantage of the management options for people at high risk. If they test negative, they can continue to undergo the same screening as recommended for the general population. Also, they may be relieved to know that their cancer risk is no greater than that in the general population.

The American Society of Clinical Oncology9 recommends genetic counseling and testing when all of the following are true:

  • There is a personal or family history suggesting genetic cancer susceptibility
  • The test can be adequately interpreted
  • The results will aid in the diagnosis or influence the medical or surgical management of the patient or family at hereditary risk of cancer.

Professional society guidelines also recommend that genetic testing be done only with genetic counseling before and after.5,6,8 The National Society of Genetic Counselors provides a list of clinical genetic counselors, organized by geographical area, at www.nsgc.org.

PATIENT 1 RECEIVES GENETIC TESTING AND COUNSELING

Let’s return to the Ashkenazi Jewish patient who has a personal and family history of breast cancer, whom you referred for cancer genetics consultation and who attends this appointment. A detailed personal and family history is gathered, and a brief physical examination is done, which reveals that the patient has macrocephaly and a history of multiple uterine fibroids.

The genetic differential diagnosis for your patient includes hereditary breast and ovarian cancer syndrome (resulting from mutations in the BRCA1 and BRCA2 genes) and Cowden syndrome (from mutations in the PTEN gene) (TABLE 3). The counselor uses BRCAPRO, a statistical risk-assessment tool that estimates a patient’s risk of harboring a BRCA1 or BRCA2 mutation based on ethnicity and personal and family history of cancer, and finds her risk to be 31%. In view of this risk, genetic testing for BRCA1 and BRCA2 is offered after a detailed discussion of the genetic differential diagnosis, the implications of a positive vs a negative test result, the possibility of finding gene changes (variants) of unknown significance, and the implications of the test results for family members.

Your patient elects to pursue BRCA1 and BRCA2 genetic testing and the results are negative—no mutations in either gene are found. PTEN testing is recommended next, which your patient elects to undergo. A mutation in the PTEN gene is found, indicating that she has Cowden syndrome. This result and its implications are discussed in a posttest genetic counseling session.

Cowden syndrome is an autosomal-dominant condition that carries a heightened risk of benign and malignant neoplasms, including a lifetime risk of breast cancer of up to 85%, with the average age at diagnosis in the 40s. Mutations in the PTEN gene also predispose to other cancer types, including nonmedullary thyroid, uterine, renal, and colorectal cancers, as well as melanoma.9 Multiple benign skin lesions and gastrointestinal polyposis are common.20

During the appointment, medical management options for patients with PTEN mutations are presented (Table 4).9 Given that your patient’s breast cancer was initially treated with lumpectomy, her remaining breast tissue is at risk of a second malignancy. She has never undergone thyroid imaging, colonoscopy, or kidney imaging. She reports that lately she has had occasional abnormal uterine bleeding and pain, which she believes are caused by her uterine fibroids. Given these symptoms and in light of her PTEN mutation, hysterectomy may be presented to her as an option. The genetics team sends a detailed clinical note directly to the primary care physician so they can coordinate and “quarterback” the patient’s care.

Like many patients, your patient is very concerned about how this information may affect her daughter. She first expresses some guilt at having to tell her daughter that she may have “given” her a risk of cancer. However, during the course of the genetic counseling session, she accepts that she could not have prevented her daughter from possibly inheriting this mutation, and understands that sharing this information will enable her daughter to pursue testing to help her understand her own risks.

When a known mutation exists in the family, as is the case with your patient, predictive testing only for that mutation gives a 100% accurate result. During a separate genetic counseling appointment, the patient’s daughter opts to proceed with testing and is found to be negative for her mother’s PTEN mutation.

 

 

 

 

WHAT HAPPENS WHEN GENETIC TESTING IS NOT INDICATED?

Cancer genetic risk assessment and counseling provides benefits even when genetic testing is not indicated. In some situations genetic testing is not warranted, but referral for heightened surveillance for breast cancer is deemed necessary. Patients who have a personal or family history of cancer can still gain from a detailed assessment of their personal and family history and may come away relieved after learning that they or their family members are not at high risk of developing cancer. Such patients or families may be classified as demonstrating either familial or sporadic breast cancer diagnoses.

Familial breast cancer

Familial breast cancers, believed to account for 15% to 20% of all cases of breast cancer, share features with hereditary breast cancer syndromes.4 In affected families, the frequency of breast cancer is higher than in the general population (multiple family members may be affected), and the age of onset tends to be close to that in the general population.

Members of a family with familial breast cancer who have not yet developed the disease may be at increased risk of it. Several risk-assessment tools (the Gail, Tyrer-Cuzick, Claus, and other models)21–25 use personal and family history to estimate breast cancer risk.

Depending on the assessed risk, additional options for screening and surveillance are available. The American Cancer Society recommends magnetic resonance imaging (MRI) in addition to annual mammography for women whose lifetime risk of breast cancer is greater than 20%. They also recommend that women at moderately increased risk (ie, 15%–20% lifetime risk) talk to their doctor about the benefits and limitations of adding MRI screening to yearly mammography.1

Sporadic breast cancer

Sporadic forms of breast cancer account for 70% to 80% of cases of breast cancer. Sporadic breast cancers are thought to have mainly nonhereditary causes, with environment and personal risk factors playing a large role.

Women with apparently sporadic breast cancers are diagnosed at or beyond the average age at diagnosis in the general population and do not have a family history that suggests either a hereditary cancer syndrome or familial breast cancer. If they undergo a cancer risk assessment, they may be relieved to learn that other women in their family do not have a high probability of being affected, and that they themselves do not appear to be at increased risk of other malignancies.

PATIENT 2: NEGATIVE TEST RESULTS ARE SOMETIMES ‘UNINFORMATIVE’

A healthy 35-year-old woman is referred for a genetics consultation by her gynecologist because her mother developed breast cancer at age 40 and died of the disease. A detailed personal and family history and risk assessment are done. After pretest genetic counseling, testing for BRCA1 and BRCA2 mutations (hereditary breast and ovarian cancer syndrome) is ordered, and the patient’s test results are negative. Risk assessment determines that no other hereditary cancer syndrome is likely. Therefore, no other genetic testing is offered at this time.

Genetic testing is most informative when performed first on the family member at highest risk of having a mutation. For families with breast cancer, this is typically the person with cancer diagnosed at the earliest age.

Unfortunately, sometimes these family members cannot be tested because they are deceased or otherwise unavailable. In such situations, it is acceptable to offer testing to a close, unaffected relative, such as your patient. Pretest genetic counseling in these circumstances is key, highlighting the fact that negative (normal) results would be uninformative. In your case, we cannot know whether the patient’s mother would have tested positive for a BRCA1 or BRCA2 mutation and your patient is a “true negative,” or whether her mother would have tested negative as well.

In unaffected patients with uninformative genetic testing results, medical management is based on the patient’s personal risk factors and family history of cancer. For your patient, statistical risk modeling tools (the Gail, Claus, Couch, and Tyrer-Cuzick models) determine that her risk of developing breast cancer is 22% to 28.5%, qualifying her for MRI along with yearly mammography per the American Cancer Society guidelines previously discussed.

KNOWLEDGE CONTINUES TO EXPAND

Major advances in the understanding of breast cancer susceptibility were made in the last decade through genetic linkage mapping in families that have an overabundance of members with breast cancer.26–28 Additionally, as more information is acquired, other genes predisposing to cancer or modifying cancer risk may be identified and additional knowledge gained.

With the advent of gene-panel-based testing and exome sequencing, we will incidentally discover mutations that predispose to cancer in patients in whom we were not looking for these mutations. With improving technology and value-based health care delivery, providers must continue to embrace multidisciplinary care, and genetics will become central in guiding medical management. In the event of an incidental finding suggesting susceptibility to heritable cancer, a consult to genetic counseling is recommended.

Many studies of the genetics of breast cancer are now focusing on known hereditary breast cancer syndromes and on possibilities for risk reduction, lifestyle modification, and identification of genetic variations that may increase or decrease cancer risk for an individual patient. The Center for Personalized Genetic Healthcare at Cleveland Clinic is collaborating in one such study. Titled “Risk Factor Analysis of Hereditary Breast and Ovarian Cancer Syndrome,” it is an international study led by a leading breast cancer researcher, Dr. Steven Narod from the Women’s College Research Institute in Toronto, ON. This study is focusing on women with a BRCA1 or BRCA2 mutation and their personal cancer risk factors, lifestyle choices, and overall development of cancer. This research group and others are also focusing on identifying genetic “modifiers” of cancer risk in these high-risk women.29

For patients who do not have a hereditary cancer syndrome, research is further exploring novel genes and their relation to breast cancer risk. One such study in our laboratory has found that several genes once thought only to cause an increased risk of hereditary paraganglioma may also predispose to breast and thyroid cancer.29,31 Additional research in this area is under way to clarify these risks.

GOOD SCIENCE, BAD MEDICINE?

Other research studies have identified a number of genes currently thought to be “moderately penetrant” for breast cancer risk, meaning that they may confer a risk of breast cancer slightly greater than that in the general population, but in some instances the risk has not been proven to be high enough to alter a patient’s management.32,33

Although a few clinical laboratories currently offer testing for these kinds of genes, the clinical utility of this testing is questionable. Before offering testing on a clinical basis, we need clear, consistent data on the types of cancers associated with these genes and on the lifetime percentage risk of acquiring these cancers. Currently, it is difficult to understand whether a variant in a moderately penetrant gene is the true explanation behind a patient’s breast cancer diagnosis. If such a variant is identified and family members pursue testing for it, should those family members who test negative be considered to have the same risk of cancer as the general population? And should family members testing positive be offered prophylactic surgical options?

Without more data these questions cannot be answered, and until such data are gathered, we believe that testing for moderately penetrant genes should not be performed outside of a research study. The Center for Personalized Genetic Healthcare in Cleveland Clinic’s Genomic Medicine Institute can assist in educating and coordinating patients’ enrollment in such research studies.

PUTTING IT ALL TOGETHER

Primary care physicians are the first-line providers to individuals and families, many of whom have a personal or family history of breast cancer. Identifying patients at risk of breast cancer and hereditary cancer syndromes can be challenging in this era of shortened appointment times and patients with complex medical histories.

Reviewing an individual’s personal and cancer family history is a necessary first step in considering appropriate medical management recommendations for cancer screening and prevention, the cornerstone of personalized health care. Patients with hereditary breast cancer syndromes and those with familial breast cancer can benefit from high-risk breast cancer surveillance.

Cancer genetics risk assessment ensures that the correct genetic testing is offered to the most appropriate patients, with personalized interpretation of results and provision of future management recommendations based on the individual patient’s personal and family history. Genetic counselors empower patients to make educated and informed decisions about genetic testing, cancer screening, and prevention.

As health care continues to focus more on prevention in this new era of genomic medicine and value-based delivery of health care, genetic counselors will serve as powerful allies to physicians.34


Acknowledgments: We would like to thank Dr. Colleen Clayton and Dr. Lynn Pattimakiel of the Medicine Institute, Cleveland Clinic, for their critical review of and thoughtful feedback on this manuscript.

References
  1. American Cancer Society. Breast cancer: detailed guide( 2013). http://www.cancer.org/Cancer/BreastCancer/DetailedGuide/index. Accessed November 12, 2013.
  2. McTiernan A, Gilligan MA, Redmond C. Assessing individual risk for breast cancer: risky business. J Clin Epidemiol 1997; 50:547556.
  3. Teerlink CC, Albright FS, Lins L, Cannon-Albright LA. A comprehensive survey of cancer risks in extended families. Genet Med 2012; 14:107114.
  4. National Comprehensive Cancer Network (NCCN). NCCN clinical practice guidelines in oncology. Breast cancer risk reduction (version 1.2013). http://www.nccn.org. Accessed November 21, 2013.
  5. National Comprehensive Cancer Network (NCCN). NCCN clinical practice guidelines in oncology. Genetic/familial high risk assessment: breast and ovarian (version 4.2013). http://www.nccn.org. Accessed November 21, 2013.
  6. National Comprehensive Cancer Network (NCCN). NCCN clinical practice guidelines in oncology. Breast cancer screening and diagnosis (version 2.2013). http://www.nccn.org. Accessed November 21, 2013.
  7. Mester JL, Schreiber AH, Moran RT. Genetic counselors: your partners in clinical practice. Cleve Clin J Med 2012; 79:560568.
  8. Trepanier A, Ahrens M, McKinnon W, et al; National Society of Genetic Counselors. Genetic cancer risk assessment and counseling: recommendations of the National Society of Genetic Counselors. J Genet Couns 2004; 13:83114.
  9. Tan MH, Mester JL, Ngeow J, Rybicki LA, Orloff MS, Eng C. Lifetime cancer risks in individuals with germline PTEN mutations. Clin Cancer Res 2012; 18:400407.
  10. Ford D, Easton DF, Stratton M, et al. Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. The Breast Cancer Linkage Consortium. Am J Hum Genet 1998; 62:676689.
  11. Liede A, Karlan BY, Narod SA. Cancer risks for male carriers of germline mutations in BRCA1 or BRCA2: a review of the literature. J Clin Oncol 2004; 22:735742.
  12. Struewing JP, Hartge P, Wacholder S, et al. The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi Jews. N Engl J Med 1997; 336:14011408.
  13. Birch JM, Hartley AL, Tricker KJ, et al. Prevalence and diversity of constitutional mutations in the p53 gene among 21 Li-Fraumeni families. Cancer Res 1994; 54:12981304.
  14. Chompret A, Brugières L, Ronsin M, et al. P53 germline mutations in childhood cancers and cancer risk for carrier individuals. Br J Cancer 2000; 82:19321937.
  15. Gonzalez KD, Noltner KA, Buzin CH, et al. Beyond Li Fraumeni syndrome: clinical characteristics of families with p53 germline mutations. J Clin Oncol 2009; 27:12501256.
  16. Varley JM. Germline TP53 mutations and Li-Fraumeni syndrome. Hum Mutat 2003; 21:313320.
  17. Fitzgerald RC, Hardwick R, Huntsman D, et al; International Gastric Cancer Linkage Consortium. Hereditary diffuse gastric cancer: updated consensus guidelines for clinical management and directions for future research. J Med Genet 2010; 47:436444.
  18. Hearle N, Schumacher V, Menko FH, et al. Frequency and spectrum of cancers in the Peutz-Jeghers syndrome. Clin Cancer Res 2006; 12:32093215.
  19. American Society of Clinical Oncology. American Society of Clinical Oncology policy statement update: genetic testing for cancer susceptibility. J Clin Oncol 2003; 21:23972406.
  20. Mester J, Eng C. When overgrowth bumps into cancer: the PTEN-opathies. Am J Med Genet C Semin Med Genet 2013; 163:114121.
  21. Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer 1994; 73:643651.
  22. Couch FJ, DeShano ML, Blackwood MA, et al. BRCA1 mutations in women attending clinics that evaluate the risk of breast cancer. N Engl J Med 1997; 336:14091415.
  23. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004; 23:11111130.
  24. Gail MH, Anderson WF, Garcia-Closas M, Sherman ME. Absolute risk models for subtypes of breast cancer. J Natl Cancer Inst 2007; 99:16571659.
  25. Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989; 81:18791886.
  26. Kent P, O’Donoghue JM, O’Hanlon DM, Kerin MJ, Maher DJ, Given HF. Linkage analysis and the susceptibility gene (BRCA-1) in familial breast cancer. Eur J Surg Oncol 1995; 21:240241.
  27. Easton DF, Bishop DT, Ford D, Crockford GP. Genetic linkage analysis in familial breast and ovarian cancer: results from 214 families. The Breast Cancer Linkage Consortium. Am J Hum Genet 1993; 52:678701.
  28. Ormiston W. Hereditary breast cancer. Eur J Cancer Care (Engl) 1996; 5:1320.
  29. Couch FJ, Wang X, McGuffog L, et al. Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk. PLoS Genet 2013; 9:e1003212.
  30. Bennett KL, Mester J, Eng C. Germline epigenetic regulation of KILLIN in Cowden and Cowden-like syndrome. JAMA 2010; 304:27242731.
  31. Ni Y, He X, Chen J, et al. Germline SDHx variants modify breast and thyroid cancer risks in Cowden and Cowden-like syndrome via FAD/NAD-dependent destabilization of p53. Hum Mol Genet 2012; 21:300310.
  32. Casadei S, Norquist BM, Walsh T, et al. Contribution of inherited mutations in the BRCA2-interacting protein PALB2 to familial breast cancer. Cancer Res 2011; 71:22222229.
  33. Walsh T, Lee MK, Casadei S, et al. Detection of inherited mutations for breast and ovarian cancer using genomic capture and massively parallel sequencing. Proc Natl Acad Sci U S A 2010; 107:1262912633.
  34. Eng C. Molecular genetics to genomic medicine: at the heart of value-based delivery of healthcare. Mol Genet Genom Med 2013; 1:46.
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Marissa Smith, MS, CGC
Genetic Counselor, Center for Personalized Genetic Healthcare, Genomic Medicine Institute, and Taussig Cancer Institute, Cleveland Clinic

Jessica Mester, MS, CGC
Genetic Counselor, Center for Personalized Genetic Healthcare, Genomic Medicine Institute, and Taussig Cancer Institute, Cleveland Clinic

Charis Eng, MD, PhD
Hardis and ACS Professor and Chair, Genomic Medicine Institute, Director, Center for Personalized Genetic Healthcare, and Medical Director, Cancer Genetics Clinical Service, Cleveland Clinic; Professor and Vice Chair, Department of Genetics and Genome Sciences, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH

Address: Marissa Smith, MS, Genomic Medicine Institute, NE50, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

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Genetic Counselor, Center for Personalized Genetic Healthcare, Genomic Medicine Institute, and Taussig Cancer Institute, Cleveland Clinic

Jessica Mester, MS, CGC
Genetic Counselor, Center for Personalized Genetic Healthcare, Genomic Medicine Institute, and Taussig Cancer Institute, Cleveland Clinic

Charis Eng, MD, PhD
Hardis and ACS Professor and Chair, Genomic Medicine Institute, Director, Center for Personalized Genetic Healthcare, and Medical Director, Cancer Genetics Clinical Service, Cleveland Clinic; Professor and Vice Chair, Department of Genetics and Genome Sciences, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH

Address: Marissa Smith, MS, Genomic Medicine Institute, NE50, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

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Jessica Mester, MS, CGC
Genetic Counselor, Center for Personalized Genetic Healthcare, Genomic Medicine Institute, and Taussig Cancer Institute, Cleveland Clinic

Charis Eng, MD, PhD
Hardis and ACS Professor and Chair, Genomic Medicine Institute, Director, Center for Personalized Genetic Healthcare, and Medical Director, Cancer Genetics Clinical Service, Cleveland Clinic; Professor and Vice Chair, Department of Genetics and Genome Sciences, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH

Address: Marissa Smith, MS, Genomic Medicine Institute, NE50, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

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PATIENT 1: A PERSONAL AND FAMILY HISTORY OF BREAST CANCER

A 55-year-old Ashkenazi Jewish woman presents to your clinic for her annual physical. She reports that she had been diagnosed with breast cancer 10 years ago and that it had been treated with lumpectomy. You recall that Ashkenazi Jewish ethnicity and a diagnosis of breast cancer before age 50 are red flags for a hereditary cancer syndrome, and you ask about her family history of cancer. She reports that her mother was diagnosed with breast cancer in her 60s. The patient wants to know if her daughter should start breast cancer screening.

What do you do next?

Facing increasing demands and a plethora of information to be discussed in a short time, primary care physicians may find it challenging to inform patients about the possibility of a hereditary cancer syndrome, to assess the risk, to organize genetic testing if appropriate, and to counsel patients about their management options. As our knowledge of the genetics of breast cancer continues to expand, this information will become more detailed and complex.

Nevertheless, primary care physicians can help identify patients who may have a syndrome of inherited cancer predisposition or whose family history raises concern for familial breast cancer. Patients in both groups may be candidates for genetic risk assessment, for special management options for women at high risk, or for both.

This article provides an overview of inherited conditions associated with higher breast cancer risk, and guidelines to help physicians recognize patients in their own practice for whom a genetics referral may be appropriate.

BREAST CANCER IS COMPLEX AND HETEROGENEOUS

Breast cancer is the second-leading cause of cancer deaths in women. According to the American Cancer Society, an estimated 234,340 new cases of breast cancer are expected to be diagnosed in women in the United States in 2013, and about 2,240 new cases are expected in men; 39,620 women and 410 men are expected to die of it.1

Breast cancer is a complex and heterogeneous disease, influenced by many factors, of which female sex and increasing age are the most significant. Modifiable risk factors include obesity, use of combined hormone replacement therapy, and physical inactivity. Other risk factors include dense breast tissue, having had a breast biopsy in the past, the finding of atypical hyperplasia on biopsy, a history of high-dose chest radiation, and reproductive factors that include early menarche, late menopause, nulliparity, and birth of first child after age 30.

After female sex and age, family history of the disease is the most significant risk factor for breast cancer.2 If a woman has a first-degree relative (mother, sister, daughter) with breast cancer, her risk is 1.8 times higher, and if she has a second-degree relative (aunt, grandmother) with breast cancer, her risk is 1.3 times higher.3

Hereditary cancer predisposition syndromes account for 5% to 10% of cases of breast cancer. These are caused by a germline mutation in a highly penetrant gene that considerably increases the risk of malignancies of the breast and other tissues. These conditions are inherited in an autosomal-dominant fashion, with age of onset tending to be significantly—several decades—younger than the median age of onset in the general population. The most common of these is hereditary breast and ovarian cancer syndrome, caused by germline mutations of the BRCA1 or BRCA2 gene.

Familial breast cancers account for 15% to 20% of cases. Here, the women who develop breast cancer have multiple family members who are also affected but without an obvious inheritance pattern, and the age of onset is similar to that in the general population.4

Sporadic forms of breast cancer account for the remaining 70% to 80% of cases. Their development can be attributed mainly to nonhereditary causes, such as the environmental and personal risk factors listed above. In general, sporadic forms of breast cancer occur at older ages, with no particular inheritance pattern and with frequency of occurrence in a family comparable to that in the general population.

IS A GENETICS CONSULTATION NEEDED?

In the case described above, the primary care physician gathered basic information about the patient’s cancer-related personal and family history. Asking a few key questions (Table  1)5,6 can help physicians understand two important things: whether a more detailed assessment of genetic risk and counseling by a genetics professional are indicated, and whether the patient would benefit from additional cancer screening and prevention.

Table 2 summarizes the National Comprehensive Cancer Network’s recommendations for cancer genetics consultation.5 These red flags for a hereditary breast cancer syndrome can help primary care providers identify patients for whom a cancer genetics referral is appropriate. Of note: the maternal and paternal family histories are equally important.

Because our patient was diagnosed with breast cancer before age 50 and is of Ashkenazi Jewish ethnicity, she meets these criteria and warrants a cancer genetics consultation.

 

 

What is a cancer-focused genetic counseling session?

The tenets of genetic counseling, described previously in this series,7 are relevant to hereditary cancer syndromes. Cancer risk assessment and genetic counseling constitute the process of identifying and counseling individuals at risk of familial or hereditary cancer.8

As in other genetic counseling scenarios, a detailed pedigree (family tree) is taken, and this information, along with the patient’s personal medical history, allows a genetics specialist to determine if the presentation is most suggestive of sporadic, familial, or hereditary cancer.

A common misconception among patients is that there is a single genetic test for hereditary breast cancer, when in fact many highly penetrant predisposition genes have been linked to heightened risk (see below). The syndromes summarized in Table 35,9–18 are part of the differential diagnosis for every patient presenting with a personal or family history of breast cancer, and the detailed information from the personal and family history, ascertained during the assessment, ensures the right syndrome is explored within a family.

Cancer-focused genetic counseling may also help a patient or family process the psychological and emotional responses that can occur when cancer risk is discussed: eg, fear of cancer and death; guilt a parent may feel for passing on a genetic predisposition; and survivor guilt experienced by family members who test negative.

Genetic counselors are trained to recognize patients who may benefit from additional counseling. Not all patients pursuing cancer-focused genetic testing need a thorough evaluation by a psychologist, unlike those with adult-onset neurodegenerative conditions such as Huntington disease. Rather, the genetic counselor discusses the psychological implications of cancer-focused genetic testing and can refer the patient to a psychologist, therapist, social worker, or others if he or she feels the patient may benefit.8

Some patients come to a genetic counseling session with concerns about whether their insurance will pay for testing, and about whether they will face discrimination because of the testing results. In most situations, genetic testing is deemed medically necessary and is covered by the patient’s insurance. When testing is necessary, genetic counselors are skilled at preauthorizing it and writing letters of medical necessity. They are also familiar with laws and regulations that protect patients, such as the Genetic Information Nondiscrimination Act, which protects patients from insurance and employment discrimination.

Because a cancer-focused genetic counseling session typically lasts 1 hour, the counselor has enough time to address these and any other concerns that might prevent a patient who is otherwise interested in genetic testing from pursuing it.

HOW CAN GENETIC TESTING HELP?

Genetic testing for hereditary cancer syndromes can have personal benefit for the patient and at-risk family members.

Note that the syndromes in Table 3 all increase the risk of more than one type of cancer. Patients with these syndromes frequently receive care from multiple subspecialists to mitigate those risks. Guidelines exist for each of these syndromes and, if followed, may prevent the morbidity and possibly death from the genotype-specific cancers that would otherwise be in the patient’s future. For patients found to have a hereditary cancer syndrome, medical management options include more-frequent cancer screening or surveillance, prophylactic surgery, and preventive medical treatment, which will be reviewed in a future article in this series.

Identifying the specific mutation in one family member allows at-risk relatives, both female and male, to then take advantage of predictive testing, with genetic counseling. If they test positive for the risk-increasing mutation, they too can take advantage of the management options for people at high risk. If they test negative, they can continue to undergo the same screening as recommended for the general population. Also, they may be relieved to know that their cancer risk is no greater than that in the general population.

The American Society of Clinical Oncology9 recommends genetic counseling and testing when all of the following are true:

  • There is a personal or family history suggesting genetic cancer susceptibility
  • The test can be adequately interpreted
  • The results will aid in the diagnosis or influence the medical or surgical management of the patient or family at hereditary risk of cancer.

Professional society guidelines also recommend that genetic testing be done only with genetic counseling before and after.5,6,8 The National Society of Genetic Counselors provides a list of clinical genetic counselors, organized by geographical area, at www.nsgc.org.

PATIENT 1 RECEIVES GENETIC TESTING AND COUNSELING

Let’s return to the Ashkenazi Jewish patient who has a personal and family history of breast cancer, whom you referred for cancer genetics consultation and who attends this appointment. A detailed personal and family history is gathered, and a brief physical examination is done, which reveals that the patient has macrocephaly and a history of multiple uterine fibroids.

The genetic differential diagnosis for your patient includes hereditary breast and ovarian cancer syndrome (resulting from mutations in the BRCA1 and BRCA2 genes) and Cowden syndrome (from mutations in the PTEN gene) (TABLE 3). The counselor uses BRCAPRO, a statistical risk-assessment tool that estimates a patient’s risk of harboring a BRCA1 or BRCA2 mutation based on ethnicity and personal and family history of cancer, and finds her risk to be 31%. In view of this risk, genetic testing for BRCA1 and BRCA2 is offered after a detailed discussion of the genetic differential diagnosis, the implications of a positive vs a negative test result, the possibility of finding gene changes (variants) of unknown significance, and the implications of the test results for family members.

Your patient elects to pursue BRCA1 and BRCA2 genetic testing and the results are negative—no mutations in either gene are found. PTEN testing is recommended next, which your patient elects to undergo. A mutation in the PTEN gene is found, indicating that she has Cowden syndrome. This result and its implications are discussed in a posttest genetic counseling session.

Cowden syndrome is an autosomal-dominant condition that carries a heightened risk of benign and malignant neoplasms, including a lifetime risk of breast cancer of up to 85%, with the average age at diagnosis in the 40s. Mutations in the PTEN gene also predispose to other cancer types, including nonmedullary thyroid, uterine, renal, and colorectal cancers, as well as melanoma.9 Multiple benign skin lesions and gastrointestinal polyposis are common.20

During the appointment, medical management options for patients with PTEN mutations are presented (Table 4).9 Given that your patient’s breast cancer was initially treated with lumpectomy, her remaining breast tissue is at risk of a second malignancy. She has never undergone thyroid imaging, colonoscopy, or kidney imaging. She reports that lately she has had occasional abnormal uterine bleeding and pain, which she believes are caused by her uterine fibroids. Given these symptoms and in light of her PTEN mutation, hysterectomy may be presented to her as an option. The genetics team sends a detailed clinical note directly to the primary care physician so they can coordinate and “quarterback” the patient’s care.

Like many patients, your patient is very concerned about how this information may affect her daughter. She first expresses some guilt at having to tell her daughter that she may have “given” her a risk of cancer. However, during the course of the genetic counseling session, she accepts that she could not have prevented her daughter from possibly inheriting this mutation, and understands that sharing this information will enable her daughter to pursue testing to help her understand her own risks.

When a known mutation exists in the family, as is the case with your patient, predictive testing only for that mutation gives a 100% accurate result. During a separate genetic counseling appointment, the patient’s daughter opts to proceed with testing and is found to be negative for her mother’s PTEN mutation.

 

 

 

 

WHAT HAPPENS WHEN GENETIC TESTING IS NOT INDICATED?

Cancer genetic risk assessment and counseling provides benefits even when genetic testing is not indicated. In some situations genetic testing is not warranted, but referral for heightened surveillance for breast cancer is deemed necessary. Patients who have a personal or family history of cancer can still gain from a detailed assessment of their personal and family history and may come away relieved after learning that they or their family members are not at high risk of developing cancer. Such patients or families may be classified as demonstrating either familial or sporadic breast cancer diagnoses.

Familial breast cancer

Familial breast cancers, believed to account for 15% to 20% of all cases of breast cancer, share features with hereditary breast cancer syndromes.4 In affected families, the frequency of breast cancer is higher than in the general population (multiple family members may be affected), and the age of onset tends to be close to that in the general population.

Members of a family with familial breast cancer who have not yet developed the disease may be at increased risk of it. Several risk-assessment tools (the Gail, Tyrer-Cuzick, Claus, and other models)21–25 use personal and family history to estimate breast cancer risk.

Depending on the assessed risk, additional options for screening and surveillance are available. The American Cancer Society recommends magnetic resonance imaging (MRI) in addition to annual mammography for women whose lifetime risk of breast cancer is greater than 20%. They also recommend that women at moderately increased risk (ie, 15%–20% lifetime risk) talk to their doctor about the benefits and limitations of adding MRI screening to yearly mammography.1

Sporadic breast cancer

Sporadic forms of breast cancer account for 70% to 80% of cases of breast cancer. Sporadic breast cancers are thought to have mainly nonhereditary causes, with environment and personal risk factors playing a large role.

Women with apparently sporadic breast cancers are diagnosed at or beyond the average age at diagnosis in the general population and do not have a family history that suggests either a hereditary cancer syndrome or familial breast cancer. If they undergo a cancer risk assessment, they may be relieved to learn that other women in their family do not have a high probability of being affected, and that they themselves do not appear to be at increased risk of other malignancies.

PATIENT 2: NEGATIVE TEST RESULTS ARE SOMETIMES ‘UNINFORMATIVE’

A healthy 35-year-old woman is referred for a genetics consultation by her gynecologist because her mother developed breast cancer at age 40 and died of the disease. A detailed personal and family history and risk assessment are done. After pretest genetic counseling, testing for BRCA1 and BRCA2 mutations (hereditary breast and ovarian cancer syndrome) is ordered, and the patient’s test results are negative. Risk assessment determines that no other hereditary cancer syndrome is likely. Therefore, no other genetic testing is offered at this time.

Genetic testing is most informative when performed first on the family member at highest risk of having a mutation. For families with breast cancer, this is typically the person with cancer diagnosed at the earliest age.

Unfortunately, sometimes these family members cannot be tested because they are deceased or otherwise unavailable. In such situations, it is acceptable to offer testing to a close, unaffected relative, such as your patient. Pretest genetic counseling in these circumstances is key, highlighting the fact that negative (normal) results would be uninformative. In your case, we cannot know whether the patient’s mother would have tested positive for a BRCA1 or BRCA2 mutation and your patient is a “true negative,” or whether her mother would have tested negative as well.

In unaffected patients with uninformative genetic testing results, medical management is based on the patient’s personal risk factors and family history of cancer. For your patient, statistical risk modeling tools (the Gail, Claus, Couch, and Tyrer-Cuzick models) determine that her risk of developing breast cancer is 22% to 28.5%, qualifying her for MRI along with yearly mammography per the American Cancer Society guidelines previously discussed.

KNOWLEDGE CONTINUES TO EXPAND

Major advances in the understanding of breast cancer susceptibility were made in the last decade through genetic linkage mapping in families that have an overabundance of members with breast cancer.26–28 Additionally, as more information is acquired, other genes predisposing to cancer or modifying cancer risk may be identified and additional knowledge gained.

With the advent of gene-panel-based testing and exome sequencing, we will incidentally discover mutations that predispose to cancer in patients in whom we were not looking for these mutations. With improving technology and value-based health care delivery, providers must continue to embrace multidisciplinary care, and genetics will become central in guiding medical management. In the event of an incidental finding suggesting susceptibility to heritable cancer, a consult to genetic counseling is recommended.

Many studies of the genetics of breast cancer are now focusing on known hereditary breast cancer syndromes and on possibilities for risk reduction, lifestyle modification, and identification of genetic variations that may increase or decrease cancer risk for an individual patient. The Center for Personalized Genetic Healthcare at Cleveland Clinic is collaborating in one such study. Titled “Risk Factor Analysis of Hereditary Breast and Ovarian Cancer Syndrome,” it is an international study led by a leading breast cancer researcher, Dr. Steven Narod from the Women’s College Research Institute in Toronto, ON. This study is focusing on women with a BRCA1 or BRCA2 mutation and their personal cancer risk factors, lifestyle choices, and overall development of cancer. This research group and others are also focusing on identifying genetic “modifiers” of cancer risk in these high-risk women.29

For patients who do not have a hereditary cancer syndrome, research is further exploring novel genes and their relation to breast cancer risk. One such study in our laboratory has found that several genes once thought only to cause an increased risk of hereditary paraganglioma may also predispose to breast and thyroid cancer.29,31 Additional research in this area is under way to clarify these risks.

GOOD SCIENCE, BAD MEDICINE?

Other research studies have identified a number of genes currently thought to be “moderately penetrant” for breast cancer risk, meaning that they may confer a risk of breast cancer slightly greater than that in the general population, but in some instances the risk has not been proven to be high enough to alter a patient’s management.32,33

Although a few clinical laboratories currently offer testing for these kinds of genes, the clinical utility of this testing is questionable. Before offering testing on a clinical basis, we need clear, consistent data on the types of cancers associated with these genes and on the lifetime percentage risk of acquiring these cancers. Currently, it is difficult to understand whether a variant in a moderately penetrant gene is the true explanation behind a patient’s breast cancer diagnosis. If such a variant is identified and family members pursue testing for it, should those family members who test negative be considered to have the same risk of cancer as the general population? And should family members testing positive be offered prophylactic surgical options?

Without more data these questions cannot be answered, and until such data are gathered, we believe that testing for moderately penetrant genes should not be performed outside of a research study. The Center for Personalized Genetic Healthcare in Cleveland Clinic’s Genomic Medicine Institute can assist in educating and coordinating patients’ enrollment in such research studies.

PUTTING IT ALL TOGETHER

Primary care physicians are the first-line providers to individuals and families, many of whom have a personal or family history of breast cancer. Identifying patients at risk of breast cancer and hereditary cancer syndromes can be challenging in this era of shortened appointment times and patients with complex medical histories.

Reviewing an individual’s personal and cancer family history is a necessary first step in considering appropriate medical management recommendations for cancer screening and prevention, the cornerstone of personalized health care. Patients with hereditary breast cancer syndromes and those with familial breast cancer can benefit from high-risk breast cancer surveillance.

Cancer genetics risk assessment ensures that the correct genetic testing is offered to the most appropriate patients, with personalized interpretation of results and provision of future management recommendations based on the individual patient’s personal and family history. Genetic counselors empower patients to make educated and informed decisions about genetic testing, cancer screening, and prevention.

As health care continues to focus more on prevention in this new era of genomic medicine and value-based delivery of health care, genetic counselors will serve as powerful allies to physicians.34


Acknowledgments: We would like to thank Dr. Colleen Clayton and Dr. Lynn Pattimakiel of the Medicine Institute, Cleveland Clinic, for their critical review of and thoughtful feedback on this manuscript.

PATIENT 1: A PERSONAL AND FAMILY HISTORY OF BREAST CANCER

A 55-year-old Ashkenazi Jewish woman presents to your clinic for her annual physical. She reports that she had been diagnosed with breast cancer 10 years ago and that it had been treated with lumpectomy. You recall that Ashkenazi Jewish ethnicity and a diagnosis of breast cancer before age 50 are red flags for a hereditary cancer syndrome, and you ask about her family history of cancer. She reports that her mother was diagnosed with breast cancer in her 60s. The patient wants to know if her daughter should start breast cancer screening.

What do you do next?

Facing increasing demands and a plethora of information to be discussed in a short time, primary care physicians may find it challenging to inform patients about the possibility of a hereditary cancer syndrome, to assess the risk, to organize genetic testing if appropriate, and to counsel patients about their management options. As our knowledge of the genetics of breast cancer continues to expand, this information will become more detailed and complex.

Nevertheless, primary care physicians can help identify patients who may have a syndrome of inherited cancer predisposition or whose family history raises concern for familial breast cancer. Patients in both groups may be candidates for genetic risk assessment, for special management options for women at high risk, or for both.

This article provides an overview of inherited conditions associated with higher breast cancer risk, and guidelines to help physicians recognize patients in their own practice for whom a genetics referral may be appropriate.

BREAST CANCER IS COMPLEX AND HETEROGENEOUS

Breast cancer is the second-leading cause of cancer deaths in women. According to the American Cancer Society, an estimated 234,340 new cases of breast cancer are expected to be diagnosed in women in the United States in 2013, and about 2,240 new cases are expected in men; 39,620 women and 410 men are expected to die of it.1

Breast cancer is a complex and heterogeneous disease, influenced by many factors, of which female sex and increasing age are the most significant. Modifiable risk factors include obesity, use of combined hormone replacement therapy, and physical inactivity. Other risk factors include dense breast tissue, having had a breast biopsy in the past, the finding of atypical hyperplasia on biopsy, a history of high-dose chest radiation, and reproductive factors that include early menarche, late menopause, nulliparity, and birth of first child after age 30.

After female sex and age, family history of the disease is the most significant risk factor for breast cancer.2 If a woman has a first-degree relative (mother, sister, daughter) with breast cancer, her risk is 1.8 times higher, and if she has a second-degree relative (aunt, grandmother) with breast cancer, her risk is 1.3 times higher.3

Hereditary cancer predisposition syndromes account for 5% to 10% of cases of breast cancer. These are caused by a germline mutation in a highly penetrant gene that considerably increases the risk of malignancies of the breast and other tissues. These conditions are inherited in an autosomal-dominant fashion, with age of onset tending to be significantly—several decades—younger than the median age of onset in the general population. The most common of these is hereditary breast and ovarian cancer syndrome, caused by germline mutations of the BRCA1 or BRCA2 gene.

Familial breast cancers account for 15% to 20% of cases. Here, the women who develop breast cancer have multiple family members who are also affected but without an obvious inheritance pattern, and the age of onset is similar to that in the general population.4

Sporadic forms of breast cancer account for the remaining 70% to 80% of cases. Their development can be attributed mainly to nonhereditary causes, such as the environmental and personal risk factors listed above. In general, sporadic forms of breast cancer occur at older ages, with no particular inheritance pattern and with frequency of occurrence in a family comparable to that in the general population.

IS A GENETICS CONSULTATION NEEDED?

In the case described above, the primary care physician gathered basic information about the patient’s cancer-related personal and family history. Asking a few key questions (Table  1)5,6 can help physicians understand two important things: whether a more detailed assessment of genetic risk and counseling by a genetics professional are indicated, and whether the patient would benefit from additional cancer screening and prevention.

Table 2 summarizes the National Comprehensive Cancer Network’s recommendations for cancer genetics consultation.5 These red flags for a hereditary breast cancer syndrome can help primary care providers identify patients for whom a cancer genetics referral is appropriate. Of note: the maternal and paternal family histories are equally important.

Because our patient was diagnosed with breast cancer before age 50 and is of Ashkenazi Jewish ethnicity, she meets these criteria and warrants a cancer genetics consultation.

 

 

What is a cancer-focused genetic counseling session?

The tenets of genetic counseling, described previously in this series,7 are relevant to hereditary cancer syndromes. Cancer risk assessment and genetic counseling constitute the process of identifying and counseling individuals at risk of familial or hereditary cancer.8

As in other genetic counseling scenarios, a detailed pedigree (family tree) is taken, and this information, along with the patient’s personal medical history, allows a genetics specialist to determine if the presentation is most suggestive of sporadic, familial, or hereditary cancer.

A common misconception among patients is that there is a single genetic test for hereditary breast cancer, when in fact many highly penetrant predisposition genes have been linked to heightened risk (see below). The syndromes summarized in Table 35,9–18 are part of the differential diagnosis for every patient presenting with a personal or family history of breast cancer, and the detailed information from the personal and family history, ascertained during the assessment, ensures the right syndrome is explored within a family.

Cancer-focused genetic counseling may also help a patient or family process the psychological and emotional responses that can occur when cancer risk is discussed: eg, fear of cancer and death; guilt a parent may feel for passing on a genetic predisposition; and survivor guilt experienced by family members who test negative.

Genetic counselors are trained to recognize patients who may benefit from additional counseling. Not all patients pursuing cancer-focused genetic testing need a thorough evaluation by a psychologist, unlike those with adult-onset neurodegenerative conditions such as Huntington disease. Rather, the genetic counselor discusses the psychological implications of cancer-focused genetic testing and can refer the patient to a psychologist, therapist, social worker, or others if he or she feels the patient may benefit.8

Some patients come to a genetic counseling session with concerns about whether their insurance will pay for testing, and about whether they will face discrimination because of the testing results. In most situations, genetic testing is deemed medically necessary and is covered by the patient’s insurance. When testing is necessary, genetic counselors are skilled at preauthorizing it and writing letters of medical necessity. They are also familiar with laws and regulations that protect patients, such as the Genetic Information Nondiscrimination Act, which protects patients from insurance and employment discrimination.

Because a cancer-focused genetic counseling session typically lasts 1 hour, the counselor has enough time to address these and any other concerns that might prevent a patient who is otherwise interested in genetic testing from pursuing it.

HOW CAN GENETIC TESTING HELP?

Genetic testing for hereditary cancer syndromes can have personal benefit for the patient and at-risk family members.

Note that the syndromes in Table 3 all increase the risk of more than one type of cancer. Patients with these syndromes frequently receive care from multiple subspecialists to mitigate those risks. Guidelines exist for each of these syndromes and, if followed, may prevent the morbidity and possibly death from the genotype-specific cancers that would otherwise be in the patient’s future. For patients found to have a hereditary cancer syndrome, medical management options include more-frequent cancer screening or surveillance, prophylactic surgery, and preventive medical treatment, which will be reviewed in a future article in this series.

Identifying the specific mutation in one family member allows at-risk relatives, both female and male, to then take advantage of predictive testing, with genetic counseling. If they test positive for the risk-increasing mutation, they too can take advantage of the management options for people at high risk. If they test negative, they can continue to undergo the same screening as recommended for the general population. Also, they may be relieved to know that their cancer risk is no greater than that in the general population.

The American Society of Clinical Oncology9 recommends genetic counseling and testing when all of the following are true:

  • There is a personal or family history suggesting genetic cancer susceptibility
  • The test can be adequately interpreted
  • The results will aid in the diagnosis or influence the medical or surgical management of the patient or family at hereditary risk of cancer.

Professional society guidelines also recommend that genetic testing be done only with genetic counseling before and after.5,6,8 The National Society of Genetic Counselors provides a list of clinical genetic counselors, organized by geographical area, at www.nsgc.org.

PATIENT 1 RECEIVES GENETIC TESTING AND COUNSELING

Let’s return to the Ashkenazi Jewish patient who has a personal and family history of breast cancer, whom you referred for cancer genetics consultation and who attends this appointment. A detailed personal and family history is gathered, and a brief physical examination is done, which reveals that the patient has macrocephaly and a history of multiple uterine fibroids.

The genetic differential diagnosis for your patient includes hereditary breast and ovarian cancer syndrome (resulting from mutations in the BRCA1 and BRCA2 genes) and Cowden syndrome (from mutations in the PTEN gene) (TABLE 3). The counselor uses BRCAPRO, a statistical risk-assessment tool that estimates a patient’s risk of harboring a BRCA1 or BRCA2 mutation based on ethnicity and personal and family history of cancer, and finds her risk to be 31%. In view of this risk, genetic testing for BRCA1 and BRCA2 is offered after a detailed discussion of the genetic differential diagnosis, the implications of a positive vs a negative test result, the possibility of finding gene changes (variants) of unknown significance, and the implications of the test results for family members.

Your patient elects to pursue BRCA1 and BRCA2 genetic testing and the results are negative—no mutations in either gene are found. PTEN testing is recommended next, which your patient elects to undergo. A mutation in the PTEN gene is found, indicating that she has Cowden syndrome. This result and its implications are discussed in a posttest genetic counseling session.

Cowden syndrome is an autosomal-dominant condition that carries a heightened risk of benign and malignant neoplasms, including a lifetime risk of breast cancer of up to 85%, with the average age at diagnosis in the 40s. Mutations in the PTEN gene also predispose to other cancer types, including nonmedullary thyroid, uterine, renal, and colorectal cancers, as well as melanoma.9 Multiple benign skin lesions and gastrointestinal polyposis are common.20

During the appointment, medical management options for patients with PTEN mutations are presented (Table 4).9 Given that your patient’s breast cancer was initially treated with lumpectomy, her remaining breast tissue is at risk of a second malignancy. She has never undergone thyroid imaging, colonoscopy, or kidney imaging. She reports that lately she has had occasional abnormal uterine bleeding and pain, which she believes are caused by her uterine fibroids. Given these symptoms and in light of her PTEN mutation, hysterectomy may be presented to her as an option. The genetics team sends a detailed clinical note directly to the primary care physician so they can coordinate and “quarterback” the patient’s care.

Like many patients, your patient is very concerned about how this information may affect her daughter. She first expresses some guilt at having to tell her daughter that she may have “given” her a risk of cancer. However, during the course of the genetic counseling session, she accepts that she could not have prevented her daughter from possibly inheriting this mutation, and understands that sharing this information will enable her daughter to pursue testing to help her understand her own risks.

When a known mutation exists in the family, as is the case with your patient, predictive testing only for that mutation gives a 100% accurate result. During a separate genetic counseling appointment, the patient’s daughter opts to proceed with testing and is found to be negative for her mother’s PTEN mutation.

 

 

 

 

WHAT HAPPENS WHEN GENETIC TESTING IS NOT INDICATED?

Cancer genetic risk assessment and counseling provides benefits even when genetic testing is not indicated. In some situations genetic testing is not warranted, but referral for heightened surveillance for breast cancer is deemed necessary. Patients who have a personal or family history of cancer can still gain from a detailed assessment of their personal and family history and may come away relieved after learning that they or their family members are not at high risk of developing cancer. Such patients or families may be classified as demonstrating either familial or sporadic breast cancer diagnoses.

Familial breast cancer

Familial breast cancers, believed to account for 15% to 20% of all cases of breast cancer, share features with hereditary breast cancer syndromes.4 In affected families, the frequency of breast cancer is higher than in the general population (multiple family members may be affected), and the age of onset tends to be close to that in the general population.

Members of a family with familial breast cancer who have not yet developed the disease may be at increased risk of it. Several risk-assessment tools (the Gail, Tyrer-Cuzick, Claus, and other models)21–25 use personal and family history to estimate breast cancer risk.

Depending on the assessed risk, additional options for screening and surveillance are available. The American Cancer Society recommends magnetic resonance imaging (MRI) in addition to annual mammography for women whose lifetime risk of breast cancer is greater than 20%. They also recommend that women at moderately increased risk (ie, 15%–20% lifetime risk) talk to their doctor about the benefits and limitations of adding MRI screening to yearly mammography.1

Sporadic breast cancer

Sporadic forms of breast cancer account for 70% to 80% of cases of breast cancer. Sporadic breast cancers are thought to have mainly nonhereditary causes, with environment and personal risk factors playing a large role.

Women with apparently sporadic breast cancers are diagnosed at or beyond the average age at diagnosis in the general population and do not have a family history that suggests either a hereditary cancer syndrome or familial breast cancer. If they undergo a cancer risk assessment, they may be relieved to learn that other women in their family do not have a high probability of being affected, and that they themselves do not appear to be at increased risk of other malignancies.

PATIENT 2: NEGATIVE TEST RESULTS ARE SOMETIMES ‘UNINFORMATIVE’

A healthy 35-year-old woman is referred for a genetics consultation by her gynecologist because her mother developed breast cancer at age 40 and died of the disease. A detailed personal and family history and risk assessment are done. After pretest genetic counseling, testing for BRCA1 and BRCA2 mutations (hereditary breast and ovarian cancer syndrome) is ordered, and the patient’s test results are negative. Risk assessment determines that no other hereditary cancer syndrome is likely. Therefore, no other genetic testing is offered at this time.

Genetic testing is most informative when performed first on the family member at highest risk of having a mutation. For families with breast cancer, this is typically the person with cancer diagnosed at the earliest age.

Unfortunately, sometimes these family members cannot be tested because they are deceased or otherwise unavailable. In such situations, it is acceptable to offer testing to a close, unaffected relative, such as your patient. Pretest genetic counseling in these circumstances is key, highlighting the fact that negative (normal) results would be uninformative. In your case, we cannot know whether the patient’s mother would have tested positive for a BRCA1 or BRCA2 mutation and your patient is a “true negative,” or whether her mother would have tested negative as well.

In unaffected patients with uninformative genetic testing results, medical management is based on the patient’s personal risk factors and family history of cancer. For your patient, statistical risk modeling tools (the Gail, Claus, Couch, and Tyrer-Cuzick models) determine that her risk of developing breast cancer is 22% to 28.5%, qualifying her for MRI along with yearly mammography per the American Cancer Society guidelines previously discussed.

KNOWLEDGE CONTINUES TO EXPAND

Major advances in the understanding of breast cancer susceptibility were made in the last decade through genetic linkage mapping in families that have an overabundance of members with breast cancer.26–28 Additionally, as more information is acquired, other genes predisposing to cancer or modifying cancer risk may be identified and additional knowledge gained.

With the advent of gene-panel-based testing and exome sequencing, we will incidentally discover mutations that predispose to cancer in patients in whom we were not looking for these mutations. With improving technology and value-based health care delivery, providers must continue to embrace multidisciplinary care, and genetics will become central in guiding medical management. In the event of an incidental finding suggesting susceptibility to heritable cancer, a consult to genetic counseling is recommended.

Many studies of the genetics of breast cancer are now focusing on known hereditary breast cancer syndromes and on possibilities for risk reduction, lifestyle modification, and identification of genetic variations that may increase or decrease cancer risk for an individual patient. The Center for Personalized Genetic Healthcare at Cleveland Clinic is collaborating in one such study. Titled “Risk Factor Analysis of Hereditary Breast and Ovarian Cancer Syndrome,” it is an international study led by a leading breast cancer researcher, Dr. Steven Narod from the Women’s College Research Institute in Toronto, ON. This study is focusing on women with a BRCA1 or BRCA2 mutation and their personal cancer risk factors, lifestyle choices, and overall development of cancer. This research group and others are also focusing on identifying genetic “modifiers” of cancer risk in these high-risk women.29

For patients who do not have a hereditary cancer syndrome, research is further exploring novel genes and their relation to breast cancer risk. One such study in our laboratory has found that several genes once thought only to cause an increased risk of hereditary paraganglioma may also predispose to breast and thyroid cancer.29,31 Additional research in this area is under way to clarify these risks.

GOOD SCIENCE, BAD MEDICINE?

Other research studies have identified a number of genes currently thought to be “moderately penetrant” for breast cancer risk, meaning that they may confer a risk of breast cancer slightly greater than that in the general population, but in some instances the risk has not been proven to be high enough to alter a patient’s management.32,33

Although a few clinical laboratories currently offer testing for these kinds of genes, the clinical utility of this testing is questionable. Before offering testing on a clinical basis, we need clear, consistent data on the types of cancers associated with these genes and on the lifetime percentage risk of acquiring these cancers. Currently, it is difficult to understand whether a variant in a moderately penetrant gene is the true explanation behind a patient’s breast cancer diagnosis. If such a variant is identified and family members pursue testing for it, should those family members who test negative be considered to have the same risk of cancer as the general population? And should family members testing positive be offered prophylactic surgical options?

Without more data these questions cannot be answered, and until such data are gathered, we believe that testing for moderately penetrant genes should not be performed outside of a research study. The Center for Personalized Genetic Healthcare in Cleveland Clinic’s Genomic Medicine Institute can assist in educating and coordinating patients’ enrollment in such research studies.

PUTTING IT ALL TOGETHER

Primary care physicians are the first-line providers to individuals and families, many of whom have a personal or family history of breast cancer. Identifying patients at risk of breast cancer and hereditary cancer syndromes can be challenging in this era of shortened appointment times and patients with complex medical histories.

Reviewing an individual’s personal and cancer family history is a necessary first step in considering appropriate medical management recommendations for cancer screening and prevention, the cornerstone of personalized health care. Patients with hereditary breast cancer syndromes and those with familial breast cancer can benefit from high-risk breast cancer surveillance.

Cancer genetics risk assessment ensures that the correct genetic testing is offered to the most appropriate patients, with personalized interpretation of results and provision of future management recommendations based on the individual patient’s personal and family history. Genetic counselors empower patients to make educated and informed decisions about genetic testing, cancer screening, and prevention.

As health care continues to focus more on prevention in this new era of genomic medicine and value-based delivery of health care, genetic counselors will serve as powerful allies to physicians.34


Acknowledgments: We would like to thank Dr. Colleen Clayton and Dr. Lynn Pattimakiel of the Medicine Institute, Cleveland Clinic, for their critical review of and thoughtful feedback on this manuscript.

References
  1. American Cancer Society. Breast cancer: detailed guide( 2013). http://www.cancer.org/Cancer/BreastCancer/DetailedGuide/index. Accessed November 12, 2013.
  2. McTiernan A, Gilligan MA, Redmond C. Assessing individual risk for breast cancer: risky business. J Clin Epidemiol 1997; 50:547556.
  3. Teerlink CC, Albright FS, Lins L, Cannon-Albright LA. A comprehensive survey of cancer risks in extended families. Genet Med 2012; 14:107114.
  4. National Comprehensive Cancer Network (NCCN). NCCN clinical practice guidelines in oncology. Breast cancer risk reduction (version 1.2013). http://www.nccn.org. Accessed November 21, 2013.
  5. National Comprehensive Cancer Network (NCCN). NCCN clinical practice guidelines in oncology. Genetic/familial high risk assessment: breast and ovarian (version 4.2013). http://www.nccn.org. Accessed November 21, 2013.
  6. National Comprehensive Cancer Network (NCCN). NCCN clinical practice guidelines in oncology. Breast cancer screening and diagnosis (version 2.2013). http://www.nccn.org. Accessed November 21, 2013.
  7. Mester JL, Schreiber AH, Moran RT. Genetic counselors: your partners in clinical practice. Cleve Clin J Med 2012; 79:560568.
  8. Trepanier A, Ahrens M, McKinnon W, et al; National Society of Genetic Counselors. Genetic cancer risk assessment and counseling: recommendations of the National Society of Genetic Counselors. J Genet Couns 2004; 13:83114.
  9. Tan MH, Mester JL, Ngeow J, Rybicki LA, Orloff MS, Eng C. Lifetime cancer risks in individuals with germline PTEN mutations. Clin Cancer Res 2012; 18:400407.
  10. Ford D, Easton DF, Stratton M, et al. Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. The Breast Cancer Linkage Consortium. Am J Hum Genet 1998; 62:676689.
  11. Liede A, Karlan BY, Narod SA. Cancer risks for male carriers of germline mutations in BRCA1 or BRCA2: a review of the literature. J Clin Oncol 2004; 22:735742.
  12. Struewing JP, Hartge P, Wacholder S, et al. The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi Jews. N Engl J Med 1997; 336:14011408.
  13. Birch JM, Hartley AL, Tricker KJ, et al. Prevalence and diversity of constitutional mutations in the p53 gene among 21 Li-Fraumeni families. Cancer Res 1994; 54:12981304.
  14. Chompret A, Brugières L, Ronsin M, et al. P53 germline mutations in childhood cancers and cancer risk for carrier individuals. Br J Cancer 2000; 82:19321937.
  15. Gonzalez KD, Noltner KA, Buzin CH, et al. Beyond Li Fraumeni syndrome: clinical characteristics of families with p53 germline mutations. J Clin Oncol 2009; 27:12501256.
  16. Varley JM. Germline TP53 mutations and Li-Fraumeni syndrome. Hum Mutat 2003; 21:313320.
  17. Fitzgerald RC, Hardwick R, Huntsman D, et al; International Gastric Cancer Linkage Consortium. Hereditary diffuse gastric cancer: updated consensus guidelines for clinical management and directions for future research. J Med Genet 2010; 47:436444.
  18. Hearle N, Schumacher V, Menko FH, et al. Frequency and spectrum of cancers in the Peutz-Jeghers syndrome. Clin Cancer Res 2006; 12:32093215.
  19. American Society of Clinical Oncology. American Society of Clinical Oncology policy statement update: genetic testing for cancer susceptibility. J Clin Oncol 2003; 21:23972406.
  20. Mester J, Eng C. When overgrowth bumps into cancer: the PTEN-opathies. Am J Med Genet C Semin Med Genet 2013; 163:114121.
  21. Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer 1994; 73:643651.
  22. Couch FJ, DeShano ML, Blackwood MA, et al. BRCA1 mutations in women attending clinics that evaluate the risk of breast cancer. N Engl J Med 1997; 336:14091415.
  23. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004; 23:11111130.
  24. Gail MH, Anderson WF, Garcia-Closas M, Sherman ME. Absolute risk models for subtypes of breast cancer. J Natl Cancer Inst 2007; 99:16571659.
  25. Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989; 81:18791886.
  26. Kent P, O’Donoghue JM, O’Hanlon DM, Kerin MJ, Maher DJ, Given HF. Linkage analysis and the susceptibility gene (BRCA-1) in familial breast cancer. Eur J Surg Oncol 1995; 21:240241.
  27. Easton DF, Bishop DT, Ford D, Crockford GP. Genetic linkage analysis in familial breast and ovarian cancer: results from 214 families. The Breast Cancer Linkage Consortium. Am J Hum Genet 1993; 52:678701.
  28. Ormiston W. Hereditary breast cancer. Eur J Cancer Care (Engl) 1996; 5:1320.
  29. Couch FJ, Wang X, McGuffog L, et al. Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk. PLoS Genet 2013; 9:e1003212.
  30. Bennett KL, Mester J, Eng C. Germline epigenetic regulation of KILLIN in Cowden and Cowden-like syndrome. JAMA 2010; 304:27242731.
  31. Ni Y, He X, Chen J, et al. Germline SDHx variants modify breast and thyroid cancer risks in Cowden and Cowden-like syndrome via FAD/NAD-dependent destabilization of p53. Hum Mol Genet 2012; 21:300310.
  32. Casadei S, Norquist BM, Walsh T, et al. Contribution of inherited mutations in the BRCA2-interacting protein PALB2 to familial breast cancer. Cancer Res 2011; 71:22222229.
  33. Walsh T, Lee MK, Casadei S, et al. Detection of inherited mutations for breast and ovarian cancer using genomic capture and massively parallel sequencing. Proc Natl Acad Sci U S A 2010; 107:1262912633.
  34. Eng C. Molecular genetics to genomic medicine: at the heart of value-based delivery of healthcare. Mol Genet Genom Med 2013; 1:46.
References
  1. American Cancer Society. Breast cancer: detailed guide( 2013). http://www.cancer.org/Cancer/BreastCancer/DetailedGuide/index. Accessed November 12, 2013.
  2. McTiernan A, Gilligan MA, Redmond C. Assessing individual risk for breast cancer: risky business. J Clin Epidemiol 1997; 50:547556.
  3. Teerlink CC, Albright FS, Lins L, Cannon-Albright LA. A comprehensive survey of cancer risks in extended families. Genet Med 2012; 14:107114.
  4. National Comprehensive Cancer Network (NCCN). NCCN clinical practice guidelines in oncology. Breast cancer risk reduction (version 1.2013). http://www.nccn.org. Accessed November 21, 2013.
  5. National Comprehensive Cancer Network (NCCN). NCCN clinical practice guidelines in oncology. Genetic/familial high risk assessment: breast and ovarian (version 4.2013). http://www.nccn.org. Accessed November 21, 2013.
  6. National Comprehensive Cancer Network (NCCN). NCCN clinical practice guidelines in oncology. Breast cancer screening and diagnosis (version 2.2013). http://www.nccn.org. Accessed November 21, 2013.
  7. Mester JL, Schreiber AH, Moran RT. Genetic counselors: your partners in clinical practice. Cleve Clin J Med 2012; 79:560568.
  8. Trepanier A, Ahrens M, McKinnon W, et al; National Society of Genetic Counselors. Genetic cancer risk assessment and counseling: recommendations of the National Society of Genetic Counselors. J Genet Couns 2004; 13:83114.
  9. Tan MH, Mester JL, Ngeow J, Rybicki LA, Orloff MS, Eng C. Lifetime cancer risks in individuals with germline PTEN mutations. Clin Cancer Res 2012; 18:400407.
  10. Ford D, Easton DF, Stratton M, et al. Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. The Breast Cancer Linkage Consortium. Am J Hum Genet 1998; 62:676689.
  11. Liede A, Karlan BY, Narod SA. Cancer risks for male carriers of germline mutations in BRCA1 or BRCA2: a review of the literature. J Clin Oncol 2004; 22:735742.
  12. Struewing JP, Hartge P, Wacholder S, et al. The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi Jews. N Engl J Med 1997; 336:14011408.
  13. Birch JM, Hartley AL, Tricker KJ, et al. Prevalence and diversity of constitutional mutations in the p53 gene among 21 Li-Fraumeni families. Cancer Res 1994; 54:12981304.
  14. Chompret A, Brugières L, Ronsin M, et al. P53 germline mutations in childhood cancers and cancer risk for carrier individuals. Br J Cancer 2000; 82:19321937.
  15. Gonzalez KD, Noltner KA, Buzin CH, et al. Beyond Li Fraumeni syndrome: clinical characteristics of families with p53 germline mutations. J Clin Oncol 2009; 27:12501256.
  16. Varley JM. Germline TP53 mutations and Li-Fraumeni syndrome. Hum Mutat 2003; 21:313320.
  17. Fitzgerald RC, Hardwick R, Huntsman D, et al; International Gastric Cancer Linkage Consortium. Hereditary diffuse gastric cancer: updated consensus guidelines for clinical management and directions for future research. J Med Genet 2010; 47:436444.
  18. Hearle N, Schumacher V, Menko FH, et al. Frequency and spectrum of cancers in the Peutz-Jeghers syndrome. Clin Cancer Res 2006; 12:32093215.
  19. American Society of Clinical Oncology. American Society of Clinical Oncology policy statement update: genetic testing for cancer susceptibility. J Clin Oncol 2003; 21:23972406.
  20. Mester J, Eng C. When overgrowth bumps into cancer: the PTEN-opathies. Am J Med Genet C Semin Med Genet 2013; 163:114121.
  21. Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer 1994; 73:643651.
  22. Couch FJ, DeShano ML, Blackwood MA, et al. BRCA1 mutations in women attending clinics that evaluate the risk of breast cancer. N Engl J Med 1997; 336:14091415.
  23. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004; 23:11111130.
  24. Gail MH, Anderson WF, Garcia-Closas M, Sherman ME. Absolute risk models for subtypes of breast cancer. J Natl Cancer Inst 2007; 99:16571659.
  25. Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989; 81:18791886.
  26. Kent P, O’Donoghue JM, O’Hanlon DM, Kerin MJ, Maher DJ, Given HF. Linkage analysis and the susceptibility gene (BRCA-1) in familial breast cancer. Eur J Surg Oncol 1995; 21:240241.
  27. Easton DF, Bishop DT, Ford D, Crockford GP. Genetic linkage analysis in familial breast and ovarian cancer: results from 214 families. The Breast Cancer Linkage Consortium. Am J Hum Genet 1993; 52:678701.
  28. Ormiston W. Hereditary breast cancer. Eur J Cancer Care (Engl) 1996; 5:1320.
  29. Couch FJ, Wang X, McGuffog L, et al. Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk. PLoS Genet 2013; 9:e1003212.
  30. Bennett KL, Mester J, Eng C. Germline epigenetic regulation of KILLIN in Cowden and Cowden-like syndrome. JAMA 2010; 304:27242731.
  31. Ni Y, He X, Chen J, et al. Germline SDHx variants modify breast and thyroid cancer risks in Cowden and Cowden-like syndrome via FAD/NAD-dependent destabilization of p53. Hum Mol Genet 2012; 21:300310.
  32. Casadei S, Norquist BM, Walsh T, et al. Contribution of inherited mutations in the BRCA2-interacting protein PALB2 to familial breast cancer. Cancer Res 2011; 71:22222229.
  33. Walsh T, Lee MK, Casadei S, et al. Detection of inherited mutations for breast and ovarian cancer using genomic capture and massively parallel sequencing. Proc Natl Acad Sci U S A 2010; 107:1262912633.
  34. Eng C. Molecular genetics to genomic medicine: at the heart of value-based delivery of healthcare. Mol Genet Genom Med 2013; 1:46.
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How to spot heritable breast cancer: A primary care physician’s guide
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KEY POINTS

  • Primary care physicians play a critical role in identifying patients at risk of inherited health problems.
  • Hereditary cancers are important to detect because the age of onset is early, multiple primary cancers can develop, and cancer predisposition may be inherited.
  • Hereditary syndromes account for only a minority of cases of breast cancer, but women who bear the responsible mutations have an extremely high risk.
  • Patients with hereditary breast cancer syndromes and those with familial breast cancer can benefit from heightened surveillance for breast cancer.
  • Cancer genetics risk assessment ensures that the correct genetic testing is offered to the most appropriate patients, with personalized interpretation of results and provision of future management recommendations based on the individual patient’s personal and family history.
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Do all hospitalized patients need stress ulcer prophylaxis?

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Do all hospitalized patients need stress ulcer prophylaxis?

No. Based on current evidence and guidelines, routine acid-suppressive therapy to prevent stress ulcers has no benefit in hospitalized patients outside the critical-care setting. Only critically ill patients who meet specific criteria, as described in the guidelines of the American Society of Health System Pharmacists, should receive acid-suppressive therapy.

Unfortunately, routine stress ulcer prophylaxis is common in US hospitals, unnecessarily putting patients at risk of complications and adding costs.

STRESS ULCER AND CRITICAL ILLNESS

Stress ulcers—ulcerations of the upper part of the gastrointestinal (GI) mucosa in the setting of acute disease—usually involve the fundus and body of the stomach. The stomach is lined with a glycoprotein mucous layer rich in bicarbonates, forming a physiologic barrier to protect the gastric wall from acid insult by neutralizing hydrogen ions. Disruption of this protective layer can occur in critically ill patients (eg, those with shock or sepsis) through overproduction of uremic toxins, increased reflux of bile salts, compromised blood flow, and increased stomach acidity through gastrin stimulation of parietal cells.

More than 75% of patients with major burns or cranial trauma develop endoscopic mucosal abnormalities within 72 hours of injury.1 In critically ill patients, the risk of ulcer-related overt bleeding is estimated to be 5% to 25%. Furthermore, 1% to 5% of stress ulcers can be deep enough to erode into the submucosa, causing clinically significant GI bleeding, defined as bleeding complicated by hemodynamic compromise or a drop in hemoglobin that requires a blood transfusion.2 In contrast, in inpatients who are not critically ill, the risk of overt bleeding from stress ulcers is less than 1%.3

ADDRESSING RISK

A multicenter prospective cohort study of 2,252 intensive care patients2 reported two main risk factors for significant bleeding caused by stress ulcers: mechanical ventilation for more than 48 hours and coagulopathy, defined as a platelet count below 50 × 109/L, an international normalized ratio greater than 1.5, or a partial thromboplastin time more than twice the control value.4 In hemodynamically stable patients receiving anticoagulation in a general medical or surgical ward, the risk of GI bleeding was low, and acid suppression failed to lower the rate of stress ulcer occurrence.3

Other risk factors include severe sepsis, shock, liver failure, kidney failure, burns over 35% of the total body surface, organ transplantation, cranial trauma, spinal cord trauma, history of peptic ulcer disease, and history of upper GI bleeding.3,5,6 Steroid therapy is not considered a risk factor for stress ulcers unless it is used in the presence of another risk factor such as use of aspirin or nonsteroidal antiinflammatory drugs (NSAIDs).2

INDICATIONS FOR PROPHYLAXIS

Prophylaxis with a proton pump inhibitor (PPI) is indicated in specific conditions—ie, peptic ulcer disease, gastroesophageal reflux disease, chronic NSAID therapy, and Zollinger-Ellison syndrome—and to eradicate Helicobacter pylori infection.7 But in the United States, stress ulcer prophylaxis is overused in general-care floors despite the lack of supporting evidence.

The American Society of Health System Pharmacists guidelines recommend it in the intensive care unit for patients with any of the following: coagulopathy, prolonged mechanical ventilation (more than 48 hours), GI ulcer or bleeding within the past year, sepsis, a stay longer than 1 week in the intensive care unit, occult GI bleeding for 6 or more days, and steroid therapy with more than 250 mg of hydrocortisone daily.8 Hemodynamically stable patients admitted to general-care floors should not receive stress ulcer prophylaxis, as it only negligibly decreases the rate of GI bleeding, from 0.33% to 0.22%.9

 

 

WHY ROUTINE ULCER PROPHYLAXIS IS NOT FOR ALL HOSPITALIZED PATIENTS

Although stress ulcer prophylaxis is often considered benign, its lack of proven benefit, additional cost, and risk of adverse effects, including interactions with foods and other drugs, preclude using it routinely for all hospitalized patients.10,11 Chronic use of PPIs has been associated with complications, as discussed below.

Infection

Acid suppression may impair the destruction of ingested microorganisms, resulting in overgrowth of bacteria.12 Overuse of PPIs may increase the risk of several infections:

  • Diarrhea due to Clostridium difficile12
  • Community-acquired pneumonia, from increased microaspiration of overgrown microorganisms into the lung.12
  • Spontaneous bacterial peritonitis in patients with cirrhosis,13 although the mechanism is not clear. (Small-bowel bacterial overgrowth is the hypothesized cause.)

Bone fracture

PPIs lower gastric acidity, and this can inhibit intestinal calcium absorption. Furthermore, PPIs may directly inhibit bone resorption by osteoclasts.14

Reduction in clopidogrel efficacy

PPIs may reduce the efficacy of clopidogrel as a result of competitive inhibition of cytochrome CYP2C19, which is necessary to metabolize clopidogrel to its active forms. Therefore, concomitant use of clopidogrel with omeprazole, esomeprazole, or other CYP2C19 inhibitors is not recommended.15

Nutritional deficiencies

The overgrown microorganisms consume cobalamin in the stomach, resulting in vitamin B12 deficiency. Acid-suppressive therapy can also reduce the absorption of magnesium and iron.12

Unnecessary cost

Heidelbaugh and Inadomi16 reviewed the non-evidence-based use of stress ulcer prophylaxis in patients admitted to a large university hospital and estimated that it entailed a cost to the hospital of $111,791 over the course of a year.

WHICH ULCER PROPHYLAXIS SHOULD BE USED IN CRITICALLY ILL PATIENTS?

Studies have shown histamine-2 blockers to be superior to antacids and sucralfate in preventing stress ulcer and GI bleeding,8,15 but no study has compared PPIs with sucralfate and antacids.

When indicated, an oral PPI is preferred over an oral histamine-2 blocker for GI prophylaxis.17 This practice is considered cost-effective and is associated with lower rates of stress ulcer and GI bleeding. In intubated patients, however, an intravenous histamine-2 blocker is preferable to an intravenous PPI.3,8,11 Interestingly, no difference was reported between PPIs and histamine-2 blockers in terms of mortality rate or reduction in the incidence of nosocomial pneumonia.17

OUR RECOMMENDATION

Only critically ill patients who meet the specific criteria described here should receive stress ulcer prophylaxis. More effort is needed to educate residents, medical staff, and pharmacists about current guidelines. Computerized ordering templates and reminders to discontinue prophylaxis at discharge or step-down may decrease overall use, reduce costs, and limit potential side effects.18

References
  1. DePriest JL. Stress ulcer prophylaxis. Do critically ill patients need it? Postgrad Med 1995; 98:159168.
  2. Cook DJ, Fuller HD, Guyatt GH, et al. Risk factors for gastrointestinal bleeding in critically ill patients. Canadian Critical Care Trials Group. N Engl J Med 1994; 330:377381.
  3. Qadeer MA, Richter JE, Brotman DJ. Hospital-acquired gastrointestinal bleeding outside the critical care unit: risk factors, role of acid suppression, and endoscopy findings. J Hosp Med 2006; 1:1320.
  4. Shuman RB, Schuster DP, Zuckerman GR. Prophylactic therapy for stress ulcer bleeding: a reappraisal. Ann Intern Med 1987; 106:562567.
  5. Dellinger RP, Levy MM, Rhodes A, et al; Surviving Sepsis Campaign Guidelines Committee including the Pediatric Subgroup. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med 2013; 41:580637.
  6. Cook DJ, Reeve BK, Guyatt GH, et al. Stress ulcer prophylaxis in critically ill patients. Resolving discordant meta-analyses. JAMA 1996; 275:308314.
  7. Kahrilas PJ, Shaheen NJ, Vaezi MF, et al; American Gastroenterological Association. American Gastroenterological Association Medical Position Statement on the management of gastroesophageal reflux disease. Gastroenterology 2008; 135:13831391.e11391.e5.
  8. Barkun AN, Bardou M, Pham CQ, Martel M. Proton pump inhibitors vs histamine 2 receptor antagonists for stress-related mucosal bleeding prophylaxis in critically ill patients: a meta-analysis. Am J Gastroenterol 2012; 107:507520.
  9. Herzig SJ, Vaughn BP, Howell MD, Ngo LH, Marcantonio ER. Acid-suppressive medication use and the risk for nosocomial gastrointestinal tract bleeding. Arch Intern Med 2011; 171:991997.
  10. Cook DJ. Stress ulcer prophylaxis: gastrointestinal bleeding and nosocomial pneumonia. Best evidence synthesis. Scand J Gastroenterol Suppl 1995; 210:4852.
  11. Messori A, Trippoli S, Vaiani M, Gorini M, Corrado A. Bleeding and pneumonia in intensive care patients given ranitidine and sucralfate for prevention of stress ulcer: meta-analysis of randomised controlled trials. BMJ 2000; 321:11031106.
  12. Heidelbaugh JJ, Kim AH, Chang R, Walker PC. Overutilization of proton-pump inhibitors: what the clinician needs to know. Therap Adv Gastroenterol 2012; 5:219232.
  13. Deshpande A, Pasupuleti V, Thota P, et al. Acid-suppressive therapy is associated with spontaneous bacterial peritonitis in cirrhotic patients: a meta-analysis. J Gastroenterol Hepatol 2013; 28:235242.
  14. Farina C, Gagliardi S. Selective inhibition of osteoclast vacuolar H(+)- ATPase. Curr Pharm Des 2002; 8:20332048.
  15. ASHP Therapeutic Guidelines on Stress Ulcer Prophylaxis. ASHP Commission on Therapeutics and approved by the ASHP Board of Directors on November 14, 1998. Am J Health Syst Pharm 1999; 56:347379.
  16. Heidelbaugh JJ, Inadomi JM. Magnitude and economic impact of inappropriate use of stress ulcer prophylaxis in non-ICU hospitalized patients. Am J Gastroenterol 2006; 101:22002205.
  17. Alhazzani W, Alenezi F, Jaeschke RZ, Moayyedi P, Cook DJ. Proton pump inhibitors versus histamine 2 receptor antagonists for stress ulcer prophylaxis in critically ill patients: a systematic review and meta-analysis. Crit Care Med 2013; 41:693705.
  18. Liberman JD, Whelan CT. Brief report: reducing inappropriate usage of stress ulcer prophylaxis among internal medicine residents. A practice-based educational intervention. J Gen Intern Med 2006; 21:498500.
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Naseem Eisa, MD
Department of Hospital Medicine, Cleveland Clinic, Cleveland, OH

Fateh Bazerbachi, MD
Department of Medicine, University of Minnesota, Minneapolis, MN

Abdul Hamid Alraiyes, MD, FCCP
Department of Pulmonary Diseases, Critical Care, and Environmental Medicine, Tulane University Health Sciences Center, New Orleans, LA

M. Chadi Alraies, MD, FACP
Division of Cardiology, University of Minnesota, Minneapolis

Address: M. Chadi Alraies, MD, 3635 E 43rd Street, Apartment 317, Minneapolis, MN 55406; e-mail: [email protected]

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Naseem Eisa, MD
Department of Hospital Medicine, Cleveland Clinic, Cleveland, OH

Fateh Bazerbachi, MD
Department of Medicine, University of Minnesota, Minneapolis, MN

Abdul Hamid Alraiyes, MD, FCCP
Department of Pulmonary Diseases, Critical Care, and Environmental Medicine, Tulane University Health Sciences Center, New Orleans, LA

M. Chadi Alraies, MD, FACP
Division of Cardiology, University of Minnesota, Minneapolis

Address: M. Chadi Alraies, MD, 3635 E 43rd Street, Apartment 317, Minneapolis, MN 55406; e-mail: [email protected]

Author and Disclosure Information

Naseem Eisa, MD
Department of Hospital Medicine, Cleveland Clinic, Cleveland, OH

Fateh Bazerbachi, MD
Department of Medicine, University of Minnesota, Minneapolis, MN

Abdul Hamid Alraiyes, MD, FCCP
Department of Pulmonary Diseases, Critical Care, and Environmental Medicine, Tulane University Health Sciences Center, New Orleans, LA

M. Chadi Alraies, MD, FACP
Division of Cardiology, University of Minnesota, Minneapolis

Address: M. Chadi Alraies, MD, 3635 E 43rd Street, Apartment 317, Minneapolis, MN 55406; e-mail: [email protected]

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No. Based on current evidence and guidelines, routine acid-suppressive therapy to prevent stress ulcers has no benefit in hospitalized patients outside the critical-care setting. Only critically ill patients who meet specific criteria, as described in the guidelines of the American Society of Health System Pharmacists, should receive acid-suppressive therapy.

Unfortunately, routine stress ulcer prophylaxis is common in US hospitals, unnecessarily putting patients at risk of complications and adding costs.

STRESS ULCER AND CRITICAL ILLNESS

Stress ulcers—ulcerations of the upper part of the gastrointestinal (GI) mucosa in the setting of acute disease—usually involve the fundus and body of the stomach. The stomach is lined with a glycoprotein mucous layer rich in bicarbonates, forming a physiologic barrier to protect the gastric wall from acid insult by neutralizing hydrogen ions. Disruption of this protective layer can occur in critically ill patients (eg, those with shock or sepsis) through overproduction of uremic toxins, increased reflux of bile salts, compromised blood flow, and increased stomach acidity through gastrin stimulation of parietal cells.

More than 75% of patients with major burns or cranial trauma develop endoscopic mucosal abnormalities within 72 hours of injury.1 In critically ill patients, the risk of ulcer-related overt bleeding is estimated to be 5% to 25%. Furthermore, 1% to 5% of stress ulcers can be deep enough to erode into the submucosa, causing clinically significant GI bleeding, defined as bleeding complicated by hemodynamic compromise or a drop in hemoglobin that requires a blood transfusion.2 In contrast, in inpatients who are not critically ill, the risk of overt bleeding from stress ulcers is less than 1%.3

ADDRESSING RISK

A multicenter prospective cohort study of 2,252 intensive care patients2 reported two main risk factors for significant bleeding caused by stress ulcers: mechanical ventilation for more than 48 hours and coagulopathy, defined as a platelet count below 50 × 109/L, an international normalized ratio greater than 1.5, or a partial thromboplastin time more than twice the control value.4 In hemodynamically stable patients receiving anticoagulation in a general medical or surgical ward, the risk of GI bleeding was low, and acid suppression failed to lower the rate of stress ulcer occurrence.3

Other risk factors include severe sepsis, shock, liver failure, kidney failure, burns over 35% of the total body surface, organ transplantation, cranial trauma, spinal cord trauma, history of peptic ulcer disease, and history of upper GI bleeding.3,5,6 Steroid therapy is not considered a risk factor for stress ulcers unless it is used in the presence of another risk factor such as use of aspirin or nonsteroidal antiinflammatory drugs (NSAIDs).2

INDICATIONS FOR PROPHYLAXIS

Prophylaxis with a proton pump inhibitor (PPI) is indicated in specific conditions—ie, peptic ulcer disease, gastroesophageal reflux disease, chronic NSAID therapy, and Zollinger-Ellison syndrome—and to eradicate Helicobacter pylori infection.7 But in the United States, stress ulcer prophylaxis is overused in general-care floors despite the lack of supporting evidence.

The American Society of Health System Pharmacists guidelines recommend it in the intensive care unit for patients with any of the following: coagulopathy, prolonged mechanical ventilation (more than 48 hours), GI ulcer or bleeding within the past year, sepsis, a stay longer than 1 week in the intensive care unit, occult GI bleeding for 6 or more days, and steroid therapy with more than 250 mg of hydrocortisone daily.8 Hemodynamically stable patients admitted to general-care floors should not receive stress ulcer prophylaxis, as it only negligibly decreases the rate of GI bleeding, from 0.33% to 0.22%.9

 

 

WHY ROUTINE ULCER PROPHYLAXIS IS NOT FOR ALL HOSPITALIZED PATIENTS

Although stress ulcer prophylaxis is often considered benign, its lack of proven benefit, additional cost, and risk of adverse effects, including interactions with foods and other drugs, preclude using it routinely for all hospitalized patients.10,11 Chronic use of PPIs has been associated with complications, as discussed below.

Infection

Acid suppression may impair the destruction of ingested microorganisms, resulting in overgrowth of bacteria.12 Overuse of PPIs may increase the risk of several infections:

  • Diarrhea due to Clostridium difficile12
  • Community-acquired pneumonia, from increased microaspiration of overgrown microorganisms into the lung.12
  • Spontaneous bacterial peritonitis in patients with cirrhosis,13 although the mechanism is not clear. (Small-bowel bacterial overgrowth is the hypothesized cause.)

Bone fracture

PPIs lower gastric acidity, and this can inhibit intestinal calcium absorption. Furthermore, PPIs may directly inhibit bone resorption by osteoclasts.14

Reduction in clopidogrel efficacy

PPIs may reduce the efficacy of clopidogrel as a result of competitive inhibition of cytochrome CYP2C19, which is necessary to metabolize clopidogrel to its active forms. Therefore, concomitant use of clopidogrel with omeprazole, esomeprazole, or other CYP2C19 inhibitors is not recommended.15

Nutritional deficiencies

The overgrown microorganisms consume cobalamin in the stomach, resulting in vitamin B12 deficiency. Acid-suppressive therapy can also reduce the absorption of magnesium and iron.12

Unnecessary cost

Heidelbaugh and Inadomi16 reviewed the non-evidence-based use of stress ulcer prophylaxis in patients admitted to a large university hospital and estimated that it entailed a cost to the hospital of $111,791 over the course of a year.

WHICH ULCER PROPHYLAXIS SHOULD BE USED IN CRITICALLY ILL PATIENTS?

Studies have shown histamine-2 blockers to be superior to antacids and sucralfate in preventing stress ulcer and GI bleeding,8,15 but no study has compared PPIs with sucralfate and antacids.

When indicated, an oral PPI is preferred over an oral histamine-2 blocker for GI prophylaxis.17 This practice is considered cost-effective and is associated with lower rates of stress ulcer and GI bleeding. In intubated patients, however, an intravenous histamine-2 blocker is preferable to an intravenous PPI.3,8,11 Interestingly, no difference was reported between PPIs and histamine-2 blockers in terms of mortality rate or reduction in the incidence of nosocomial pneumonia.17

OUR RECOMMENDATION

Only critically ill patients who meet the specific criteria described here should receive stress ulcer prophylaxis. More effort is needed to educate residents, medical staff, and pharmacists about current guidelines. Computerized ordering templates and reminders to discontinue prophylaxis at discharge or step-down may decrease overall use, reduce costs, and limit potential side effects.18

No. Based on current evidence and guidelines, routine acid-suppressive therapy to prevent stress ulcers has no benefit in hospitalized patients outside the critical-care setting. Only critically ill patients who meet specific criteria, as described in the guidelines of the American Society of Health System Pharmacists, should receive acid-suppressive therapy.

Unfortunately, routine stress ulcer prophylaxis is common in US hospitals, unnecessarily putting patients at risk of complications and adding costs.

STRESS ULCER AND CRITICAL ILLNESS

Stress ulcers—ulcerations of the upper part of the gastrointestinal (GI) mucosa in the setting of acute disease—usually involve the fundus and body of the stomach. The stomach is lined with a glycoprotein mucous layer rich in bicarbonates, forming a physiologic barrier to protect the gastric wall from acid insult by neutralizing hydrogen ions. Disruption of this protective layer can occur in critically ill patients (eg, those with shock or sepsis) through overproduction of uremic toxins, increased reflux of bile salts, compromised blood flow, and increased stomach acidity through gastrin stimulation of parietal cells.

More than 75% of patients with major burns or cranial trauma develop endoscopic mucosal abnormalities within 72 hours of injury.1 In critically ill patients, the risk of ulcer-related overt bleeding is estimated to be 5% to 25%. Furthermore, 1% to 5% of stress ulcers can be deep enough to erode into the submucosa, causing clinically significant GI bleeding, defined as bleeding complicated by hemodynamic compromise or a drop in hemoglobin that requires a blood transfusion.2 In contrast, in inpatients who are not critically ill, the risk of overt bleeding from stress ulcers is less than 1%.3

ADDRESSING RISK

A multicenter prospective cohort study of 2,252 intensive care patients2 reported two main risk factors for significant bleeding caused by stress ulcers: mechanical ventilation for more than 48 hours and coagulopathy, defined as a platelet count below 50 × 109/L, an international normalized ratio greater than 1.5, or a partial thromboplastin time more than twice the control value.4 In hemodynamically stable patients receiving anticoagulation in a general medical or surgical ward, the risk of GI bleeding was low, and acid suppression failed to lower the rate of stress ulcer occurrence.3

Other risk factors include severe sepsis, shock, liver failure, kidney failure, burns over 35% of the total body surface, organ transplantation, cranial trauma, spinal cord trauma, history of peptic ulcer disease, and history of upper GI bleeding.3,5,6 Steroid therapy is not considered a risk factor for stress ulcers unless it is used in the presence of another risk factor such as use of aspirin or nonsteroidal antiinflammatory drugs (NSAIDs).2

INDICATIONS FOR PROPHYLAXIS

Prophylaxis with a proton pump inhibitor (PPI) is indicated in specific conditions—ie, peptic ulcer disease, gastroesophageal reflux disease, chronic NSAID therapy, and Zollinger-Ellison syndrome—and to eradicate Helicobacter pylori infection.7 But in the United States, stress ulcer prophylaxis is overused in general-care floors despite the lack of supporting evidence.

The American Society of Health System Pharmacists guidelines recommend it in the intensive care unit for patients with any of the following: coagulopathy, prolonged mechanical ventilation (more than 48 hours), GI ulcer or bleeding within the past year, sepsis, a stay longer than 1 week in the intensive care unit, occult GI bleeding for 6 or more days, and steroid therapy with more than 250 mg of hydrocortisone daily.8 Hemodynamically stable patients admitted to general-care floors should not receive stress ulcer prophylaxis, as it only negligibly decreases the rate of GI bleeding, from 0.33% to 0.22%.9

 

 

WHY ROUTINE ULCER PROPHYLAXIS IS NOT FOR ALL HOSPITALIZED PATIENTS

Although stress ulcer prophylaxis is often considered benign, its lack of proven benefit, additional cost, and risk of adverse effects, including interactions with foods and other drugs, preclude using it routinely for all hospitalized patients.10,11 Chronic use of PPIs has been associated with complications, as discussed below.

Infection

Acid suppression may impair the destruction of ingested microorganisms, resulting in overgrowth of bacteria.12 Overuse of PPIs may increase the risk of several infections:

  • Diarrhea due to Clostridium difficile12
  • Community-acquired pneumonia, from increased microaspiration of overgrown microorganisms into the lung.12
  • Spontaneous bacterial peritonitis in patients with cirrhosis,13 although the mechanism is not clear. (Small-bowel bacterial overgrowth is the hypothesized cause.)

Bone fracture

PPIs lower gastric acidity, and this can inhibit intestinal calcium absorption. Furthermore, PPIs may directly inhibit bone resorption by osteoclasts.14

Reduction in clopidogrel efficacy

PPIs may reduce the efficacy of clopidogrel as a result of competitive inhibition of cytochrome CYP2C19, which is necessary to metabolize clopidogrel to its active forms. Therefore, concomitant use of clopidogrel with omeprazole, esomeprazole, or other CYP2C19 inhibitors is not recommended.15

Nutritional deficiencies

The overgrown microorganisms consume cobalamin in the stomach, resulting in vitamin B12 deficiency. Acid-suppressive therapy can also reduce the absorption of magnesium and iron.12

Unnecessary cost

Heidelbaugh and Inadomi16 reviewed the non-evidence-based use of stress ulcer prophylaxis in patients admitted to a large university hospital and estimated that it entailed a cost to the hospital of $111,791 over the course of a year.

WHICH ULCER PROPHYLAXIS SHOULD BE USED IN CRITICALLY ILL PATIENTS?

Studies have shown histamine-2 blockers to be superior to antacids and sucralfate in preventing stress ulcer and GI bleeding,8,15 but no study has compared PPIs with sucralfate and antacids.

When indicated, an oral PPI is preferred over an oral histamine-2 blocker for GI prophylaxis.17 This practice is considered cost-effective and is associated with lower rates of stress ulcer and GI bleeding. In intubated patients, however, an intravenous histamine-2 blocker is preferable to an intravenous PPI.3,8,11 Interestingly, no difference was reported between PPIs and histamine-2 blockers in terms of mortality rate or reduction in the incidence of nosocomial pneumonia.17

OUR RECOMMENDATION

Only critically ill patients who meet the specific criteria described here should receive stress ulcer prophylaxis. More effort is needed to educate residents, medical staff, and pharmacists about current guidelines. Computerized ordering templates and reminders to discontinue prophylaxis at discharge or step-down may decrease overall use, reduce costs, and limit potential side effects.18

References
  1. DePriest JL. Stress ulcer prophylaxis. Do critically ill patients need it? Postgrad Med 1995; 98:159168.
  2. Cook DJ, Fuller HD, Guyatt GH, et al. Risk factors for gastrointestinal bleeding in critically ill patients. Canadian Critical Care Trials Group. N Engl J Med 1994; 330:377381.
  3. Qadeer MA, Richter JE, Brotman DJ. Hospital-acquired gastrointestinal bleeding outside the critical care unit: risk factors, role of acid suppression, and endoscopy findings. J Hosp Med 2006; 1:1320.
  4. Shuman RB, Schuster DP, Zuckerman GR. Prophylactic therapy for stress ulcer bleeding: a reappraisal. Ann Intern Med 1987; 106:562567.
  5. Dellinger RP, Levy MM, Rhodes A, et al; Surviving Sepsis Campaign Guidelines Committee including the Pediatric Subgroup. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med 2013; 41:580637.
  6. Cook DJ, Reeve BK, Guyatt GH, et al. Stress ulcer prophylaxis in critically ill patients. Resolving discordant meta-analyses. JAMA 1996; 275:308314.
  7. Kahrilas PJ, Shaheen NJ, Vaezi MF, et al; American Gastroenterological Association. American Gastroenterological Association Medical Position Statement on the management of gastroesophageal reflux disease. Gastroenterology 2008; 135:13831391.e11391.e5.
  8. Barkun AN, Bardou M, Pham CQ, Martel M. Proton pump inhibitors vs histamine 2 receptor antagonists for stress-related mucosal bleeding prophylaxis in critically ill patients: a meta-analysis. Am J Gastroenterol 2012; 107:507520.
  9. Herzig SJ, Vaughn BP, Howell MD, Ngo LH, Marcantonio ER. Acid-suppressive medication use and the risk for nosocomial gastrointestinal tract bleeding. Arch Intern Med 2011; 171:991997.
  10. Cook DJ. Stress ulcer prophylaxis: gastrointestinal bleeding and nosocomial pneumonia. Best evidence synthesis. Scand J Gastroenterol Suppl 1995; 210:4852.
  11. Messori A, Trippoli S, Vaiani M, Gorini M, Corrado A. Bleeding and pneumonia in intensive care patients given ranitidine and sucralfate for prevention of stress ulcer: meta-analysis of randomised controlled trials. BMJ 2000; 321:11031106.
  12. Heidelbaugh JJ, Kim AH, Chang R, Walker PC. Overutilization of proton-pump inhibitors: what the clinician needs to know. Therap Adv Gastroenterol 2012; 5:219232.
  13. Deshpande A, Pasupuleti V, Thota P, et al. Acid-suppressive therapy is associated with spontaneous bacterial peritonitis in cirrhotic patients: a meta-analysis. J Gastroenterol Hepatol 2013; 28:235242.
  14. Farina C, Gagliardi S. Selective inhibition of osteoclast vacuolar H(+)- ATPase. Curr Pharm Des 2002; 8:20332048.
  15. ASHP Therapeutic Guidelines on Stress Ulcer Prophylaxis. ASHP Commission on Therapeutics and approved by the ASHP Board of Directors on November 14, 1998. Am J Health Syst Pharm 1999; 56:347379.
  16. Heidelbaugh JJ, Inadomi JM. Magnitude and economic impact of inappropriate use of stress ulcer prophylaxis in non-ICU hospitalized patients. Am J Gastroenterol 2006; 101:22002205.
  17. Alhazzani W, Alenezi F, Jaeschke RZ, Moayyedi P, Cook DJ. Proton pump inhibitors versus histamine 2 receptor antagonists for stress ulcer prophylaxis in critically ill patients: a systematic review and meta-analysis. Crit Care Med 2013; 41:693705.
  18. Liberman JD, Whelan CT. Brief report: reducing inappropriate usage of stress ulcer prophylaxis among internal medicine residents. A practice-based educational intervention. J Gen Intern Med 2006; 21:498500.
References
  1. DePriest JL. Stress ulcer prophylaxis. Do critically ill patients need it? Postgrad Med 1995; 98:159168.
  2. Cook DJ, Fuller HD, Guyatt GH, et al. Risk factors for gastrointestinal bleeding in critically ill patients. Canadian Critical Care Trials Group. N Engl J Med 1994; 330:377381.
  3. Qadeer MA, Richter JE, Brotman DJ. Hospital-acquired gastrointestinal bleeding outside the critical care unit: risk factors, role of acid suppression, and endoscopy findings. J Hosp Med 2006; 1:1320.
  4. Shuman RB, Schuster DP, Zuckerman GR. Prophylactic therapy for stress ulcer bleeding: a reappraisal. Ann Intern Med 1987; 106:562567.
  5. Dellinger RP, Levy MM, Rhodes A, et al; Surviving Sepsis Campaign Guidelines Committee including the Pediatric Subgroup. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med 2013; 41:580637.
  6. Cook DJ, Reeve BK, Guyatt GH, et al. Stress ulcer prophylaxis in critically ill patients. Resolving discordant meta-analyses. JAMA 1996; 275:308314.
  7. Kahrilas PJ, Shaheen NJ, Vaezi MF, et al; American Gastroenterological Association. American Gastroenterological Association Medical Position Statement on the management of gastroesophageal reflux disease. Gastroenterology 2008; 135:13831391.e11391.e5.
  8. Barkun AN, Bardou M, Pham CQ, Martel M. Proton pump inhibitors vs histamine 2 receptor antagonists for stress-related mucosal bleeding prophylaxis in critically ill patients: a meta-analysis. Am J Gastroenterol 2012; 107:507520.
  9. Herzig SJ, Vaughn BP, Howell MD, Ngo LH, Marcantonio ER. Acid-suppressive medication use and the risk for nosocomial gastrointestinal tract bleeding. Arch Intern Med 2011; 171:991997.
  10. Cook DJ. Stress ulcer prophylaxis: gastrointestinal bleeding and nosocomial pneumonia. Best evidence synthesis. Scand J Gastroenterol Suppl 1995; 210:4852.
  11. Messori A, Trippoli S, Vaiani M, Gorini M, Corrado A. Bleeding and pneumonia in intensive care patients given ranitidine and sucralfate for prevention of stress ulcer: meta-analysis of randomised controlled trials. BMJ 2000; 321:11031106.
  12. Heidelbaugh JJ, Kim AH, Chang R, Walker PC. Overutilization of proton-pump inhibitors: what the clinician needs to know. Therap Adv Gastroenterol 2012; 5:219232.
  13. Deshpande A, Pasupuleti V, Thota P, et al. Acid-suppressive therapy is associated with spontaneous bacterial peritonitis in cirrhotic patients: a meta-analysis. J Gastroenterol Hepatol 2013; 28:235242.
  14. Farina C, Gagliardi S. Selective inhibition of osteoclast vacuolar H(+)- ATPase. Curr Pharm Des 2002; 8:20332048.
  15. ASHP Therapeutic Guidelines on Stress Ulcer Prophylaxis. ASHP Commission on Therapeutics and approved by the ASHP Board of Directors on November 14, 1998. Am J Health Syst Pharm 1999; 56:347379.
  16. Heidelbaugh JJ, Inadomi JM. Magnitude and economic impact of inappropriate use of stress ulcer prophylaxis in non-ICU hospitalized patients. Am J Gastroenterol 2006; 101:22002205.
  17. Alhazzani W, Alenezi F, Jaeschke RZ, Moayyedi P, Cook DJ. Proton pump inhibitors versus histamine 2 receptor antagonists for stress ulcer prophylaxis in critically ill patients: a systematic review and meta-analysis. Crit Care Med 2013; 41:693705.
  18. Liberman JD, Whelan CT. Brief report: reducing inappropriate usage of stress ulcer prophylaxis among internal medicine residents. A practice-based educational intervention. J Gen Intern Med 2006; 21:498500.
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Deep T waves and chest pain

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A 67-year-old man with a history of hypertension and hyperlipidemia presented to the emergency department after 3 hours of what he described as a burning sensation in his chest that woke him from sleep. He attributed it at first to a late-night meal and treated himself with some milk and yogurt, which seemed to relieve the symptoms. However, the pain recurred and was associated with difficulty breathing. At that point, he drove himself to the emergency department.

On arrival, his temperature was 36.5°C (97.7°F), blood pressure 134/67 mm Hg, heart rate 89 bpm, respirations 18/min, and oxygen saturation 98% on room air. His cardiovascular, lung, and neurologic examinations were normal. His cardiac enzyme levels (creatine kinase, creatine kinase MB fraction, and troponin T) were within normal limits.

Figure 1. The patient’s electrocardiogram on admission. Note the T-wave inversions in precordial leads V2 and V3 (red arrows) and ST-segment changes in V1 (black arrow).

Figure 1 depicts his initial electrocardiogram. It showed deep, symmetric T-wave inversions in the precordial leads especially in V2 and V3, changes known as Wellens syndrome. The ST-T changes in lead V1 suggested a very proximal lesion in the left anterior descending artery (LAD), before the first septal perforator. Also, lateral and high lateral (V5 and V6) findings indicated stenoses of the branching diagonals and left circumflex myocardial territory. Furthermore, the inferior ST-T changes indicated that his LAD may have wrapped around the cardiac apex. All of these findings were prognostically significant.

Figure 2. Coronary angiography showed intraluminal disease, with 50% to 60% stenosis of the left main coronary artery (A), 90% steno-sis in the proximal left anterior descending artery (B), 80% stenosis in the middle segment of the left anterior descending artery (C), and 40% stenosis in a large (> 3.0-mm) second diagonal artery (D).

The patient was given aspirin and was started on intravenous unfractionated heparin and nitroglycerin. He was sent for urgent left-heart catheterization, which showed a 50% to 60% stenosis in the left main coronary artery, with involvement of the left circumflex artery proximally, in addition to a “tight” first-diagonal stenosis, a 90% stenosis in a large (> 3.0-mm) proximal segment of the LAD, an 80% stenosis in a large (> 3.0-mm) mid-LAD segment, and a 40% stenosis in a large (> 3.0-mm) second diagonal artery (Figure 2).

He was referred for cardiac surgery and underwent triple coronary artery bypass grafting: the left internal thoracic artery was grafted to the LAD, a reverse saphenous vein graft was performed to the diagonal artery, and a reverse saphenous vein graft was performed to the obtuse marginal artery.

A PRECURSOR TO INFARCTION

Wellens et al described specific precordial T-wave changes in patients with unstable angina who subsequently developed anterior wall myocardial infarction.1

The importance of Wellens syndrome is that it occurs in the pain-free interval when no other evidence of ischemia or angina may be present.1 Cardiac enzyme levels are typically normal or only minimally elevated; only 12% of patients with this syndrome have elevated cardiac biomarker levels.2

Given the extent of myocardial injury, urgent echocardiography can show a wall-motion abnormality even if cardiac enzyme levels are normal. This gives important insight into electrocardiographic changes and should prompt consideration of revascularization.

Even with extensive medical management, Wellens syndrome progresses to acute anterior wall ischemia. About 75% of patients with Wellens syndrome who receive medical management but do not undergo revascularization (eg, coronary artery bypass grafting, percutaneous coronary intervention) develop extensive anterior wall infarction within days.1,3 Despite negative cardiac biomarkers, Wellens syndrome is considered an acute coronary syndrome requiring urgent cardiac intervention.

References
  1. Movahed MR. Wellens’ syndrome or inverted U-waves? Clin Cardiol 2008; 31:133134.
  2. de Zwaan C, Bär FW, Janssen JH, et al. Angiographic and clinical characteristics of patients with unstable angina showing an ECG pattern indicating critical narrowing of the proximal LAD coronary artery. Am Heart J 1989; 117:657665.
  3. de Zwaan C, Bär FW, Wellens HJ. Characteristic electrocardiographic pattern indicating a critical stenosis high in left anterior descending coronary artery in patients admitted because of impending myocardial infarction. Am Heart J 1982; 103:730736.
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Wael Aljaroudi, MD
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Usman Ayub Khan, MBBS
Department of Hospital Medicine, Cleveland Clinic

Abdul Hamid Alraiyes, MD, FCCP
Respiratory institute, Cleveland Clinic

Address: M. Chadi Alraies, MD, Department of Hospital Medicine, A13, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

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Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic

Usman Ayub Khan, MBBS
Department of Hospital Medicine, Cleveland Clinic

Abdul Hamid Alraiyes, MD, FCCP
Respiratory institute, Cleveland Clinic

Address: M. Chadi Alraies, MD, Department of Hospital Medicine, A13, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

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Clinical Assistant Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, Department of Hospital Medicine, Cleveland Clinic

Wael Aljaroudi, MD
Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic

Usman Ayub Khan, MBBS
Department of Hospital Medicine, Cleveland Clinic

Abdul Hamid Alraiyes, MD, FCCP
Respiratory institute, Cleveland Clinic

Address: M. Chadi Alraies, MD, Department of Hospital Medicine, A13, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

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A 67-year-old man with a history of hypertension and hyperlipidemia presented to the emergency department after 3 hours of what he described as a burning sensation in his chest that woke him from sleep. He attributed it at first to a late-night meal and treated himself with some milk and yogurt, which seemed to relieve the symptoms. However, the pain recurred and was associated with difficulty breathing. At that point, he drove himself to the emergency department.

On arrival, his temperature was 36.5°C (97.7°F), blood pressure 134/67 mm Hg, heart rate 89 bpm, respirations 18/min, and oxygen saturation 98% on room air. His cardiovascular, lung, and neurologic examinations were normal. His cardiac enzyme levels (creatine kinase, creatine kinase MB fraction, and troponin T) were within normal limits.

Figure 1. The patient’s electrocardiogram on admission. Note the T-wave inversions in precordial leads V2 and V3 (red arrows) and ST-segment changes in V1 (black arrow).

Figure 1 depicts his initial electrocardiogram. It showed deep, symmetric T-wave inversions in the precordial leads especially in V2 and V3, changes known as Wellens syndrome. The ST-T changes in lead V1 suggested a very proximal lesion in the left anterior descending artery (LAD), before the first septal perforator. Also, lateral and high lateral (V5 and V6) findings indicated stenoses of the branching diagonals and left circumflex myocardial territory. Furthermore, the inferior ST-T changes indicated that his LAD may have wrapped around the cardiac apex. All of these findings were prognostically significant.

Figure 2. Coronary angiography showed intraluminal disease, with 50% to 60% stenosis of the left main coronary artery (A), 90% steno-sis in the proximal left anterior descending artery (B), 80% stenosis in the middle segment of the left anterior descending artery (C), and 40% stenosis in a large (> 3.0-mm) second diagonal artery (D).

The patient was given aspirin and was started on intravenous unfractionated heparin and nitroglycerin. He was sent for urgent left-heart catheterization, which showed a 50% to 60% stenosis in the left main coronary artery, with involvement of the left circumflex artery proximally, in addition to a “tight” first-diagonal stenosis, a 90% stenosis in a large (> 3.0-mm) proximal segment of the LAD, an 80% stenosis in a large (> 3.0-mm) mid-LAD segment, and a 40% stenosis in a large (> 3.0-mm) second diagonal artery (Figure 2).

He was referred for cardiac surgery and underwent triple coronary artery bypass grafting: the left internal thoracic artery was grafted to the LAD, a reverse saphenous vein graft was performed to the diagonal artery, and a reverse saphenous vein graft was performed to the obtuse marginal artery.

A PRECURSOR TO INFARCTION

Wellens et al described specific precordial T-wave changes in patients with unstable angina who subsequently developed anterior wall myocardial infarction.1

The importance of Wellens syndrome is that it occurs in the pain-free interval when no other evidence of ischemia or angina may be present.1 Cardiac enzyme levels are typically normal or only minimally elevated; only 12% of patients with this syndrome have elevated cardiac biomarker levels.2

Given the extent of myocardial injury, urgent echocardiography can show a wall-motion abnormality even if cardiac enzyme levels are normal. This gives important insight into electrocardiographic changes and should prompt consideration of revascularization.

Even with extensive medical management, Wellens syndrome progresses to acute anterior wall ischemia. About 75% of patients with Wellens syndrome who receive medical management but do not undergo revascularization (eg, coronary artery bypass grafting, percutaneous coronary intervention) develop extensive anterior wall infarction within days.1,3 Despite negative cardiac biomarkers, Wellens syndrome is considered an acute coronary syndrome requiring urgent cardiac intervention.

A 67-year-old man with a history of hypertension and hyperlipidemia presented to the emergency department after 3 hours of what he described as a burning sensation in his chest that woke him from sleep. He attributed it at first to a late-night meal and treated himself with some milk and yogurt, which seemed to relieve the symptoms. However, the pain recurred and was associated with difficulty breathing. At that point, he drove himself to the emergency department.

On arrival, his temperature was 36.5°C (97.7°F), blood pressure 134/67 mm Hg, heart rate 89 bpm, respirations 18/min, and oxygen saturation 98% on room air. His cardiovascular, lung, and neurologic examinations were normal. His cardiac enzyme levels (creatine kinase, creatine kinase MB fraction, and troponin T) were within normal limits.

Figure 1. The patient’s electrocardiogram on admission. Note the T-wave inversions in precordial leads V2 and V3 (red arrows) and ST-segment changes in V1 (black arrow).

Figure 1 depicts his initial electrocardiogram. It showed deep, symmetric T-wave inversions in the precordial leads especially in V2 and V3, changes known as Wellens syndrome. The ST-T changes in lead V1 suggested a very proximal lesion in the left anterior descending artery (LAD), before the first septal perforator. Also, lateral and high lateral (V5 and V6) findings indicated stenoses of the branching diagonals and left circumflex myocardial territory. Furthermore, the inferior ST-T changes indicated that his LAD may have wrapped around the cardiac apex. All of these findings were prognostically significant.

Figure 2. Coronary angiography showed intraluminal disease, with 50% to 60% stenosis of the left main coronary artery (A), 90% steno-sis in the proximal left anterior descending artery (B), 80% stenosis in the middle segment of the left anterior descending artery (C), and 40% stenosis in a large (> 3.0-mm) second diagonal artery (D).

The patient was given aspirin and was started on intravenous unfractionated heparin and nitroglycerin. He was sent for urgent left-heart catheterization, which showed a 50% to 60% stenosis in the left main coronary artery, with involvement of the left circumflex artery proximally, in addition to a “tight” first-diagonal stenosis, a 90% stenosis in a large (> 3.0-mm) proximal segment of the LAD, an 80% stenosis in a large (> 3.0-mm) mid-LAD segment, and a 40% stenosis in a large (> 3.0-mm) second diagonal artery (Figure 2).

He was referred for cardiac surgery and underwent triple coronary artery bypass grafting: the left internal thoracic artery was grafted to the LAD, a reverse saphenous vein graft was performed to the diagonal artery, and a reverse saphenous vein graft was performed to the obtuse marginal artery.

A PRECURSOR TO INFARCTION

Wellens et al described specific precordial T-wave changes in patients with unstable angina who subsequently developed anterior wall myocardial infarction.1

The importance of Wellens syndrome is that it occurs in the pain-free interval when no other evidence of ischemia or angina may be present.1 Cardiac enzyme levels are typically normal or only minimally elevated; only 12% of patients with this syndrome have elevated cardiac biomarker levels.2

Given the extent of myocardial injury, urgent echocardiography can show a wall-motion abnormality even if cardiac enzyme levels are normal. This gives important insight into electrocardiographic changes and should prompt consideration of revascularization.

Even with extensive medical management, Wellens syndrome progresses to acute anterior wall ischemia. About 75% of patients with Wellens syndrome who receive medical management but do not undergo revascularization (eg, coronary artery bypass grafting, percutaneous coronary intervention) develop extensive anterior wall infarction within days.1,3 Despite negative cardiac biomarkers, Wellens syndrome is considered an acute coronary syndrome requiring urgent cardiac intervention.

References
  1. Movahed MR. Wellens’ syndrome or inverted U-waves? Clin Cardiol 2008; 31:133134.
  2. de Zwaan C, Bär FW, Janssen JH, et al. Angiographic and clinical characteristics of patients with unstable angina showing an ECG pattern indicating critical narrowing of the proximal LAD coronary artery. Am Heart J 1989; 117:657665.
  3. de Zwaan C, Bär FW, Wellens HJ. Characteristic electrocardiographic pattern indicating a critical stenosis high in left anterior descending coronary artery in patients admitted because of impending myocardial infarction. Am Heart J 1982; 103:730736.
References
  1. Movahed MR. Wellens’ syndrome or inverted U-waves? Clin Cardiol 2008; 31:133134.
  2. de Zwaan C, Bär FW, Janssen JH, et al. Angiographic and clinical characteristics of patients with unstable angina showing an ECG pattern indicating critical narrowing of the proximal LAD coronary artery. Am Heart J 1989; 117:657665.
  3. de Zwaan C, Bär FW, Wellens HJ. Characteristic electrocardiographic pattern indicating a critical stenosis high in left anterior descending coronary artery in patients admitted because of impending myocardial infarction. Am Heart J 1982; 103:730736.
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Return of the 'pisse-mongers,' this time with data

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Great effort has been spent on identifying easily measured biomarkers to predict the progression of coronary disease and chronic kidney disease (CKD). Interestingly, these two disease processes seem to share some biomarkers and perhaps some pathogenic mechanisms. An ultimate hope is that some of these markers will be found to also contribute directly to organ dysfunction and be amenable to therapy. Blood pressure and (in many people’s minds) low-density lipoprotein cholesterol fulfill this hope. The jury remains out on C-reactive protein and serum urate. There are others.

In this issue of the Journal, Stephen et al review the data indicating that albuminuria helps predict the progression of CKD, coronary disease, ventricular remodeling, and, in some studies, all-cause mortality. Proteinuria has generally been assumed to be a marker of renal injury, but, the authors point out, albumin can under some circumstances initiate inflammatory mechanisms and stimulate mediators of fibrosis.

Although not mentioned by Stephen et al, albumin (like hemoglobin) is susceptible to nonenzymatic glycosylation in patients with diabetes. There is a hint in the literature that glycosylated albumin may be preferentially excreted. Its effects on various tissues are incompletely studied, but it strikes me that perhaps this molecule plays a unique pathogenic role in diabetic renal and vascular disease, even more than native albumin. Further evaluation of this specific marker may lead to even stronger associations (although in a select population of patients with poorly controlled diabetes).

The focus on urine as a fluid with diagnostic and predictive characteristics is certainly not new. Both Hippocrates and Galen recognized the value of inspecting urine. Uroscopy (now urinalysis) may be the oldest surviving laboratory test. Recently, my friend Joe Nally, a coauthor with Stephen et al, shared with me a paper detailing the romantic yet checkered history of urinalysis.1

Figure 1. Urinalysis on horseback. From the Physician’s Tale in the Ellesmere manuscript of Geoffrey Chaucer’s Canterbury Tales, c. 1400.

Gilles de Corbeil in the 12th century wrote a poem on judging urine, intending it as an aid for remembering the supposed 20 different diagnostic colors of urine and describing in detail the use of the urine flask, a bladder-shaped container for studying the partitioning of the urine colors and substance as representative of the diseased parts of the body. A urine flask was even illustrated in a version of Chaucer’s Canterbury Tales as a recognized accoutrement of the stylish physician (Figure 1). The “art” of uroscopy grew so successful over the centuries as a component of rampant medical charlatanry (casting no aspersions, of course, on current nephrologists) that the Royal College of Physicians in 1601 felt pressed to attack the “pisse-mongers” by stating, “It is ridiculous and foolish to divine the…course of disease…from the inspection of urine.”1 This dictate was apparently ignored then, but seemingly is too frequently followed by clinicians today, contributing to the oft-delayed diagnosis of glomerulonephritis and other renal diseases.

In 1637, Thomas Brian published The Pisse-Prophet or Certaine Pisse Pot Lectures, in which he railed against the witchcraft of uroscopy, which he said should only be performed by university-trained physicians. Jump forward to 1827, when Richard Bright elegantly described acute glomerulonephritis, although not the microscopic findings that would be illustrated in accurate detail by Golding Bird in his 1844 treatise, Urinary Deposits. Sitting on the bookshelf behind my desk is a copy of Richard W. Lippman’s Urine and Urinary Sediment: A Practical Manual and Atlas (1957). I have no urine flask—rheumatologists know their limitations.

As we enter 2014, all of us at the Journal offer you our sincere wishes for a personally healthy and universally peaceful new year.

References
  1. Haber MH. Pisse prophecy: a brief history of urinalysis. Clin Lab Med 1988; 8:415430.
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Great effort has been spent on identifying easily measured biomarkers to predict the progression of coronary disease and chronic kidney disease (CKD). Interestingly, these two disease processes seem to share some biomarkers and perhaps some pathogenic mechanisms. An ultimate hope is that some of these markers will be found to also contribute directly to organ dysfunction and be amenable to therapy. Blood pressure and (in many people’s minds) low-density lipoprotein cholesterol fulfill this hope. The jury remains out on C-reactive protein and serum urate. There are others.

In this issue of the Journal, Stephen et al review the data indicating that albuminuria helps predict the progression of CKD, coronary disease, ventricular remodeling, and, in some studies, all-cause mortality. Proteinuria has generally been assumed to be a marker of renal injury, but, the authors point out, albumin can under some circumstances initiate inflammatory mechanisms and stimulate mediators of fibrosis.

Although not mentioned by Stephen et al, albumin (like hemoglobin) is susceptible to nonenzymatic glycosylation in patients with diabetes. There is a hint in the literature that glycosylated albumin may be preferentially excreted. Its effects on various tissues are incompletely studied, but it strikes me that perhaps this molecule plays a unique pathogenic role in diabetic renal and vascular disease, even more than native albumin. Further evaluation of this specific marker may lead to even stronger associations (although in a select population of patients with poorly controlled diabetes).

The focus on urine as a fluid with diagnostic and predictive characteristics is certainly not new. Both Hippocrates and Galen recognized the value of inspecting urine. Uroscopy (now urinalysis) may be the oldest surviving laboratory test. Recently, my friend Joe Nally, a coauthor with Stephen et al, shared with me a paper detailing the romantic yet checkered history of urinalysis.1

Figure 1. Urinalysis on horseback. From the Physician’s Tale in the Ellesmere manuscript of Geoffrey Chaucer’s Canterbury Tales, c. 1400.

Gilles de Corbeil in the 12th century wrote a poem on judging urine, intending it as an aid for remembering the supposed 20 different diagnostic colors of urine and describing in detail the use of the urine flask, a bladder-shaped container for studying the partitioning of the urine colors and substance as representative of the diseased parts of the body. A urine flask was even illustrated in a version of Chaucer’s Canterbury Tales as a recognized accoutrement of the stylish physician (Figure 1). The “art” of uroscopy grew so successful over the centuries as a component of rampant medical charlatanry (casting no aspersions, of course, on current nephrologists) that the Royal College of Physicians in 1601 felt pressed to attack the “pisse-mongers” by stating, “It is ridiculous and foolish to divine the…course of disease…from the inspection of urine.”1 This dictate was apparently ignored then, but seemingly is too frequently followed by clinicians today, contributing to the oft-delayed diagnosis of glomerulonephritis and other renal diseases.

In 1637, Thomas Brian published The Pisse-Prophet or Certaine Pisse Pot Lectures, in which he railed against the witchcraft of uroscopy, which he said should only be performed by university-trained physicians. Jump forward to 1827, when Richard Bright elegantly described acute glomerulonephritis, although not the microscopic findings that would be illustrated in accurate detail by Golding Bird in his 1844 treatise, Urinary Deposits. Sitting on the bookshelf behind my desk is a copy of Richard W. Lippman’s Urine and Urinary Sediment: A Practical Manual and Atlas (1957). I have no urine flask—rheumatologists know their limitations.

As we enter 2014, all of us at the Journal offer you our sincere wishes for a personally healthy and universally peaceful new year.

Great effort has been spent on identifying easily measured biomarkers to predict the progression of coronary disease and chronic kidney disease (CKD). Interestingly, these two disease processes seem to share some biomarkers and perhaps some pathogenic mechanisms. An ultimate hope is that some of these markers will be found to also contribute directly to organ dysfunction and be amenable to therapy. Blood pressure and (in many people’s minds) low-density lipoprotein cholesterol fulfill this hope. The jury remains out on C-reactive protein and serum urate. There are others.

In this issue of the Journal, Stephen et al review the data indicating that albuminuria helps predict the progression of CKD, coronary disease, ventricular remodeling, and, in some studies, all-cause mortality. Proteinuria has generally been assumed to be a marker of renal injury, but, the authors point out, albumin can under some circumstances initiate inflammatory mechanisms and stimulate mediators of fibrosis.

Although not mentioned by Stephen et al, albumin (like hemoglobin) is susceptible to nonenzymatic glycosylation in patients with diabetes. There is a hint in the literature that glycosylated albumin may be preferentially excreted. Its effects on various tissues are incompletely studied, but it strikes me that perhaps this molecule plays a unique pathogenic role in diabetic renal and vascular disease, even more than native albumin. Further evaluation of this specific marker may lead to even stronger associations (although in a select population of patients with poorly controlled diabetes).

The focus on urine as a fluid with diagnostic and predictive characteristics is certainly not new. Both Hippocrates and Galen recognized the value of inspecting urine. Uroscopy (now urinalysis) may be the oldest surviving laboratory test. Recently, my friend Joe Nally, a coauthor with Stephen et al, shared with me a paper detailing the romantic yet checkered history of urinalysis.1

Figure 1. Urinalysis on horseback. From the Physician’s Tale in the Ellesmere manuscript of Geoffrey Chaucer’s Canterbury Tales, c. 1400.

Gilles de Corbeil in the 12th century wrote a poem on judging urine, intending it as an aid for remembering the supposed 20 different diagnostic colors of urine and describing in detail the use of the urine flask, a bladder-shaped container for studying the partitioning of the urine colors and substance as representative of the diseased parts of the body. A urine flask was even illustrated in a version of Chaucer’s Canterbury Tales as a recognized accoutrement of the stylish physician (Figure 1). The “art” of uroscopy grew so successful over the centuries as a component of rampant medical charlatanry (casting no aspersions, of course, on current nephrologists) that the Royal College of Physicians in 1601 felt pressed to attack the “pisse-mongers” by stating, “It is ridiculous and foolish to divine the…course of disease…from the inspection of urine.”1 This dictate was apparently ignored then, but seemingly is too frequently followed by clinicians today, contributing to the oft-delayed diagnosis of glomerulonephritis and other renal diseases.

In 1637, Thomas Brian published The Pisse-Prophet or Certaine Pisse Pot Lectures, in which he railed against the witchcraft of uroscopy, which he said should only be performed by university-trained physicians. Jump forward to 1827, when Richard Bright elegantly described acute glomerulonephritis, although not the microscopic findings that would be illustrated in accurate detail by Golding Bird in his 1844 treatise, Urinary Deposits. Sitting on the bookshelf behind my desk is a copy of Richard W. Lippman’s Urine and Urinary Sediment: A Practical Manual and Atlas (1957). I have no urine flask—rheumatologists know their limitations.

As we enter 2014, all of us at the Journal offer you our sincere wishes for a personally healthy and universally peaceful new year.

References
  1. Haber MH. Pisse prophecy: a brief history of urinalysis. Clin Lab Med 1988; 8:415430.
References
  1. Haber MH. Pisse prophecy: a brief history of urinalysis. Clin Lab Med 1988; 8:415430.
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Albuminuria: When urine predicts kidney and cardiovascular disease

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Albuminuria: When urine predicts kidney and cardiovascular disease

One can obtain considerable information concerning the general health by examining the urine.” 
—Hippocrates (460?–355? BCE)

Chronic kidney disease is a notable public health concern because it is an important risk factor for end-stage renal disease, cardiovascular disease, and death. Its prevalence1 exceeds 10% and is considerably higher in high-risk groups, such as those with diabetes or hypertension, which are growing in the United States.

While high levels of total protein in the urine have always been recognized as pathologic, a growing body of evidence links excretion of the protein albumin to adverse cardiovascular outcomes, and most international guidelines now recommend measuring albumin specifically. Albuminuria is a predictor of declining renal function and is independently associated with adverse cardiovascular outcomes. Thus, clinicians need to detect it early, manage it effectively, and reduce concurrent risk factors for cardiovascular disease.

Therefore, this review will focus on albuminuria. However, because the traditional standard for urinary protein measurement was total protein, and because a few guidelines still recommend measuring total protein rather than albumin, we will also briefly discuss total urinary protein.

MOST URINARY PROTEIN IS ALBUMIN

Most of the protein in the urine is albumin filtered from the plasma. Less than half of the rest is derived from the distal renal tubules (uromodulin or Tamm-Horsfall mucoprotein), 2 and urine also contains a small and varying proportion of immunoglobulins, low-molecular-weight proteins, and light chains.

Normal healthy people lose less than 30 mg of albumin in the urine per day. In greater amounts, albumin is the major urinary protein in most kidney diseases. Other proteins in urine can be specific markers of less-common illnesses such as plasma cell dyscrasia, glomerulopathy, and renal tubular disease.

MEASURING PROTEINURIA AND ALBUMINURIA

Albumin is not a homogeneous molecule in urine. It undergoes changes to its molecular configuration in the presence of certain ions, peptides, hormones, and drugs, and as a result of proteolytic fragmentation both in the plasma and in renal tubules.3 Consequently, measuring urinary albumin involves a trade-off between convenience and accuracy.

A 24-hour timed urine sample has long been the gold standard for measuring albuminuria, but the collection is cumbersome and time-consuming, and the test is prone to laboratory error.

Dipstick measurements are more convenient and are better at detecting albumin than other proteins in urine, but they have low sensitivity and high interobserver variation.3–5

The albumin-to-creatinine ratio (ACR). As the quantity of protein in the urine changes with time of day, exertion, stress level, and posture, spot-checking of urine samples is not as good as timed collection. However, a simultaneous measurement of creatinine in a spot urine sample adjusts for protein concentration, which can vary with a person’s hydration status. The ACR so obtained is consistent with the 24-hour timed collection (the gold standard) and is the recommended method of assessing albuminuria.3 An early morning urine sample is favored, as it avoids orthostatic variations and varies less in the same individual.

In a study in the general population comparing the ACR in a random sample and in an early morning sample, only 44% of those who had an ACR of 30 mg/g or higher in the random sample had one this high in the early morning sample.6 However, getting an early morning sample is not always feasible in clinical practice. If you are going to measure albuminuria, the Kidney Disease Outcomes and Quality Initiative7 suggests checking the ACR in a random sample and then, if the test is positive, following up and confirming it within 3 months with an early morning sample.

Also, since creatinine excretion differs with race, diet, and muscle mass, if the 24-hour creatinine excretion is not close to 1 g, the ACR will give an erroneous estimate of the 24-hour excretion rate.3

Table 1 compares the various methods of measuring protein in the urine.3,5,8,9 Of note, methods of measuring albumin and total protein vary considerably in their precision and accuracy, making it impossible to reliably translate values from one to the other.5

National and international guidelines (Table 2)7,10–13 agree that albuminuria should be tested in diabetic patients, as it is a surrogate marker for early diabetic nephropathy.3,13 Most guidelines also recommend measuring albuminuria by a urine ACR test as the preferred measure, even in people without diabetes.

Also, no single cutoff is universally accepted for distinguishing pathologic albuminuria from physiologic albuminuria, nor is there a universally accepted unit of measure.14 Because approximately 1 g of creatinine is lost in the urine per day, the ACR has the convenient property of numerically matching the albumin excretory rate expressed in milligrams per 24 hours. The other commonly used unit is milligrams of albumin per millimole of creatinine; 30 mg/g is roughly equal to 3 mg/mmol.

The term microalbuminuria was traditionally used to refer to albumin excretion of 30 to 299 mg per 24 hours, and macroalbuminuria to 300 mg or more per 24 hours. However, as there is no pathophysiologic basis to these thresholds (see outcomes data below), the current Kidney Disease Improving Global Outcomes (KDIGO) guidelines do not recommend using these terms.13,15

 

 

RENAL COMPLICATIONS OF ALBUMINURIA

A failure of the glomerular filtration barrier or of proximal tubular reabsorption accounts for most cases of pathologic albuminuria.16 Processes affecting the glomerular filtration of albumin include endothelial cell dysfunction and abnormalities with the glomerular basement membrane, podocytes, or the slit diaphragms among the podocytic processes.17

Ultrafiltrated albumin has been directly implicated in tubulointerstitial damage and glomerulosclerosis through diverse pathways. In the proximal tubule, albumin up-regulates interleukin 8 (a chemoattractant for lymphocytes and neutrophils), induces synthesis of endothelin 1 (which stimulates renal cell proliferation, extracellular matrix production, and monocyte attraction), and causes apoptosis of tubular cells.18 In response to albumin, proximal tubular cells also stimulate interstitial fibroblasts via paracrine release of transforming growth factor beta, either directly or by activating complement or macrophages.18,19

Studies linking albuminuria to kidney disease

Albuminuria has traditionally been associated with diabetes mellitus as a predictor of overt diabetic nephropathy,20,21 although in type 1 diabetes, established albuminuria can spontaneously regress.22

Albuminuria is also a strong predictor of progression in chronic kidney disease.23 In fact, in the last decade, albuminuria has become an independent criterion in the definition of chronic kidney disease; excretion of more than 30 mg of albumin per day, sustained for at least 3 months, qualifies as chronic kidney disease, with independent prognostic implications (Table  3).13

Astor et al,24 in a meta-analysis of 13 studies with more than 21,000 patients with chronic kidney disease, found that the risk of end-stage renal disease was three times higher in those with albuminuria.

Gansevoort et al,23 in a meta-analysis of nine studies with nearly 850,000 participants from the general population, found that the risk of end-stage renal disease increased continuously as albumin excretion increased. They also found that as albuminuria increased, so did the risk of progression of chronic kidney disease and the incidence of acute kidney injury.

Hemmelgarn et al,25 in a large pooled cohort study with more than 1.5 million participants from the general population, showed that increasing albuminuria was associated with a decline in the estimated glomerular filtration rate (GFR) and with progression to end-stage renal disease across all strata of baseline renal function. For example, in persons with an estimated GFR of 60 mL/min/1.73 m2

  • 1 per 1,000 person-years for those with no proteinuria
  • 2.8 per 1,000 person-years for those with mild proteinuria (trace or 1+ by dipstick or ACR 30–300 mg/g)
  • 13.4 per 1,000 person-years for those with heavy proteinuria (2+ or ACR > 300 mg/g).

Rates of progression to end-stage renal disease were:

  • 0.03 per 1,000 person-years with no proteinuria
  • 0.05 per 1,000 person-years with mild proteinuria
  • 1 per 1,000 person-years with heavy proteinuria.25

CARDIOVASCULAR CONSEQUENCES OF ALBUMINURIA

The exact pathophysiologic link between albuminuria and cardiovascular disease is unknown, but several mechanisms have been proposed.

One is that generalized endothelial dysfunction causes both albuminuria and cardiovascular disease.26 Endothelium-derived nitric oxide has vasodilator, antiplatelet, antiproliferative, antiadhesive, permeability-decreasing, and anti-inflammatory properties. Impaired endothelial synthesis of nitric oxide has been independently associated with both microalbuminuria and diabetes.27

Low levels of heparan sulfate (which has antithrombogenic effects and decreases vessel permeability) in the glycocalyx lining vessel walls may also account for albuminuria and for the other cardiovascular effects.28–30 These changes may be the consequence of chronic low-grade inflammation, which precedes the occurrence and progression of both albuminuria and atherothrombotic disease. The resulting abnormalities in the endothelial glycocalyx could lead to increased glomerular permeability to albumin and may also be implicated in the pathogenesis of atherosclerosis.26

In an atherosclerotic aorta and coronary arteries, the endothelial dysfunction may cause increased leakage of cholesterol and glycated end-products into the myocardium, resulting in increasing wall stiffness and left ventricular mass. A similar atherosclerotic process may account for coronary artery microthrombi, resulting in subendocardial ischemia that could lead to systolic and diastolic heart dysfunction.31

Studies linking albuminuria to heart disease

There is convincing evidence that albuminuria is associated with cardiovascular disease. An ACR between 30 and 300 mg/g was independently associated with myocardial infarction and ischemia.32 People with albuminuria have more than twice the risk of severe coronary artery disease, and albuminuria is also associated with increased intimal thickening in the carotid arteries.33,34 An ACR in the same range has been associated with increased incidence and progression of coronary artery calcification.35 Higher brachial-ankle pulse-wave velocity has also been demonstrated with albuminuria in a dose-dependent fashion.36,37

An ACR of 30 to 300 mg/g has been linked to left ventricular hypertrophy independently of other risk factors,38 and functionally with diastolic dysfunction and abnormal midwall shortening.39 In a study of a subgroup of patients with diabetes from a population-based cohort of Native American patients (the Strong Heart Study),39 the prevalence of diastolic dysfunction was:

  • 16% in those with no albuminuria
  • 26% in those with an ACR of 30 to 300 mg/g
  • 31% in those with an ACR greater than 300 mg/g.

The association persisted even after controlling for age, sex, hypertension, and other covariates.

Those pathologic associations have been directly linked to clinical outcomes. For patients with heart failure (New York Heart Association class II–IV), a study found that albuminuria (an ACR > 30 mg/g) conferred a 41% higher risk of admission for heart failure, and an ACR greater than 300 mg/g was associated with an 88% higher risk.40

In an analysis of a prospective cohort from the general population with albuminuria and a low prevalence of renal dysfunction (the Prevention of Renal and Vascular Endstage Disease study),41 albuminuria was associated with a modest increase in the incidence of the composite end point of myocardial infarction, stroke, ischemic heart disease, revascularization procedures, and all-cause mortality per doubling of the urine albumin excretion (hazard ratio 1.08, range 1.04 –1.12).

The relationship to cardiovascular outcomes extends below traditional lower-limit thresholds of albuminuria (corresponding to an ACR > 30 mg/g). A subgroup of patients from the Framingham Offspring Study without prevalent cardiovascular disease, hypertension, diabetes, or kidney disease, and thus with a low to intermediate probability of cardiovascular events, were found to have thresholds for albuminuria as low as 5.3 mg/g in men and 10.8 mg/g in women to discriminate between incident coronary artery disease, heart failure, cerebrovascular disease, other peripheral vascular disease, or death.42

In a meta-analysis including more than 1 million patients in the general population, increasing albuminuria was associated with an increase in deaths from all causes in a continuous manner, with no threshold effect.43 In patients with an ACR of 30 mg/g, the hazard ratio for death was 1.63, increasing to 2.22 for those with more than 300 mg/g compared with those with no albuminuria. A similar increase in the risk of myocardial infarction, heart failure, stroke, or sudden cardiac death was noted with higher ACR.43

Important prospective cohort studies and meta-analyses related to albuminuria and kidney and cardiovascular disease and death are summarized in the eTable.23,39–50

 

 

THE CASE FOR TREATING ALBUMINURIA

Reduced progression of renal disease

Treating patients who have proteinuric chronic kidney disease with an angiotensin-converting enzyme (ACE) inhibitor or an angiotensin receptor blocker (ARB) can reduce the risk of progression of renal failure. However, it is unclear whether this benefit is the result of treating concomitant risk factors independent of the reduction in albuminuria, and there is no consistent treatment effect in improving renal outcomes across studies.

Fink et al,51 in a meta-analysis, found that chronic kidney disease patients with diabetes, hypertension, and macroalbuminuria had a 40% lower risk of progression to end-stage renal disease if they received an ACE inhibitor (relative risk [RR] 0.60, 95% confidence interval [CI] 0.43–0.83). In the same meta-analysis, ARBs also reduced the risk of progression to end-stage renal disease (RR 0.77, 95% CI 0.66–0.90).

Jafar et al,52 in an analysis of pooled patient-level data including only nondiabetic patients on ACE inhibitor therapy (n = 1,860), found that the risk of progression of renal failure, defined as a doubling of serum creatinine or end-stage renal disease, was reduced (RR 0.70, 95% CI 0.55–0.88). Patients with higher levels of albuminuria showed the most benefit, but the effect was not conclusive for albuminuria below 500 mg/day at baseline.

Maione et al,53 in a meta-analysis that included patients with albuminuria who were treated with ACE inhibitors vs placebo (n = 8,231), found a similar reduction in risk of:

  • Progression to end-stage renal disease (RR 0.67, 95% CI 0.54–0.84)
  • Doubling of serum creatinine (RR 0.62, 95% CI 0.46–0.84)
  • Progression of albuminuria (RR 0.49, 95% CI 0.36–0.65)
  • Normalization of pathologic albuminuria (as defined by the trialists in the individual studies) (RR 2.99, 95% CI 1.82–4.91).

Similar results were obtained for patients with albuminuria who were treated with ARBs.53

ONTARGET.54 In contrast, in the Ongoing Telmisartan Alone and in Combination With Ramipril Global Endpoint Trial, the combination of an ACE inhibitor and an ARB showed no benefit in reducing the progression of renal failure, and in those patients with chronic kidney disease there was a higher risk of a doubling of serum creatinine or of the development of end-stage renal disease and hyperkalemia.

Also, in a pooled analysis of the ONTARGET and Telmisartan Randomized Assessment Study in ACE Intolerant Subjects With Cardiovascular Disease (TRANSCEND) trials, a 50% reduction in baseline albuminuria was associated with reduced progression of renal failure in those with a baseline ACR less than 10 mg/g.55

Improved cardiovascular outcomes

There is also evidence of better cardiovascular outcomes with treatment of albuminuria. Again, it is uncertain whether this is a result of treating risk factors other than albuminuria with ACE inhibitors or ARBs, and there is no parallel benefit demonstrated across all studies.

LIFE.47,48 In the Losartan Intervention for Endpoint Reduction in Hypertension trial, survival analyses suggested a decrease in risk of cardiovascular adverse events as the degree of proteinuria improved with ARB therapy.

Maione et al,53 in a meta-analysis including 8,231 patients with albuminuria and at least one other risk factor, found a significant reduction in the rate of nonfatal cardiovascular outcomes (angina, myocardial infarction, revascularization, stroke, transient ischemic attack, or heart failure) with ACE inhibitors vs placebo (RR 0.88, CI 0.82–0.94) and also in 3,888 patients treated with ARBs vs placebo (RR 0.77, CI 0.61–0.98). However, the meta-analysis did not show that ACE inhibitor or ARB therapy reduced rate of cardiovascular or all-cause mortality.

Fink et al,51 in their meta-analysis of 18 trials of ACE inhibitors and four trials of ARBs, also found no evidence that ACE inhibitor or ARB therapy reduced cardiovascular mortality rates.38

The ONTARGET trial evaluated the combination of an ACE inhibitor and ARB therapy in patients with diabetes or preexisting peripheral vascular disease. Reductions in the rate of cardiovascular disease or death were not observed, and in those with chronic kidney disease, there was a higher risk of doubling of serum creatinine or development of end-stage renal disease and adverse events of hyperkalemia.56 And although an increase in baseline albuminuria was associated with worse cardiovascular outcomes, its reduction in the ONTARGET and TRANSCEND trials did not demonstrate better outcomes when the baseline ACR was greater than 10 mg/g.55

WHO SHOULD BE TESTED?

The benefit of adding albuminuria to conventional cardiovascular risk stratification such as Framingham risk scoring is not conclusive. However, today’s clinician may view albuminuria as a biomarker for renal and cardiovascular disease, as albuminuria might be a surrogate marker for endothelial dysfunction in the glomerular capillaries or other vital vascular beds.

High-risk populations and chronic kidney disease patients

Nearly all the current guidelines recommend annual screening for albuminuria in patients with diabetes and hypertension (Table 2).7,10–13 Other high-risk populations include people with cardiovascular disease, a family history of end-stage renal disease, and metabolic syndrome. Additionally, chronic kidney disease patients whose estimated GFR defines them as being in stage 3 or higher (ie, GFR < 60 mL/min/1.73m2), regardless of other comorbidities, should be tested for albuminuria, as it is an important risk predictor.

Most experts prefer that albuminuria be measured by urine ACR in a first morning voided sample, though this is not the only option.

Screening the general population

Given that albuminuria has been shown to be such an important prognosticator for patients at high risk and those with chronic kidney disease, the question arises whether screening for albuminuria in the asymptomatic low-risk general population would foster earlier detection and therefore enable earlier intervention and result in improved outcomes. However, a systematic review done for the United States Preventive Services Task Force and for an American College of Physicians clinical practice guideline did not find robust evidence to support this.51

OUR RECOMMENDATIONS

Who should be tested?

  • Patients with chronic kidney disease stage 3, 4, or 5 (GFR < 60 mL/min/1.73m2) who are not on dialysis
  • Patients who are considered at higher risk of adverse outcomes, such as those with diabetes, hypertension, a family history of end-stage renal disease, or cardiovascular disease. Testing is useful for recognizing increased renal and cardiovascular risk and may lead clinicians to prescribe or titrate a renin-angiotensin system antagonist, a statin, or both, or to modify other cardiovascular risk factors.
  • Not recommended: routine screening in the general population who are asymptomatic or are considered at low risk.

Which test should be used?

Based on current evidence and most guidelines, we recommend the urine ACR test as the screening test for people with diabetes and others deemed to be at high risk.

What should be done about albuminuria?

  • Controlling blood pressure is important, and though there is debate about the target blood pressure, an individualized plan should be developed with the patient based on age, comorbidities, and goals of care.
  • An ACE inhibitor or ARB, if not contraindicated, is recommended for patients with diabetes whose ACR is greater than 30 mg/g and for patients with chronic kidney disease and an ACR greater than 300 mg/g.
  • Current evidence does not support the combined use of an ACE inhibitor and an ARB, as proof of benefit is lacking and the risk of adverse events is higher.
  • Refer patients with high or unexplained albuminuria to a nephrologist or clinic specializing in chronic kidney disease.
References
  1. Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA 2007; 298:20382047.
  2. Hoyer JR, Seiler MW. Pathophysiology of Tamm-Horsfall protein. Kidney Int 1979; 16:279289.
  3. Viswanathan G, Upadhyay A. Assessment of proteinuria. Adv Chronic Kidney Dis 2011; 18:243248.
  4. Guh JY. Proteinuria versus albuminuria in chronic kidney disease. Nephrology (Carlton) 2010; 15(suppl 2):5356.
  5. Lamb EJ, MacKenzie F, Stevens PE. How should proteinuria be detected and measured? Ann Clin Biochem 2009; 46:205217.
  6. Saydah SH, Pavkov ME, Zhang C, et al. Albuminuria prevalence in first morning void compared with previous random urine from adults in the National Health and Nutrition Examination Survey, 2009-2010. Clin Chem 2013; 59:675683.
  7. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002; 39(suppl 1):S1S266.
  8. Younes N, Cleary PA, Steffes MW, et al; DCCT/EDIC Research Group. Comparison of urinary albumin-creatinine ratio and albumin excretion rate in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study. Clin J Am Soc Nephrol 2010; 5:12351242.
  9. Brinkman JW, de Zeeuw D, Duker JJ, et al. Falsely low urinary albumin concentrations after prolonged frozen storage of urine samples. Clin Chem 2005; 51:21812183.
  10. National Collaborating Centre for Chronic Conditions (UK). Chronic Kidney Disease: National Clinical Guideline for Early Identification and Management in Adults in Primary and Secondary Care. London: Royal College of Physicians (UK) 2008.
  11. American Diabetes Association. Standards of medical care in diabetes—2013. Diabetes Care 2013; 36(suppl 1):S11S66.
  12. Chobanian AV, Bakris GL, Black HR, et al; Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003; 42:12061252.
  13. Kidney Disease Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013; 3:1150.
  14. Johnson DW. Global proteinuria guidelines: are we nearly there yet? Clin Biochem Rev 2011; 32:8995.
  15. Ruggenenti P, Remuzzi G. Time to abandon microalbuminuria? Kidney Int 2006; 70:12141222.
  16. Glassock RJ. Is the presence of microalbuminuria a relevant marker of kidney disease? Curr Hypertens Rep 2010; 12:364368.
  17. Zhang A, Huang S. Progress in pathogenesis of proteinuria. Int J Nephrol 2012; 2012:314251.
  18. Abbate M, Zoja C, Remuzzi G. How does proteinuria cause progressive renal damage? J Am Soc Nephrol 2006; 17:29742984.
  19. Karalliedde J, Viberti G. Proteinuria in diabetes: bystander or pathway to cardiorenal disease? J Am Soc Nephrol 2010; 21:20202027.
  20. Svendsen PA, Oxenbøll B, Christiansen JS. Microalbuminuria in diabetic patients—a longitudinal study. Acta Endocrinol Suppl (Copenh) 1981; 242:5354.
  21. Viberti GC, Hill RD, Jarrett RJ, Argyropoulos A, Mahmud U, Keen H. Microalbuminuria as a predictor of clinical nephropathy in insulin-dependent diabetes mellitus. Lancet 1982; 1:14301432.
  22. Perkins BA, Ficociello LH, Silva KH, Finkelstein DM, Warram JH, Krolewski AS. Regression of microalbuminuria in type 1 diabetes. N Engl J Med 2003; 348:22852293.
  23. Gansevoort RT, Matsushita K, van der Velde M, et al; Chronic Kidney Disease Prognosis Consortium. Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. Kidney Int 2011; 80:93104.
  24. Astor BC, Matsushita K, Gansevoort RT, et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts. Kidney Int 2011; 79:13311340.
  25. Hemmelgarn BR, Manns BJ, Lloyd A, et al; Alberta Kidney Disease Network. Relation between kidney function, proteinuria, and adverse outcomes. JAMA 2010; 303:423429.
  26. Stehouwer CD, Smulders YM. Microalbuminuria and risk for cardiovascular disease: analysis of potential mechanisms. J Am Soc Nephrol 2006; 17:21062111.
  27. Stehouwer CD, Henry RM, Dekker JM, Nijpels G, Heine RJ, Bouter LM. Microalbuminuria is associated with impaired brachial artery, flow-mediated vasodilation in elderly individuals without and with diabetes: further evidence for a link between microalbuminuria and endothelial dysfunction—the Hoorn Study. Kidney Int Suppl 2004; 92:S42S44.
  28. Wasty F, Alavi MZ, Moore S. Distribution of glycosaminoglycans in the intima of human aortas: changes in atherosclerosis and diabetes mellitus. Diabetologia 1993; 36:316322.
  29. Ylä-Herttuala S, Sumuvuori H, Karkola K, Möttönen M, Nikkari T. Glycosaminoglycans in normal and atherosclerotic human coronary arteries. Lab Invest 1986; 54:402407.
  30. Deckert T, Feldt-Rasmussen B, Borch-Johnsen K, Jensen T, Kofoed-Enevoldsen A. Albuminuria reflects widespread vascular damage. The Steno hypothesis. Diabetologia 1989; 32:219226.
  31. van Hoeven KH, Factor SM. A comparison of the pathological spectrum of hypertensive, diabetic, and hypertensive-diabetic heart disease. Circulation 1990; 82:848855.
  32. Diercks GF, van Boven AJ, Hillege HL, et al. Microalbuminuria is independently associated with ischaemic electrocardiographic abnormalities in a large non-diabetic population. The PREVEND (Prevention of REnal and Vascular ENdstage Disease) study. Eur Heart J 2000; 21:19221927.
  33. Bigazzi R, Bianchi S, Nenci R, Baldari D, Baldari G, Campese VM. Increased thickness of the carotid artery in patients with essential hypertension and microalbuminuria. J Hum Hypertens 1995; 9:827833.
  34. Tuttle KR, Puhlman ME, Cooney SK, Short R. Urinary albumin and insulin as predictors of coronary artery disease: an angiographic study. Am J Kidney Dis 1999; 34:918925.
  35. DeFilippis AP, Kramer HJ, Katz R, et al. Association between coronary artery calcification progression and microalbuminuria: the MESA study. JACC Cardiovasc Imaging 2010; 3:595604.
  36. Liu CS, Pi-Sunyer FX, Li CI, et al. Albuminuria is strongly associated with arterial stiffness, especially in diabetic or hypertensive subjects—a population-based study (Taichung Community Health Study, TCHS). Atherosclerosis 2010; 211:315321.
  37. Upadhyay A, Hwang SJ, Mitchell GF, et al. Arterial stiffness in mild-to-moderate CKD. J Am Soc Nephrol 2009; 20:20442053.
  38. Pontremoli R, Sofia A, Ravera M, et al. Prevalence and clinical correlates of microalbuminuria in essential hypertension: the MAGIC Study. Microalbuminuria: a Genoa Investigation on Complications. Hypertension 1997; 30:11351143.
  39. Liu JE, Robbins DC, Palmieri V, et al. Association of albuminuria with systolic and diastolic left ventricular dysfunction in type 2 diabetes: the Strong Heart Study. J Am Coll Cardiol 2003; 41:20222028.
  40. Jackson CE, Solomon SD, Gerstein HC, et al; CHARM Investigators and Committees. Albuminuria in chronic heart failure: prevalence and prognostic importance. Lancet 2009; 374:543550.
  41. Smink PA, Lambers Heerspink HJ, Gansevoort RT, et al. Albuminuria, estimated GFR, traditional risk factors, and incident cardiovascular disease: the PREVEND (Prevention of Renal and Vascular Endstage Disease) study. Am J Kidney Dis 2012; 60:804811.
  42. Arnlöv J, Evans JC, Meigs JB, et al. Low-grade albuminuria and incidence of cardiovascular disease events in nonhypertensive and nondiabetic individuals: the Framingham Heart Study. Circulation 2005; 112:969975.
  43. Chronic Kidney Disease Prognosis Consortium; Matsushita K, van der Velde M, Astor BC, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 2010; 375:20732081.
  44. van der Velde M, Matsushita K, Coresh J, et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney Int 2011; 79:13411352.
  45. Ruggenenti P, Porrini E, Motterlini N, et al; BENEDICT Study Investigators. Measurable urinary albumin predicts cardiovascular risk among normoalbuminuric patients with type 2 diabetes. J Am Soc Nephrol 2012; 23:17171724.
  46. Hallan S, Astor B, Romundstad S, Aasarød K, Kvenild K, Coresh J. Association of kidney function and albuminuria with cardiovascular mortality in older vs younger individuals: the HUNT II Study. Arch Intern Med 2007; 167:24902496.
  47. Ibsen H, Wachtell K, Olsen MH, et al. Albuminuria and cardiovascular risk in hypertensive patients with left ventricular hypertrophy: the LIFE Study. Kidney Int Suppl 2004; 92:S56S58.
  48. Olsen MH, Wachtell K, Bella JN, et al. Albuminuria predicts cardiovascular events independently of left ventricular mass in hypertension: a LIFE substudy. J Hum Hypertens 2004; 18:453459.
  49. Klausen K, Borch-Johnsen K, Feldt-Rasmussen B, et al. Very low levels of microalbuminuria are associated with increased risk of coronary heart disease and death independently of renal function, hypertension, and diabetes. Circulation 2004; 110:3235.
  50. Gerstein HC, Mann JF, Yi Q, et al; HOPE Study Investigators. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA 2001; 286:421426.
  51. Fink HA, Ishani A, Taylor BC, et al. Screening for, monitoring, and treatment of chronic kidney disease stages 1 to 3: a systematic review for the US Preventive Services Task Force and for an American College of Physicians Clinical Practice Guideline. Ann Intern Med 2012; 156:570581.
  52. Jafar TH, Schmid CH, Landa M, et al. Angiotensin-converting enzyme inhibitors and progression of nondiabetic renal disease. A meta-analysis of patient-level data. Ann Intern Med 2001; 135:7387.
  53. Maione A, Navaneethan SD, Graziano G, et al. Angiotensin-converting enzyme inhibitors, angiotensin receptor blockers and combined therapy in patients with micro- and macroalbuminuria and other cardiovascular risk factors: a systematic review of randomized controlled trials. Nephrol Dial Transplant 2011; 26:28272847.
  54. Mann JF, Schmieder RE, McQueen M, et al; ONTARGET investigators. Renal outcomes with telmisartan, ramipril, or both, in people at high vascular risk (the ONTARGET study): a multicentre, randomised, double-blind, controlled trial. Lancet 2008; 372:547553.
  55. Schmieder RE, Mann JF, Schumacher H, et al; ONTARGET Investigators. Changes in albuminuria predict mortality and morbidity in patients with vascular disease. J Am Soc Nephrol 2011; 22:13531364.
  56. Tobe SW, Clase CM, Gao P, et al; ONTARGET and TRANSCEND Investigators. Cardiovascular and renal outcomes with telmisartan, ramipril, or both in people at high renal risk: results from the ONTARGET and TRANSCEND studies. Circulation 2011; 123:10981107.
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Bridgeport Hospital, Yale New Haven Health System, Department of Internal Medicine, Bridgeport, CT

Stacey E. Jolly, MD, MAS
Department of General Internal Medicine, Medicine Institute, Cleveland Clinic

Joseph V. Nally, MD
Department of Nephrology and Hypertension, Glickman Urological and Kidney Institute, Cleveland Clinic; Clinical Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Sankar D. Navaneethan, MD, MPH
Department of Nephrology and Hypertension, Vice-Chair, Novick Center for Clinical and Translational Research, Glickman Urological and Kidney Institute, Cleveland Clinic; Assistant Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Address: Sankar D. Navaneethan, MD, MPH, Department of Nephrology and Hypertension, Glickman Urological and Kidney Institute, Q7, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

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Bridgeport Hospital, Yale New Haven Health System, Department of Internal Medicine, Bridgeport, CT

Stacey E. Jolly, MD, MAS
Department of General Internal Medicine, Medicine Institute, Cleveland Clinic

Joseph V. Nally, MD
Department of Nephrology and Hypertension, Glickman Urological and Kidney Institute, Cleveland Clinic; Clinical Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Sankar D. Navaneethan, MD, MPH
Department of Nephrology and Hypertension, Vice-Chair, Novick Center for Clinical and Translational Research, Glickman Urological and Kidney Institute, Cleveland Clinic; Assistant Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Address: Sankar D. Navaneethan, MD, MPH, Department of Nephrology and Hypertension, Glickman Urological and Kidney Institute, Q7, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

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Bridgeport Hospital, Yale New Haven Health System, Department of Internal Medicine, Bridgeport, CT

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Joseph V. Nally, MD
Department of Nephrology and Hypertension, Glickman Urological and Kidney Institute, Cleveland Clinic; Clinical Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Sankar D. Navaneethan, MD, MPH
Department of Nephrology and Hypertension, Vice-Chair, Novick Center for Clinical and Translational Research, Glickman Urological and Kidney Institute, Cleveland Clinic; Assistant Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Address: Sankar D. Navaneethan, MD, MPH, Department of Nephrology and Hypertension, Glickman Urological and Kidney Institute, Q7, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

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Related Articles

One can obtain considerable information concerning the general health by examining the urine.” 
—Hippocrates (460?–355? BCE)

Chronic kidney disease is a notable public health concern because it is an important risk factor for end-stage renal disease, cardiovascular disease, and death. Its prevalence1 exceeds 10% and is considerably higher in high-risk groups, such as those with diabetes or hypertension, which are growing in the United States.

While high levels of total protein in the urine have always been recognized as pathologic, a growing body of evidence links excretion of the protein albumin to adverse cardiovascular outcomes, and most international guidelines now recommend measuring albumin specifically. Albuminuria is a predictor of declining renal function and is independently associated with adverse cardiovascular outcomes. Thus, clinicians need to detect it early, manage it effectively, and reduce concurrent risk factors for cardiovascular disease.

Therefore, this review will focus on albuminuria. However, because the traditional standard for urinary protein measurement was total protein, and because a few guidelines still recommend measuring total protein rather than albumin, we will also briefly discuss total urinary protein.

MOST URINARY PROTEIN IS ALBUMIN

Most of the protein in the urine is albumin filtered from the plasma. Less than half of the rest is derived from the distal renal tubules (uromodulin or Tamm-Horsfall mucoprotein), 2 and urine also contains a small and varying proportion of immunoglobulins, low-molecular-weight proteins, and light chains.

Normal healthy people lose less than 30 mg of albumin in the urine per day. In greater amounts, albumin is the major urinary protein in most kidney diseases. Other proteins in urine can be specific markers of less-common illnesses such as plasma cell dyscrasia, glomerulopathy, and renal tubular disease.

MEASURING PROTEINURIA AND ALBUMINURIA

Albumin is not a homogeneous molecule in urine. It undergoes changes to its molecular configuration in the presence of certain ions, peptides, hormones, and drugs, and as a result of proteolytic fragmentation both in the plasma and in renal tubules.3 Consequently, measuring urinary albumin involves a trade-off between convenience and accuracy.

A 24-hour timed urine sample has long been the gold standard for measuring albuminuria, but the collection is cumbersome and time-consuming, and the test is prone to laboratory error.

Dipstick measurements are more convenient and are better at detecting albumin than other proteins in urine, but they have low sensitivity and high interobserver variation.3–5

The albumin-to-creatinine ratio (ACR). As the quantity of protein in the urine changes with time of day, exertion, stress level, and posture, spot-checking of urine samples is not as good as timed collection. However, a simultaneous measurement of creatinine in a spot urine sample adjusts for protein concentration, which can vary with a person’s hydration status. The ACR so obtained is consistent with the 24-hour timed collection (the gold standard) and is the recommended method of assessing albuminuria.3 An early morning urine sample is favored, as it avoids orthostatic variations and varies less in the same individual.

In a study in the general population comparing the ACR in a random sample and in an early morning sample, only 44% of those who had an ACR of 30 mg/g or higher in the random sample had one this high in the early morning sample.6 However, getting an early morning sample is not always feasible in clinical practice. If you are going to measure albuminuria, the Kidney Disease Outcomes and Quality Initiative7 suggests checking the ACR in a random sample and then, if the test is positive, following up and confirming it within 3 months with an early morning sample.

Also, since creatinine excretion differs with race, diet, and muscle mass, if the 24-hour creatinine excretion is not close to 1 g, the ACR will give an erroneous estimate of the 24-hour excretion rate.3

Table 1 compares the various methods of measuring protein in the urine.3,5,8,9 Of note, methods of measuring albumin and total protein vary considerably in their precision and accuracy, making it impossible to reliably translate values from one to the other.5

National and international guidelines (Table 2)7,10–13 agree that albuminuria should be tested in diabetic patients, as it is a surrogate marker for early diabetic nephropathy.3,13 Most guidelines also recommend measuring albuminuria by a urine ACR test as the preferred measure, even in people without diabetes.

Also, no single cutoff is universally accepted for distinguishing pathologic albuminuria from physiologic albuminuria, nor is there a universally accepted unit of measure.14 Because approximately 1 g of creatinine is lost in the urine per day, the ACR has the convenient property of numerically matching the albumin excretory rate expressed in milligrams per 24 hours. The other commonly used unit is milligrams of albumin per millimole of creatinine; 30 mg/g is roughly equal to 3 mg/mmol.

The term microalbuminuria was traditionally used to refer to albumin excretion of 30 to 299 mg per 24 hours, and macroalbuminuria to 300 mg or more per 24 hours. However, as there is no pathophysiologic basis to these thresholds (see outcomes data below), the current Kidney Disease Improving Global Outcomes (KDIGO) guidelines do not recommend using these terms.13,15

 

 

RENAL COMPLICATIONS OF ALBUMINURIA

A failure of the glomerular filtration barrier or of proximal tubular reabsorption accounts for most cases of pathologic albuminuria.16 Processes affecting the glomerular filtration of albumin include endothelial cell dysfunction and abnormalities with the glomerular basement membrane, podocytes, or the slit diaphragms among the podocytic processes.17

Ultrafiltrated albumin has been directly implicated in tubulointerstitial damage and glomerulosclerosis through diverse pathways. In the proximal tubule, albumin up-regulates interleukin 8 (a chemoattractant for lymphocytes and neutrophils), induces synthesis of endothelin 1 (which stimulates renal cell proliferation, extracellular matrix production, and monocyte attraction), and causes apoptosis of tubular cells.18 In response to albumin, proximal tubular cells also stimulate interstitial fibroblasts via paracrine release of transforming growth factor beta, either directly or by activating complement or macrophages.18,19

Studies linking albuminuria to kidney disease

Albuminuria has traditionally been associated with diabetes mellitus as a predictor of overt diabetic nephropathy,20,21 although in type 1 diabetes, established albuminuria can spontaneously regress.22

Albuminuria is also a strong predictor of progression in chronic kidney disease.23 In fact, in the last decade, albuminuria has become an independent criterion in the definition of chronic kidney disease; excretion of more than 30 mg of albumin per day, sustained for at least 3 months, qualifies as chronic kidney disease, with independent prognostic implications (Table  3).13

Astor et al,24 in a meta-analysis of 13 studies with more than 21,000 patients with chronic kidney disease, found that the risk of end-stage renal disease was three times higher in those with albuminuria.

Gansevoort et al,23 in a meta-analysis of nine studies with nearly 850,000 participants from the general population, found that the risk of end-stage renal disease increased continuously as albumin excretion increased. They also found that as albuminuria increased, so did the risk of progression of chronic kidney disease and the incidence of acute kidney injury.

Hemmelgarn et al,25 in a large pooled cohort study with more than 1.5 million participants from the general population, showed that increasing albuminuria was associated with a decline in the estimated glomerular filtration rate (GFR) and with progression to end-stage renal disease across all strata of baseline renal function. For example, in persons with an estimated GFR of 60 mL/min/1.73 m2

  • 1 per 1,000 person-years for those with no proteinuria
  • 2.8 per 1,000 person-years for those with mild proteinuria (trace or 1+ by dipstick or ACR 30–300 mg/g)
  • 13.4 per 1,000 person-years for those with heavy proteinuria (2+ or ACR > 300 mg/g).

Rates of progression to end-stage renal disease were:

  • 0.03 per 1,000 person-years with no proteinuria
  • 0.05 per 1,000 person-years with mild proteinuria
  • 1 per 1,000 person-years with heavy proteinuria.25

CARDIOVASCULAR CONSEQUENCES OF ALBUMINURIA

The exact pathophysiologic link between albuminuria and cardiovascular disease is unknown, but several mechanisms have been proposed.

One is that generalized endothelial dysfunction causes both albuminuria and cardiovascular disease.26 Endothelium-derived nitric oxide has vasodilator, antiplatelet, antiproliferative, antiadhesive, permeability-decreasing, and anti-inflammatory properties. Impaired endothelial synthesis of nitric oxide has been independently associated with both microalbuminuria and diabetes.27

Low levels of heparan sulfate (which has antithrombogenic effects and decreases vessel permeability) in the glycocalyx lining vessel walls may also account for albuminuria and for the other cardiovascular effects.28–30 These changes may be the consequence of chronic low-grade inflammation, which precedes the occurrence and progression of both albuminuria and atherothrombotic disease. The resulting abnormalities in the endothelial glycocalyx could lead to increased glomerular permeability to albumin and may also be implicated in the pathogenesis of atherosclerosis.26

In an atherosclerotic aorta and coronary arteries, the endothelial dysfunction may cause increased leakage of cholesterol and glycated end-products into the myocardium, resulting in increasing wall stiffness and left ventricular mass. A similar atherosclerotic process may account for coronary artery microthrombi, resulting in subendocardial ischemia that could lead to systolic and diastolic heart dysfunction.31

Studies linking albuminuria to heart disease

There is convincing evidence that albuminuria is associated with cardiovascular disease. An ACR between 30 and 300 mg/g was independently associated with myocardial infarction and ischemia.32 People with albuminuria have more than twice the risk of severe coronary artery disease, and albuminuria is also associated with increased intimal thickening in the carotid arteries.33,34 An ACR in the same range has been associated with increased incidence and progression of coronary artery calcification.35 Higher brachial-ankle pulse-wave velocity has also been demonstrated with albuminuria in a dose-dependent fashion.36,37

An ACR of 30 to 300 mg/g has been linked to left ventricular hypertrophy independently of other risk factors,38 and functionally with diastolic dysfunction and abnormal midwall shortening.39 In a study of a subgroup of patients with diabetes from a population-based cohort of Native American patients (the Strong Heart Study),39 the prevalence of diastolic dysfunction was:

  • 16% in those with no albuminuria
  • 26% in those with an ACR of 30 to 300 mg/g
  • 31% in those with an ACR greater than 300 mg/g.

The association persisted even after controlling for age, sex, hypertension, and other covariates.

Those pathologic associations have been directly linked to clinical outcomes. For patients with heart failure (New York Heart Association class II–IV), a study found that albuminuria (an ACR > 30 mg/g) conferred a 41% higher risk of admission for heart failure, and an ACR greater than 300 mg/g was associated with an 88% higher risk.40

In an analysis of a prospective cohort from the general population with albuminuria and a low prevalence of renal dysfunction (the Prevention of Renal and Vascular Endstage Disease study),41 albuminuria was associated with a modest increase in the incidence of the composite end point of myocardial infarction, stroke, ischemic heart disease, revascularization procedures, and all-cause mortality per doubling of the urine albumin excretion (hazard ratio 1.08, range 1.04 –1.12).

The relationship to cardiovascular outcomes extends below traditional lower-limit thresholds of albuminuria (corresponding to an ACR > 30 mg/g). A subgroup of patients from the Framingham Offspring Study without prevalent cardiovascular disease, hypertension, diabetes, or kidney disease, and thus with a low to intermediate probability of cardiovascular events, were found to have thresholds for albuminuria as low as 5.3 mg/g in men and 10.8 mg/g in women to discriminate between incident coronary artery disease, heart failure, cerebrovascular disease, other peripheral vascular disease, or death.42

In a meta-analysis including more than 1 million patients in the general population, increasing albuminuria was associated with an increase in deaths from all causes in a continuous manner, with no threshold effect.43 In patients with an ACR of 30 mg/g, the hazard ratio for death was 1.63, increasing to 2.22 for those with more than 300 mg/g compared with those with no albuminuria. A similar increase in the risk of myocardial infarction, heart failure, stroke, or sudden cardiac death was noted with higher ACR.43

Important prospective cohort studies and meta-analyses related to albuminuria and kidney and cardiovascular disease and death are summarized in the eTable.23,39–50

 

 

THE CASE FOR TREATING ALBUMINURIA

Reduced progression of renal disease

Treating patients who have proteinuric chronic kidney disease with an angiotensin-converting enzyme (ACE) inhibitor or an angiotensin receptor blocker (ARB) can reduce the risk of progression of renal failure. However, it is unclear whether this benefit is the result of treating concomitant risk factors independent of the reduction in albuminuria, and there is no consistent treatment effect in improving renal outcomes across studies.

Fink et al,51 in a meta-analysis, found that chronic kidney disease patients with diabetes, hypertension, and macroalbuminuria had a 40% lower risk of progression to end-stage renal disease if they received an ACE inhibitor (relative risk [RR] 0.60, 95% confidence interval [CI] 0.43–0.83). In the same meta-analysis, ARBs also reduced the risk of progression to end-stage renal disease (RR 0.77, 95% CI 0.66–0.90).

Jafar et al,52 in an analysis of pooled patient-level data including only nondiabetic patients on ACE inhibitor therapy (n = 1,860), found that the risk of progression of renal failure, defined as a doubling of serum creatinine or end-stage renal disease, was reduced (RR 0.70, 95% CI 0.55–0.88). Patients with higher levels of albuminuria showed the most benefit, but the effect was not conclusive for albuminuria below 500 mg/day at baseline.

Maione et al,53 in a meta-analysis that included patients with albuminuria who were treated with ACE inhibitors vs placebo (n = 8,231), found a similar reduction in risk of:

  • Progression to end-stage renal disease (RR 0.67, 95% CI 0.54–0.84)
  • Doubling of serum creatinine (RR 0.62, 95% CI 0.46–0.84)
  • Progression of albuminuria (RR 0.49, 95% CI 0.36–0.65)
  • Normalization of pathologic albuminuria (as defined by the trialists in the individual studies) (RR 2.99, 95% CI 1.82–4.91).

Similar results were obtained for patients with albuminuria who were treated with ARBs.53

ONTARGET.54 In contrast, in the Ongoing Telmisartan Alone and in Combination With Ramipril Global Endpoint Trial, the combination of an ACE inhibitor and an ARB showed no benefit in reducing the progression of renal failure, and in those patients with chronic kidney disease there was a higher risk of a doubling of serum creatinine or of the development of end-stage renal disease and hyperkalemia.

Also, in a pooled analysis of the ONTARGET and Telmisartan Randomized Assessment Study in ACE Intolerant Subjects With Cardiovascular Disease (TRANSCEND) trials, a 50% reduction in baseline albuminuria was associated with reduced progression of renal failure in those with a baseline ACR less than 10 mg/g.55

Improved cardiovascular outcomes

There is also evidence of better cardiovascular outcomes with treatment of albuminuria. Again, it is uncertain whether this is a result of treating risk factors other than albuminuria with ACE inhibitors or ARBs, and there is no parallel benefit demonstrated across all studies.

LIFE.47,48 In the Losartan Intervention for Endpoint Reduction in Hypertension trial, survival analyses suggested a decrease in risk of cardiovascular adverse events as the degree of proteinuria improved with ARB therapy.

Maione et al,53 in a meta-analysis including 8,231 patients with albuminuria and at least one other risk factor, found a significant reduction in the rate of nonfatal cardiovascular outcomes (angina, myocardial infarction, revascularization, stroke, transient ischemic attack, or heart failure) with ACE inhibitors vs placebo (RR 0.88, CI 0.82–0.94) and also in 3,888 patients treated with ARBs vs placebo (RR 0.77, CI 0.61–0.98). However, the meta-analysis did not show that ACE inhibitor or ARB therapy reduced rate of cardiovascular or all-cause mortality.

Fink et al,51 in their meta-analysis of 18 trials of ACE inhibitors and four trials of ARBs, also found no evidence that ACE inhibitor or ARB therapy reduced cardiovascular mortality rates.38

The ONTARGET trial evaluated the combination of an ACE inhibitor and ARB therapy in patients with diabetes or preexisting peripheral vascular disease. Reductions in the rate of cardiovascular disease or death were not observed, and in those with chronic kidney disease, there was a higher risk of doubling of serum creatinine or development of end-stage renal disease and adverse events of hyperkalemia.56 And although an increase in baseline albuminuria was associated with worse cardiovascular outcomes, its reduction in the ONTARGET and TRANSCEND trials did not demonstrate better outcomes when the baseline ACR was greater than 10 mg/g.55

WHO SHOULD BE TESTED?

The benefit of adding albuminuria to conventional cardiovascular risk stratification such as Framingham risk scoring is not conclusive. However, today’s clinician may view albuminuria as a biomarker for renal and cardiovascular disease, as albuminuria might be a surrogate marker for endothelial dysfunction in the glomerular capillaries or other vital vascular beds.

High-risk populations and chronic kidney disease patients

Nearly all the current guidelines recommend annual screening for albuminuria in patients with diabetes and hypertension (Table 2).7,10–13 Other high-risk populations include people with cardiovascular disease, a family history of end-stage renal disease, and metabolic syndrome. Additionally, chronic kidney disease patients whose estimated GFR defines them as being in stage 3 or higher (ie, GFR < 60 mL/min/1.73m2), regardless of other comorbidities, should be tested for albuminuria, as it is an important risk predictor.

Most experts prefer that albuminuria be measured by urine ACR in a first morning voided sample, though this is not the only option.

Screening the general population

Given that albuminuria has been shown to be such an important prognosticator for patients at high risk and those with chronic kidney disease, the question arises whether screening for albuminuria in the asymptomatic low-risk general population would foster earlier detection and therefore enable earlier intervention and result in improved outcomes. However, a systematic review done for the United States Preventive Services Task Force and for an American College of Physicians clinical practice guideline did not find robust evidence to support this.51

OUR RECOMMENDATIONS

Who should be tested?

  • Patients with chronic kidney disease stage 3, 4, or 5 (GFR < 60 mL/min/1.73m2) who are not on dialysis
  • Patients who are considered at higher risk of adverse outcomes, such as those with diabetes, hypertension, a family history of end-stage renal disease, or cardiovascular disease. Testing is useful for recognizing increased renal and cardiovascular risk and may lead clinicians to prescribe or titrate a renin-angiotensin system antagonist, a statin, or both, or to modify other cardiovascular risk factors.
  • Not recommended: routine screening in the general population who are asymptomatic or are considered at low risk.

Which test should be used?

Based on current evidence and most guidelines, we recommend the urine ACR test as the screening test for people with diabetes and others deemed to be at high risk.

What should be done about albuminuria?

  • Controlling blood pressure is important, and though there is debate about the target blood pressure, an individualized plan should be developed with the patient based on age, comorbidities, and goals of care.
  • An ACE inhibitor or ARB, if not contraindicated, is recommended for patients with diabetes whose ACR is greater than 30 mg/g and for patients with chronic kidney disease and an ACR greater than 300 mg/g.
  • Current evidence does not support the combined use of an ACE inhibitor and an ARB, as proof of benefit is lacking and the risk of adverse events is higher.
  • Refer patients with high or unexplained albuminuria to a nephrologist or clinic specializing in chronic kidney disease.

One can obtain considerable information concerning the general health by examining the urine.” 
—Hippocrates (460?–355? BCE)

Chronic kidney disease is a notable public health concern because it is an important risk factor for end-stage renal disease, cardiovascular disease, and death. Its prevalence1 exceeds 10% and is considerably higher in high-risk groups, such as those with diabetes or hypertension, which are growing in the United States.

While high levels of total protein in the urine have always been recognized as pathologic, a growing body of evidence links excretion of the protein albumin to adverse cardiovascular outcomes, and most international guidelines now recommend measuring albumin specifically. Albuminuria is a predictor of declining renal function and is independently associated with adverse cardiovascular outcomes. Thus, clinicians need to detect it early, manage it effectively, and reduce concurrent risk factors for cardiovascular disease.

Therefore, this review will focus on albuminuria. However, because the traditional standard for urinary protein measurement was total protein, and because a few guidelines still recommend measuring total protein rather than albumin, we will also briefly discuss total urinary protein.

MOST URINARY PROTEIN IS ALBUMIN

Most of the protein in the urine is albumin filtered from the plasma. Less than half of the rest is derived from the distal renal tubules (uromodulin or Tamm-Horsfall mucoprotein), 2 and urine also contains a small and varying proportion of immunoglobulins, low-molecular-weight proteins, and light chains.

Normal healthy people lose less than 30 mg of albumin in the urine per day. In greater amounts, albumin is the major urinary protein in most kidney diseases. Other proteins in urine can be specific markers of less-common illnesses such as plasma cell dyscrasia, glomerulopathy, and renal tubular disease.

MEASURING PROTEINURIA AND ALBUMINURIA

Albumin is not a homogeneous molecule in urine. It undergoes changes to its molecular configuration in the presence of certain ions, peptides, hormones, and drugs, and as a result of proteolytic fragmentation both in the plasma and in renal tubules.3 Consequently, measuring urinary albumin involves a trade-off between convenience and accuracy.

A 24-hour timed urine sample has long been the gold standard for measuring albuminuria, but the collection is cumbersome and time-consuming, and the test is prone to laboratory error.

Dipstick measurements are more convenient and are better at detecting albumin than other proteins in urine, but they have low sensitivity and high interobserver variation.3–5

The albumin-to-creatinine ratio (ACR). As the quantity of protein in the urine changes with time of day, exertion, stress level, and posture, spot-checking of urine samples is not as good as timed collection. However, a simultaneous measurement of creatinine in a spot urine sample adjusts for protein concentration, which can vary with a person’s hydration status. The ACR so obtained is consistent with the 24-hour timed collection (the gold standard) and is the recommended method of assessing albuminuria.3 An early morning urine sample is favored, as it avoids orthostatic variations and varies less in the same individual.

In a study in the general population comparing the ACR in a random sample and in an early morning sample, only 44% of those who had an ACR of 30 mg/g or higher in the random sample had one this high in the early morning sample.6 However, getting an early morning sample is not always feasible in clinical practice. If you are going to measure albuminuria, the Kidney Disease Outcomes and Quality Initiative7 suggests checking the ACR in a random sample and then, if the test is positive, following up and confirming it within 3 months with an early morning sample.

Also, since creatinine excretion differs with race, diet, and muscle mass, if the 24-hour creatinine excretion is not close to 1 g, the ACR will give an erroneous estimate of the 24-hour excretion rate.3

Table 1 compares the various methods of measuring protein in the urine.3,5,8,9 Of note, methods of measuring albumin and total protein vary considerably in their precision and accuracy, making it impossible to reliably translate values from one to the other.5

National and international guidelines (Table 2)7,10–13 agree that albuminuria should be tested in diabetic patients, as it is a surrogate marker for early diabetic nephropathy.3,13 Most guidelines also recommend measuring albuminuria by a urine ACR test as the preferred measure, even in people without diabetes.

Also, no single cutoff is universally accepted for distinguishing pathologic albuminuria from physiologic albuminuria, nor is there a universally accepted unit of measure.14 Because approximately 1 g of creatinine is lost in the urine per day, the ACR has the convenient property of numerically matching the albumin excretory rate expressed in milligrams per 24 hours. The other commonly used unit is milligrams of albumin per millimole of creatinine; 30 mg/g is roughly equal to 3 mg/mmol.

The term microalbuminuria was traditionally used to refer to albumin excretion of 30 to 299 mg per 24 hours, and macroalbuminuria to 300 mg or more per 24 hours. However, as there is no pathophysiologic basis to these thresholds (see outcomes data below), the current Kidney Disease Improving Global Outcomes (KDIGO) guidelines do not recommend using these terms.13,15

 

 

RENAL COMPLICATIONS OF ALBUMINURIA

A failure of the glomerular filtration barrier or of proximal tubular reabsorption accounts for most cases of pathologic albuminuria.16 Processes affecting the glomerular filtration of albumin include endothelial cell dysfunction and abnormalities with the glomerular basement membrane, podocytes, or the slit diaphragms among the podocytic processes.17

Ultrafiltrated albumin has been directly implicated in tubulointerstitial damage and glomerulosclerosis through diverse pathways. In the proximal tubule, albumin up-regulates interleukin 8 (a chemoattractant for lymphocytes and neutrophils), induces synthesis of endothelin 1 (which stimulates renal cell proliferation, extracellular matrix production, and monocyte attraction), and causes apoptosis of tubular cells.18 In response to albumin, proximal tubular cells also stimulate interstitial fibroblasts via paracrine release of transforming growth factor beta, either directly or by activating complement or macrophages.18,19

Studies linking albuminuria to kidney disease

Albuminuria has traditionally been associated with diabetes mellitus as a predictor of overt diabetic nephropathy,20,21 although in type 1 diabetes, established albuminuria can spontaneously regress.22

Albuminuria is also a strong predictor of progression in chronic kidney disease.23 In fact, in the last decade, albuminuria has become an independent criterion in the definition of chronic kidney disease; excretion of more than 30 mg of albumin per day, sustained for at least 3 months, qualifies as chronic kidney disease, with independent prognostic implications (Table  3).13

Astor et al,24 in a meta-analysis of 13 studies with more than 21,000 patients with chronic kidney disease, found that the risk of end-stage renal disease was three times higher in those with albuminuria.

Gansevoort et al,23 in a meta-analysis of nine studies with nearly 850,000 participants from the general population, found that the risk of end-stage renal disease increased continuously as albumin excretion increased. They also found that as albuminuria increased, so did the risk of progression of chronic kidney disease and the incidence of acute kidney injury.

Hemmelgarn et al,25 in a large pooled cohort study with more than 1.5 million participants from the general population, showed that increasing albuminuria was associated with a decline in the estimated glomerular filtration rate (GFR) and with progression to end-stage renal disease across all strata of baseline renal function. For example, in persons with an estimated GFR of 60 mL/min/1.73 m2

  • 1 per 1,000 person-years for those with no proteinuria
  • 2.8 per 1,000 person-years for those with mild proteinuria (trace or 1+ by dipstick or ACR 30–300 mg/g)
  • 13.4 per 1,000 person-years for those with heavy proteinuria (2+ or ACR > 300 mg/g).

Rates of progression to end-stage renal disease were:

  • 0.03 per 1,000 person-years with no proteinuria
  • 0.05 per 1,000 person-years with mild proteinuria
  • 1 per 1,000 person-years with heavy proteinuria.25

CARDIOVASCULAR CONSEQUENCES OF ALBUMINURIA

The exact pathophysiologic link between albuminuria and cardiovascular disease is unknown, but several mechanisms have been proposed.

One is that generalized endothelial dysfunction causes both albuminuria and cardiovascular disease.26 Endothelium-derived nitric oxide has vasodilator, antiplatelet, antiproliferative, antiadhesive, permeability-decreasing, and anti-inflammatory properties. Impaired endothelial synthesis of nitric oxide has been independently associated with both microalbuminuria and diabetes.27

Low levels of heparan sulfate (which has antithrombogenic effects and decreases vessel permeability) in the glycocalyx lining vessel walls may also account for albuminuria and for the other cardiovascular effects.28–30 These changes may be the consequence of chronic low-grade inflammation, which precedes the occurrence and progression of both albuminuria and atherothrombotic disease. The resulting abnormalities in the endothelial glycocalyx could lead to increased glomerular permeability to albumin and may also be implicated in the pathogenesis of atherosclerosis.26

In an atherosclerotic aorta and coronary arteries, the endothelial dysfunction may cause increased leakage of cholesterol and glycated end-products into the myocardium, resulting in increasing wall stiffness and left ventricular mass. A similar atherosclerotic process may account for coronary artery microthrombi, resulting in subendocardial ischemia that could lead to systolic and diastolic heart dysfunction.31

Studies linking albuminuria to heart disease

There is convincing evidence that albuminuria is associated with cardiovascular disease. An ACR between 30 and 300 mg/g was independently associated with myocardial infarction and ischemia.32 People with albuminuria have more than twice the risk of severe coronary artery disease, and albuminuria is also associated with increased intimal thickening in the carotid arteries.33,34 An ACR in the same range has been associated with increased incidence and progression of coronary artery calcification.35 Higher brachial-ankle pulse-wave velocity has also been demonstrated with albuminuria in a dose-dependent fashion.36,37

An ACR of 30 to 300 mg/g has been linked to left ventricular hypertrophy independently of other risk factors,38 and functionally with diastolic dysfunction and abnormal midwall shortening.39 In a study of a subgroup of patients with diabetes from a population-based cohort of Native American patients (the Strong Heart Study),39 the prevalence of diastolic dysfunction was:

  • 16% in those with no albuminuria
  • 26% in those with an ACR of 30 to 300 mg/g
  • 31% in those with an ACR greater than 300 mg/g.

The association persisted even after controlling for age, sex, hypertension, and other covariates.

Those pathologic associations have been directly linked to clinical outcomes. For patients with heart failure (New York Heart Association class II–IV), a study found that albuminuria (an ACR > 30 mg/g) conferred a 41% higher risk of admission for heart failure, and an ACR greater than 300 mg/g was associated with an 88% higher risk.40

In an analysis of a prospective cohort from the general population with albuminuria and a low prevalence of renal dysfunction (the Prevention of Renal and Vascular Endstage Disease study),41 albuminuria was associated with a modest increase in the incidence of the composite end point of myocardial infarction, stroke, ischemic heart disease, revascularization procedures, and all-cause mortality per doubling of the urine albumin excretion (hazard ratio 1.08, range 1.04 –1.12).

The relationship to cardiovascular outcomes extends below traditional lower-limit thresholds of albuminuria (corresponding to an ACR > 30 mg/g). A subgroup of patients from the Framingham Offspring Study without prevalent cardiovascular disease, hypertension, diabetes, or kidney disease, and thus with a low to intermediate probability of cardiovascular events, were found to have thresholds for albuminuria as low as 5.3 mg/g in men and 10.8 mg/g in women to discriminate between incident coronary artery disease, heart failure, cerebrovascular disease, other peripheral vascular disease, or death.42

In a meta-analysis including more than 1 million patients in the general population, increasing albuminuria was associated with an increase in deaths from all causes in a continuous manner, with no threshold effect.43 In patients with an ACR of 30 mg/g, the hazard ratio for death was 1.63, increasing to 2.22 for those with more than 300 mg/g compared with those with no albuminuria. A similar increase in the risk of myocardial infarction, heart failure, stroke, or sudden cardiac death was noted with higher ACR.43

Important prospective cohort studies and meta-analyses related to albuminuria and kidney and cardiovascular disease and death are summarized in the eTable.23,39–50

 

 

THE CASE FOR TREATING ALBUMINURIA

Reduced progression of renal disease

Treating patients who have proteinuric chronic kidney disease with an angiotensin-converting enzyme (ACE) inhibitor or an angiotensin receptor blocker (ARB) can reduce the risk of progression of renal failure. However, it is unclear whether this benefit is the result of treating concomitant risk factors independent of the reduction in albuminuria, and there is no consistent treatment effect in improving renal outcomes across studies.

Fink et al,51 in a meta-analysis, found that chronic kidney disease patients with diabetes, hypertension, and macroalbuminuria had a 40% lower risk of progression to end-stage renal disease if they received an ACE inhibitor (relative risk [RR] 0.60, 95% confidence interval [CI] 0.43–0.83). In the same meta-analysis, ARBs also reduced the risk of progression to end-stage renal disease (RR 0.77, 95% CI 0.66–0.90).

Jafar et al,52 in an analysis of pooled patient-level data including only nondiabetic patients on ACE inhibitor therapy (n = 1,860), found that the risk of progression of renal failure, defined as a doubling of serum creatinine or end-stage renal disease, was reduced (RR 0.70, 95% CI 0.55–0.88). Patients with higher levels of albuminuria showed the most benefit, but the effect was not conclusive for albuminuria below 500 mg/day at baseline.

Maione et al,53 in a meta-analysis that included patients with albuminuria who were treated with ACE inhibitors vs placebo (n = 8,231), found a similar reduction in risk of:

  • Progression to end-stage renal disease (RR 0.67, 95% CI 0.54–0.84)
  • Doubling of serum creatinine (RR 0.62, 95% CI 0.46–0.84)
  • Progression of albuminuria (RR 0.49, 95% CI 0.36–0.65)
  • Normalization of pathologic albuminuria (as defined by the trialists in the individual studies) (RR 2.99, 95% CI 1.82–4.91).

Similar results were obtained for patients with albuminuria who were treated with ARBs.53

ONTARGET.54 In contrast, in the Ongoing Telmisartan Alone and in Combination With Ramipril Global Endpoint Trial, the combination of an ACE inhibitor and an ARB showed no benefit in reducing the progression of renal failure, and in those patients with chronic kidney disease there was a higher risk of a doubling of serum creatinine or of the development of end-stage renal disease and hyperkalemia.

Also, in a pooled analysis of the ONTARGET and Telmisartan Randomized Assessment Study in ACE Intolerant Subjects With Cardiovascular Disease (TRANSCEND) trials, a 50% reduction in baseline albuminuria was associated with reduced progression of renal failure in those with a baseline ACR less than 10 mg/g.55

Improved cardiovascular outcomes

There is also evidence of better cardiovascular outcomes with treatment of albuminuria. Again, it is uncertain whether this is a result of treating risk factors other than albuminuria with ACE inhibitors or ARBs, and there is no parallel benefit demonstrated across all studies.

LIFE.47,48 In the Losartan Intervention for Endpoint Reduction in Hypertension trial, survival analyses suggested a decrease in risk of cardiovascular adverse events as the degree of proteinuria improved with ARB therapy.

Maione et al,53 in a meta-analysis including 8,231 patients with albuminuria and at least one other risk factor, found a significant reduction in the rate of nonfatal cardiovascular outcomes (angina, myocardial infarction, revascularization, stroke, transient ischemic attack, or heart failure) with ACE inhibitors vs placebo (RR 0.88, CI 0.82–0.94) and also in 3,888 patients treated with ARBs vs placebo (RR 0.77, CI 0.61–0.98). However, the meta-analysis did not show that ACE inhibitor or ARB therapy reduced rate of cardiovascular or all-cause mortality.

Fink et al,51 in their meta-analysis of 18 trials of ACE inhibitors and four trials of ARBs, also found no evidence that ACE inhibitor or ARB therapy reduced cardiovascular mortality rates.38

The ONTARGET trial evaluated the combination of an ACE inhibitor and ARB therapy in patients with diabetes or preexisting peripheral vascular disease. Reductions in the rate of cardiovascular disease or death were not observed, and in those with chronic kidney disease, there was a higher risk of doubling of serum creatinine or development of end-stage renal disease and adverse events of hyperkalemia.56 And although an increase in baseline albuminuria was associated with worse cardiovascular outcomes, its reduction in the ONTARGET and TRANSCEND trials did not demonstrate better outcomes when the baseline ACR was greater than 10 mg/g.55

WHO SHOULD BE TESTED?

The benefit of adding albuminuria to conventional cardiovascular risk stratification such as Framingham risk scoring is not conclusive. However, today’s clinician may view albuminuria as a biomarker for renal and cardiovascular disease, as albuminuria might be a surrogate marker for endothelial dysfunction in the glomerular capillaries or other vital vascular beds.

High-risk populations and chronic kidney disease patients

Nearly all the current guidelines recommend annual screening for albuminuria in patients with diabetes and hypertension (Table 2).7,10–13 Other high-risk populations include people with cardiovascular disease, a family history of end-stage renal disease, and metabolic syndrome. Additionally, chronic kidney disease patients whose estimated GFR defines them as being in stage 3 or higher (ie, GFR < 60 mL/min/1.73m2), regardless of other comorbidities, should be tested for albuminuria, as it is an important risk predictor.

Most experts prefer that albuminuria be measured by urine ACR in a first morning voided sample, though this is not the only option.

Screening the general population

Given that albuminuria has been shown to be such an important prognosticator for patients at high risk and those with chronic kidney disease, the question arises whether screening for albuminuria in the asymptomatic low-risk general population would foster earlier detection and therefore enable earlier intervention and result in improved outcomes. However, a systematic review done for the United States Preventive Services Task Force and for an American College of Physicians clinical practice guideline did not find robust evidence to support this.51

OUR RECOMMENDATIONS

Who should be tested?

  • Patients with chronic kidney disease stage 3, 4, or 5 (GFR < 60 mL/min/1.73m2) who are not on dialysis
  • Patients who are considered at higher risk of adverse outcomes, such as those with diabetes, hypertension, a family history of end-stage renal disease, or cardiovascular disease. Testing is useful for recognizing increased renal and cardiovascular risk and may lead clinicians to prescribe or titrate a renin-angiotensin system antagonist, a statin, or both, or to modify other cardiovascular risk factors.
  • Not recommended: routine screening in the general population who are asymptomatic or are considered at low risk.

Which test should be used?

Based on current evidence and most guidelines, we recommend the urine ACR test as the screening test for people with diabetes and others deemed to be at high risk.

What should be done about albuminuria?

  • Controlling blood pressure is important, and though there is debate about the target blood pressure, an individualized plan should be developed with the patient based on age, comorbidities, and goals of care.
  • An ACE inhibitor or ARB, if not contraindicated, is recommended for patients with diabetes whose ACR is greater than 30 mg/g and for patients with chronic kidney disease and an ACR greater than 300 mg/g.
  • Current evidence does not support the combined use of an ACE inhibitor and an ARB, as proof of benefit is lacking and the risk of adverse events is higher.
  • Refer patients with high or unexplained albuminuria to a nephrologist or clinic specializing in chronic kidney disease.
References
  1. Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA 2007; 298:20382047.
  2. Hoyer JR, Seiler MW. Pathophysiology of Tamm-Horsfall protein. Kidney Int 1979; 16:279289.
  3. Viswanathan G, Upadhyay A. Assessment of proteinuria. Adv Chronic Kidney Dis 2011; 18:243248.
  4. Guh JY. Proteinuria versus albuminuria in chronic kidney disease. Nephrology (Carlton) 2010; 15(suppl 2):5356.
  5. Lamb EJ, MacKenzie F, Stevens PE. How should proteinuria be detected and measured? Ann Clin Biochem 2009; 46:205217.
  6. Saydah SH, Pavkov ME, Zhang C, et al. Albuminuria prevalence in first morning void compared with previous random urine from adults in the National Health and Nutrition Examination Survey, 2009-2010. Clin Chem 2013; 59:675683.
  7. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002; 39(suppl 1):S1S266.
  8. Younes N, Cleary PA, Steffes MW, et al; DCCT/EDIC Research Group. Comparison of urinary albumin-creatinine ratio and albumin excretion rate in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study. Clin J Am Soc Nephrol 2010; 5:12351242.
  9. Brinkman JW, de Zeeuw D, Duker JJ, et al. Falsely low urinary albumin concentrations after prolonged frozen storage of urine samples. Clin Chem 2005; 51:21812183.
  10. National Collaborating Centre for Chronic Conditions (UK). Chronic Kidney Disease: National Clinical Guideline for Early Identification and Management in Adults in Primary and Secondary Care. London: Royal College of Physicians (UK) 2008.
  11. American Diabetes Association. Standards of medical care in diabetes—2013. Diabetes Care 2013; 36(suppl 1):S11S66.
  12. Chobanian AV, Bakris GL, Black HR, et al; Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003; 42:12061252.
  13. Kidney Disease Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013; 3:1150.
  14. Johnson DW. Global proteinuria guidelines: are we nearly there yet? Clin Biochem Rev 2011; 32:8995.
  15. Ruggenenti P, Remuzzi G. Time to abandon microalbuminuria? Kidney Int 2006; 70:12141222.
  16. Glassock RJ. Is the presence of microalbuminuria a relevant marker of kidney disease? Curr Hypertens Rep 2010; 12:364368.
  17. Zhang A, Huang S. Progress in pathogenesis of proteinuria. Int J Nephrol 2012; 2012:314251.
  18. Abbate M, Zoja C, Remuzzi G. How does proteinuria cause progressive renal damage? J Am Soc Nephrol 2006; 17:29742984.
  19. Karalliedde J, Viberti G. Proteinuria in diabetes: bystander or pathway to cardiorenal disease? J Am Soc Nephrol 2010; 21:20202027.
  20. Svendsen PA, Oxenbøll B, Christiansen JS. Microalbuminuria in diabetic patients—a longitudinal study. Acta Endocrinol Suppl (Copenh) 1981; 242:5354.
  21. Viberti GC, Hill RD, Jarrett RJ, Argyropoulos A, Mahmud U, Keen H. Microalbuminuria as a predictor of clinical nephropathy in insulin-dependent diabetes mellitus. Lancet 1982; 1:14301432.
  22. Perkins BA, Ficociello LH, Silva KH, Finkelstein DM, Warram JH, Krolewski AS. Regression of microalbuminuria in type 1 diabetes. N Engl J Med 2003; 348:22852293.
  23. Gansevoort RT, Matsushita K, van der Velde M, et al; Chronic Kidney Disease Prognosis Consortium. Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. Kidney Int 2011; 80:93104.
  24. Astor BC, Matsushita K, Gansevoort RT, et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts. Kidney Int 2011; 79:13311340.
  25. Hemmelgarn BR, Manns BJ, Lloyd A, et al; Alberta Kidney Disease Network. Relation between kidney function, proteinuria, and adverse outcomes. JAMA 2010; 303:423429.
  26. Stehouwer CD, Smulders YM. Microalbuminuria and risk for cardiovascular disease: analysis of potential mechanisms. J Am Soc Nephrol 2006; 17:21062111.
  27. Stehouwer CD, Henry RM, Dekker JM, Nijpels G, Heine RJ, Bouter LM. Microalbuminuria is associated with impaired brachial artery, flow-mediated vasodilation in elderly individuals without and with diabetes: further evidence for a link between microalbuminuria and endothelial dysfunction—the Hoorn Study. Kidney Int Suppl 2004; 92:S42S44.
  28. Wasty F, Alavi MZ, Moore S. Distribution of glycosaminoglycans in the intima of human aortas: changes in atherosclerosis and diabetes mellitus. Diabetologia 1993; 36:316322.
  29. Ylä-Herttuala S, Sumuvuori H, Karkola K, Möttönen M, Nikkari T. Glycosaminoglycans in normal and atherosclerotic human coronary arteries. Lab Invest 1986; 54:402407.
  30. Deckert T, Feldt-Rasmussen B, Borch-Johnsen K, Jensen T, Kofoed-Enevoldsen A. Albuminuria reflects widespread vascular damage. The Steno hypothesis. Diabetologia 1989; 32:219226.
  31. van Hoeven KH, Factor SM. A comparison of the pathological spectrum of hypertensive, diabetic, and hypertensive-diabetic heart disease. Circulation 1990; 82:848855.
  32. Diercks GF, van Boven AJ, Hillege HL, et al. Microalbuminuria is independently associated with ischaemic electrocardiographic abnormalities in a large non-diabetic population. The PREVEND (Prevention of REnal and Vascular ENdstage Disease) study. Eur Heart J 2000; 21:19221927.
  33. Bigazzi R, Bianchi S, Nenci R, Baldari D, Baldari G, Campese VM. Increased thickness of the carotid artery in patients with essential hypertension and microalbuminuria. J Hum Hypertens 1995; 9:827833.
  34. Tuttle KR, Puhlman ME, Cooney SK, Short R. Urinary albumin and insulin as predictors of coronary artery disease: an angiographic study. Am J Kidney Dis 1999; 34:918925.
  35. DeFilippis AP, Kramer HJ, Katz R, et al. Association between coronary artery calcification progression and microalbuminuria: the MESA study. JACC Cardiovasc Imaging 2010; 3:595604.
  36. Liu CS, Pi-Sunyer FX, Li CI, et al. Albuminuria is strongly associated with arterial stiffness, especially in diabetic or hypertensive subjects—a population-based study (Taichung Community Health Study, TCHS). Atherosclerosis 2010; 211:315321.
  37. Upadhyay A, Hwang SJ, Mitchell GF, et al. Arterial stiffness in mild-to-moderate CKD. J Am Soc Nephrol 2009; 20:20442053.
  38. Pontremoli R, Sofia A, Ravera M, et al. Prevalence and clinical correlates of microalbuminuria in essential hypertension: the MAGIC Study. Microalbuminuria: a Genoa Investigation on Complications. Hypertension 1997; 30:11351143.
  39. Liu JE, Robbins DC, Palmieri V, et al. Association of albuminuria with systolic and diastolic left ventricular dysfunction in type 2 diabetes: the Strong Heart Study. J Am Coll Cardiol 2003; 41:20222028.
  40. Jackson CE, Solomon SD, Gerstein HC, et al; CHARM Investigators and Committees. Albuminuria in chronic heart failure: prevalence and prognostic importance. Lancet 2009; 374:543550.
  41. Smink PA, Lambers Heerspink HJ, Gansevoort RT, et al. Albuminuria, estimated GFR, traditional risk factors, and incident cardiovascular disease: the PREVEND (Prevention of Renal and Vascular Endstage Disease) study. Am J Kidney Dis 2012; 60:804811.
  42. Arnlöv J, Evans JC, Meigs JB, et al. Low-grade albuminuria and incidence of cardiovascular disease events in nonhypertensive and nondiabetic individuals: the Framingham Heart Study. Circulation 2005; 112:969975.
  43. Chronic Kidney Disease Prognosis Consortium; Matsushita K, van der Velde M, Astor BC, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 2010; 375:20732081.
  44. van der Velde M, Matsushita K, Coresh J, et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney Int 2011; 79:13411352.
  45. Ruggenenti P, Porrini E, Motterlini N, et al; BENEDICT Study Investigators. Measurable urinary albumin predicts cardiovascular risk among normoalbuminuric patients with type 2 diabetes. J Am Soc Nephrol 2012; 23:17171724.
  46. Hallan S, Astor B, Romundstad S, Aasarød K, Kvenild K, Coresh J. Association of kidney function and albuminuria with cardiovascular mortality in older vs younger individuals: the HUNT II Study. Arch Intern Med 2007; 167:24902496.
  47. Ibsen H, Wachtell K, Olsen MH, et al. Albuminuria and cardiovascular risk in hypertensive patients with left ventricular hypertrophy: the LIFE Study. Kidney Int Suppl 2004; 92:S56S58.
  48. Olsen MH, Wachtell K, Bella JN, et al. Albuminuria predicts cardiovascular events independently of left ventricular mass in hypertension: a LIFE substudy. J Hum Hypertens 2004; 18:453459.
  49. Klausen K, Borch-Johnsen K, Feldt-Rasmussen B, et al. Very low levels of microalbuminuria are associated with increased risk of coronary heart disease and death independently of renal function, hypertension, and diabetes. Circulation 2004; 110:3235.
  50. Gerstein HC, Mann JF, Yi Q, et al; HOPE Study Investigators. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA 2001; 286:421426.
  51. Fink HA, Ishani A, Taylor BC, et al. Screening for, monitoring, and treatment of chronic kidney disease stages 1 to 3: a systematic review for the US Preventive Services Task Force and for an American College of Physicians Clinical Practice Guideline. Ann Intern Med 2012; 156:570581.
  52. Jafar TH, Schmid CH, Landa M, et al. Angiotensin-converting enzyme inhibitors and progression of nondiabetic renal disease. A meta-analysis of patient-level data. Ann Intern Med 2001; 135:7387.
  53. Maione A, Navaneethan SD, Graziano G, et al. Angiotensin-converting enzyme inhibitors, angiotensin receptor blockers and combined therapy in patients with micro- and macroalbuminuria and other cardiovascular risk factors: a systematic review of randomized controlled trials. Nephrol Dial Transplant 2011; 26:28272847.
  54. Mann JF, Schmieder RE, McQueen M, et al; ONTARGET investigators. Renal outcomes with telmisartan, ramipril, or both, in people at high vascular risk (the ONTARGET study): a multicentre, randomised, double-blind, controlled trial. Lancet 2008; 372:547553.
  55. Schmieder RE, Mann JF, Schumacher H, et al; ONTARGET Investigators. Changes in albuminuria predict mortality and morbidity in patients with vascular disease. J Am Soc Nephrol 2011; 22:13531364.
  56. Tobe SW, Clase CM, Gao P, et al; ONTARGET and TRANSCEND Investigators. Cardiovascular and renal outcomes with telmisartan, ramipril, or both in people at high renal risk: results from the ONTARGET and TRANSCEND studies. Circulation 2011; 123:10981107.
References
  1. Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA 2007; 298:20382047.
  2. Hoyer JR, Seiler MW. Pathophysiology of Tamm-Horsfall protein. Kidney Int 1979; 16:279289.
  3. Viswanathan G, Upadhyay A. Assessment of proteinuria. Adv Chronic Kidney Dis 2011; 18:243248.
  4. Guh JY. Proteinuria versus albuminuria in chronic kidney disease. Nephrology (Carlton) 2010; 15(suppl 2):5356.
  5. Lamb EJ, MacKenzie F, Stevens PE. How should proteinuria be detected and measured? Ann Clin Biochem 2009; 46:205217.
  6. Saydah SH, Pavkov ME, Zhang C, et al. Albuminuria prevalence in first morning void compared with previous random urine from adults in the National Health and Nutrition Examination Survey, 2009-2010. Clin Chem 2013; 59:675683.
  7. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002; 39(suppl 1):S1S266.
  8. Younes N, Cleary PA, Steffes MW, et al; DCCT/EDIC Research Group. Comparison of urinary albumin-creatinine ratio and albumin excretion rate in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study. Clin J Am Soc Nephrol 2010; 5:12351242.
  9. Brinkman JW, de Zeeuw D, Duker JJ, et al. Falsely low urinary albumin concentrations after prolonged frozen storage of urine samples. Clin Chem 2005; 51:21812183.
  10. National Collaborating Centre for Chronic Conditions (UK). Chronic Kidney Disease: National Clinical Guideline for Early Identification and Management in Adults in Primary and Secondary Care. London: Royal College of Physicians (UK) 2008.
  11. American Diabetes Association. Standards of medical care in diabetes—2013. Diabetes Care 2013; 36(suppl 1):S11S66.
  12. Chobanian AV, Bakris GL, Black HR, et al; Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003; 42:12061252.
  13. Kidney Disease Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013; 3:1150.
  14. Johnson DW. Global proteinuria guidelines: are we nearly there yet? Clin Biochem Rev 2011; 32:8995.
  15. Ruggenenti P, Remuzzi G. Time to abandon microalbuminuria? Kidney Int 2006; 70:12141222.
  16. Glassock RJ. Is the presence of microalbuminuria a relevant marker of kidney disease? Curr Hypertens Rep 2010; 12:364368.
  17. Zhang A, Huang S. Progress in pathogenesis of proteinuria. Int J Nephrol 2012; 2012:314251.
  18. Abbate M, Zoja C, Remuzzi G. How does proteinuria cause progressive renal damage? J Am Soc Nephrol 2006; 17:29742984.
  19. Karalliedde J, Viberti G. Proteinuria in diabetes: bystander or pathway to cardiorenal disease? J Am Soc Nephrol 2010; 21:20202027.
  20. Svendsen PA, Oxenbøll B, Christiansen JS. Microalbuminuria in diabetic patients—a longitudinal study. Acta Endocrinol Suppl (Copenh) 1981; 242:5354.
  21. Viberti GC, Hill RD, Jarrett RJ, Argyropoulos A, Mahmud U, Keen H. Microalbuminuria as a predictor of clinical nephropathy in insulin-dependent diabetes mellitus. Lancet 1982; 1:14301432.
  22. Perkins BA, Ficociello LH, Silva KH, Finkelstein DM, Warram JH, Krolewski AS. Regression of microalbuminuria in type 1 diabetes. N Engl J Med 2003; 348:22852293.
  23. Gansevoort RT, Matsushita K, van der Velde M, et al; Chronic Kidney Disease Prognosis Consortium. Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. Kidney Int 2011; 80:93104.
  24. Astor BC, Matsushita K, Gansevoort RT, et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts. Kidney Int 2011; 79:13311340.
  25. Hemmelgarn BR, Manns BJ, Lloyd A, et al; Alberta Kidney Disease Network. Relation between kidney function, proteinuria, and adverse outcomes. JAMA 2010; 303:423429.
  26. Stehouwer CD, Smulders YM. Microalbuminuria and risk for cardiovascular disease: analysis of potential mechanisms. J Am Soc Nephrol 2006; 17:21062111.
  27. Stehouwer CD, Henry RM, Dekker JM, Nijpels G, Heine RJ, Bouter LM. Microalbuminuria is associated with impaired brachial artery, flow-mediated vasodilation in elderly individuals without and with diabetes: further evidence for a link between microalbuminuria and endothelial dysfunction—the Hoorn Study. Kidney Int Suppl 2004; 92:S42S44.
  28. Wasty F, Alavi MZ, Moore S. Distribution of glycosaminoglycans in the intima of human aortas: changes in atherosclerosis and diabetes mellitus. Diabetologia 1993; 36:316322.
  29. Ylä-Herttuala S, Sumuvuori H, Karkola K, Möttönen M, Nikkari T. Glycosaminoglycans in normal and atherosclerotic human coronary arteries. Lab Invest 1986; 54:402407.
  30. Deckert T, Feldt-Rasmussen B, Borch-Johnsen K, Jensen T, Kofoed-Enevoldsen A. Albuminuria reflects widespread vascular damage. The Steno hypothesis. Diabetologia 1989; 32:219226.
  31. van Hoeven KH, Factor SM. A comparison of the pathological spectrum of hypertensive, diabetic, and hypertensive-diabetic heart disease. Circulation 1990; 82:848855.
  32. Diercks GF, van Boven AJ, Hillege HL, et al. Microalbuminuria is independently associated with ischaemic electrocardiographic abnormalities in a large non-diabetic population. The PREVEND (Prevention of REnal and Vascular ENdstage Disease) study. Eur Heart J 2000; 21:19221927.
  33. Bigazzi R, Bianchi S, Nenci R, Baldari D, Baldari G, Campese VM. Increased thickness of the carotid artery in patients with essential hypertension and microalbuminuria. J Hum Hypertens 1995; 9:827833.
  34. Tuttle KR, Puhlman ME, Cooney SK, Short R. Urinary albumin and insulin as predictors of coronary artery disease: an angiographic study. Am J Kidney Dis 1999; 34:918925.
  35. DeFilippis AP, Kramer HJ, Katz R, et al. Association between coronary artery calcification progression and microalbuminuria: the MESA study. JACC Cardiovasc Imaging 2010; 3:595604.
  36. Liu CS, Pi-Sunyer FX, Li CI, et al. Albuminuria is strongly associated with arterial stiffness, especially in diabetic or hypertensive subjects—a population-based study (Taichung Community Health Study, TCHS). Atherosclerosis 2010; 211:315321.
  37. Upadhyay A, Hwang SJ, Mitchell GF, et al. Arterial stiffness in mild-to-moderate CKD. J Am Soc Nephrol 2009; 20:20442053.
  38. Pontremoli R, Sofia A, Ravera M, et al. Prevalence and clinical correlates of microalbuminuria in essential hypertension: the MAGIC Study. Microalbuminuria: a Genoa Investigation on Complications. Hypertension 1997; 30:11351143.
  39. Liu JE, Robbins DC, Palmieri V, et al. Association of albuminuria with systolic and diastolic left ventricular dysfunction in type 2 diabetes: the Strong Heart Study. J Am Coll Cardiol 2003; 41:20222028.
  40. Jackson CE, Solomon SD, Gerstein HC, et al; CHARM Investigators and Committees. Albuminuria in chronic heart failure: prevalence and prognostic importance. Lancet 2009; 374:543550.
  41. Smink PA, Lambers Heerspink HJ, Gansevoort RT, et al. Albuminuria, estimated GFR, traditional risk factors, and incident cardiovascular disease: the PREVEND (Prevention of Renal and Vascular Endstage Disease) study. Am J Kidney Dis 2012; 60:804811.
  42. Arnlöv J, Evans JC, Meigs JB, et al. Low-grade albuminuria and incidence of cardiovascular disease events in nonhypertensive and nondiabetic individuals: the Framingham Heart Study. Circulation 2005; 112:969975.
  43. Chronic Kidney Disease Prognosis Consortium; Matsushita K, van der Velde M, Astor BC, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 2010; 375:20732081.
  44. van der Velde M, Matsushita K, Coresh J, et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney Int 2011; 79:13411352.
  45. Ruggenenti P, Porrini E, Motterlini N, et al; BENEDICT Study Investigators. Measurable urinary albumin predicts cardiovascular risk among normoalbuminuric patients with type 2 diabetes. J Am Soc Nephrol 2012; 23:17171724.
  46. Hallan S, Astor B, Romundstad S, Aasarød K, Kvenild K, Coresh J. Association of kidney function and albuminuria with cardiovascular mortality in older vs younger individuals: the HUNT II Study. Arch Intern Med 2007; 167:24902496.
  47. Ibsen H, Wachtell K, Olsen MH, et al. Albuminuria and cardiovascular risk in hypertensive patients with left ventricular hypertrophy: the LIFE Study. Kidney Int Suppl 2004; 92:S56S58.
  48. Olsen MH, Wachtell K, Bella JN, et al. Albuminuria predicts cardiovascular events independently of left ventricular mass in hypertension: a LIFE substudy. J Hum Hypertens 2004; 18:453459.
  49. Klausen K, Borch-Johnsen K, Feldt-Rasmussen B, et al. Very low levels of microalbuminuria are associated with increased risk of coronary heart disease and death independently of renal function, hypertension, and diabetes. Circulation 2004; 110:3235.
  50. Gerstein HC, Mann JF, Yi Q, et al; HOPE Study Investigators. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA 2001; 286:421426.
  51. Fink HA, Ishani A, Taylor BC, et al. Screening for, monitoring, and treatment of chronic kidney disease stages 1 to 3: a systematic review for the US Preventive Services Task Force and for an American College of Physicians Clinical Practice Guideline. Ann Intern Med 2012; 156:570581.
  52. Jafar TH, Schmid CH, Landa M, et al. Angiotensin-converting enzyme inhibitors and progression of nondiabetic renal disease. A meta-analysis of patient-level data. Ann Intern Med 2001; 135:7387.
  53. Maione A, Navaneethan SD, Graziano G, et al. Angiotensin-converting enzyme inhibitors, angiotensin receptor blockers and combined therapy in patients with micro- and macroalbuminuria and other cardiovascular risk factors: a systematic review of randomized controlled trials. Nephrol Dial Transplant 2011; 26:28272847.
  54. Mann JF, Schmieder RE, McQueen M, et al; ONTARGET investigators. Renal outcomes with telmisartan, ramipril, or both, in people at high vascular risk (the ONTARGET study): a multicentre, randomised, double-blind, controlled trial. Lancet 2008; 372:547553.
  55. Schmieder RE, Mann JF, Schumacher H, et al; ONTARGET Investigators. Changes in albuminuria predict mortality and morbidity in patients with vascular disease. J Am Soc Nephrol 2011; 22:13531364.
  56. Tobe SW, Clase CM, Gao P, et al; ONTARGET and TRANSCEND Investigators. Cardiovascular and renal outcomes with telmisartan, ramipril, or both in people at high renal risk: results from the ONTARGET and TRANSCEND studies. Circulation 2011; 123:10981107.
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KEY POINTS

  • Albuminuria is best measured by the albumin-to-creatinine ratio.
  • In several studies, albuminuria has been independently associated with a higher risk of death, cardiovascular events, heart failure, stroke, and progression of chronic kidney disease.
  • Despite strong evidence linking albuminuria to adverse outcomes, evidence is limited in favor of routinely screening for it in the general population.
  • Evaluating and managing albuminuria require understanding the limits of its clinical measures, controlling other risk factors for progression of renal disease, managing it medically, and referring to a specialist in certain situations.
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An 85-year-old with muscle pain

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An 85-year-old with muscle pain

An 85-year-old man with hypertension, hyperlipidemia, and coronary artery disease presented to our clinic with diffuse muscle pain. The pain had been present for about 3 months, but it had become noticeably worse over the past few weeks.

He was not aware of any trauma. He described the muscle pain as dull and particularly severe in his lower extremities (his thighs and calves). The pain did not limit his daily activities, nor did physical exertion or the time of day have any effect on the level of the pain.

His medications at that time included metoprolol, aspirin, hydrochlorothiazide, simvastatin, and a daily multivitamin.

He was not in acute distress. On neurologic and musculoskeletal examinations, all deep-tendon reflexes were intact, with no tenderness to palpation of the upper and lower extremities. No abnormalities were noted on the joint examination. He had full range of motion, with 5/5 muscle strength in the upper and lower extremities bilaterally and normal muscle tone. He was able to walk with ease. Results of initial laboratory testing, including creatine kinase and erythrocyte sedimentation rate, were normal.

1. What should be the next best step in the evaluation of this patient’s muscle pain?

  • Order tests for cyclic citrullinated peptide (CCP) antibody and rheumatoid factor
  • Advise him to refrain from physical activity until his symptoms resolve
  • Take a more detailed history, including a review of medications and supplements
  • Recommend a trial of a nonsteroidal anti-inflammatory drug (NSAID)
  • Send him for radiographic imaging

Since his muscle pain has persisted for several months without improvement, a more detailed history should be taken, including a review of current medications and supplements.

Testing CCP antibody and rheumatoid factor would be useful if rheumatoid arthritis were suspected, but in the absence of demonstrable arthritis on examination, these tests would have low specificity even if the results were positive.

An NSAID may temporarily alleviate his pain, but it will not help establish a diagnosis. And in elderly patients, NSAIDs are not without complications and so should be prescribed only in appropriate situations.

Imaging would be appropriate at this point only if there was clinical suspicion of a specific disease. However, our patient has no focal deficits, and the suspicion of fracture or malignancy is low.

The medical history should include asking about current drug regimens, recent medication changes, and the use of herbal supplements, since polypharmacy is common in elderly patients with multiple comorbidities.

On further questioning, our patient said that his dose of simvastatin had been increased from 40 mg daily to 80 mg daily about 1 month before his symptoms appeared. He was taking a daily multivitamin but was not using herbal supplements or other over-the-counter products. He did not recall any constitutional symptoms before the onset of his current symptoms, and he had never had similar muscle pain in the past.

2. Based on the additional information from the history, what is the most likely cause of his muscle pain?

  • Limited myositis secondary to recent viral infection
  • Rhabdomyolysis
  • Hypothyroidism
  • Drug-drug interaction
  • Statin-induced myalgia

Our patient’s history provided nothing to suggest viral myositis. Hypothyroidism should always be considered in patients with myalgia, but this is not likely in our patient, as he does not display other characteristics, such as diminished reflexes, hypotonia, cold intolerance, and mood instability. Even though calcium channel blockers have been known to cause myalgia in patients on statins, a drug-drug reaction is not likely, as he had not started taking a calcium channel blocker before his symptoms began. This patient did not show signs or symptoms of rhabdomyolysis, a type of myopathy in which necrosis of the muscle tissue occurs, generally causing profound weakness and pain.1

Therefore, statin-induced myopathy is the most likely cause of his diffuse muscle pain, particularly since his simvastatin had been increased 1 month before the onset of symptoms.

3. What should be the next step in his management?

  • Decrease the dose of simvastatin to the last known dose he was able to tolerate
  • Continue simvastatin at the same dose and then monitor
  • Switch to another statin
  • Add coenzyme Q10
  • Stop simvastatin

Decreasing the statin dosage to the last well-tolerated dose would not be appropriate in a patient with myopathy, as the symptoms would probably not improve.2–4 Also, one should not switch to a different statin while a patient is experiencing symptoms. Rather, the statin should be stopped for at least 6 weeks or until the symptoms have fully resolved.1

Adding coenzyme Q10 is another option, especially in a patient with previously diagnosed coronary artery disease,5 when continued statin therapy is thought necessary to reduce the likelihood of repeat coronary events.

We discontinued his simvastatin. Followup 3 weeks later in the outpatient clinic showed that his symptoms were slowly improving. The symptoms had resolved completely 4 months later.

 

 

4. How should we manage our patient’s hyperlipidemia once his symptoms have resolved?

  • Restart simvastatin at the 80-mg dose
  • Restart simvastatin at the 40-mg dose
  • Start a hydrophilic statin at full dose
  • Use a drug from another class of lipid-lowering drugs
  • Wait another 3 months before prescribing any lipid-lowering drug

His treatment for hyperlipidemia should be continued, considering his comorbidities. However, restarting the same statin, even at a lower dose, will likely cause his symptoms to recur. Thus, a different statin should be tried once his muscle pain has resolved.

Other classes of lipid-lowering drugs are usually less efficacious than statins, particularly when trying to control low-density lipoprotein (LDL) cholesterol, so a drug from another class should not be used until other statin options have been attempted.2,6,7

Simvastatin is lipophilic. Trying a statin with hydrophilic properties (eg, pravastatin, rosuvastatin, fluvastatin) has been shown to convey similar cardioprotective effects with a lower propensity for myalgia, as lipophilic statins have a higher propensity to penetrate muscle tissue than do hydrophilic statins.3,4,8

Once his symptoms resolved, our patient was started on a hydrophilic statin, fluvastatin 20 mg daily. Unfortunately, his pain recurred 3 weeks later. The statin was stopped, and his symptoms again resolved.

5. Since our patient was unable to tolerate a second statin, what should be the next step in his management?

  • Restart simvastatin 
  • Use a drug from another class to control the hyperlipidemia
  • Wait at least 6 months after symptoms resolve before trying any lipid-lowering drug
  • Initiate therapy with coenzyme Q10 and fish oil
  • Wait for symptoms to resolve, then restart a hydrophilic statin at a lower dose and lower frequency

Restarting simvastatin will likely cause a recurrence of the myalgia. Other lipid-lowering drugs such as nicotinic acid, bile acid resins, and fibrates are not as efficacious as statins. Coenzyme Q10 and fish oil can reduce lipid levels, but they are not as efficacious as statins.

In view of our patient’s lipid profile—LDL cholesterol elevated at 167 mg/dL, high-density lipoprotein cholesterol 31 mg/dL, triglycerides 47 mg/dL—it is important to treat his hyperlipidemia. Therefore, another attempt at statin therapy should be made once his symptoms have resolved.

Studies have shown that restarting a statin at a low dose and low frequency is effective in patients who have experienced intolerance to a statin.3,4 Our patient was treated with low-dose pravastatin (20 mg), resulting in a moderate improvement in his LDL cholesterol to 123 mg/dL.

STATIN-INDUCED MYOPATHY: ADDRESSING THE DILEMMA

Treating hyperlipidemia is important to prevent vascular events in patients with or without coronary artery disease. Statins are the most effective agents available for controlling hypercholesterolemia, specifically LDL levels, as well as for preventing myocardial infarction.

Unfortunately, significant side effects have been reported, and myopathy is the most prevalent. Statin-induced myopathy includes a combination of muscle tenderness, myalgia, and weakness.2–11 In randomized controlled trials, the risk of myopathy was estimated to be between 1.5% and 5%.6 In unselected clinic patients on high-dose statins, the rate of muscle complaints may be as high as 20%.12

The cause of statin-induced myopathy is not known, although studies have linked it to genetic defects.7 Risk factors have been identified and include personal and family history of myalgia, Asian ethnicity, hypothyroidism, and type 1 diabetes. The incidence of statin-induced myalgia is two to three times higher in patients on corticosteroid therapy. Other risk factors include female sex, liver disease, and renal dysfunction.7,8

A less common etiology is anti-HMG coenzyme A reductase antibodies. Studies have shown that these antibody levels correlate well with the amount of myositis as measured by creatine kinase levels. However, there is no consensus yet on screening for these antibodies.13

Statin therapy poses a dilemma, as there is a thin line between the benefits and the risks of side effects, especially statin-induced myopathy.3,4 Current recommendations include discontinuing the statin until symptoms fully resolve. Creatine kinase levels may be useful in assessing for potential muscle breakdown, especially in patients with reduced renal function, as this predisposes them to statin-induced myopathy, yet normal values do not preclude the diagnosis of statin-induced myopathy.3,4,7,8

Once symptoms resolve and laboratory test results normalize, a trial of a different statin is recommended. If patients become symptomatic, a trial of a low-dose hydrophilic statin at a once- or twice-weekly interval has been recommended. Several studies have assessed the efficacy of a low-dose statin with decreased frequency of administration and have consistently shown significant improvement in lipid levels.3,4 For instance, once-weekly rosuvastatin at a dose between 5 mg and 20 mg resulted in a 29% reduction in LDL cholesterol levels, and 80% of patients did not experience a recurrence of myalgia.3 Furthermore, a study of patients treated with 5 mg to 10 mg of rosuvastatin twice a week resulted in a 26% decrease in LDL cholesterol levels.4 This study also showed that when an additional non-statin lipid-lowering drug was prescribed (eg, ezetimibe, bile acid resin, nicotinic acid), more than half of the patients reached their goal lipid level.4

The addition of coenzyme Q10 and fish oil has also been suggested. Although, the evidence to support this is inconclusive, the potential benefit outweighs the risk, since the side effects are minimal.1 However, no study yet has evaluated the risks vs the benefits in patients with elevated creatine kinase.

Statin-induced myopathy is a commonly encountered adverse effect. Currently, there are no guidelines on restarting statin therapy after statin-induced myopathy; however, data suggest that statin therapy should be restarted once symptoms resolve, and that variations in dose and frequency may be necessary.1–8,14

References
  1. Fernandez G, Spatz ES, Jablecki C, Phillips PS. Statin myopathy: a common dilemma not reflected in clinical trials. Cleve Clin J Med 2011; 78:393403.
  2. Foley KA, Simpson RJ, Crouse JR, Weiss TW, Markson LE, Alexander CM. Effectiveness of statin titration on low-density lipoprotein cholesterol goal attainment in patients at high risk of atherogenic events. Am J Cardiol 2003; 92:7981.
  3. Backes JM, Moriarty PM, Ruisinger JF, Gibson CA. Effects of once weekly rosuvastatin among patients with a prior statin intolerance. Am J Cardiol 2007; 100:554555.
  4. Gadarla M, Kearns AK, Thompson PD. Efficacy of rosuvastatin (5 mg and 10 mg) twice a week in patients intolerant to daily statins. Am J Cardiol 2008; 101:17471748.
  5. Caso G, Kelly P, McNurlan MA, Lawson WE. Effect of coenzyme q10 on myopathic symptoms in patients treated with statins. Am J Cardiol 2007; 99:14091412.
  6. Baigent C, Keech A, Kearney PM, et al; Cholesterol Treatment Trialists’ (CTT) Collaborators. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet 2005; 366:12671278.
  7. Tomaszewski M, Stepien KM, Tomaszewska J, Czuczwar SJ. Statin-induced myopathies. Pharmacol Rep 2011; 63:859866.
  8. SEARCH Collaborative Group; Link E, Parish S, Armitage J, et al. SLCO1B1 variants and statin-induced myopathy—a genomewide study. N Engl J Med 2008; 359:789799.
  9. Thompson PD, Clarkson P, Karas RH. Statin-associated myopathy. JAMA 2003; 289:16811690.
  10. Heart Protection Study Collaborative Group. MRC/BHF heart protection study of cholesterol lowering with simvastatin in 20,536 high-risk individuals: a randomised placebo-controlled trial. Lancet 2002; 360:722.
  11. Guyton JR. Benefit versus risk in statin treatment. Am J Cardiol 2006; 97:95C97C.
  12. Buettner C, Davis RB, Leveille SG, Mittleman MA, Mukamal KJ. Prevalence of musculoskeletal pain and statin use. J Gen Intern Med 2008; 23:11821186.
  13. Werner JL, Christopher-Stine L, Ghazarian SR, et al. Antibody levels correlate with creatine kinase levels and strength in anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase-associated autoimmune myopathy. Arthritis Rheum 2012; 64:40874093.
  14. The Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID) Study Group. Prevention of cardiovascular events and death with pravastatin in patients with coronary heart disease and a broad range of initial cholesterol levels. N Engl J Med 1998; 339:13491357.
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An 85-year-old man with hypertension, hyperlipidemia, and coronary artery disease presented to our clinic with diffuse muscle pain. The pain had been present for about 3 months, but it had become noticeably worse over the past few weeks.

He was not aware of any trauma. He described the muscle pain as dull and particularly severe in his lower extremities (his thighs and calves). The pain did not limit his daily activities, nor did physical exertion or the time of day have any effect on the level of the pain.

His medications at that time included metoprolol, aspirin, hydrochlorothiazide, simvastatin, and a daily multivitamin.

He was not in acute distress. On neurologic and musculoskeletal examinations, all deep-tendon reflexes were intact, with no tenderness to palpation of the upper and lower extremities. No abnormalities were noted on the joint examination. He had full range of motion, with 5/5 muscle strength in the upper and lower extremities bilaterally and normal muscle tone. He was able to walk with ease. Results of initial laboratory testing, including creatine kinase and erythrocyte sedimentation rate, were normal.

1. What should be the next best step in the evaluation of this patient’s muscle pain?

  • Order tests for cyclic citrullinated peptide (CCP) antibody and rheumatoid factor
  • Advise him to refrain from physical activity until his symptoms resolve
  • Take a more detailed history, including a review of medications and supplements
  • Recommend a trial of a nonsteroidal anti-inflammatory drug (NSAID)
  • Send him for radiographic imaging

Since his muscle pain has persisted for several months without improvement, a more detailed history should be taken, including a review of current medications and supplements.

Testing CCP antibody and rheumatoid factor would be useful if rheumatoid arthritis were suspected, but in the absence of demonstrable arthritis on examination, these tests would have low specificity even if the results were positive.

An NSAID may temporarily alleviate his pain, but it will not help establish a diagnosis. And in elderly patients, NSAIDs are not without complications and so should be prescribed only in appropriate situations.

Imaging would be appropriate at this point only if there was clinical suspicion of a specific disease. However, our patient has no focal deficits, and the suspicion of fracture or malignancy is low.

The medical history should include asking about current drug regimens, recent medication changes, and the use of herbal supplements, since polypharmacy is common in elderly patients with multiple comorbidities.

On further questioning, our patient said that his dose of simvastatin had been increased from 40 mg daily to 80 mg daily about 1 month before his symptoms appeared. He was taking a daily multivitamin but was not using herbal supplements or other over-the-counter products. He did not recall any constitutional symptoms before the onset of his current symptoms, and he had never had similar muscle pain in the past.

2. Based on the additional information from the history, what is the most likely cause of his muscle pain?

  • Limited myositis secondary to recent viral infection
  • Rhabdomyolysis
  • Hypothyroidism
  • Drug-drug interaction
  • Statin-induced myalgia

Our patient’s history provided nothing to suggest viral myositis. Hypothyroidism should always be considered in patients with myalgia, but this is not likely in our patient, as he does not display other characteristics, such as diminished reflexes, hypotonia, cold intolerance, and mood instability. Even though calcium channel blockers have been known to cause myalgia in patients on statins, a drug-drug reaction is not likely, as he had not started taking a calcium channel blocker before his symptoms began. This patient did not show signs or symptoms of rhabdomyolysis, a type of myopathy in which necrosis of the muscle tissue occurs, generally causing profound weakness and pain.1

Therefore, statin-induced myopathy is the most likely cause of his diffuse muscle pain, particularly since his simvastatin had been increased 1 month before the onset of symptoms.

3. What should be the next step in his management?

  • Decrease the dose of simvastatin to the last known dose he was able to tolerate
  • Continue simvastatin at the same dose and then monitor
  • Switch to another statin
  • Add coenzyme Q10
  • Stop simvastatin

Decreasing the statin dosage to the last well-tolerated dose would not be appropriate in a patient with myopathy, as the symptoms would probably not improve.2–4 Also, one should not switch to a different statin while a patient is experiencing symptoms. Rather, the statin should be stopped for at least 6 weeks or until the symptoms have fully resolved.1

Adding coenzyme Q10 is another option, especially in a patient with previously diagnosed coronary artery disease,5 when continued statin therapy is thought necessary to reduce the likelihood of repeat coronary events.

We discontinued his simvastatin. Followup 3 weeks later in the outpatient clinic showed that his symptoms were slowly improving. The symptoms had resolved completely 4 months later.

 

 

4. How should we manage our patient’s hyperlipidemia once his symptoms have resolved?

  • Restart simvastatin at the 80-mg dose
  • Restart simvastatin at the 40-mg dose
  • Start a hydrophilic statin at full dose
  • Use a drug from another class of lipid-lowering drugs
  • Wait another 3 months before prescribing any lipid-lowering drug

His treatment for hyperlipidemia should be continued, considering his comorbidities. However, restarting the same statin, even at a lower dose, will likely cause his symptoms to recur. Thus, a different statin should be tried once his muscle pain has resolved.

Other classes of lipid-lowering drugs are usually less efficacious than statins, particularly when trying to control low-density lipoprotein (LDL) cholesterol, so a drug from another class should not be used until other statin options have been attempted.2,6,7

Simvastatin is lipophilic. Trying a statin with hydrophilic properties (eg, pravastatin, rosuvastatin, fluvastatin) has been shown to convey similar cardioprotective effects with a lower propensity for myalgia, as lipophilic statins have a higher propensity to penetrate muscle tissue than do hydrophilic statins.3,4,8

Once his symptoms resolved, our patient was started on a hydrophilic statin, fluvastatin 20 mg daily. Unfortunately, his pain recurred 3 weeks later. The statin was stopped, and his symptoms again resolved.

5. Since our patient was unable to tolerate a second statin, what should be the next step in his management?

  • Restart simvastatin 
  • Use a drug from another class to control the hyperlipidemia
  • Wait at least 6 months after symptoms resolve before trying any lipid-lowering drug
  • Initiate therapy with coenzyme Q10 and fish oil
  • Wait for symptoms to resolve, then restart a hydrophilic statin at a lower dose and lower frequency

Restarting simvastatin will likely cause a recurrence of the myalgia. Other lipid-lowering drugs such as nicotinic acid, bile acid resins, and fibrates are not as efficacious as statins. Coenzyme Q10 and fish oil can reduce lipid levels, but they are not as efficacious as statins.

In view of our patient’s lipid profile—LDL cholesterol elevated at 167 mg/dL, high-density lipoprotein cholesterol 31 mg/dL, triglycerides 47 mg/dL—it is important to treat his hyperlipidemia. Therefore, another attempt at statin therapy should be made once his symptoms have resolved.

Studies have shown that restarting a statin at a low dose and low frequency is effective in patients who have experienced intolerance to a statin.3,4 Our patient was treated with low-dose pravastatin (20 mg), resulting in a moderate improvement in his LDL cholesterol to 123 mg/dL.

STATIN-INDUCED MYOPATHY: ADDRESSING THE DILEMMA

Treating hyperlipidemia is important to prevent vascular events in patients with or without coronary artery disease. Statins are the most effective agents available for controlling hypercholesterolemia, specifically LDL levels, as well as for preventing myocardial infarction.

Unfortunately, significant side effects have been reported, and myopathy is the most prevalent. Statin-induced myopathy includes a combination of muscle tenderness, myalgia, and weakness.2–11 In randomized controlled trials, the risk of myopathy was estimated to be between 1.5% and 5%.6 In unselected clinic patients on high-dose statins, the rate of muscle complaints may be as high as 20%.12

The cause of statin-induced myopathy is not known, although studies have linked it to genetic defects.7 Risk factors have been identified and include personal and family history of myalgia, Asian ethnicity, hypothyroidism, and type 1 diabetes. The incidence of statin-induced myalgia is two to three times higher in patients on corticosteroid therapy. Other risk factors include female sex, liver disease, and renal dysfunction.7,8

A less common etiology is anti-HMG coenzyme A reductase antibodies. Studies have shown that these antibody levels correlate well with the amount of myositis as measured by creatine kinase levels. However, there is no consensus yet on screening for these antibodies.13

Statin therapy poses a dilemma, as there is a thin line between the benefits and the risks of side effects, especially statin-induced myopathy.3,4 Current recommendations include discontinuing the statin until symptoms fully resolve. Creatine kinase levels may be useful in assessing for potential muscle breakdown, especially in patients with reduced renal function, as this predisposes them to statin-induced myopathy, yet normal values do not preclude the diagnosis of statin-induced myopathy.3,4,7,8

Once symptoms resolve and laboratory test results normalize, a trial of a different statin is recommended. If patients become symptomatic, a trial of a low-dose hydrophilic statin at a once- or twice-weekly interval has been recommended. Several studies have assessed the efficacy of a low-dose statin with decreased frequency of administration and have consistently shown significant improvement in lipid levels.3,4 For instance, once-weekly rosuvastatin at a dose between 5 mg and 20 mg resulted in a 29% reduction in LDL cholesterol levels, and 80% of patients did not experience a recurrence of myalgia.3 Furthermore, a study of patients treated with 5 mg to 10 mg of rosuvastatin twice a week resulted in a 26% decrease in LDL cholesterol levels.4 This study also showed that when an additional non-statin lipid-lowering drug was prescribed (eg, ezetimibe, bile acid resin, nicotinic acid), more than half of the patients reached their goal lipid level.4

The addition of coenzyme Q10 and fish oil has also been suggested. Although, the evidence to support this is inconclusive, the potential benefit outweighs the risk, since the side effects are minimal.1 However, no study yet has evaluated the risks vs the benefits in patients with elevated creatine kinase.

Statin-induced myopathy is a commonly encountered adverse effect. Currently, there are no guidelines on restarting statin therapy after statin-induced myopathy; however, data suggest that statin therapy should be restarted once symptoms resolve, and that variations in dose and frequency may be necessary.1–8,14

An 85-year-old man with hypertension, hyperlipidemia, and coronary artery disease presented to our clinic with diffuse muscle pain. The pain had been present for about 3 months, but it had become noticeably worse over the past few weeks.

He was not aware of any trauma. He described the muscle pain as dull and particularly severe in his lower extremities (his thighs and calves). The pain did not limit his daily activities, nor did physical exertion or the time of day have any effect on the level of the pain.

His medications at that time included metoprolol, aspirin, hydrochlorothiazide, simvastatin, and a daily multivitamin.

He was not in acute distress. On neurologic and musculoskeletal examinations, all deep-tendon reflexes were intact, with no tenderness to palpation of the upper and lower extremities. No abnormalities were noted on the joint examination. He had full range of motion, with 5/5 muscle strength in the upper and lower extremities bilaterally and normal muscle tone. He was able to walk with ease. Results of initial laboratory testing, including creatine kinase and erythrocyte sedimentation rate, were normal.

1. What should be the next best step in the evaluation of this patient’s muscle pain?

  • Order tests for cyclic citrullinated peptide (CCP) antibody and rheumatoid factor
  • Advise him to refrain from physical activity until his symptoms resolve
  • Take a more detailed history, including a review of medications and supplements
  • Recommend a trial of a nonsteroidal anti-inflammatory drug (NSAID)
  • Send him for radiographic imaging

Since his muscle pain has persisted for several months without improvement, a more detailed history should be taken, including a review of current medications and supplements.

Testing CCP antibody and rheumatoid factor would be useful if rheumatoid arthritis were suspected, but in the absence of demonstrable arthritis on examination, these tests would have low specificity even if the results were positive.

An NSAID may temporarily alleviate his pain, but it will not help establish a diagnosis. And in elderly patients, NSAIDs are not without complications and so should be prescribed only in appropriate situations.

Imaging would be appropriate at this point only if there was clinical suspicion of a specific disease. However, our patient has no focal deficits, and the suspicion of fracture or malignancy is low.

The medical history should include asking about current drug regimens, recent medication changes, and the use of herbal supplements, since polypharmacy is common in elderly patients with multiple comorbidities.

On further questioning, our patient said that his dose of simvastatin had been increased from 40 mg daily to 80 mg daily about 1 month before his symptoms appeared. He was taking a daily multivitamin but was not using herbal supplements or other over-the-counter products. He did not recall any constitutional symptoms before the onset of his current symptoms, and he had never had similar muscle pain in the past.

2. Based on the additional information from the history, what is the most likely cause of his muscle pain?

  • Limited myositis secondary to recent viral infection
  • Rhabdomyolysis
  • Hypothyroidism
  • Drug-drug interaction
  • Statin-induced myalgia

Our patient’s history provided nothing to suggest viral myositis. Hypothyroidism should always be considered in patients with myalgia, but this is not likely in our patient, as he does not display other characteristics, such as diminished reflexes, hypotonia, cold intolerance, and mood instability. Even though calcium channel blockers have been known to cause myalgia in patients on statins, a drug-drug reaction is not likely, as he had not started taking a calcium channel blocker before his symptoms began. This patient did not show signs or symptoms of rhabdomyolysis, a type of myopathy in which necrosis of the muscle tissue occurs, generally causing profound weakness and pain.1

Therefore, statin-induced myopathy is the most likely cause of his diffuse muscle pain, particularly since his simvastatin had been increased 1 month before the onset of symptoms.

3. What should be the next step in his management?

  • Decrease the dose of simvastatin to the last known dose he was able to tolerate
  • Continue simvastatin at the same dose and then monitor
  • Switch to another statin
  • Add coenzyme Q10
  • Stop simvastatin

Decreasing the statin dosage to the last well-tolerated dose would not be appropriate in a patient with myopathy, as the symptoms would probably not improve.2–4 Also, one should not switch to a different statin while a patient is experiencing symptoms. Rather, the statin should be stopped for at least 6 weeks or until the symptoms have fully resolved.1

Adding coenzyme Q10 is another option, especially in a patient with previously diagnosed coronary artery disease,5 when continued statin therapy is thought necessary to reduce the likelihood of repeat coronary events.

We discontinued his simvastatin. Followup 3 weeks later in the outpatient clinic showed that his symptoms were slowly improving. The symptoms had resolved completely 4 months later.

 

 

4. How should we manage our patient’s hyperlipidemia once his symptoms have resolved?

  • Restart simvastatin at the 80-mg dose
  • Restart simvastatin at the 40-mg dose
  • Start a hydrophilic statin at full dose
  • Use a drug from another class of lipid-lowering drugs
  • Wait another 3 months before prescribing any lipid-lowering drug

His treatment for hyperlipidemia should be continued, considering his comorbidities. However, restarting the same statin, even at a lower dose, will likely cause his symptoms to recur. Thus, a different statin should be tried once his muscle pain has resolved.

Other classes of lipid-lowering drugs are usually less efficacious than statins, particularly when trying to control low-density lipoprotein (LDL) cholesterol, so a drug from another class should not be used until other statin options have been attempted.2,6,7

Simvastatin is lipophilic. Trying a statin with hydrophilic properties (eg, pravastatin, rosuvastatin, fluvastatin) has been shown to convey similar cardioprotective effects with a lower propensity for myalgia, as lipophilic statins have a higher propensity to penetrate muscle tissue than do hydrophilic statins.3,4,8

Once his symptoms resolved, our patient was started on a hydrophilic statin, fluvastatin 20 mg daily. Unfortunately, his pain recurred 3 weeks later. The statin was stopped, and his symptoms again resolved.

5. Since our patient was unable to tolerate a second statin, what should be the next step in his management?

  • Restart simvastatin 
  • Use a drug from another class to control the hyperlipidemia
  • Wait at least 6 months after symptoms resolve before trying any lipid-lowering drug
  • Initiate therapy with coenzyme Q10 and fish oil
  • Wait for symptoms to resolve, then restart a hydrophilic statin at a lower dose and lower frequency

Restarting simvastatin will likely cause a recurrence of the myalgia. Other lipid-lowering drugs such as nicotinic acid, bile acid resins, and fibrates are not as efficacious as statins. Coenzyme Q10 and fish oil can reduce lipid levels, but they are not as efficacious as statins.

In view of our patient’s lipid profile—LDL cholesterol elevated at 167 mg/dL, high-density lipoprotein cholesterol 31 mg/dL, triglycerides 47 mg/dL—it is important to treat his hyperlipidemia. Therefore, another attempt at statin therapy should be made once his symptoms have resolved.

Studies have shown that restarting a statin at a low dose and low frequency is effective in patients who have experienced intolerance to a statin.3,4 Our patient was treated with low-dose pravastatin (20 mg), resulting in a moderate improvement in his LDL cholesterol to 123 mg/dL.

STATIN-INDUCED MYOPATHY: ADDRESSING THE DILEMMA

Treating hyperlipidemia is important to prevent vascular events in patients with or without coronary artery disease. Statins are the most effective agents available for controlling hypercholesterolemia, specifically LDL levels, as well as for preventing myocardial infarction.

Unfortunately, significant side effects have been reported, and myopathy is the most prevalent. Statin-induced myopathy includes a combination of muscle tenderness, myalgia, and weakness.2–11 In randomized controlled trials, the risk of myopathy was estimated to be between 1.5% and 5%.6 In unselected clinic patients on high-dose statins, the rate of muscle complaints may be as high as 20%.12

The cause of statin-induced myopathy is not known, although studies have linked it to genetic defects.7 Risk factors have been identified and include personal and family history of myalgia, Asian ethnicity, hypothyroidism, and type 1 diabetes. The incidence of statin-induced myalgia is two to three times higher in patients on corticosteroid therapy. Other risk factors include female sex, liver disease, and renal dysfunction.7,8

A less common etiology is anti-HMG coenzyme A reductase antibodies. Studies have shown that these antibody levels correlate well with the amount of myositis as measured by creatine kinase levels. However, there is no consensus yet on screening for these antibodies.13

Statin therapy poses a dilemma, as there is a thin line between the benefits and the risks of side effects, especially statin-induced myopathy.3,4 Current recommendations include discontinuing the statin until symptoms fully resolve. Creatine kinase levels may be useful in assessing for potential muscle breakdown, especially in patients with reduced renal function, as this predisposes them to statin-induced myopathy, yet normal values do not preclude the diagnosis of statin-induced myopathy.3,4,7,8

Once symptoms resolve and laboratory test results normalize, a trial of a different statin is recommended. If patients become symptomatic, a trial of a low-dose hydrophilic statin at a once- or twice-weekly interval has been recommended. Several studies have assessed the efficacy of a low-dose statin with decreased frequency of administration and have consistently shown significant improvement in lipid levels.3,4 For instance, once-weekly rosuvastatin at a dose between 5 mg and 20 mg resulted in a 29% reduction in LDL cholesterol levels, and 80% of patients did not experience a recurrence of myalgia.3 Furthermore, a study of patients treated with 5 mg to 10 mg of rosuvastatin twice a week resulted in a 26% decrease in LDL cholesterol levels.4 This study also showed that when an additional non-statin lipid-lowering drug was prescribed (eg, ezetimibe, bile acid resin, nicotinic acid), more than half of the patients reached their goal lipid level.4

The addition of coenzyme Q10 and fish oil has also been suggested. Although, the evidence to support this is inconclusive, the potential benefit outweighs the risk, since the side effects are minimal.1 However, no study yet has evaluated the risks vs the benefits in patients with elevated creatine kinase.

Statin-induced myopathy is a commonly encountered adverse effect. Currently, there are no guidelines on restarting statin therapy after statin-induced myopathy; however, data suggest that statin therapy should be restarted once symptoms resolve, and that variations in dose and frequency may be necessary.1–8,14

References
  1. Fernandez G, Spatz ES, Jablecki C, Phillips PS. Statin myopathy: a common dilemma not reflected in clinical trials. Cleve Clin J Med 2011; 78:393403.
  2. Foley KA, Simpson RJ, Crouse JR, Weiss TW, Markson LE, Alexander CM. Effectiveness of statin titration on low-density lipoprotein cholesterol goal attainment in patients at high risk of atherogenic events. Am J Cardiol 2003; 92:7981.
  3. Backes JM, Moriarty PM, Ruisinger JF, Gibson CA. Effects of once weekly rosuvastatin among patients with a prior statin intolerance. Am J Cardiol 2007; 100:554555.
  4. Gadarla M, Kearns AK, Thompson PD. Efficacy of rosuvastatin (5 mg and 10 mg) twice a week in patients intolerant to daily statins. Am J Cardiol 2008; 101:17471748.
  5. Caso G, Kelly P, McNurlan MA, Lawson WE. Effect of coenzyme q10 on myopathic symptoms in patients treated with statins. Am J Cardiol 2007; 99:14091412.
  6. Baigent C, Keech A, Kearney PM, et al; Cholesterol Treatment Trialists’ (CTT) Collaborators. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet 2005; 366:12671278.
  7. Tomaszewski M, Stepien KM, Tomaszewska J, Czuczwar SJ. Statin-induced myopathies. Pharmacol Rep 2011; 63:859866.
  8. SEARCH Collaborative Group; Link E, Parish S, Armitage J, et al. SLCO1B1 variants and statin-induced myopathy—a genomewide study. N Engl J Med 2008; 359:789799.
  9. Thompson PD, Clarkson P, Karas RH. Statin-associated myopathy. JAMA 2003; 289:16811690.
  10. Heart Protection Study Collaborative Group. MRC/BHF heart protection study of cholesterol lowering with simvastatin in 20,536 high-risk individuals: a randomised placebo-controlled trial. Lancet 2002; 360:722.
  11. Guyton JR. Benefit versus risk in statin treatment. Am J Cardiol 2006; 97:95C97C.
  12. Buettner C, Davis RB, Leveille SG, Mittleman MA, Mukamal KJ. Prevalence of musculoskeletal pain and statin use. J Gen Intern Med 2008; 23:11821186.
  13. Werner JL, Christopher-Stine L, Ghazarian SR, et al. Antibody levels correlate with creatine kinase levels and strength in anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase-associated autoimmune myopathy. Arthritis Rheum 2012; 64:40874093.
  14. The Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID) Study Group. Prevention of cardiovascular events and death with pravastatin in patients with coronary heart disease and a broad range of initial cholesterol levels. N Engl J Med 1998; 339:13491357.
References
  1. Fernandez G, Spatz ES, Jablecki C, Phillips PS. Statin myopathy: a common dilemma not reflected in clinical trials. Cleve Clin J Med 2011; 78:393403.
  2. Foley KA, Simpson RJ, Crouse JR, Weiss TW, Markson LE, Alexander CM. Effectiveness of statin titration on low-density lipoprotein cholesterol goal attainment in patients at high risk of atherogenic events. Am J Cardiol 2003; 92:7981.
  3. Backes JM, Moriarty PM, Ruisinger JF, Gibson CA. Effects of once weekly rosuvastatin among patients with a prior statin intolerance. Am J Cardiol 2007; 100:554555.
  4. Gadarla M, Kearns AK, Thompson PD. Efficacy of rosuvastatin (5 mg and 10 mg) twice a week in patients intolerant to daily statins. Am J Cardiol 2008; 101:17471748.
  5. Caso G, Kelly P, McNurlan MA, Lawson WE. Effect of coenzyme q10 on myopathic symptoms in patients treated with statins. Am J Cardiol 2007; 99:14091412.
  6. Baigent C, Keech A, Kearney PM, et al; Cholesterol Treatment Trialists’ (CTT) Collaborators. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet 2005; 366:12671278.
  7. Tomaszewski M, Stepien KM, Tomaszewska J, Czuczwar SJ. Statin-induced myopathies. Pharmacol Rep 2011; 63:859866.
  8. SEARCH Collaborative Group; Link E, Parish S, Armitage J, et al. SLCO1B1 variants and statin-induced myopathy—a genomewide study. N Engl J Med 2008; 359:789799.
  9. Thompson PD, Clarkson P, Karas RH. Statin-associated myopathy. JAMA 2003; 289:16811690.
  10. Heart Protection Study Collaborative Group. MRC/BHF heart protection study of cholesterol lowering with simvastatin in 20,536 high-risk individuals: a randomised placebo-controlled trial. Lancet 2002; 360:722.
  11. Guyton JR. Benefit versus risk in statin treatment. Am J Cardiol 2006; 97:95C97C.
  12. Buettner C, Davis RB, Leveille SG, Mittleman MA, Mukamal KJ. Prevalence of musculoskeletal pain and statin use. J Gen Intern Med 2008; 23:11821186.
  13. Werner JL, Christopher-Stine L, Ghazarian SR, et al. Antibody levels correlate with creatine kinase levels and strength in anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase-associated autoimmune myopathy. Arthritis Rheum 2012; 64:40874093.
  14. The Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID) Study Group. Prevention of cardiovascular events and death with pravastatin in patients with coronary heart disease and a broad range of initial cholesterol levels. N Engl J Med 1998; 339:13491357.
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New cholesterol guidelines: Worth the wait?

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New cholesterol guidelines: Worth the wait?

On November 12, 2013, a joint task force for the American College of Cardiology and American Heart Association released new guidelines for treating high blood cholesterol to reduce the risk of atherosclerotic cardiovascular disease (ASCVD) in adults.1

This document arrives after several years of intense deliberation, 12 years after the third Adult Treatment Panel (ATP III) guidelines,2 and 8 years after an ATP III update recommending that low-density lipoprotein cholesterol (LDL-C) levels be lowered aggressively (to less than 70 mg/dL) as an option in patients at high risk.3 It represents a major shift in the approach to and management of high blood cholesterol and has sparked considerable controversy.

In the following commentary, we summarize the new guidelines and the philosophy employed by the task force in generating them. We will also examine some advantages and what we believe to be several shortcomings of the new guidelines. These latter points are illustrated through case examples.

IN RANDOMIZED CONTROLLED TRIALS WE TRUST

In collaboration with the National Heart, Lung, and Blood Institute of the National Institutes of Health, the American College of Cardiology and American Heart Association formed an expert panel task force in 2008.

The task force elected to use only evidence from randomized controlled trials, systematic reviews, and meta-analyses of randomized controlled trials (and only predefined outcomes of the trials, not post hoc analyses) in formulating its recommendations, with the goal of providing the strongest possible evidence.

The authors state that “By using [randomized controlled trial] data to identify those most likely to benefit [emphasis in original] from cholesterol-lowering statin therapy, the recommendations will be of value to primary care clinicians as well as specialists concerned with ASCVD prevention. Importantly, the recommendations were designed to be easy to use in the clinical setting, facilitating the implementation of a strategy of risk assessment and treatment focused on the prevention of ASCVD.”3 They also state the guidelines are meant to “inform clinical judgment, not replace it” and that clinician judgment in addition to discussion with patients remains vital.

During the deliberations, the National Heart, Lung, and Blood Institute removed itself from participating, stating its mission no longer included drafting new guidelines. Additionally, other initial members of the task force removed themselves because of disagreement and concerns about the direction of the new guidelines.

These guidelines, and their accompanying new cardiovascular risk calculator,4 were released without a preliminary period to allow for open discussion, comment, and critique by physicians outside the panel. No attempt was made to harmonize the guidelines with previous versions (eg, ATP III) or with current international guidelines.

WHAT’S NEW IN THE GUIDELINES?

The following are the major changes in the new guidelines for treating high blood cholesterol:

  • Treatment goals for LDL-C and non-high-density lipoprotein cholesterol (non-HDL-C) are no longer recommended.
  • High-intensity and moderate-intensity statin treatment is emphasized, and low-intensity statin therapy is nearly eliminated.
  • “ASCVD” now includes stroke in addition to coronary heart disease and peripheral arterial disease.
  • Four groups are targeted for treatment (see below).
  • Nonstatin therapies have been markedly de-emphasized.
  • No guidelines are provided for treating high triglyceride levels.

The new guidelines emphasize lifestyle modification as the foundation for reducing risk, regardless of cholesterol therapy. No recommendations are given for patients with New York Heart Association class II, III, or IV heart failure or for hemodialysis patients, because there were insufficient data from randomized controlled trials to support recommendations. Similarly, the guidelines apply only to people between the ages of 40 and 75 (risk calculator ages 40–79), because the authors believed there was not enough evidence from randomized controlled trials to allow development of guidelines outside of this age range.

FOUR MAJOR STATIN TREATMENT GROUPS

The new guidelines specify four groups that merit intensive or moderately intensive statin therapy (Table 1)1:

  • People with clinical ASCVD
  • People with LDL-C levels of 190 mg/dL or higher
  • People with diabetes, age 40 to 75
  • People without diabetes, age 40 to 75, with LDL-C levels 70–189 mg/dL, and a 10-year ASCVD risk of 7.5% or higher as determined by the new risk calculator4 (which also calculates the lifetime risk of ASCVD).

Below, we will address each of these four groups and provide case scenarios to consider. In general, our major disagreements with the new recommendations pertain to the first and fourth categories.

 

 

GROUP 1: PEOPLE WITH CLINICAL ASCVD

Advantages of the new guidelines

  • They appropriately recommend statins in the highest tolerated doses as first-line treatment for this group at high risk.
  • They designate all patients with ASCVD, including those with coronary, peripheral, and cerebrovascular disease, as a high-risk group.
  • Without target LDL-C levels, treatment is simpler than before, requiring less monitoring of lipid levels. (This can also be seen as a limitation, as we discuss below.)

Limitations of the new guidelines

  • They make follow-up LDL-C levels irrelevant, seeming to assume that there is no gradation in residual risk and, thus, no need to tailor therapy to the individual.
  • Patients no longer have a goal to strive for or a way to monitor their progress.
  • The guidelines ignore the pathophysiology of coronary artery disease and evidence of residual risk in patients on both moderate-intensity and high-intensity statin therapy.
  • They also ignore the potential benefits of treating to lower LDL-C or non-HDL-C goals, thus eliminating consideration of multidrug therapy. They do not address patients with recurrent cardiovascular events already on maximal tolerated statin doses.
  • They undermine the potential development and use of new therapies for dysplipidemia in patients with ASCVD.

Case 1: Is LDL-C 110 mg/dL low enough?

A 52-year-old African American man presents with newly discovered moderate coronary artery disease that is not severe enough to warrant stenting. He has no history of hypertension, diabetes mellitus, or smoking. His systolic blood pressure is 130 mm Hg, and his body mass index is 26 kg/m2. He exercises regularly and follows a low-cholesterol diet. He has the following fasting lipid values:

  • Total cholesterol 290 mg/dL
  • HDL-C 50 mg/dL
  • Triglycerides 250 mg/dL
  • Calculated LDL-C 190 mg/dL.

Two months later, after beginning atorvastatin 80 mg daily, meeting with a nutritionist, and redoubling his dietary efforts, his fasting lipid concentrations are:

  • Total cholesterol 180 mg/dL
  • HDL-C 55 mg/dL
  • Triglycerides 75 mg/dL
  • Calculated LDL-C 110 mg/dL.

Comment: Lack of LDL-C goals is a flaw

The new guidelines call for patients with known ASCVD, such as this patient, to receive intensive statin therapy—which he got.

However, once a patient is on therapy, the new guidelines do not encourage repeating the lipid panel other than to assess compliance. With intensive therapy, we expect a reduction in LDL-C of at least 50% (Table 1), but patient-to-patient differences in response to medications are common, and without repeat testing we would have no way of gauging this patient’s residual risk.

Further, the new guidelines emphasize the lack of hard outcome data supporting the addition of another lipid-lowering drug to a statin, although they do indicate that one can consider it. In a patient at high risk, such as this one, would you be comfortable with an LDL-C value of 110 mg/dL on maximum statin therapy? Would you consider adding a nonstatin drug?

Figure 1. Scatter plot with best-fit lines of major lipid trials (statin and nonstatin trials) for both primary and secondary prevention of coronary heart disease events. Even though the trials were not designed to show differences based on a target LDL-C level, there is a clear relationship of fewer events with lower LDL-C levels.

A preponderance of data shows that LDL plays a causal role in ASCVD development and adverse events. Genetic data show that the LDL particle and the LDL receptor pathway are mechanistically linked to ASCVD pathogenesis, with lifetime exposure as a critical determinant of risk.5,6 Moreover, randomized controlled trials of statins and other studies of cholesterol-lowering show a reproducible relationship between the LDL-C level achieved and absolute risk (Figure 1).7–24 We believe the totality of data constitutes a strong rationale for targeting LDL-C and establishing goals for lowering its levels. For these reasons, we believe that removing LDL-C goals is a fundamental flaw of the new guidelines.

The reason for the lack of data from randomized controlled trials demonstrating benefits of adding therapies to statins (when LDL-C is still high) or benefits of treating to specific goals is that no such trials have been performed. Even trials of nonpharmacologic means of lowering LDL-C, such as ileal bypass, which was used in the Program on the Surgical Control of the Hyperlipidemias trial,20 provide independent evidence that lowering LDL-C reduces the risk of ASCVD (Figure 1).

In addition, trials of nonstatin drugs, such as the Coronary Drug Project,25 which tested niacin, also showed outcome benefits. On the other hand, studies such as the Atherothrombosis Intervention in Metabolic Syndrome With Low HDL/High Triglycerides: Impact on Global Health26 and Treatment of HDL to Reduce the Incidence of Vascular Events27 trials did not show additional risk reduction when niacin was added to statin therapy. However, the study designs arguably had flaws, including requirement of aggressive LDL-lowering with statins, with LDL-C levels below 70 to 80 mg/dL before randomization.

Therefore, these trials do not tell us what to do for a patient on maximal intensive therapy who has recurrent ASCVD events or who, like our patient, has an LDL-C level higher than previous targets.

For this patient, we would recommend adding a second medication to further lower his LDL-C, but discussing with him the absence of proven benefit in clinical trials and the risks of side effects. At present, lacking LDL-C goals in the new guidelines, we are keeping with the ATP III goals to help guide therapeutic choices and individualize patient management.

GROUP 2: PEOPLE WITH LDL-C ≥ 190

Advantages of the new guidelines

  • They state that these patients should receive statins in the highest tolerated doses, which is universally accepted.

Limitations of the new guidelines

  • The new guidelines mention only that one “may consider” adding a second agent if LDL-C remains above 190 mg/dL after maximum-dose therapy. Patients with familial hypercholesterolemia or other severe forms of hypercholesterolemia typically end up on multidrug therapy to further reduce LDL-C. The absence of randomized controlled trial data in this setting to show an additive value of second and third lipid-lowering agents does not mean these agents do not provide benefit.
 

 

GROUP 3: DIABETES, AGE 40–75, LDL-C 70–189, NO CLINICAL ASCVD

Advantages of the new guidelines

  • They call for aggressive treatment of people with diabetes, a group at high risk that derives significant benefit from statin therapy, as shown in randomized controlled trials.

Limitations of the new guidelines

  • Although high-intensity statin therapy is indicated for this group, we believe that, using the new risk calculator, some patients may receive overly aggressive treatment, thus increasing the possibility of statin side effects.
  • The guidelines do not address patients younger than 40 or older than 75.
  • Diabetic patients have a high residual risk of ASCVD events, even on statin therapy. Yet the guidelines ignore the potential benefits of more aggressive LDL-lowering or non-LDL secondary targets for therapy.

Case 2: How low is too low?

A 63-year-old white woman, a nonsmoker with recently diagnosed diabetes, is seen by her primary care physician. She has hypertension, for which she takes lisinopril 5 mg daily. Her fasting lipid values are:

  • Total cholesterol 160 mg/dL
  • HDL-C 64 mg/dL
  • Triglycerides 100 mg/dL
  • Calculated LDL-C 76 mg/dL.

Her systolic blood pressure is 129 mm Hg, and based on the new risk calculator, her 10-year risk of cardiovascular disease is 10.2%. According to the new guidelines, she should be started on high-intensity statin treatment (Table 1).

Although this is an acceptable initial course of action, it necessitates close vigilance, since it may actually drive her LDL-C level too low. Randomized controlled trials have typically used an LDL-C concentration of less than or equal to 25 mg/dL as the safety cutoff. With a typical LDL-C reduction of at least 50% on high-intensity statins, our patient’s expected LDL-C level will likely be in the low 30s. We believe this would be a good outcome, provided that she tolerates the medication without adverse effects. However, responses to statins vary from patient to patient.

High-intensity statin therapy may not be necessary to reduce risk adequately in all patients who have diabetes without preexisting vascular disease. The Collaborative Atorvastatin Diabetes Study12 compared atorvastatin 10 mg vs placebo in people with type 2 diabetes, age 40 to 75, who had one or more cardiovascular risk factors but no signs or symptoms of preexisting ASCVD and who had only average or below-average cholesterol levels—precisely like this patient. The trial was terminated early because of a clear benefit (a 37% reduction in the composite end point of major adverse cardiovascular events) in the intervention group. For our patient, we believe an alternative and acceptable approach would be to begin moderate-intensity statin therapy (eg, with atorvastatin 10 mg) (Table 1).

Alternatively, in a patient with diabetes and previous atherosclerotic vascular disease or with a high 10-year risk and high LDL-C, limiting treatment to high-intensity statin therapy by itself may deny them the potential benefits of combination therapies and targeting to lower LDL-C levels or non-HDL-C secondary targets. Guidelines from the American Diabetes Association28 and the American Association of Clinical Endocrinologists29 continue to recommend an LDL-C goal of less than 70 mg/dL in patients at high risk, a non-HDL-C less than 100 mg/dL, an apolipoprotein B less than 80 mg/dL, and an LDL particle number less than 1,000 nmol/L.

GROUP 4: AGE 40–75, LDL-C 70–189, NO ASCVD, BUT 10-YEAR RISK ≥ 7.5%

Advantages of the new guidelines

  • They may reduce ASCVD events for patients at higher risk.
  • The risk calculator is easy to use and focuses on global risk, ie, all forms of ASCVD.
  • The guidelines promote discussion of risks and benefits between patients and providers.

Limitations of the new guidelines

  • The new risk calculator is controversial (see below).
  • There is potential for overtreatment, particularly in older patients.
  • There is potential for undertreatment, particularly in patients with an elevated LDL-C but whose 10-year risk is less than 7.5% because they are young.
  • The guidelines do not address patients younger than 40 or older than 75.
  • They do not take into account some traditional risk factors, such as family history, and nontraditional risk factors such as C-reactive protein as measured by ultrasensitive assays, lipoprotein(a), and apolipoprotein B.

Risk calculator controversy

The new risk calculator has aroused strong opinions on both sides of the aisle.

Shortly after the new guidelines were released, cardiologists Dr. Paul Ridker and Dr. Nancy Cook from Brigham and Women’s Hospital in Boston published analyses30 showing that the new risk calculator, which was based on older data from several large cohorts such as the Atherosclerosis Risk in Communities study,31 the Cardiovascular Health Study,32 the Coronary Artery Risk Development in Young Adults study,33 and the Framingham Heart Study,34,35 was inaccurate in other cohorts. Specifically, in more-recent cohorts (the Women’s Health Study,36 Physicians’ Health Study,37 and Women’s Health Initiative38), the new calculator overestimates the 10-year risk of ASCVD by 75% to 150%.30 Using the new calculator would make approximately 30 million more Americans eligible for statin treatment. The concern is that patients at lower risk would be treated and exposed to potential side effects of statin therapy.

In addition, the risk calculator relies heavily on age and sex and does not include other factors such as triglyceride level, family history, C-reactive protein, or lipoprotein(a). Importantly, and somewhat ironically given the otherwise absolute adherence to randomized controlled trial data for guideline development, the risk calculator has never been verified in prospective studies to adequately show that using it reduces ASCVD events.

 

 

Case 3: Overtreating a primary prevention patient

Based on the risk calculator, essentially any African American man in his early 60s with no other risk factors has a 10-year risk of ASCVD of 7.5% or higher and, according to the new guidelines, should receive at least moderate-intensity statin therapy.

For example, consider a 64-year-old African American man whose systolic blood pressure is 129 mm Hg, who does not smoke, does not have diabetes, and does not have hypertension, and whose total cholesterol level is 180 mg/dL, HDL-C 70 mg/dL, triglycerides 130 mg/dL, and calculated LDL-C 84 mg/dL. His calculated 10-year risk is, surprisingly, 7.5%.

Alternatively, his twin brother is a two-pack-per-day smoker with untreated hypertension and systolic blood pressure 150 mm Hg, with fasting total cholesterol 153 mg/dL, HDL-C 70 mg/dL, triglycerides 60 mg/dL, and LDL-C 71 mg/dL. His calculated 10-year risk is 10.5%, so according to the new guidelines, he too should receive high-intensity statin therapy. Yet this patient clearly needs better blood pressure control and smoking cessation as his primary risk-reduction efforts, not a statin. While assessing global risk is important, a shortcoming of the new guidelines is that they can inappropriately lead to treating the risk score, not individualizing the treatment to the patient. Because of the errors inherent in the risk calculator, some experts have called for a temporary halt on implementing the new guidelines until the risk calculator can be further validated. In November 2013, the American Heart Association and the American College of Cardiology reaffirmed their support of the new guidelines and recommended that they be implemented as planned. As of the time this manuscript goes to print, there are no plans to halt implementation of the new guidelines.

Case 4: Undertreating a primary prevention patient

A 25-year-old white man with no medical history has a total cholesterol level of 310 mg/dL, HDL-C 50 mg/dL, triglycerides 400 mg/dL, and calculated LDL-C 180 mg/dL. He does not smoke or have hypertension or diabetes but has a strong family history of premature coronary disease (his father died of myocardial infarction at age 42). His body mass index is 25 kg/m2. Because he is less than 40 years old, the risk calculator does not apply to him.

If we assume he remains untreated and returns at age 40 with the same clinical factors and laboratory values, his calculated 10-year risk of an ASCVD event according to the new risk calculator will still be only 3.1%. Assuming his medical history remains unchanged as he continues to age, his 10-year risk would not reach 7.5% until he is 58. Would you feel comfortable waiting 33 years before starting statin therapy in this patient?

Waiting for dyslipidemic patients to reach middle age before starting LDL-C-lowering therapy is a failure of prevention. For practical reasons, there are no data from randomized controlled trials with hard outcomes in younger people. Nevertheless, a tenet of preventive cardiology is that cumulative exposure accelerates the “vascular age” ahead of the chronological age. This case illustrates why individualized recommendations guided by LDL-C goals as a target for therapy are needed. For this 25-year-old patient, we would recommend starting an intermediate- or high-potency statin.

Case 5: Rheumatoid arthritis

A 60-year-old postmenopausal white woman with severe rheumatoid arthritis presents for cholesterol evaluation. Her total cholesterol level is 235 mg/dL, HDL-C 50 mg/dL, and LDL-C 165 mg/dL. She does not smoke or have hypertension or diabetes. Her systolic blood pressure is 110 mm Hg. She has elevated C-reactive protein on an ultrasensitive assay and elevated lipoprotein(a).

Her calculated 10-year risk of ASCVD is 3.0%. Assuming her medical history remains the same, she would not reach a calculated 10-year risk of at least 7.5% until age 70. We suggest starting moderate- or high-dose statin therapy in this case, based on data (not from randomized controlled trials) showing an increased risk of ASCVD events in patients with rheumatologic disease, increased lipoprotein(a), and inflammatory markers like C-reactive protein. However, the current guidelines do not address this scenario, other than to suggest that clinician consideration can be given to other risk markers such as these, and that these findings should be discussed in detail with the patient. The Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin trial16 showed a dramatic ASCVD risk reduction in just such patients (Figure 1).

APPLAUSE—AND RESERVATIONS

The newest guidelines for treating high blood cholesterol represent a monumental shift away from using target levels of LDL-C and non-HDL-C and toward a focus on statin intensity for patients in the four highest-risk groups.

We applaud the expert panel for its idealistic approach of using only data from randomized controlled trials, for placing more emphasis on higher-intensity statin treatment, for including stroke in the new definition of ASCVD, and for focusing more attention on treating diabetic patients more aggressively. Simplifying the guidelines is a noble goal. Emphasizing moderate-to-high-intensity statin therapy in patients at moderate-to-high risk should have substantial long-term public health benefits.

However, as we have shown in the case examples, there are significant limitations, and some patients can end up being overtreated, while others may be undertreated.

Guidelines need to be crafted by looking at all the evidence, including the pathophysiology of the disease process, not just data from randomized controlled trials. It is difficult to implement a guideline that on one hand used randomized controlled trials exclusively for recommendations, but on the other hand used an untested risk calculator to guide therapy. Randomized controlled trials are not available for every scenario.

Further, absence of randomized controlled trial data in a given scenario should not be interpreted as evidence of lack of benefit. An example of this is a primary-prevention patient under age 40 with elevated LDL-C below the 190 mg/dL cutoff who otherwise is healthy and without risk factors (eg, Case 4). By disregarding all evidence that is not from randomized controlled trials, the expert panel fails to account for the extensive pathophysiology of ASCVD, which often begins at a young age and takes decades to develop.5,6,39 An entire generation of patients who have not reached the age of inclusion in most randomized controlled trials with hard outcomes is excluded (unless the LDL-C level is very high), potentially setting back decades of progress in the field of prevention. Prevention only works if started. With childhood and young adult obesity sharply rising, we should not fail to address the under-40-year-old patient population in our guidelines.

Guidelines are designed to be expert opinion, not to dictate practice. Focusing on the individual patient instead of the general population at risk, the expert panel appropriately emphasizes the “importance of clinician judgment, weighing potential benefits, adverse effects, drug-drug interactions and patient preferences.” However, by excluding all data that do not come from randomized controlled trials, the panel neglects a very large base of knowledge and leaves many clinicians without as much expert opinion as we had hoped for.

LDL-C goals are important: they provide a scorecard to help the patient with lifestyle and dietary changes. They provide the health care provider guidance in making treatment decisions and focusing on treatment of a single patient, not a population. Moreover, if a patient has difficulty taking standard doses of statins because of side effects, the absence of LDL-C goals makes decision-making nearly impossible. We hope physicians will rely on LDL-C goals in such situations, falling back on the ATP III recommendations, although many patients may simply go untreated until they present with ASCVD or until they “age in” to a higher risk category.

We suggest caution in strict adherence to the new guidelines and instead urge physicians to consider a hybrid of the old guidelines (using the ATP III LDL-C goals) and the new ones (emphasizing global risk assessment and high-intensity statin treatment).

References
  1. Stone NJ, Robinson J, Lichtenstein AH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013; published online Nov 13. DOI: 10.1016/j.jacc.2013.11.002.
  2. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002; 106:3143–3421.
  3. Grundy SM, Cleeman JI, Merz CN, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation 2004; 110:227–239.
  4. American Heart Association. 2013 Prevention guidelines tools. CV risk calculator. http://my.americanheart.org/professional/StatementsGuidelines/PreventionGuidelines/Prevention-Guidelines_UCM_457698_SubHomePage.jsp. Accessed December 10, 2013.
  5. Goldstein JL, Brown MS. The LDL receptor. Arterioscler Thromb Vasc Biol 2009; 29:431–438.
  6. Horton JD, Cohen JC, Hobbs HH. PCSK9: a convertase that coordinates LDL catabolism. J Lipid Res 2009; 50(suppl):S172–S177.
  7. Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 1994; 344:1383–1389.
  8. de Lemos JA, Blazing MA, Wiviott SD, et al; for the A to Z Investigators. Early intensive vs a delayed conservative simvastatin strategy in patients with acute coronary syndromes. Phase Z of the A to Z trial. JAMA 2004; 292:1307–1316.
  9. Downs JR, Clearfield M, Weis S, et al; for the AFCAPS/TexCAPS Research Group. Primary prevention of acute coronary events with lovastatin in men and women with average cholesterol levels. Results of AFCAPS/TexCAPS. JAMA 1998; 279:1615–1622.
  10. Koren MJ, Hunninghake DB, on behalf of the ALLIANCE investigators. Clinical outcomes in managed-care patients with coronary heart disease treated aggressively in lipid-lowering disease management clinics. J Am Coll Cardiol 2004; 44:1772–1779.
  11. Sever PS, Dahlof B, Poulter NR, et al; ASCOT investigators. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than-average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial - Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial. Lancet 2003; 361:1149–1158.
  12. Colhoun HM, Betteridge DJ, Durrington PN, et al; on behalf of the CARDS Investigators. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet 2004; 364:685–696.
  13. Sacks FM, Pfeffer MA, Moye LA, et al; for the Cholesterol and Recurrent Events Trial Investigators. The effect of pravastatin on coronary events after myocardial infarction in patients with average cholesterol levels. N Engl J Med 1996; 335:1001–1009.
  14. Heart Protection Study Collaborative Group. MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20 536 high-risk individuals: a randomised placebo-controlled trial. Lancet 2002; 360:7–22.
  15. Pedersen TR, Faegeman O, Kastelein JJ, et al. Incremental Decrease in End Points Through Aggressive Lipid Lowering Study Group. High-dose atorvastatin vs usual-dose simvastatin for secondary prevention after myocardial infarction: the IDEAL study: a randomized controlled trial. JAMA 2005; 294:2437–2445.
  16. Ridker PM, Danielson E, Fonseca FAH, et al; for the JUPITER Study Group. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med 2008; 359:2195–2207.
  17. LIPID Study Group. Prevention of cardiovascular events and death with pravastatin in patients with coronary heart disease and a broad range of initial cholesterol levels. N Engl J Med 1998; 339:1349–1357.
  18. Nakamura H, Arakawa K, Itakura H, et al; for the MEGA Study Group. Primary prevention of cardiovascular disease with pravastatin Japan (MEGA Study): a prospective rabndomised controlled trial. Lancet 2006; 368:1155–1163.
  19. Schwartz GG, Olsson AG, Ezekowitz MD, et al. Myocardial Ischemia Reduction with Aggreessive Cholesterol Lowering (MIRACL) Study Investigators. Effects of atorvastatin on early recurrent ischemic events in acute coronary syndromes: the MIRACL study: a randomized controlled trial. JAMA 2001; 285:1711–1718.
  20. Buchwald H, Varco RL, Matts JP, et al. Effect of partial ileal bypass surgery on mortality and morbidity from coronary heart disease in patients with hypercholesterolemia: report of the Program on the Surgical Control of the Hyperlipidemias (POSCH). N Engl J Med 1990; 323:946–955.
  21. Cannon CP, Braunwald E, McCabe CH, et al; for the Pravastatin or Atorvastatin Evaluation and Infection Therapy-Thrombolysis in Myocardial Infarction 22 Investigators. Intensive versus moderate lipid lowering with statins after acute coronary syndromes. N Engl J Med 2004; 350:1495–1504.
  22. Baigent C, Landray MJ, Reith C, et al; SHARP Investigators. The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial. Lancet 2011; 377:2181–2192.
  23. LaRosa JC, Grundy SM, Waters DD, et al. Intensive lipid lowering with atorvastatin in patients with stable coronary disease. N Engl J Med 2005; 352:1425–1435.
  24. Shepherd J, Cobbe SM, Ford I, et al; for the West of Scotland Coronary Prevention Study Group. Prevention of coronary heart disease with pravastatin in men with hypercholesterolemia. N Engl J Med 1995; 333:1301–1308.
  25. Canner PL, Berge KG, Wenger NK, et al. Fifteen year mortality in Coronary Drug Project patients: long-term benefit with niacin. J Am Coll Cardiol 1989; 8:1245–1255.
  26. AIM-HIGH Investigators, Boden WE, Probstfield JL, Anderson T, et al.  Niacin in patients with low HDL cholesterol levels receiving intensive statin therapy. N Engl J Med 2011; 365:2255–2267.
  27. HPS2-Thrive Collaborative Group. HPS2-THRIVE randomized placebo-controlled trial in 25 673 high-risk patients of ER niacin/laropiprant: trial design, pre-specified muscle and liver outcomes, and reasons for stopping study treatment. Eur Heart J 2013; 34:1279–1291.
  28. American Diabetes Association. Standards of medical care in diabetes—2013. Diabetes Care 2013; 36(suppl 1):S11–S66.
  29. Garber AJ, Abrahamson MJ, Barzilay JI, et al. American Association of Clinical Endocrinologists’comprehensive diabetes management algorithm 2013 consensus statement—executive summary. Endocr Pract 2013; 19:536–557.
  30. Ridker PM, Cook NR. Statins: new American guidelines for prevention of cardiovascular disease. Lancet 2013doi: 10.1016/S0140-6736(13)62388-0. [Epub ahead of print]
  31. The ARIC investigators. The Atherosclerosis Risk in Communities (ARIC) study: design and objectives. Am J Epidemiol 1989; 129:687–702.
  32. Fried LP, Borhani NO, Enright P, et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol 1991; 1:263–276.
  33. Friedman GD, Cutter GR, Donahue RP, et al. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol 1988; 41:1105–1116.
  34. Dawber TR, Kannel WB, Lyell LP. An approach to longitudinal studies in a community: the Framingham study. Ann N Y Acad Sci 1963; 107:539–556.
  35. Kannel WB, Feinleib M, McNamara PM, Garrison RJ, Castelli WP. An investigation of coronary heart disease in families. The Framingham offspring study. Am J Epidemiol 1979; 110:281–290.
  36. Ridker PM, Cook NR, Lee IM, et al. A randomized trial of low-dose aspirin in the primary prevention of cardiovascular disease in women. N Engl J Med 2005; 352:1293–1304.
  37. Belancer C, Buring JE, Cook N, et al; The Steering Committee of the Physicians’ Health Study Research Group. Final report on the aspirin component of the ongoing Physicians’ Health Study. N Engl J Med 1989; 321:129–135.
  38. Langer R, White E, Lewis C, et al. The Women’s Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures. Ann Epidemiol 2003; 13:S107–S121.
  39. Strong JP, Malcom GT, Oalmann MC, Wissler RW. The PDAY study: natural history, risk factors, and pathobiology. Ann N Y Acad Sci 1997; 811:226–235.
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Leslie Cho, MD
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Michael Rocco, MD
Section of Preventive Cardiology, Heart and Vascular Institute, Cleveland Clinic; Assistant Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Stanley L. Hazen, MD, PhD
Co-Section Head, Section of Preventive Cardiology, Heart and Vascular Institute, Cleveland Clinic; Professor of Molecular Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Address: Stanley L. Hazen, MD, PhD, Lerner Research Institute, NC10, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

M.R. is a speaker for Abbott and Amarin.

S.L.H. is named as co-inventor on pending and issued patents held by Cleveland Clinic relating to cardiovascular diagnostics and therapeutics. S.L.H. reports he has been paid as a consultant by the following companies: Cleveland Heart Lab, Esperion, Liposciences, Merck & Co., Pfizer, and Procter & Gamble. S.L.H. reports he has received research funds from Abbott, Astra Zeneca, Cleveland Heart Lab, Esperion, Liposciences, Procter & Gamble, and Takeda. S.L.H. has the right to receive royalty payments for inventions or discoveries related to cardiovascular diagnostics and therapeutics from Abbott Laboratories, Cleveland Heart Lab, Esperion, Frantz Biomarkers, and Liposciences.

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Michael Rocco, MD
Section of Preventive Cardiology, Heart and Vascular Institute, Cleveland Clinic; Assistant Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Stanley L. Hazen, MD, PhD
Co-Section Head, Section of Preventive Cardiology, Heart and Vascular Institute, Cleveland Clinic; Professor of Molecular Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Address: Stanley L. Hazen, MD, PhD, Lerner Research Institute, NC10, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

M.R. is a speaker for Abbott and Amarin.

S.L.H. is named as co-inventor on pending and issued patents held by Cleveland Clinic relating to cardiovascular diagnostics and therapeutics. S.L.H. reports he has been paid as a consultant by the following companies: Cleveland Heart Lab, Esperion, Liposciences, Merck & Co., Pfizer, and Procter & Gamble. S.L.H. reports he has received research funds from Abbott, Astra Zeneca, Cleveland Heart Lab, Esperion, Liposciences, Procter & Gamble, and Takeda. S.L.H. has the right to receive royalty payments for inventions or discoveries related to cardiovascular diagnostics and therapeutics from Abbott Laboratories, Cleveland Heart Lab, Esperion, Frantz Biomarkers, and Liposciences.

Author and Disclosure Information

Chad Raymond, DO
Section of Preventive Cardiology, Heart and Vascular Institute, Cleveland Clinic

Leslie Cho, MD
Co-Section Head, Medical Director, Section of Preventive Cardiology, Heart and Vascular Institute, Cleveland Clinic

Michael Rocco, MD
Section of Preventive Cardiology, Heart and Vascular Institute, Cleveland Clinic; Assistant Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Stanley L. Hazen, MD, PhD
Co-Section Head, Section of Preventive Cardiology, Heart and Vascular Institute, Cleveland Clinic; Professor of Molecular Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH

Address: Stanley L. Hazen, MD, PhD, Lerner Research Institute, NC10, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail: [email protected]

M.R. is a speaker for Abbott and Amarin.

S.L.H. is named as co-inventor on pending and issued patents held by Cleveland Clinic relating to cardiovascular diagnostics and therapeutics. S.L.H. reports he has been paid as a consultant by the following companies: Cleveland Heart Lab, Esperion, Liposciences, Merck & Co., Pfizer, and Procter & Gamble. S.L.H. reports he has received research funds from Abbott, Astra Zeneca, Cleveland Heart Lab, Esperion, Liposciences, Procter & Gamble, and Takeda. S.L.H. has the right to receive royalty payments for inventions or discoveries related to cardiovascular diagnostics and therapeutics from Abbott Laboratories, Cleveland Heart Lab, Esperion, Frantz Biomarkers, and Liposciences.

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On November 12, 2013, a joint task force for the American College of Cardiology and American Heart Association released new guidelines for treating high blood cholesterol to reduce the risk of atherosclerotic cardiovascular disease (ASCVD) in adults.1

This document arrives after several years of intense deliberation, 12 years after the third Adult Treatment Panel (ATP III) guidelines,2 and 8 years after an ATP III update recommending that low-density lipoprotein cholesterol (LDL-C) levels be lowered aggressively (to less than 70 mg/dL) as an option in patients at high risk.3 It represents a major shift in the approach to and management of high blood cholesterol and has sparked considerable controversy.

In the following commentary, we summarize the new guidelines and the philosophy employed by the task force in generating them. We will also examine some advantages and what we believe to be several shortcomings of the new guidelines. These latter points are illustrated through case examples.

IN RANDOMIZED CONTROLLED TRIALS WE TRUST

In collaboration with the National Heart, Lung, and Blood Institute of the National Institutes of Health, the American College of Cardiology and American Heart Association formed an expert panel task force in 2008.

The task force elected to use only evidence from randomized controlled trials, systematic reviews, and meta-analyses of randomized controlled trials (and only predefined outcomes of the trials, not post hoc analyses) in formulating its recommendations, with the goal of providing the strongest possible evidence.

The authors state that “By using [randomized controlled trial] data to identify those most likely to benefit [emphasis in original] from cholesterol-lowering statin therapy, the recommendations will be of value to primary care clinicians as well as specialists concerned with ASCVD prevention. Importantly, the recommendations were designed to be easy to use in the clinical setting, facilitating the implementation of a strategy of risk assessment and treatment focused on the prevention of ASCVD.”3 They also state the guidelines are meant to “inform clinical judgment, not replace it” and that clinician judgment in addition to discussion with patients remains vital.

During the deliberations, the National Heart, Lung, and Blood Institute removed itself from participating, stating its mission no longer included drafting new guidelines. Additionally, other initial members of the task force removed themselves because of disagreement and concerns about the direction of the new guidelines.

These guidelines, and their accompanying new cardiovascular risk calculator,4 were released without a preliminary period to allow for open discussion, comment, and critique by physicians outside the panel. No attempt was made to harmonize the guidelines with previous versions (eg, ATP III) or with current international guidelines.

WHAT’S NEW IN THE GUIDELINES?

The following are the major changes in the new guidelines for treating high blood cholesterol:

  • Treatment goals for LDL-C and non-high-density lipoprotein cholesterol (non-HDL-C) are no longer recommended.
  • High-intensity and moderate-intensity statin treatment is emphasized, and low-intensity statin therapy is nearly eliminated.
  • “ASCVD” now includes stroke in addition to coronary heart disease and peripheral arterial disease.
  • Four groups are targeted for treatment (see below).
  • Nonstatin therapies have been markedly de-emphasized.
  • No guidelines are provided for treating high triglyceride levels.

The new guidelines emphasize lifestyle modification as the foundation for reducing risk, regardless of cholesterol therapy. No recommendations are given for patients with New York Heart Association class II, III, or IV heart failure or for hemodialysis patients, because there were insufficient data from randomized controlled trials to support recommendations. Similarly, the guidelines apply only to people between the ages of 40 and 75 (risk calculator ages 40–79), because the authors believed there was not enough evidence from randomized controlled trials to allow development of guidelines outside of this age range.

FOUR MAJOR STATIN TREATMENT GROUPS

The new guidelines specify four groups that merit intensive or moderately intensive statin therapy (Table 1)1:

  • People with clinical ASCVD
  • People with LDL-C levels of 190 mg/dL or higher
  • People with diabetes, age 40 to 75
  • People without diabetes, age 40 to 75, with LDL-C levels 70–189 mg/dL, and a 10-year ASCVD risk of 7.5% or higher as determined by the new risk calculator4 (which also calculates the lifetime risk of ASCVD).

Below, we will address each of these four groups and provide case scenarios to consider. In general, our major disagreements with the new recommendations pertain to the first and fourth categories.

 

 

GROUP 1: PEOPLE WITH CLINICAL ASCVD

Advantages of the new guidelines

  • They appropriately recommend statins in the highest tolerated doses as first-line treatment for this group at high risk.
  • They designate all patients with ASCVD, including those with coronary, peripheral, and cerebrovascular disease, as a high-risk group.
  • Without target LDL-C levels, treatment is simpler than before, requiring less monitoring of lipid levels. (This can also be seen as a limitation, as we discuss below.)

Limitations of the new guidelines

  • They make follow-up LDL-C levels irrelevant, seeming to assume that there is no gradation in residual risk and, thus, no need to tailor therapy to the individual.
  • Patients no longer have a goal to strive for or a way to monitor their progress.
  • The guidelines ignore the pathophysiology of coronary artery disease and evidence of residual risk in patients on both moderate-intensity and high-intensity statin therapy.
  • They also ignore the potential benefits of treating to lower LDL-C or non-HDL-C goals, thus eliminating consideration of multidrug therapy. They do not address patients with recurrent cardiovascular events already on maximal tolerated statin doses.
  • They undermine the potential development and use of new therapies for dysplipidemia in patients with ASCVD.

Case 1: Is LDL-C 110 mg/dL low enough?

A 52-year-old African American man presents with newly discovered moderate coronary artery disease that is not severe enough to warrant stenting. He has no history of hypertension, diabetes mellitus, or smoking. His systolic blood pressure is 130 mm Hg, and his body mass index is 26 kg/m2. He exercises regularly and follows a low-cholesterol diet. He has the following fasting lipid values:

  • Total cholesterol 290 mg/dL
  • HDL-C 50 mg/dL
  • Triglycerides 250 mg/dL
  • Calculated LDL-C 190 mg/dL.

Two months later, after beginning atorvastatin 80 mg daily, meeting with a nutritionist, and redoubling his dietary efforts, his fasting lipid concentrations are:

  • Total cholesterol 180 mg/dL
  • HDL-C 55 mg/dL
  • Triglycerides 75 mg/dL
  • Calculated LDL-C 110 mg/dL.

Comment: Lack of LDL-C goals is a flaw

The new guidelines call for patients with known ASCVD, such as this patient, to receive intensive statin therapy—which he got.

However, once a patient is on therapy, the new guidelines do not encourage repeating the lipid panel other than to assess compliance. With intensive therapy, we expect a reduction in LDL-C of at least 50% (Table 1), but patient-to-patient differences in response to medications are common, and without repeat testing we would have no way of gauging this patient’s residual risk.

Further, the new guidelines emphasize the lack of hard outcome data supporting the addition of another lipid-lowering drug to a statin, although they do indicate that one can consider it. In a patient at high risk, such as this one, would you be comfortable with an LDL-C value of 110 mg/dL on maximum statin therapy? Would you consider adding a nonstatin drug?

Figure 1. Scatter plot with best-fit lines of major lipid trials (statin and nonstatin trials) for both primary and secondary prevention of coronary heart disease events. Even though the trials were not designed to show differences based on a target LDL-C level, there is a clear relationship of fewer events with lower LDL-C levels.

A preponderance of data shows that LDL plays a causal role in ASCVD development and adverse events. Genetic data show that the LDL particle and the LDL receptor pathway are mechanistically linked to ASCVD pathogenesis, with lifetime exposure as a critical determinant of risk.5,6 Moreover, randomized controlled trials of statins and other studies of cholesterol-lowering show a reproducible relationship between the LDL-C level achieved and absolute risk (Figure 1).7–24 We believe the totality of data constitutes a strong rationale for targeting LDL-C and establishing goals for lowering its levels. For these reasons, we believe that removing LDL-C goals is a fundamental flaw of the new guidelines.

The reason for the lack of data from randomized controlled trials demonstrating benefits of adding therapies to statins (when LDL-C is still high) or benefits of treating to specific goals is that no such trials have been performed. Even trials of nonpharmacologic means of lowering LDL-C, such as ileal bypass, which was used in the Program on the Surgical Control of the Hyperlipidemias trial,20 provide independent evidence that lowering LDL-C reduces the risk of ASCVD (Figure 1).

In addition, trials of nonstatin drugs, such as the Coronary Drug Project,25 which tested niacin, also showed outcome benefits. On the other hand, studies such as the Atherothrombosis Intervention in Metabolic Syndrome With Low HDL/High Triglycerides: Impact on Global Health26 and Treatment of HDL to Reduce the Incidence of Vascular Events27 trials did not show additional risk reduction when niacin was added to statin therapy. However, the study designs arguably had flaws, including requirement of aggressive LDL-lowering with statins, with LDL-C levels below 70 to 80 mg/dL before randomization.

Therefore, these trials do not tell us what to do for a patient on maximal intensive therapy who has recurrent ASCVD events or who, like our patient, has an LDL-C level higher than previous targets.

For this patient, we would recommend adding a second medication to further lower his LDL-C, but discussing with him the absence of proven benefit in clinical trials and the risks of side effects. At present, lacking LDL-C goals in the new guidelines, we are keeping with the ATP III goals to help guide therapeutic choices and individualize patient management.

GROUP 2: PEOPLE WITH LDL-C ≥ 190

Advantages of the new guidelines

  • They state that these patients should receive statins in the highest tolerated doses, which is universally accepted.

Limitations of the new guidelines

  • The new guidelines mention only that one “may consider” adding a second agent if LDL-C remains above 190 mg/dL after maximum-dose therapy. Patients with familial hypercholesterolemia or other severe forms of hypercholesterolemia typically end up on multidrug therapy to further reduce LDL-C. The absence of randomized controlled trial data in this setting to show an additive value of second and third lipid-lowering agents does not mean these agents do not provide benefit.
 

 

GROUP 3: DIABETES, AGE 40–75, LDL-C 70–189, NO CLINICAL ASCVD

Advantages of the new guidelines

  • They call for aggressive treatment of people with diabetes, a group at high risk that derives significant benefit from statin therapy, as shown in randomized controlled trials.

Limitations of the new guidelines

  • Although high-intensity statin therapy is indicated for this group, we believe that, using the new risk calculator, some patients may receive overly aggressive treatment, thus increasing the possibility of statin side effects.
  • The guidelines do not address patients younger than 40 or older than 75.
  • Diabetic patients have a high residual risk of ASCVD events, even on statin therapy. Yet the guidelines ignore the potential benefits of more aggressive LDL-lowering or non-LDL secondary targets for therapy.

Case 2: How low is too low?

A 63-year-old white woman, a nonsmoker with recently diagnosed diabetes, is seen by her primary care physician. She has hypertension, for which she takes lisinopril 5 mg daily. Her fasting lipid values are:

  • Total cholesterol 160 mg/dL
  • HDL-C 64 mg/dL
  • Triglycerides 100 mg/dL
  • Calculated LDL-C 76 mg/dL.

Her systolic blood pressure is 129 mm Hg, and based on the new risk calculator, her 10-year risk of cardiovascular disease is 10.2%. According to the new guidelines, she should be started on high-intensity statin treatment (Table 1).

Although this is an acceptable initial course of action, it necessitates close vigilance, since it may actually drive her LDL-C level too low. Randomized controlled trials have typically used an LDL-C concentration of less than or equal to 25 mg/dL as the safety cutoff. With a typical LDL-C reduction of at least 50% on high-intensity statins, our patient’s expected LDL-C level will likely be in the low 30s. We believe this would be a good outcome, provided that she tolerates the medication without adverse effects. However, responses to statins vary from patient to patient.

High-intensity statin therapy may not be necessary to reduce risk adequately in all patients who have diabetes without preexisting vascular disease. The Collaborative Atorvastatin Diabetes Study12 compared atorvastatin 10 mg vs placebo in people with type 2 diabetes, age 40 to 75, who had one or more cardiovascular risk factors but no signs or symptoms of preexisting ASCVD and who had only average or below-average cholesterol levels—precisely like this patient. The trial was terminated early because of a clear benefit (a 37% reduction in the composite end point of major adverse cardiovascular events) in the intervention group. For our patient, we believe an alternative and acceptable approach would be to begin moderate-intensity statin therapy (eg, with atorvastatin 10 mg) (Table 1).

Alternatively, in a patient with diabetes and previous atherosclerotic vascular disease or with a high 10-year risk and high LDL-C, limiting treatment to high-intensity statin therapy by itself may deny them the potential benefits of combination therapies and targeting to lower LDL-C levels or non-HDL-C secondary targets. Guidelines from the American Diabetes Association28 and the American Association of Clinical Endocrinologists29 continue to recommend an LDL-C goal of less than 70 mg/dL in patients at high risk, a non-HDL-C less than 100 mg/dL, an apolipoprotein B less than 80 mg/dL, and an LDL particle number less than 1,000 nmol/L.

GROUP 4: AGE 40–75, LDL-C 70–189, NO ASCVD, BUT 10-YEAR RISK ≥ 7.5%

Advantages of the new guidelines

  • They may reduce ASCVD events for patients at higher risk.
  • The risk calculator is easy to use and focuses on global risk, ie, all forms of ASCVD.
  • The guidelines promote discussion of risks and benefits between patients and providers.

Limitations of the new guidelines

  • The new risk calculator is controversial (see below).
  • There is potential for overtreatment, particularly in older patients.
  • There is potential for undertreatment, particularly in patients with an elevated LDL-C but whose 10-year risk is less than 7.5% because they are young.
  • The guidelines do not address patients younger than 40 or older than 75.
  • They do not take into account some traditional risk factors, such as family history, and nontraditional risk factors such as C-reactive protein as measured by ultrasensitive assays, lipoprotein(a), and apolipoprotein B.

Risk calculator controversy

The new risk calculator has aroused strong opinions on both sides of the aisle.

Shortly after the new guidelines were released, cardiologists Dr. Paul Ridker and Dr. Nancy Cook from Brigham and Women’s Hospital in Boston published analyses30 showing that the new risk calculator, which was based on older data from several large cohorts such as the Atherosclerosis Risk in Communities study,31 the Cardiovascular Health Study,32 the Coronary Artery Risk Development in Young Adults study,33 and the Framingham Heart Study,34,35 was inaccurate in other cohorts. Specifically, in more-recent cohorts (the Women’s Health Study,36 Physicians’ Health Study,37 and Women’s Health Initiative38), the new calculator overestimates the 10-year risk of ASCVD by 75% to 150%.30 Using the new calculator would make approximately 30 million more Americans eligible for statin treatment. The concern is that patients at lower risk would be treated and exposed to potential side effects of statin therapy.

In addition, the risk calculator relies heavily on age and sex and does not include other factors such as triglyceride level, family history, C-reactive protein, or lipoprotein(a). Importantly, and somewhat ironically given the otherwise absolute adherence to randomized controlled trial data for guideline development, the risk calculator has never been verified in prospective studies to adequately show that using it reduces ASCVD events.

 

 

Case 3: Overtreating a primary prevention patient

Based on the risk calculator, essentially any African American man in his early 60s with no other risk factors has a 10-year risk of ASCVD of 7.5% or higher and, according to the new guidelines, should receive at least moderate-intensity statin therapy.

For example, consider a 64-year-old African American man whose systolic blood pressure is 129 mm Hg, who does not smoke, does not have diabetes, and does not have hypertension, and whose total cholesterol level is 180 mg/dL, HDL-C 70 mg/dL, triglycerides 130 mg/dL, and calculated LDL-C 84 mg/dL. His calculated 10-year risk is, surprisingly, 7.5%.

Alternatively, his twin brother is a two-pack-per-day smoker with untreated hypertension and systolic blood pressure 150 mm Hg, with fasting total cholesterol 153 mg/dL, HDL-C 70 mg/dL, triglycerides 60 mg/dL, and LDL-C 71 mg/dL. His calculated 10-year risk is 10.5%, so according to the new guidelines, he too should receive high-intensity statin therapy. Yet this patient clearly needs better blood pressure control and smoking cessation as his primary risk-reduction efforts, not a statin. While assessing global risk is important, a shortcoming of the new guidelines is that they can inappropriately lead to treating the risk score, not individualizing the treatment to the patient. Because of the errors inherent in the risk calculator, some experts have called for a temporary halt on implementing the new guidelines until the risk calculator can be further validated. In November 2013, the American Heart Association and the American College of Cardiology reaffirmed their support of the new guidelines and recommended that they be implemented as planned. As of the time this manuscript goes to print, there are no plans to halt implementation of the new guidelines.

Case 4: Undertreating a primary prevention patient

A 25-year-old white man with no medical history has a total cholesterol level of 310 mg/dL, HDL-C 50 mg/dL, triglycerides 400 mg/dL, and calculated LDL-C 180 mg/dL. He does not smoke or have hypertension or diabetes but has a strong family history of premature coronary disease (his father died of myocardial infarction at age 42). His body mass index is 25 kg/m2. Because he is less than 40 years old, the risk calculator does not apply to him.

If we assume he remains untreated and returns at age 40 with the same clinical factors and laboratory values, his calculated 10-year risk of an ASCVD event according to the new risk calculator will still be only 3.1%. Assuming his medical history remains unchanged as he continues to age, his 10-year risk would not reach 7.5% until he is 58. Would you feel comfortable waiting 33 years before starting statin therapy in this patient?

Waiting for dyslipidemic patients to reach middle age before starting LDL-C-lowering therapy is a failure of prevention. For practical reasons, there are no data from randomized controlled trials with hard outcomes in younger people. Nevertheless, a tenet of preventive cardiology is that cumulative exposure accelerates the “vascular age” ahead of the chronological age. This case illustrates why individualized recommendations guided by LDL-C goals as a target for therapy are needed. For this 25-year-old patient, we would recommend starting an intermediate- or high-potency statin.

Case 5: Rheumatoid arthritis

A 60-year-old postmenopausal white woman with severe rheumatoid arthritis presents for cholesterol evaluation. Her total cholesterol level is 235 mg/dL, HDL-C 50 mg/dL, and LDL-C 165 mg/dL. She does not smoke or have hypertension or diabetes. Her systolic blood pressure is 110 mm Hg. She has elevated C-reactive protein on an ultrasensitive assay and elevated lipoprotein(a).

Her calculated 10-year risk of ASCVD is 3.0%. Assuming her medical history remains the same, she would not reach a calculated 10-year risk of at least 7.5% until age 70. We suggest starting moderate- or high-dose statin therapy in this case, based on data (not from randomized controlled trials) showing an increased risk of ASCVD events in patients with rheumatologic disease, increased lipoprotein(a), and inflammatory markers like C-reactive protein. However, the current guidelines do not address this scenario, other than to suggest that clinician consideration can be given to other risk markers such as these, and that these findings should be discussed in detail with the patient. The Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin trial16 showed a dramatic ASCVD risk reduction in just such patients (Figure 1).

APPLAUSE—AND RESERVATIONS

The newest guidelines for treating high blood cholesterol represent a monumental shift away from using target levels of LDL-C and non-HDL-C and toward a focus on statin intensity for patients in the four highest-risk groups.

We applaud the expert panel for its idealistic approach of using only data from randomized controlled trials, for placing more emphasis on higher-intensity statin treatment, for including stroke in the new definition of ASCVD, and for focusing more attention on treating diabetic patients more aggressively. Simplifying the guidelines is a noble goal. Emphasizing moderate-to-high-intensity statin therapy in patients at moderate-to-high risk should have substantial long-term public health benefits.

However, as we have shown in the case examples, there are significant limitations, and some patients can end up being overtreated, while others may be undertreated.

Guidelines need to be crafted by looking at all the evidence, including the pathophysiology of the disease process, not just data from randomized controlled trials. It is difficult to implement a guideline that on one hand used randomized controlled trials exclusively for recommendations, but on the other hand used an untested risk calculator to guide therapy. Randomized controlled trials are not available for every scenario.

Further, absence of randomized controlled trial data in a given scenario should not be interpreted as evidence of lack of benefit. An example of this is a primary-prevention patient under age 40 with elevated LDL-C below the 190 mg/dL cutoff who otherwise is healthy and without risk factors (eg, Case 4). By disregarding all evidence that is not from randomized controlled trials, the expert panel fails to account for the extensive pathophysiology of ASCVD, which often begins at a young age and takes decades to develop.5,6,39 An entire generation of patients who have not reached the age of inclusion in most randomized controlled trials with hard outcomes is excluded (unless the LDL-C level is very high), potentially setting back decades of progress in the field of prevention. Prevention only works if started. With childhood and young adult obesity sharply rising, we should not fail to address the under-40-year-old patient population in our guidelines.

Guidelines are designed to be expert opinion, not to dictate practice. Focusing on the individual patient instead of the general population at risk, the expert panel appropriately emphasizes the “importance of clinician judgment, weighing potential benefits, adverse effects, drug-drug interactions and patient preferences.” However, by excluding all data that do not come from randomized controlled trials, the panel neglects a very large base of knowledge and leaves many clinicians without as much expert opinion as we had hoped for.

LDL-C goals are important: they provide a scorecard to help the patient with lifestyle and dietary changes. They provide the health care provider guidance in making treatment decisions and focusing on treatment of a single patient, not a population. Moreover, if a patient has difficulty taking standard doses of statins because of side effects, the absence of LDL-C goals makes decision-making nearly impossible. We hope physicians will rely on LDL-C goals in such situations, falling back on the ATP III recommendations, although many patients may simply go untreated until they present with ASCVD or until they “age in” to a higher risk category.

We suggest caution in strict adherence to the new guidelines and instead urge physicians to consider a hybrid of the old guidelines (using the ATP III LDL-C goals) and the new ones (emphasizing global risk assessment and high-intensity statin treatment).

On November 12, 2013, a joint task force for the American College of Cardiology and American Heart Association released new guidelines for treating high blood cholesterol to reduce the risk of atherosclerotic cardiovascular disease (ASCVD) in adults.1

This document arrives after several years of intense deliberation, 12 years after the third Adult Treatment Panel (ATP III) guidelines,2 and 8 years after an ATP III update recommending that low-density lipoprotein cholesterol (LDL-C) levels be lowered aggressively (to less than 70 mg/dL) as an option in patients at high risk.3 It represents a major shift in the approach to and management of high blood cholesterol and has sparked considerable controversy.

In the following commentary, we summarize the new guidelines and the philosophy employed by the task force in generating them. We will also examine some advantages and what we believe to be several shortcomings of the new guidelines. These latter points are illustrated through case examples.

IN RANDOMIZED CONTROLLED TRIALS WE TRUST

In collaboration with the National Heart, Lung, and Blood Institute of the National Institutes of Health, the American College of Cardiology and American Heart Association formed an expert panel task force in 2008.

The task force elected to use only evidence from randomized controlled trials, systematic reviews, and meta-analyses of randomized controlled trials (and only predefined outcomes of the trials, not post hoc analyses) in formulating its recommendations, with the goal of providing the strongest possible evidence.

The authors state that “By using [randomized controlled trial] data to identify those most likely to benefit [emphasis in original] from cholesterol-lowering statin therapy, the recommendations will be of value to primary care clinicians as well as specialists concerned with ASCVD prevention. Importantly, the recommendations were designed to be easy to use in the clinical setting, facilitating the implementation of a strategy of risk assessment and treatment focused on the prevention of ASCVD.”3 They also state the guidelines are meant to “inform clinical judgment, not replace it” and that clinician judgment in addition to discussion with patients remains vital.

During the deliberations, the National Heart, Lung, and Blood Institute removed itself from participating, stating its mission no longer included drafting new guidelines. Additionally, other initial members of the task force removed themselves because of disagreement and concerns about the direction of the new guidelines.

These guidelines, and their accompanying new cardiovascular risk calculator,4 were released without a preliminary period to allow for open discussion, comment, and critique by physicians outside the panel. No attempt was made to harmonize the guidelines with previous versions (eg, ATP III) or with current international guidelines.

WHAT’S NEW IN THE GUIDELINES?

The following are the major changes in the new guidelines for treating high blood cholesterol:

  • Treatment goals for LDL-C and non-high-density lipoprotein cholesterol (non-HDL-C) are no longer recommended.
  • High-intensity and moderate-intensity statin treatment is emphasized, and low-intensity statin therapy is nearly eliminated.
  • “ASCVD” now includes stroke in addition to coronary heart disease and peripheral arterial disease.
  • Four groups are targeted for treatment (see below).
  • Nonstatin therapies have been markedly de-emphasized.
  • No guidelines are provided for treating high triglyceride levels.

The new guidelines emphasize lifestyle modification as the foundation for reducing risk, regardless of cholesterol therapy. No recommendations are given for patients with New York Heart Association class II, III, or IV heart failure or for hemodialysis patients, because there were insufficient data from randomized controlled trials to support recommendations. Similarly, the guidelines apply only to people between the ages of 40 and 75 (risk calculator ages 40–79), because the authors believed there was not enough evidence from randomized controlled trials to allow development of guidelines outside of this age range.

FOUR MAJOR STATIN TREATMENT GROUPS

The new guidelines specify four groups that merit intensive or moderately intensive statin therapy (Table 1)1:

  • People with clinical ASCVD
  • People with LDL-C levels of 190 mg/dL or higher
  • People with diabetes, age 40 to 75
  • People without diabetes, age 40 to 75, with LDL-C levels 70–189 mg/dL, and a 10-year ASCVD risk of 7.5% or higher as determined by the new risk calculator4 (which also calculates the lifetime risk of ASCVD).

Below, we will address each of these four groups and provide case scenarios to consider. In general, our major disagreements with the new recommendations pertain to the first and fourth categories.

 

 

GROUP 1: PEOPLE WITH CLINICAL ASCVD

Advantages of the new guidelines

  • They appropriately recommend statins in the highest tolerated doses as first-line treatment for this group at high risk.
  • They designate all patients with ASCVD, including those with coronary, peripheral, and cerebrovascular disease, as a high-risk group.
  • Without target LDL-C levels, treatment is simpler than before, requiring less monitoring of lipid levels. (This can also be seen as a limitation, as we discuss below.)

Limitations of the new guidelines

  • They make follow-up LDL-C levels irrelevant, seeming to assume that there is no gradation in residual risk and, thus, no need to tailor therapy to the individual.
  • Patients no longer have a goal to strive for or a way to monitor their progress.
  • The guidelines ignore the pathophysiology of coronary artery disease and evidence of residual risk in patients on both moderate-intensity and high-intensity statin therapy.
  • They also ignore the potential benefits of treating to lower LDL-C or non-HDL-C goals, thus eliminating consideration of multidrug therapy. They do not address patients with recurrent cardiovascular events already on maximal tolerated statin doses.
  • They undermine the potential development and use of new therapies for dysplipidemia in patients with ASCVD.

Case 1: Is LDL-C 110 mg/dL low enough?

A 52-year-old African American man presents with newly discovered moderate coronary artery disease that is not severe enough to warrant stenting. He has no history of hypertension, diabetes mellitus, or smoking. His systolic blood pressure is 130 mm Hg, and his body mass index is 26 kg/m2. He exercises regularly and follows a low-cholesterol diet. He has the following fasting lipid values:

  • Total cholesterol 290 mg/dL
  • HDL-C 50 mg/dL
  • Triglycerides 250 mg/dL
  • Calculated LDL-C 190 mg/dL.

Two months later, after beginning atorvastatin 80 mg daily, meeting with a nutritionist, and redoubling his dietary efforts, his fasting lipid concentrations are:

  • Total cholesterol 180 mg/dL
  • HDL-C 55 mg/dL
  • Triglycerides 75 mg/dL
  • Calculated LDL-C 110 mg/dL.

Comment: Lack of LDL-C goals is a flaw

The new guidelines call for patients with known ASCVD, such as this patient, to receive intensive statin therapy—which he got.

However, once a patient is on therapy, the new guidelines do not encourage repeating the lipid panel other than to assess compliance. With intensive therapy, we expect a reduction in LDL-C of at least 50% (Table 1), but patient-to-patient differences in response to medications are common, and without repeat testing we would have no way of gauging this patient’s residual risk.

Further, the new guidelines emphasize the lack of hard outcome data supporting the addition of another lipid-lowering drug to a statin, although they do indicate that one can consider it. In a patient at high risk, such as this one, would you be comfortable with an LDL-C value of 110 mg/dL on maximum statin therapy? Would you consider adding a nonstatin drug?

Figure 1. Scatter plot with best-fit lines of major lipid trials (statin and nonstatin trials) for both primary and secondary prevention of coronary heart disease events. Even though the trials were not designed to show differences based on a target LDL-C level, there is a clear relationship of fewer events with lower LDL-C levels.

A preponderance of data shows that LDL plays a causal role in ASCVD development and adverse events. Genetic data show that the LDL particle and the LDL receptor pathway are mechanistically linked to ASCVD pathogenesis, with lifetime exposure as a critical determinant of risk.5,6 Moreover, randomized controlled trials of statins and other studies of cholesterol-lowering show a reproducible relationship between the LDL-C level achieved and absolute risk (Figure 1).7–24 We believe the totality of data constitutes a strong rationale for targeting LDL-C and establishing goals for lowering its levels. For these reasons, we believe that removing LDL-C goals is a fundamental flaw of the new guidelines.

The reason for the lack of data from randomized controlled trials demonstrating benefits of adding therapies to statins (when LDL-C is still high) or benefits of treating to specific goals is that no such trials have been performed. Even trials of nonpharmacologic means of lowering LDL-C, such as ileal bypass, which was used in the Program on the Surgical Control of the Hyperlipidemias trial,20 provide independent evidence that lowering LDL-C reduces the risk of ASCVD (Figure 1).

In addition, trials of nonstatin drugs, such as the Coronary Drug Project,25 which tested niacin, also showed outcome benefits. On the other hand, studies such as the Atherothrombosis Intervention in Metabolic Syndrome With Low HDL/High Triglycerides: Impact on Global Health26 and Treatment of HDL to Reduce the Incidence of Vascular Events27 trials did not show additional risk reduction when niacin was added to statin therapy. However, the study designs arguably had flaws, including requirement of aggressive LDL-lowering with statins, with LDL-C levels below 70 to 80 mg/dL before randomization.

Therefore, these trials do not tell us what to do for a patient on maximal intensive therapy who has recurrent ASCVD events or who, like our patient, has an LDL-C level higher than previous targets.

For this patient, we would recommend adding a second medication to further lower his LDL-C, but discussing with him the absence of proven benefit in clinical trials and the risks of side effects. At present, lacking LDL-C goals in the new guidelines, we are keeping with the ATP III goals to help guide therapeutic choices and individualize patient management.

GROUP 2: PEOPLE WITH LDL-C ≥ 190

Advantages of the new guidelines

  • They state that these patients should receive statins in the highest tolerated doses, which is universally accepted.

Limitations of the new guidelines

  • The new guidelines mention only that one “may consider” adding a second agent if LDL-C remains above 190 mg/dL after maximum-dose therapy. Patients with familial hypercholesterolemia or other severe forms of hypercholesterolemia typically end up on multidrug therapy to further reduce LDL-C. The absence of randomized controlled trial data in this setting to show an additive value of second and third lipid-lowering agents does not mean these agents do not provide benefit.
 

 

GROUP 3: DIABETES, AGE 40–75, LDL-C 70–189, NO CLINICAL ASCVD

Advantages of the new guidelines

  • They call for aggressive treatment of people with diabetes, a group at high risk that derives significant benefit from statin therapy, as shown in randomized controlled trials.

Limitations of the new guidelines

  • Although high-intensity statin therapy is indicated for this group, we believe that, using the new risk calculator, some patients may receive overly aggressive treatment, thus increasing the possibility of statin side effects.
  • The guidelines do not address patients younger than 40 or older than 75.
  • Diabetic patients have a high residual risk of ASCVD events, even on statin therapy. Yet the guidelines ignore the potential benefits of more aggressive LDL-lowering or non-LDL secondary targets for therapy.

Case 2: How low is too low?

A 63-year-old white woman, a nonsmoker with recently diagnosed diabetes, is seen by her primary care physician. She has hypertension, for which she takes lisinopril 5 mg daily. Her fasting lipid values are:

  • Total cholesterol 160 mg/dL
  • HDL-C 64 mg/dL
  • Triglycerides 100 mg/dL
  • Calculated LDL-C 76 mg/dL.

Her systolic blood pressure is 129 mm Hg, and based on the new risk calculator, her 10-year risk of cardiovascular disease is 10.2%. According to the new guidelines, she should be started on high-intensity statin treatment (Table 1).

Although this is an acceptable initial course of action, it necessitates close vigilance, since it may actually drive her LDL-C level too low. Randomized controlled trials have typically used an LDL-C concentration of less than or equal to 25 mg/dL as the safety cutoff. With a typical LDL-C reduction of at least 50% on high-intensity statins, our patient’s expected LDL-C level will likely be in the low 30s. We believe this would be a good outcome, provided that she tolerates the medication without adverse effects. However, responses to statins vary from patient to patient.

High-intensity statin therapy may not be necessary to reduce risk adequately in all patients who have diabetes without preexisting vascular disease. The Collaborative Atorvastatin Diabetes Study12 compared atorvastatin 10 mg vs placebo in people with type 2 diabetes, age 40 to 75, who had one or more cardiovascular risk factors but no signs or symptoms of preexisting ASCVD and who had only average or below-average cholesterol levels—precisely like this patient. The trial was terminated early because of a clear benefit (a 37% reduction in the composite end point of major adverse cardiovascular events) in the intervention group. For our patient, we believe an alternative and acceptable approach would be to begin moderate-intensity statin therapy (eg, with atorvastatin 10 mg) (Table 1).

Alternatively, in a patient with diabetes and previous atherosclerotic vascular disease or with a high 10-year risk and high LDL-C, limiting treatment to high-intensity statin therapy by itself may deny them the potential benefits of combination therapies and targeting to lower LDL-C levels or non-HDL-C secondary targets. Guidelines from the American Diabetes Association28 and the American Association of Clinical Endocrinologists29 continue to recommend an LDL-C goal of less than 70 mg/dL in patients at high risk, a non-HDL-C less than 100 mg/dL, an apolipoprotein B less than 80 mg/dL, and an LDL particle number less than 1,000 nmol/L.

GROUP 4: AGE 40–75, LDL-C 70–189, NO ASCVD, BUT 10-YEAR RISK ≥ 7.5%

Advantages of the new guidelines

  • They may reduce ASCVD events for patients at higher risk.
  • The risk calculator is easy to use and focuses on global risk, ie, all forms of ASCVD.
  • The guidelines promote discussion of risks and benefits between patients and providers.

Limitations of the new guidelines

  • The new risk calculator is controversial (see below).
  • There is potential for overtreatment, particularly in older patients.
  • There is potential for undertreatment, particularly in patients with an elevated LDL-C but whose 10-year risk is less than 7.5% because they are young.
  • The guidelines do not address patients younger than 40 or older than 75.
  • They do not take into account some traditional risk factors, such as family history, and nontraditional risk factors such as C-reactive protein as measured by ultrasensitive assays, lipoprotein(a), and apolipoprotein B.

Risk calculator controversy

The new risk calculator has aroused strong opinions on both sides of the aisle.

Shortly after the new guidelines were released, cardiologists Dr. Paul Ridker and Dr. Nancy Cook from Brigham and Women’s Hospital in Boston published analyses30 showing that the new risk calculator, which was based on older data from several large cohorts such as the Atherosclerosis Risk in Communities study,31 the Cardiovascular Health Study,32 the Coronary Artery Risk Development in Young Adults study,33 and the Framingham Heart Study,34,35 was inaccurate in other cohorts. Specifically, in more-recent cohorts (the Women’s Health Study,36 Physicians’ Health Study,37 and Women’s Health Initiative38), the new calculator overestimates the 10-year risk of ASCVD by 75% to 150%.30 Using the new calculator would make approximately 30 million more Americans eligible for statin treatment. The concern is that patients at lower risk would be treated and exposed to potential side effects of statin therapy.

In addition, the risk calculator relies heavily on age and sex and does not include other factors such as triglyceride level, family history, C-reactive protein, or lipoprotein(a). Importantly, and somewhat ironically given the otherwise absolute adherence to randomized controlled trial data for guideline development, the risk calculator has never been verified in prospective studies to adequately show that using it reduces ASCVD events.

 

 

Case 3: Overtreating a primary prevention patient

Based on the risk calculator, essentially any African American man in his early 60s with no other risk factors has a 10-year risk of ASCVD of 7.5% or higher and, according to the new guidelines, should receive at least moderate-intensity statin therapy.

For example, consider a 64-year-old African American man whose systolic blood pressure is 129 mm Hg, who does not smoke, does not have diabetes, and does not have hypertension, and whose total cholesterol level is 180 mg/dL, HDL-C 70 mg/dL, triglycerides 130 mg/dL, and calculated LDL-C 84 mg/dL. His calculated 10-year risk is, surprisingly, 7.5%.

Alternatively, his twin brother is a two-pack-per-day smoker with untreated hypertension and systolic blood pressure 150 mm Hg, with fasting total cholesterol 153 mg/dL, HDL-C 70 mg/dL, triglycerides 60 mg/dL, and LDL-C 71 mg/dL. His calculated 10-year risk is 10.5%, so according to the new guidelines, he too should receive high-intensity statin therapy. Yet this patient clearly needs better blood pressure control and smoking cessation as his primary risk-reduction efforts, not a statin. While assessing global risk is important, a shortcoming of the new guidelines is that they can inappropriately lead to treating the risk score, not individualizing the treatment to the patient. Because of the errors inherent in the risk calculator, some experts have called for a temporary halt on implementing the new guidelines until the risk calculator can be further validated. In November 2013, the American Heart Association and the American College of Cardiology reaffirmed their support of the new guidelines and recommended that they be implemented as planned. As of the time this manuscript goes to print, there are no plans to halt implementation of the new guidelines.

Case 4: Undertreating a primary prevention patient

A 25-year-old white man with no medical history has a total cholesterol level of 310 mg/dL, HDL-C 50 mg/dL, triglycerides 400 mg/dL, and calculated LDL-C 180 mg/dL. He does not smoke or have hypertension or diabetes but has a strong family history of premature coronary disease (his father died of myocardial infarction at age 42). His body mass index is 25 kg/m2. Because he is less than 40 years old, the risk calculator does not apply to him.

If we assume he remains untreated and returns at age 40 with the same clinical factors and laboratory values, his calculated 10-year risk of an ASCVD event according to the new risk calculator will still be only 3.1%. Assuming his medical history remains unchanged as he continues to age, his 10-year risk would not reach 7.5% until he is 58. Would you feel comfortable waiting 33 years before starting statin therapy in this patient?

Waiting for dyslipidemic patients to reach middle age before starting LDL-C-lowering therapy is a failure of prevention. For practical reasons, there are no data from randomized controlled trials with hard outcomes in younger people. Nevertheless, a tenet of preventive cardiology is that cumulative exposure accelerates the “vascular age” ahead of the chronological age. This case illustrates why individualized recommendations guided by LDL-C goals as a target for therapy are needed. For this 25-year-old patient, we would recommend starting an intermediate- or high-potency statin.

Case 5: Rheumatoid arthritis

A 60-year-old postmenopausal white woman with severe rheumatoid arthritis presents for cholesterol evaluation. Her total cholesterol level is 235 mg/dL, HDL-C 50 mg/dL, and LDL-C 165 mg/dL. She does not smoke or have hypertension or diabetes. Her systolic blood pressure is 110 mm Hg. She has elevated C-reactive protein on an ultrasensitive assay and elevated lipoprotein(a).

Her calculated 10-year risk of ASCVD is 3.0%. Assuming her medical history remains the same, she would not reach a calculated 10-year risk of at least 7.5% until age 70. We suggest starting moderate- or high-dose statin therapy in this case, based on data (not from randomized controlled trials) showing an increased risk of ASCVD events in patients with rheumatologic disease, increased lipoprotein(a), and inflammatory markers like C-reactive protein. However, the current guidelines do not address this scenario, other than to suggest that clinician consideration can be given to other risk markers such as these, and that these findings should be discussed in detail with the patient. The Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin trial16 showed a dramatic ASCVD risk reduction in just such patients (Figure 1).

APPLAUSE—AND RESERVATIONS

The newest guidelines for treating high blood cholesterol represent a monumental shift away from using target levels of LDL-C and non-HDL-C and toward a focus on statin intensity for patients in the four highest-risk groups.

We applaud the expert panel for its idealistic approach of using only data from randomized controlled trials, for placing more emphasis on higher-intensity statin treatment, for including stroke in the new definition of ASCVD, and for focusing more attention on treating diabetic patients more aggressively. Simplifying the guidelines is a noble goal. Emphasizing moderate-to-high-intensity statin therapy in patients at moderate-to-high risk should have substantial long-term public health benefits.

However, as we have shown in the case examples, there are significant limitations, and some patients can end up being overtreated, while others may be undertreated.

Guidelines need to be crafted by looking at all the evidence, including the pathophysiology of the disease process, not just data from randomized controlled trials. It is difficult to implement a guideline that on one hand used randomized controlled trials exclusively for recommendations, but on the other hand used an untested risk calculator to guide therapy. Randomized controlled trials are not available for every scenario.

Further, absence of randomized controlled trial data in a given scenario should not be interpreted as evidence of lack of benefit. An example of this is a primary-prevention patient under age 40 with elevated LDL-C below the 190 mg/dL cutoff who otherwise is healthy and without risk factors (eg, Case 4). By disregarding all evidence that is not from randomized controlled trials, the expert panel fails to account for the extensive pathophysiology of ASCVD, which often begins at a young age and takes decades to develop.5,6,39 An entire generation of patients who have not reached the age of inclusion in most randomized controlled trials with hard outcomes is excluded (unless the LDL-C level is very high), potentially setting back decades of progress in the field of prevention. Prevention only works if started. With childhood and young adult obesity sharply rising, we should not fail to address the under-40-year-old patient population in our guidelines.

Guidelines are designed to be expert opinion, not to dictate practice. Focusing on the individual patient instead of the general population at risk, the expert panel appropriately emphasizes the “importance of clinician judgment, weighing potential benefits, adverse effects, drug-drug interactions and patient preferences.” However, by excluding all data that do not come from randomized controlled trials, the panel neglects a very large base of knowledge and leaves many clinicians without as much expert opinion as we had hoped for.

LDL-C goals are important: they provide a scorecard to help the patient with lifestyle and dietary changes. They provide the health care provider guidance in making treatment decisions and focusing on treatment of a single patient, not a population. Moreover, if a patient has difficulty taking standard doses of statins because of side effects, the absence of LDL-C goals makes decision-making nearly impossible. We hope physicians will rely on LDL-C goals in such situations, falling back on the ATP III recommendations, although many patients may simply go untreated until they present with ASCVD or until they “age in” to a higher risk category.

We suggest caution in strict adherence to the new guidelines and instead urge physicians to consider a hybrid of the old guidelines (using the ATP III LDL-C goals) and the new ones (emphasizing global risk assessment and high-intensity statin treatment).

References
  1. Stone NJ, Robinson J, Lichtenstein AH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013; published online Nov 13. DOI: 10.1016/j.jacc.2013.11.002.
  2. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002; 106:3143–3421.
  3. Grundy SM, Cleeman JI, Merz CN, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation 2004; 110:227–239.
  4. American Heart Association. 2013 Prevention guidelines tools. CV risk calculator. http://my.americanheart.org/professional/StatementsGuidelines/PreventionGuidelines/Prevention-Guidelines_UCM_457698_SubHomePage.jsp. Accessed December 10, 2013.
  5. Goldstein JL, Brown MS. The LDL receptor. Arterioscler Thromb Vasc Biol 2009; 29:431–438.
  6. Horton JD, Cohen JC, Hobbs HH. PCSK9: a convertase that coordinates LDL catabolism. J Lipid Res 2009; 50(suppl):S172–S177.
  7. Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 1994; 344:1383–1389.
  8. de Lemos JA, Blazing MA, Wiviott SD, et al; for the A to Z Investigators. Early intensive vs a delayed conservative simvastatin strategy in patients with acute coronary syndromes. Phase Z of the A to Z trial. JAMA 2004; 292:1307–1316.
  9. Downs JR, Clearfield M, Weis S, et al; for the AFCAPS/TexCAPS Research Group. Primary prevention of acute coronary events with lovastatin in men and women with average cholesterol levels. Results of AFCAPS/TexCAPS. JAMA 1998; 279:1615–1622.
  10. Koren MJ, Hunninghake DB, on behalf of the ALLIANCE investigators. Clinical outcomes in managed-care patients with coronary heart disease treated aggressively in lipid-lowering disease management clinics. J Am Coll Cardiol 2004; 44:1772–1779.
  11. Sever PS, Dahlof B, Poulter NR, et al; ASCOT investigators. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than-average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial - Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial. Lancet 2003; 361:1149–1158.
  12. Colhoun HM, Betteridge DJ, Durrington PN, et al; on behalf of the CARDS Investigators. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet 2004; 364:685–696.
  13. Sacks FM, Pfeffer MA, Moye LA, et al; for the Cholesterol and Recurrent Events Trial Investigators. The effect of pravastatin on coronary events after myocardial infarction in patients with average cholesterol levels. N Engl J Med 1996; 335:1001–1009.
  14. Heart Protection Study Collaborative Group. MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20 536 high-risk individuals: a randomised placebo-controlled trial. Lancet 2002; 360:7–22.
  15. Pedersen TR, Faegeman O, Kastelein JJ, et al. Incremental Decrease in End Points Through Aggressive Lipid Lowering Study Group. High-dose atorvastatin vs usual-dose simvastatin for secondary prevention after myocardial infarction: the IDEAL study: a randomized controlled trial. JAMA 2005; 294:2437–2445.
  16. Ridker PM, Danielson E, Fonseca FAH, et al; for the JUPITER Study Group. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med 2008; 359:2195–2207.
  17. LIPID Study Group. Prevention of cardiovascular events and death with pravastatin in patients with coronary heart disease and a broad range of initial cholesterol levels. N Engl J Med 1998; 339:1349–1357.
  18. Nakamura H, Arakawa K, Itakura H, et al; for the MEGA Study Group. Primary prevention of cardiovascular disease with pravastatin Japan (MEGA Study): a prospective rabndomised controlled trial. Lancet 2006; 368:1155–1163.
  19. Schwartz GG, Olsson AG, Ezekowitz MD, et al. Myocardial Ischemia Reduction with Aggreessive Cholesterol Lowering (MIRACL) Study Investigators. Effects of atorvastatin on early recurrent ischemic events in acute coronary syndromes: the MIRACL study: a randomized controlled trial. JAMA 2001; 285:1711–1718.
  20. Buchwald H, Varco RL, Matts JP, et al. Effect of partial ileal bypass surgery on mortality and morbidity from coronary heart disease in patients with hypercholesterolemia: report of the Program on the Surgical Control of the Hyperlipidemias (POSCH). N Engl J Med 1990; 323:946–955.
  21. Cannon CP, Braunwald E, McCabe CH, et al; for the Pravastatin or Atorvastatin Evaluation and Infection Therapy-Thrombolysis in Myocardial Infarction 22 Investigators. Intensive versus moderate lipid lowering with statins after acute coronary syndromes. N Engl J Med 2004; 350:1495–1504.
  22. Baigent C, Landray MJ, Reith C, et al; SHARP Investigators. The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial. Lancet 2011; 377:2181–2192.
  23. LaRosa JC, Grundy SM, Waters DD, et al. Intensive lipid lowering with atorvastatin in patients with stable coronary disease. N Engl J Med 2005; 352:1425–1435.
  24. Shepherd J, Cobbe SM, Ford I, et al; for the West of Scotland Coronary Prevention Study Group. Prevention of coronary heart disease with pravastatin in men with hypercholesterolemia. N Engl J Med 1995; 333:1301–1308.
  25. Canner PL, Berge KG, Wenger NK, et al. Fifteen year mortality in Coronary Drug Project patients: long-term benefit with niacin. J Am Coll Cardiol 1989; 8:1245–1255.
  26. AIM-HIGH Investigators, Boden WE, Probstfield JL, Anderson T, et al.  Niacin in patients with low HDL cholesterol levels receiving intensive statin therapy. N Engl J Med 2011; 365:2255–2267.
  27. HPS2-Thrive Collaborative Group. HPS2-THRIVE randomized placebo-controlled trial in 25 673 high-risk patients of ER niacin/laropiprant: trial design, pre-specified muscle and liver outcomes, and reasons for stopping study treatment. Eur Heart J 2013; 34:1279–1291.
  28. American Diabetes Association. Standards of medical care in diabetes—2013. Diabetes Care 2013; 36(suppl 1):S11–S66.
  29. Garber AJ, Abrahamson MJ, Barzilay JI, et al. American Association of Clinical Endocrinologists’comprehensive diabetes management algorithm 2013 consensus statement—executive summary. Endocr Pract 2013; 19:536–557.
  30. Ridker PM, Cook NR. Statins: new American guidelines for prevention of cardiovascular disease. Lancet 2013doi: 10.1016/S0140-6736(13)62388-0. [Epub ahead of print]
  31. The ARIC investigators. The Atherosclerosis Risk in Communities (ARIC) study: design and objectives. Am J Epidemiol 1989; 129:687–702.
  32. Fried LP, Borhani NO, Enright P, et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol 1991; 1:263–276.
  33. Friedman GD, Cutter GR, Donahue RP, et al. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol 1988; 41:1105–1116.
  34. Dawber TR, Kannel WB, Lyell LP. An approach to longitudinal studies in a community: the Framingham study. Ann N Y Acad Sci 1963; 107:539–556.
  35. Kannel WB, Feinleib M, McNamara PM, Garrison RJ, Castelli WP. An investigation of coronary heart disease in families. The Framingham offspring study. Am J Epidemiol 1979; 110:281–290.
  36. Ridker PM, Cook NR, Lee IM, et al. A randomized trial of low-dose aspirin in the primary prevention of cardiovascular disease in women. N Engl J Med 2005; 352:1293–1304.
  37. Belancer C, Buring JE, Cook N, et al; The Steering Committee of the Physicians’ Health Study Research Group. Final report on the aspirin component of the ongoing Physicians’ Health Study. N Engl J Med 1989; 321:129–135.
  38. Langer R, White E, Lewis C, et al. The Women’s Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures. Ann Epidemiol 2003; 13:S107–S121.
  39. Strong JP, Malcom GT, Oalmann MC, Wissler RW. The PDAY study: natural history, risk factors, and pathobiology. Ann N Y Acad Sci 1997; 811:226–235.
References
  1. Stone NJ, Robinson J, Lichtenstein AH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013; published online Nov 13. DOI: 10.1016/j.jacc.2013.11.002.
  2. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002; 106:3143–3421.
  3. Grundy SM, Cleeman JI, Merz CN, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation 2004; 110:227–239.
  4. American Heart Association. 2013 Prevention guidelines tools. CV risk calculator. http://my.americanheart.org/professional/StatementsGuidelines/PreventionGuidelines/Prevention-Guidelines_UCM_457698_SubHomePage.jsp. Accessed December 10, 2013.
  5. Goldstein JL, Brown MS. The LDL receptor. Arterioscler Thromb Vasc Biol 2009; 29:431–438.
  6. Horton JD, Cohen JC, Hobbs HH. PCSK9: a convertase that coordinates LDL catabolism. J Lipid Res 2009; 50(suppl):S172–S177.
  7. Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 1994; 344:1383–1389.
  8. de Lemos JA, Blazing MA, Wiviott SD, et al; for the A to Z Investigators. Early intensive vs a delayed conservative simvastatin strategy in patients with acute coronary syndromes. Phase Z of the A to Z trial. JAMA 2004; 292:1307–1316.
  9. Downs JR, Clearfield M, Weis S, et al; for the AFCAPS/TexCAPS Research Group. Primary prevention of acute coronary events with lovastatin in men and women with average cholesterol levels. Results of AFCAPS/TexCAPS. JAMA 1998; 279:1615–1622.
  10. Koren MJ, Hunninghake DB, on behalf of the ALLIANCE investigators. Clinical outcomes in managed-care patients with coronary heart disease treated aggressively in lipid-lowering disease management clinics. J Am Coll Cardiol 2004; 44:1772–1779.
  11. Sever PS, Dahlof B, Poulter NR, et al; ASCOT investigators. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than-average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial - Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial. Lancet 2003; 361:1149–1158.
  12. Colhoun HM, Betteridge DJ, Durrington PN, et al; on behalf of the CARDS Investigators. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet 2004; 364:685–696.
  13. Sacks FM, Pfeffer MA, Moye LA, et al; for the Cholesterol and Recurrent Events Trial Investigators. The effect of pravastatin on coronary events after myocardial infarction in patients with average cholesterol levels. N Engl J Med 1996; 335:1001–1009.
  14. Heart Protection Study Collaborative Group. MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20 536 high-risk individuals: a randomised placebo-controlled trial. Lancet 2002; 360:7–22.
  15. Pedersen TR, Faegeman O, Kastelein JJ, et al. Incremental Decrease in End Points Through Aggressive Lipid Lowering Study Group. High-dose atorvastatin vs usual-dose simvastatin for secondary prevention after myocardial infarction: the IDEAL study: a randomized controlled trial. JAMA 2005; 294:2437–2445.
  16. Ridker PM, Danielson E, Fonseca FAH, et al; for the JUPITER Study Group. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med 2008; 359:2195–2207.
  17. LIPID Study Group. Prevention of cardiovascular events and death with pravastatin in patients with coronary heart disease and a broad range of initial cholesterol levels. N Engl J Med 1998; 339:1349–1357.
  18. Nakamura H, Arakawa K, Itakura H, et al; for the MEGA Study Group. Primary prevention of cardiovascular disease with pravastatin Japan (MEGA Study): a prospective rabndomised controlled trial. Lancet 2006; 368:1155–1163.
  19. Schwartz GG, Olsson AG, Ezekowitz MD, et al. Myocardial Ischemia Reduction with Aggreessive Cholesterol Lowering (MIRACL) Study Investigators. Effects of atorvastatin on early recurrent ischemic events in acute coronary syndromes: the MIRACL study: a randomized controlled trial. JAMA 2001; 285:1711–1718.
  20. Buchwald H, Varco RL, Matts JP, et al. Effect of partial ileal bypass surgery on mortality and morbidity from coronary heart disease in patients with hypercholesterolemia: report of the Program on the Surgical Control of the Hyperlipidemias (POSCH). N Engl J Med 1990; 323:946–955.
  21. Cannon CP, Braunwald E, McCabe CH, et al; for the Pravastatin or Atorvastatin Evaluation and Infection Therapy-Thrombolysis in Myocardial Infarction 22 Investigators. Intensive versus moderate lipid lowering with statins after acute coronary syndromes. N Engl J Med 2004; 350:1495–1504.
  22. Baigent C, Landray MJ, Reith C, et al; SHARP Investigators. The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial. Lancet 2011; 377:2181–2192.
  23. LaRosa JC, Grundy SM, Waters DD, et al. Intensive lipid lowering with atorvastatin in patients with stable coronary disease. N Engl J Med 2005; 352:1425–1435.
  24. Shepherd J, Cobbe SM, Ford I, et al; for the West of Scotland Coronary Prevention Study Group. Prevention of coronary heart disease with pravastatin in men with hypercholesterolemia. N Engl J Med 1995; 333:1301–1308.
  25. Canner PL, Berge KG, Wenger NK, et al. Fifteen year mortality in Coronary Drug Project patients: long-term benefit with niacin. J Am Coll Cardiol 1989; 8:1245–1255.
  26. AIM-HIGH Investigators, Boden WE, Probstfield JL, Anderson T, et al.  Niacin in patients with low HDL cholesterol levels receiving intensive statin therapy. N Engl J Med 2011; 365:2255–2267.
  27. HPS2-Thrive Collaborative Group. HPS2-THRIVE randomized placebo-controlled trial in 25 673 high-risk patients of ER niacin/laropiprant: trial design, pre-specified muscle and liver outcomes, and reasons for stopping study treatment. Eur Heart J 2013; 34:1279–1291.
  28. American Diabetes Association. Standards of medical care in diabetes—2013. Diabetes Care 2013; 36(suppl 1):S11–S66.
  29. Garber AJ, Abrahamson MJ, Barzilay JI, et al. American Association of Clinical Endocrinologists’comprehensive diabetes management algorithm 2013 consensus statement—executive summary. Endocr Pract 2013; 19:536–557.
  30. Ridker PM, Cook NR. Statins: new American guidelines for prevention of cardiovascular disease. Lancet 2013doi: 10.1016/S0140-6736(13)62388-0. [Epub ahead of print]
  31. The ARIC investigators. The Atherosclerosis Risk in Communities (ARIC) study: design and objectives. Am J Epidemiol 1989; 129:687–702.
  32. Fried LP, Borhani NO, Enright P, et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol 1991; 1:263–276.
  33. Friedman GD, Cutter GR, Donahue RP, et al. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol 1988; 41:1105–1116.
  34. Dawber TR, Kannel WB, Lyell LP. An approach to longitudinal studies in a community: the Framingham study. Ann N Y Acad Sci 1963; 107:539–556.
  35. Kannel WB, Feinleib M, McNamara PM, Garrison RJ, Castelli WP. An investigation of coronary heart disease in families. The Framingham offspring study. Am J Epidemiol 1979; 110:281–290.
  36. Ridker PM, Cook NR, Lee IM, et al. A randomized trial of low-dose aspirin in the primary prevention of cardiovascular disease in women. N Engl J Med 2005; 352:1293–1304.
  37. Belancer C, Buring JE, Cook N, et al; The Steering Committee of the Physicians’ Health Study Research Group. Final report on the aspirin component of the ongoing Physicians’ Health Study. N Engl J Med 1989; 321:129–135.
  38. Langer R, White E, Lewis C, et al. The Women’s Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures. Ann Epidemiol 2003; 13:S107–S121.
  39. Strong JP, Malcom GT, Oalmann MC, Wissler RW. The PDAY study: natural history, risk factors, and pathobiology. Ann N Y Acad Sci 1997; 811:226–235.
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(Re)turning the Pages of Residency

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(Re)turning the pages of residency: The impact of localizing resident physicians to hospital units on paging frequency

It's hard to imagine a busy urban hospital without its chorus of beepers.[1] This statement, the first sentence of an article published in 1988, rings (or beeps or buzzes) true to any resident physician today. At that time, pagers had replaced overhead paging, and provided a rapid method to contact physicians who were often scattered throughout the hospital. Still, it was an imperfect solution as the ubiquitous pager constantly interrupted patient care and other tasks, failed to prioritize information, and added to an already stressful working environment. Notably, interns were paged on average once per hour, and occasionally 5 or more times per hour, a frequency that was felt to be detrimental to patient care and to the working environment of resident physicians.[1]

Little has changed. Despite the instant, multidirectional communication platforms available today, alphanumeric paging remains a mainstay of communication between physicians and other members of the care team. Importantly, paging contributes to communication errors (eg, by failing to convey urgency, having incomplete information, or being missed entirely by coverage gaps),[2, 3] and interrupts resident workflow, thereby negatively affecting work efficiency and educational activities, and adding to perceived workload.[4, 5]

In this era of duty hour restrictions, there has been concern that residents experience increased workload due to having fewer hours to do the same amount of work.[6, 7] As such, the Accreditation Council of Graduate Medical Education emphasizes the quality of those hours, with a focus on several aspects of the resident working environment as key to improved educational and patient safety outcomes.[8, 9, 10]

Geographic localization of physicians to patient care units has been proposed as a means to improve communication and agreement on plans of care,[11, 12] and also to reduce resident workload by decreasing inefficiencies attributable to traveling throughout the hospital.[13] O'Leary, et al. (2009) found that when physicians were localized to 1 hospital unit, there was greater agreement between physicians and nurses on various aspects of care, such as planned tests and anticipated length of stay. In addition, members of the patient care team were better able to identify one another, and there was a perceived increase in face‐to‐face communication, and a perceived decrease in text paging.[11]

In consideration of these factors, in July 2011, at New YorkPresbyterian Hospital/Weill Cornell (NYPH/WC), an 800‐bed tertiary care teaching hospital in New York, New York, we geographically localized 2 internal medicine resident teams, and partially localized 2 additional teams. We investigated whether interns on teams that were geographically localized received fewer pages than interns on teams that were not localized. This study was reviewed by the institutional review board of Weill Cornell Medical College and met the requirements for exemption.

METHODS

We conducted a retrospective analysis of the number of pages received by interns during the day (7:00 am to 7:00 pm) on 5 general internal medicine teams during a 1‐month ward rotation between October 17, 2011 and November 13, 2011 at NYPH/WC. The general medicine teams were composed of 1 attending, 1 resident, and 2 interns each. Two teams were geographically localized to a 32‐bed unit (geographic localization model [GLM]). Two teams were partially localized to a 26‐bed unit, which included a respiratory care step‐down unit (partial localization model [PLM]). A fifth and final team admitted patients irrespective of their assigned bed location (standard model [SM]). Both the GLM and the PLM occasionally carried patients on other units to allow for overall census management and patient throughput. The total number of pages received by each intern over the study period was collected by retrospective analysis of electronic paging logs. Night pages (7 pm7 am) were excluded because of night float coverage. Weekend pages were excluded because data were inaccurate due to coverage for days off.

The daily number of admissions and daily census per team were recorded by physician assistants, who also assigned new patients to appropriate teams according to an admissions algorithm (see Supporting Figure 1 in the online version of this article). The percent of geographically localized patients on each team was estimated from the percentage of localized patients on the day of discharge averaged over the study period. For the SM team, percent localization was defined as the number of patients on the patient care unit that contained the team's work area.

Figure 1
Average number of pages per intern per hour for each care model. Abbreviations: GLM, geographically localized model; PLM, partial localization model; SM, standard model.

Standard multivariate linear regression techniques were used to analyze the relationship between the number of pages received per intern and the type of team, controlling for the potential effect of total census and number of admissions. The regression model was used to determine adjusted marginal point estimates and 95% confidence intervals (CIs) for the average number of pages per intern per hour for each type of team. All statistical analyses were conducted using Stata version 12 (StataCorp, College Station, TX).

RESULTS

Over the 28‐day study period, a total of 6652 pages were received by 10 interns on 5 general internal medicine teams from 7 am to 7 pm Monday through Friday. The average daily census, average daily admissions, and percent of patients localized to patient care units for the individual teams are shown in Table 1. In univariate analysis, the mean daily pages per intern were not significantly different between the 2 teams within the GLM, nor between the 2 teams in the PLM, allowing them to be combined in multivariate analysis (data not shown). The number of pages received per intern per hour, adjusted for team census and number of admissions, was 2.2 (95% CI: 2.02.4) in the GLM, 2.8 (95% CI: 2.6‐3.0) in the PLM, and 3.9 (95% CI: 3.6‐4.2) in the SM (Table 1). All of these differences were statistically significant (P<0.001).

Geographic Distribution, Census, Admissions, and Pages per Hour per Intern for Each Patient Care Model During the Study Period
Standard Model* Partial Localization Model Geographically Localized Model
  • NOTE: Abbreviations: CI, confidence interval. *One general medicine team in the standard model, and 2 each in the partial localization model and geographically localized model, with 2 interns per team.

Percent of patients localized 37% 45% 85%
Team census, mean (range per day) 16.1 (1320) 15.9 (1120) 15.6 (1119)
Team admissions, mean (range per day) 2.7 (15) 2.9 (06) 3.5 (07)
Pages per hour per intern, unadjusted, mean (95% CI) 3.9 (3.6‐4.1) 2.8 (2.6‐3.0) 2.2 (2.02.4)
Pages per hour per intern, adjusted for census and admissions, mean (95% CI) 3.9 (3.6‐4.2) 2.8 (2.6‐3.0) 2.2 (2.02.4)

Figure 1 shows the pattern of daytime paging for each model. The GLM and PLM had a similar pattern, with an initial ramp up in the first 2 hours of the day, holding steady until approximately 4 pm, and then decrease until 7 pm. The SM had a steeper initial rise, and then continued to increase slowly until a peak at 4 pm.

DISCUSSION

This study corroborates that of Singh et al. (2012), who found that geographic localization led to significantly fewer pages.[14] Our results strengthen the evidence by demonstrating that even modest differences between the percent of patients localized to a care unit led to a significant decrease in the number of pages, indicating a dose‐response effect. The paging frequency we measured is higher than described in Singh et al. (1.4 pages per hour for localized teams), yet our average census appears to be 4 patients higher, which may account for some of that difference. We also show that interns on teams whose patients are more widely scattered throughout the hospital may experience upward of 5 pages per hour, an interruption by pager every 12 minutes, all day long.

A pager interruption is not solely limited to a disruption by noxious sound or vibration. The page recipient must then read the page and respond accordingly, which may involve a phone call, placing an order, walking to another location, or other work tasks. Although some of these interruptions must be handled immediately, such as a clinically deteriorating patient, many are not urgent, and could wait until the physician's current task or thought process is complete. There is also the potentially risky assumption on the part of the sender that the message has been received and will be acted upon. Furthermore, frequent paging is a common interruption to physician workflow; interruptions contribute to increased perceived physician workload[4, 5] and are likely detrimental to patient safety.[15, 16]

The most common metrics used to measure resident workload are patient census and number of admissions,[13] but these metrics have provided a mixed and likely incomplete picture. Recent research suggests that other factors, such as work efficiency (including interruptions, time spent obtaining test results, and time in transit) and work intensity (such as the acuity and complexity of patients), contribute significantly to actual and perceived resident workload.[13]

Our analysis was a single‐site, retrospective study, which occurred over 1 month and was limited to internal medicine teams. Additionally, geographic localization logically should lead to increased face‐to‐face interruptions, which we were unable to measure with this project, but direct communication is more efficient and less prone to error, which would likely lead to fewer overall interruptions. Although we anticipate that our findings are applicable to geographically localized patient care units in other hospitals, further investigation is warranted.

The paging chorus has only grown louder over the last 25 years, with likely downstream effects on patient safety and resident education. To mitigate these effects, it is incumbent upon us to approach our training and patient care environments with a critical and creative lens, and to explore opportunities to decrease interruptions and streamline our communication systems.

Acknowledgements

The authors acknowledge the assistance with data analysis of Arthur Evans, MD, MPH, and review of the manuscript by Brendan Reilly, MD.

Disclosures: Dr. Fanucchi and Ms. Unterbrink have no conflicts of interest to disclose. Dr. Logio reports receiving royalties from McGraw‐Hill for Core Concepts in Patient Safety online modules.

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References
  1. Katz MH, Schroeder SA. The sounds of the hospital. Paging patterns in three teaching hospitals. N Engl J Med. 1988;319(24):15851589.
  2. Sutcliffe KM, Lewton E, Rosenthal MM. Communication failures: an insidious contributor to medical mishaps. Acad Med. 2004;79(2):186194.
  3. Espino S, Cox D, Kaplan B. Alphanumeric paging: a potential source of problems in patient care and communication. J Surg Educ. 2011;68(6):447451.
  4. Weigl M, Muller A, Zupanc A, Glaser J, Angerer P. Hospital doctors' workflow interruptions and activities: an observation study. BMJ Qual Saf. 2011;20(6):491497.
  5. Weigl M, Muller A, Vincent C, Angerer P, Sevdalis N. The association of workflow interruptions and hospital doctors' workload: a prospective observational study. BMJ Qual Saf. 2012;21(5):399407.
  6. Goitein L, Ludmerer KM. Resident workload—let's treat the disease, not just the symptom. Comment on: Effect of the 2011 vs 2003 duty hour regulation‐compliant models on sleep duration, trainee education, and continuity of patient care among internal medicine house staff. JAMA Intern Med. 2013;173(8):655656.
  7. Desai SV, Feldman L, Brown L, et al. Effect of the 2011 vs 2003 duty hour regulation‐compliant models on sleep duration, trainee education, and continuity of patient care among internal medicine house staff: a randomized trial. JAMA Intern Med. 2013;173(8):649655.
  8. Philibert I, Amis S. The ACGME 2011 Duty Hour Standards: Enhancing Quality of Care, Supervision, and Resident Professional Development. Chicago, IL: Accreditation Council for Graduate Medical Education; 2011.
  9. Ulmer C, Wolman D, Johns M, eds . Institute of Medicine Resident Duty Hours: Enhancing Sleep, Supervision, and Safety. Washington, DC: National Academies Press; 2009.
  10. Schumacher DJ, Slovin SR, Riebschleger MP, Englander R, Hicks PJ, Carraccio C. Perspective: beyond counting hours: the importance of supervision, professionalism, transitions of care, and workload in residency training. Acad Med. 2012;87(7):883888.
  11. O'Leary KJ, Wayne DB, Landler MP, et al. Impact of localizing physicians to hospital units on nurse‐physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):12231227.
  12. Gordon MB, Melvin P, Graham D, et al. Unit‐based care teams and the frequency and quality of physician‐nurse communications. Arch Pediatr Adolesc Med. 2011;165(5):424428.
  13. Thanarajasingam U, McDonald FS, Halvorsen AJ, et al. Service census caps and unit‐based admissions: resident workload, conference attendance, duty hour compliance, and patient safety. Mayo Clin Proc. 2012;87(4):320327.
  14. Singh S, Tarima S, Rana V, et al. Impact of localizing general medical teams to a single nursing unit. J Hosp Med. 2012;7(7):551556.
  15. Coiera E. The science of interruption. BMJ Qual Saf. 2012;21(5):357360.
  16. Westbrook JI, Coiera E, Dunsmuir WT, et al. The impact of interruptions on clinical task completion. Qual Saf Health Care. 2010;19(4):284289.
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It's hard to imagine a busy urban hospital without its chorus of beepers.[1] This statement, the first sentence of an article published in 1988, rings (or beeps or buzzes) true to any resident physician today. At that time, pagers had replaced overhead paging, and provided a rapid method to contact physicians who were often scattered throughout the hospital. Still, it was an imperfect solution as the ubiquitous pager constantly interrupted patient care and other tasks, failed to prioritize information, and added to an already stressful working environment. Notably, interns were paged on average once per hour, and occasionally 5 or more times per hour, a frequency that was felt to be detrimental to patient care and to the working environment of resident physicians.[1]

Little has changed. Despite the instant, multidirectional communication platforms available today, alphanumeric paging remains a mainstay of communication between physicians and other members of the care team. Importantly, paging contributes to communication errors (eg, by failing to convey urgency, having incomplete information, or being missed entirely by coverage gaps),[2, 3] and interrupts resident workflow, thereby negatively affecting work efficiency and educational activities, and adding to perceived workload.[4, 5]

In this era of duty hour restrictions, there has been concern that residents experience increased workload due to having fewer hours to do the same amount of work.[6, 7] As such, the Accreditation Council of Graduate Medical Education emphasizes the quality of those hours, with a focus on several aspects of the resident working environment as key to improved educational and patient safety outcomes.[8, 9, 10]

Geographic localization of physicians to patient care units has been proposed as a means to improve communication and agreement on plans of care,[11, 12] and also to reduce resident workload by decreasing inefficiencies attributable to traveling throughout the hospital.[13] O'Leary, et al. (2009) found that when physicians were localized to 1 hospital unit, there was greater agreement between physicians and nurses on various aspects of care, such as planned tests and anticipated length of stay. In addition, members of the patient care team were better able to identify one another, and there was a perceived increase in face‐to‐face communication, and a perceived decrease in text paging.[11]

In consideration of these factors, in July 2011, at New YorkPresbyterian Hospital/Weill Cornell (NYPH/WC), an 800‐bed tertiary care teaching hospital in New York, New York, we geographically localized 2 internal medicine resident teams, and partially localized 2 additional teams. We investigated whether interns on teams that were geographically localized received fewer pages than interns on teams that were not localized. This study was reviewed by the institutional review board of Weill Cornell Medical College and met the requirements for exemption.

METHODS

We conducted a retrospective analysis of the number of pages received by interns during the day (7:00 am to 7:00 pm) on 5 general internal medicine teams during a 1‐month ward rotation between October 17, 2011 and November 13, 2011 at NYPH/WC. The general medicine teams were composed of 1 attending, 1 resident, and 2 interns each. Two teams were geographically localized to a 32‐bed unit (geographic localization model [GLM]). Two teams were partially localized to a 26‐bed unit, which included a respiratory care step‐down unit (partial localization model [PLM]). A fifth and final team admitted patients irrespective of their assigned bed location (standard model [SM]). Both the GLM and the PLM occasionally carried patients on other units to allow for overall census management and patient throughput. The total number of pages received by each intern over the study period was collected by retrospective analysis of electronic paging logs. Night pages (7 pm7 am) were excluded because of night float coverage. Weekend pages were excluded because data were inaccurate due to coverage for days off.

The daily number of admissions and daily census per team were recorded by physician assistants, who also assigned new patients to appropriate teams according to an admissions algorithm (see Supporting Figure 1 in the online version of this article). The percent of geographically localized patients on each team was estimated from the percentage of localized patients on the day of discharge averaged over the study period. For the SM team, percent localization was defined as the number of patients on the patient care unit that contained the team's work area.

Figure 1
Average number of pages per intern per hour for each care model. Abbreviations: GLM, geographically localized model; PLM, partial localization model; SM, standard model.

Standard multivariate linear regression techniques were used to analyze the relationship between the number of pages received per intern and the type of team, controlling for the potential effect of total census and number of admissions. The regression model was used to determine adjusted marginal point estimates and 95% confidence intervals (CIs) for the average number of pages per intern per hour for each type of team. All statistical analyses were conducted using Stata version 12 (StataCorp, College Station, TX).

RESULTS

Over the 28‐day study period, a total of 6652 pages were received by 10 interns on 5 general internal medicine teams from 7 am to 7 pm Monday through Friday. The average daily census, average daily admissions, and percent of patients localized to patient care units for the individual teams are shown in Table 1. In univariate analysis, the mean daily pages per intern were not significantly different between the 2 teams within the GLM, nor between the 2 teams in the PLM, allowing them to be combined in multivariate analysis (data not shown). The number of pages received per intern per hour, adjusted for team census and number of admissions, was 2.2 (95% CI: 2.02.4) in the GLM, 2.8 (95% CI: 2.6‐3.0) in the PLM, and 3.9 (95% CI: 3.6‐4.2) in the SM (Table 1). All of these differences were statistically significant (P<0.001).

Geographic Distribution, Census, Admissions, and Pages per Hour per Intern for Each Patient Care Model During the Study Period
Standard Model* Partial Localization Model Geographically Localized Model
  • NOTE: Abbreviations: CI, confidence interval. *One general medicine team in the standard model, and 2 each in the partial localization model and geographically localized model, with 2 interns per team.

Percent of patients localized 37% 45% 85%
Team census, mean (range per day) 16.1 (1320) 15.9 (1120) 15.6 (1119)
Team admissions, mean (range per day) 2.7 (15) 2.9 (06) 3.5 (07)
Pages per hour per intern, unadjusted, mean (95% CI) 3.9 (3.6‐4.1) 2.8 (2.6‐3.0) 2.2 (2.02.4)
Pages per hour per intern, adjusted for census and admissions, mean (95% CI) 3.9 (3.6‐4.2) 2.8 (2.6‐3.0) 2.2 (2.02.4)

Figure 1 shows the pattern of daytime paging for each model. The GLM and PLM had a similar pattern, with an initial ramp up in the first 2 hours of the day, holding steady until approximately 4 pm, and then decrease until 7 pm. The SM had a steeper initial rise, and then continued to increase slowly until a peak at 4 pm.

DISCUSSION

This study corroborates that of Singh et al. (2012), who found that geographic localization led to significantly fewer pages.[14] Our results strengthen the evidence by demonstrating that even modest differences between the percent of patients localized to a care unit led to a significant decrease in the number of pages, indicating a dose‐response effect. The paging frequency we measured is higher than described in Singh et al. (1.4 pages per hour for localized teams), yet our average census appears to be 4 patients higher, which may account for some of that difference. We also show that interns on teams whose patients are more widely scattered throughout the hospital may experience upward of 5 pages per hour, an interruption by pager every 12 minutes, all day long.

A pager interruption is not solely limited to a disruption by noxious sound or vibration. The page recipient must then read the page and respond accordingly, which may involve a phone call, placing an order, walking to another location, or other work tasks. Although some of these interruptions must be handled immediately, such as a clinically deteriorating patient, many are not urgent, and could wait until the physician's current task or thought process is complete. There is also the potentially risky assumption on the part of the sender that the message has been received and will be acted upon. Furthermore, frequent paging is a common interruption to physician workflow; interruptions contribute to increased perceived physician workload[4, 5] and are likely detrimental to patient safety.[15, 16]

The most common metrics used to measure resident workload are patient census and number of admissions,[13] but these metrics have provided a mixed and likely incomplete picture. Recent research suggests that other factors, such as work efficiency (including interruptions, time spent obtaining test results, and time in transit) and work intensity (such as the acuity and complexity of patients), contribute significantly to actual and perceived resident workload.[13]

Our analysis was a single‐site, retrospective study, which occurred over 1 month and was limited to internal medicine teams. Additionally, geographic localization logically should lead to increased face‐to‐face interruptions, which we were unable to measure with this project, but direct communication is more efficient and less prone to error, which would likely lead to fewer overall interruptions. Although we anticipate that our findings are applicable to geographically localized patient care units in other hospitals, further investigation is warranted.

The paging chorus has only grown louder over the last 25 years, with likely downstream effects on patient safety and resident education. To mitigate these effects, it is incumbent upon us to approach our training and patient care environments with a critical and creative lens, and to explore opportunities to decrease interruptions and streamline our communication systems.

Acknowledgements

The authors acknowledge the assistance with data analysis of Arthur Evans, MD, MPH, and review of the manuscript by Brendan Reilly, MD.

Disclosures: Dr. Fanucchi and Ms. Unterbrink have no conflicts of interest to disclose. Dr. Logio reports receiving royalties from McGraw‐Hill for Core Concepts in Patient Safety online modules.

It's hard to imagine a busy urban hospital without its chorus of beepers.[1] This statement, the first sentence of an article published in 1988, rings (or beeps or buzzes) true to any resident physician today. At that time, pagers had replaced overhead paging, and provided a rapid method to contact physicians who were often scattered throughout the hospital. Still, it was an imperfect solution as the ubiquitous pager constantly interrupted patient care and other tasks, failed to prioritize information, and added to an already stressful working environment. Notably, interns were paged on average once per hour, and occasionally 5 or more times per hour, a frequency that was felt to be detrimental to patient care and to the working environment of resident physicians.[1]

Little has changed. Despite the instant, multidirectional communication platforms available today, alphanumeric paging remains a mainstay of communication between physicians and other members of the care team. Importantly, paging contributes to communication errors (eg, by failing to convey urgency, having incomplete information, or being missed entirely by coverage gaps),[2, 3] and interrupts resident workflow, thereby negatively affecting work efficiency and educational activities, and adding to perceived workload.[4, 5]

In this era of duty hour restrictions, there has been concern that residents experience increased workload due to having fewer hours to do the same amount of work.[6, 7] As such, the Accreditation Council of Graduate Medical Education emphasizes the quality of those hours, with a focus on several aspects of the resident working environment as key to improved educational and patient safety outcomes.[8, 9, 10]

Geographic localization of physicians to patient care units has been proposed as a means to improve communication and agreement on plans of care,[11, 12] and also to reduce resident workload by decreasing inefficiencies attributable to traveling throughout the hospital.[13] O'Leary, et al. (2009) found that when physicians were localized to 1 hospital unit, there was greater agreement between physicians and nurses on various aspects of care, such as planned tests and anticipated length of stay. In addition, members of the patient care team were better able to identify one another, and there was a perceived increase in face‐to‐face communication, and a perceived decrease in text paging.[11]

In consideration of these factors, in July 2011, at New YorkPresbyterian Hospital/Weill Cornell (NYPH/WC), an 800‐bed tertiary care teaching hospital in New York, New York, we geographically localized 2 internal medicine resident teams, and partially localized 2 additional teams. We investigated whether interns on teams that were geographically localized received fewer pages than interns on teams that were not localized. This study was reviewed by the institutional review board of Weill Cornell Medical College and met the requirements for exemption.

METHODS

We conducted a retrospective analysis of the number of pages received by interns during the day (7:00 am to 7:00 pm) on 5 general internal medicine teams during a 1‐month ward rotation between October 17, 2011 and November 13, 2011 at NYPH/WC. The general medicine teams were composed of 1 attending, 1 resident, and 2 interns each. Two teams were geographically localized to a 32‐bed unit (geographic localization model [GLM]). Two teams were partially localized to a 26‐bed unit, which included a respiratory care step‐down unit (partial localization model [PLM]). A fifth and final team admitted patients irrespective of their assigned bed location (standard model [SM]). Both the GLM and the PLM occasionally carried patients on other units to allow for overall census management and patient throughput. The total number of pages received by each intern over the study period was collected by retrospective analysis of electronic paging logs. Night pages (7 pm7 am) were excluded because of night float coverage. Weekend pages were excluded because data were inaccurate due to coverage for days off.

The daily number of admissions and daily census per team were recorded by physician assistants, who also assigned new patients to appropriate teams according to an admissions algorithm (see Supporting Figure 1 in the online version of this article). The percent of geographically localized patients on each team was estimated from the percentage of localized patients on the day of discharge averaged over the study period. For the SM team, percent localization was defined as the number of patients on the patient care unit that contained the team's work area.

Figure 1
Average number of pages per intern per hour for each care model. Abbreviations: GLM, geographically localized model; PLM, partial localization model; SM, standard model.

Standard multivariate linear regression techniques were used to analyze the relationship between the number of pages received per intern and the type of team, controlling for the potential effect of total census and number of admissions. The regression model was used to determine adjusted marginal point estimates and 95% confidence intervals (CIs) for the average number of pages per intern per hour for each type of team. All statistical analyses were conducted using Stata version 12 (StataCorp, College Station, TX).

RESULTS

Over the 28‐day study period, a total of 6652 pages were received by 10 interns on 5 general internal medicine teams from 7 am to 7 pm Monday through Friday. The average daily census, average daily admissions, and percent of patients localized to patient care units for the individual teams are shown in Table 1. In univariate analysis, the mean daily pages per intern were not significantly different between the 2 teams within the GLM, nor between the 2 teams in the PLM, allowing them to be combined in multivariate analysis (data not shown). The number of pages received per intern per hour, adjusted for team census and number of admissions, was 2.2 (95% CI: 2.02.4) in the GLM, 2.8 (95% CI: 2.6‐3.0) in the PLM, and 3.9 (95% CI: 3.6‐4.2) in the SM (Table 1). All of these differences were statistically significant (P<0.001).

Geographic Distribution, Census, Admissions, and Pages per Hour per Intern for Each Patient Care Model During the Study Period
Standard Model* Partial Localization Model Geographically Localized Model
  • NOTE: Abbreviations: CI, confidence interval. *One general medicine team in the standard model, and 2 each in the partial localization model and geographically localized model, with 2 interns per team.

Percent of patients localized 37% 45% 85%
Team census, mean (range per day) 16.1 (1320) 15.9 (1120) 15.6 (1119)
Team admissions, mean (range per day) 2.7 (15) 2.9 (06) 3.5 (07)
Pages per hour per intern, unadjusted, mean (95% CI) 3.9 (3.6‐4.1) 2.8 (2.6‐3.0) 2.2 (2.02.4)
Pages per hour per intern, adjusted for census and admissions, mean (95% CI) 3.9 (3.6‐4.2) 2.8 (2.6‐3.0) 2.2 (2.02.4)

Figure 1 shows the pattern of daytime paging for each model. The GLM and PLM had a similar pattern, with an initial ramp up in the first 2 hours of the day, holding steady until approximately 4 pm, and then decrease until 7 pm. The SM had a steeper initial rise, and then continued to increase slowly until a peak at 4 pm.

DISCUSSION

This study corroborates that of Singh et al. (2012), who found that geographic localization led to significantly fewer pages.[14] Our results strengthen the evidence by demonstrating that even modest differences between the percent of patients localized to a care unit led to a significant decrease in the number of pages, indicating a dose‐response effect. The paging frequency we measured is higher than described in Singh et al. (1.4 pages per hour for localized teams), yet our average census appears to be 4 patients higher, which may account for some of that difference. We also show that interns on teams whose patients are more widely scattered throughout the hospital may experience upward of 5 pages per hour, an interruption by pager every 12 minutes, all day long.

A pager interruption is not solely limited to a disruption by noxious sound or vibration. The page recipient must then read the page and respond accordingly, which may involve a phone call, placing an order, walking to another location, or other work tasks. Although some of these interruptions must be handled immediately, such as a clinically deteriorating patient, many are not urgent, and could wait until the physician's current task or thought process is complete. There is also the potentially risky assumption on the part of the sender that the message has been received and will be acted upon. Furthermore, frequent paging is a common interruption to physician workflow; interruptions contribute to increased perceived physician workload[4, 5] and are likely detrimental to patient safety.[15, 16]

The most common metrics used to measure resident workload are patient census and number of admissions,[13] but these metrics have provided a mixed and likely incomplete picture. Recent research suggests that other factors, such as work efficiency (including interruptions, time spent obtaining test results, and time in transit) and work intensity (such as the acuity and complexity of patients), contribute significantly to actual and perceived resident workload.[13]

Our analysis was a single‐site, retrospective study, which occurred over 1 month and was limited to internal medicine teams. Additionally, geographic localization logically should lead to increased face‐to‐face interruptions, which we were unable to measure with this project, but direct communication is more efficient and less prone to error, which would likely lead to fewer overall interruptions. Although we anticipate that our findings are applicable to geographically localized patient care units in other hospitals, further investigation is warranted.

The paging chorus has only grown louder over the last 25 years, with likely downstream effects on patient safety and resident education. To mitigate these effects, it is incumbent upon us to approach our training and patient care environments with a critical and creative lens, and to explore opportunities to decrease interruptions and streamline our communication systems.

Acknowledgements

The authors acknowledge the assistance with data analysis of Arthur Evans, MD, MPH, and review of the manuscript by Brendan Reilly, MD.

Disclosures: Dr. Fanucchi and Ms. Unterbrink have no conflicts of interest to disclose. Dr. Logio reports receiving royalties from McGraw‐Hill for Core Concepts in Patient Safety online modules.

References
  1. Katz MH, Schroeder SA. The sounds of the hospital. Paging patterns in three teaching hospitals. N Engl J Med. 1988;319(24):15851589.
  2. Sutcliffe KM, Lewton E, Rosenthal MM. Communication failures: an insidious contributor to medical mishaps. Acad Med. 2004;79(2):186194.
  3. Espino S, Cox D, Kaplan B. Alphanumeric paging: a potential source of problems in patient care and communication. J Surg Educ. 2011;68(6):447451.
  4. Weigl M, Muller A, Zupanc A, Glaser J, Angerer P. Hospital doctors' workflow interruptions and activities: an observation study. BMJ Qual Saf. 2011;20(6):491497.
  5. Weigl M, Muller A, Vincent C, Angerer P, Sevdalis N. The association of workflow interruptions and hospital doctors' workload: a prospective observational study. BMJ Qual Saf. 2012;21(5):399407.
  6. Goitein L, Ludmerer KM. Resident workload—let's treat the disease, not just the symptom. Comment on: Effect of the 2011 vs 2003 duty hour regulation‐compliant models on sleep duration, trainee education, and continuity of patient care among internal medicine house staff. JAMA Intern Med. 2013;173(8):655656.
  7. Desai SV, Feldman L, Brown L, et al. Effect of the 2011 vs 2003 duty hour regulation‐compliant models on sleep duration, trainee education, and continuity of patient care among internal medicine house staff: a randomized trial. JAMA Intern Med. 2013;173(8):649655.
  8. Philibert I, Amis S. The ACGME 2011 Duty Hour Standards: Enhancing Quality of Care, Supervision, and Resident Professional Development. Chicago, IL: Accreditation Council for Graduate Medical Education; 2011.
  9. Ulmer C, Wolman D, Johns M, eds . Institute of Medicine Resident Duty Hours: Enhancing Sleep, Supervision, and Safety. Washington, DC: National Academies Press; 2009.
  10. Schumacher DJ, Slovin SR, Riebschleger MP, Englander R, Hicks PJ, Carraccio C. Perspective: beyond counting hours: the importance of supervision, professionalism, transitions of care, and workload in residency training. Acad Med. 2012;87(7):883888.
  11. O'Leary KJ, Wayne DB, Landler MP, et al. Impact of localizing physicians to hospital units on nurse‐physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):12231227.
  12. Gordon MB, Melvin P, Graham D, et al. Unit‐based care teams and the frequency and quality of physician‐nurse communications. Arch Pediatr Adolesc Med. 2011;165(5):424428.
  13. Thanarajasingam U, McDonald FS, Halvorsen AJ, et al. Service census caps and unit‐based admissions: resident workload, conference attendance, duty hour compliance, and patient safety. Mayo Clin Proc. 2012;87(4):320327.
  14. Singh S, Tarima S, Rana V, et al. Impact of localizing general medical teams to a single nursing unit. J Hosp Med. 2012;7(7):551556.
  15. Coiera E. The science of interruption. BMJ Qual Saf. 2012;21(5):357360.
  16. Westbrook JI, Coiera E, Dunsmuir WT, et al. The impact of interruptions on clinical task completion. Qual Saf Health Care. 2010;19(4):284289.
References
  1. Katz MH, Schroeder SA. The sounds of the hospital. Paging patterns in three teaching hospitals. N Engl J Med. 1988;319(24):15851589.
  2. Sutcliffe KM, Lewton E, Rosenthal MM. Communication failures: an insidious contributor to medical mishaps. Acad Med. 2004;79(2):186194.
  3. Espino S, Cox D, Kaplan B. Alphanumeric paging: a potential source of problems in patient care and communication. J Surg Educ. 2011;68(6):447451.
  4. Weigl M, Muller A, Zupanc A, Glaser J, Angerer P. Hospital doctors' workflow interruptions and activities: an observation study. BMJ Qual Saf. 2011;20(6):491497.
  5. Weigl M, Muller A, Vincent C, Angerer P, Sevdalis N. The association of workflow interruptions and hospital doctors' workload: a prospective observational study. BMJ Qual Saf. 2012;21(5):399407.
  6. Goitein L, Ludmerer KM. Resident workload—let's treat the disease, not just the symptom. Comment on: Effect of the 2011 vs 2003 duty hour regulation‐compliant models on sleep duration, trainee education, and continuity of patient care among internal medicine house staff. JAMA Intern Med. 2013;173(8):655656.
  7. Desai SV, Feldman L, Brown L, et al. Effect of the 2011 vs 2003 duty hour regulation‐compliant models on sleep duration, trainee education, and continuity of patient care among internal medicine house staff: a randomized trial. JAMA Intern Med. 2013;173(8):649655.
  8. Philibert I, Amis S. The ACGME 2011 Duty Hour Standards: Enhancing Quality of Care, Supervision, and Resident Professional Development. Chicago, IL: Accreditation Council for Graduate Medical Education; 2011.
  9. Ulmer C, Wolman D, Johns M, eds . Institute of Medicine Resident Duty Hours: Enhancing Sleep, Supervision, and Safety. Washington, DC: National Academies Press; 2009.
  10. Schumacher DJ, Slovin SR, Riebschleger MP, Englander R, Hicks PJ, Carraccio C. Perspective: beyond counting hours: the importance of supervision, professionalism, transitions of care, and workload in residency training. Acad Med. 2012;87(7):883888.
  11. O'Leary KJ, Wayne DB, Landler MP, et al. Impact of localizing physicians to hospital units on nurse‐physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):12231227.
  12. Gordon MB, Melvin P, Graham D, et al. Unit‐based care teams and the frequency and quality of physician‐nurse communications. Arch Pediatr Adolesc Med. 2011;165(5):424428.
  13. Thanarajasingam U, McDonald FS, Halvorsen AJ, et al. Service census caps and unit‐based admissions: resident workload, conference attendance, duty hour compliance, and patient safety. Mayo Clin Proc. 2012;87(4):320327.
  14. Singh S, Tarima S, Rana V, et al. Impact of localizing general medical teams to a single nursing unit. J Hosp Med. 2012;7(7):551556.
  15. Coiera E. The science of interruption. BMJ Qual Saf. 2012;21(5):357360.
  16. Westbrook JI, Coiera E, Dunsmuir WT, et al. The impact of interruptions on clinical task completion. Qual Saf Health Care. 2010;19(4):284289.
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(Re)turning the pages of residency: The impact of localizing resident physicians to hospital units on paging frequency
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Address for correspondence and reprint requests: Laura Fanucchi, MD, University of Kentucky College of Medicine, 900 South Limestone, 306B Charles T. Wethington Bldg., Lexington, KY 40536; Telephone: 859‐323‐6642; Fax: 859‐323‐1197; E‐mail: [email protected]
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Improving Admission Process Efficiency

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Tempering pediatric hospitalist supervision of residents improves admission process efficiency without decreasing quality of care

Maintaining high‐quality patient care, optimizing patient safety, and providing adequate trainee supervision has been an area of debate in medical education recently, and many physicians remain concerned that excessive regulation and duty hour restrictions may prevent residents from obtaining sufficient experience and developing an appropriate sense of autonomy.[1, 2, 3, 4] However, pediatric hospital medicine (PHM) has seen dramatic increases in evening and nighttime in‐house attending coverage, and the trend is expected to continue.[5, 6] Whether it be for financial, educational, or patient‐centered reasons, increased in‐house attending coverage at an academic medical setting, almost by definition, increases direct resident supervision.[7]

Increased supervision may result in better educational outcomes,[8] but many forces, such as night float systems and electronic medical records (EMRs), pull residents away from the bedside, leaving them with fewer opportunities to make decisions and a reduced sense of personal responsibility and patient ownership. Experiential learning is of great value in medical training, and without this, residents may exit their training with less confidence and competence, only rarely having been able to make important medical decisions on their own.[9, 10]

Counter to the shift toward increased supervision, we recently amended our process for pediatric admissions to the PHM service by transitioning from mandatory to on‐demand attending input during the admissions process. We hypothesized that this would improve its efficiency by encouraging residents to develop an increased sense of patient ownership and would not significantly impact patient care.

METHODS

Setting

This cohort study was conducted at the Golisano Children's Hospital (GCH) at the University of Rochester in Rochester, New York. The pediatric residency program at this tertiary care center includes 48 pediatric residents and 21 medicinepediatric residents. The PHM division, comprised of 8 pediatric hospitalists, provides care to approximately one‐third of the children with medical illnesses admitted to GCH. During the daytime, PHM attendings provide in‐house supervision for 2 resident teams, each consisting of a senior resident and 2 interns. At night, PHM attendings take calls from home. Residents are encouraged to contact attendings, available by cell phone and pager, with questions or concerns regarding patient care. The institutional review board of the University of Rochester Medical Center approved this study and informed consent was waived.

Process Change

Prior to the change, a pediatric emergency department (ED) provider at GCH directly contacted the PHM attending for all admissions to the PHM service (Figure 1). If the PHM attending accepted the admission, the ED provider then notified the pediatric admitting officer (PAO), a third‐year pediatric or fourth‐year medicinepediatric resident, who either performed or delegated the admission duties (eg, history and physical exam, admission orders).

Figure 1
Admission process for patients admitted to PHM service in pre‐ and post‐intervention cohorts. Abbreviations: ED, emergency department; PAO, pediatric admitting officer; PHM, pediatric hospital medicine.

On June 18, 2012, a new process for pediatric admissions was implemented (Figure 1). The ED provider now called the PAO, and not the attending, to discuss an admission to the PHM service. The PAO was empowered to accept the patient on behalf of the PHM attending, and perform or delegate the admission duties. During daytime hours (7:00 am5:00 pm), the PAO was expected to alert the PHM attending of the admission to allow the attending to see the patient on the day of admission. The PHM attending discussed the case with the admitting resident after the resident had an opportunity to assess the patient and formulate a management plan. During evening hours (5:00 pm10:00 pm), the admitting resident was expected to contact the PHM attending on call after evaluating the patient and developing a plan. Overnight (10:00 pm7:00 am), the PAO was given discretion as to whether she/he needed to contact the PHM attending on call; the PHM service attending then saw the patient in the morning. Residents were strongly encouraged to call the PHM attending with any questions or concerns or if they did not feel an admission was appropriate to the PHM service.

Study Population

The study population included all patients <19 years of age admitted to the PHM service from the ED. The pre‐ and post‐intervention cohorts included patients admitted from July 1, 2011 to September 30, 2011 and July 1, 2012 to September 30, 2012, respectively. These dates were chosen because residents are least experienced in the summer months, and hence we would predict the greatest disparity during this time. Patients who were directly admitted via transport from an outside facility, office or from home, or who were transferred from another service within GCH were excluded. Patients were identified from administrative databases.

Data Collection

Date and time of admission, severity of illness (SOI) scores, and risk of mortality (ROM) scores were obtained from the administrative dataset. The EMR was then used to extract the following variables: gender; date and time of the ED provider's admission request and first inpatient resident order; date and time of patient discharge, defined as the time the after‐visit summary was finalized by an inpatient provider; and the number of rapid response team (RRT) activations within 24 hours of the first inpatient resident order. The order time difference was calculated by subtracting the date and time of the ED provider admission request from the first inpatient order. Cases in which the order time difference was negative were excluded from the order time analysis due to the possibility that some extenuating circumstance for these patients, not related to the admission process, caused the early inpatient order. Length of stay (LOS) was calculated as the difference between the date and time of ED admission request and date and time of patient discharge.

The first 24 hours of each admission were reviewed independently by 3 PHM attending investigators. Neither reviewer evaluated a chart for which he had cosigned the admission note. Charts were assessed to determine whether a reasonable standard of care (SOC) was provided by the inpatient resident during admission. For instances in which SOC was not felt to have been provided by the resident, the chart was reviewed by the second investigator. If there was disagreement between the 2 investigators, a third PHM attending was used to determine the majority opinion. Due to the nature of data collected, it was not possible to blind reviewers.

PHM attending investigators also assessed how often the inpatient resident's antibiotic choice was changed by the admitting PHM attending. This evaluation excluded topical antibiotics and antibiotics not related to the admitting diagnosis (eg, continuation of outpatient antibiotics for otitis media). A change in antibiotics was defined as a change in class or a change within classes, initiation, or discontinuation of an antibiotic by the attending. Switching the route of administration was considered a change if it was not done as part of the transition to discharge. Antibiotic choice was considered in agreement if a change was made by the PHM attending based on new patient information that was not available to the admitting inpatient resident if it could be reasonably concluded that the attending would have otherwise agreed with the original choice. If this determination could not be made, the antibiotic agreement was classified as unknown. Data regarding antibiotic agreement were analyzed in 2 ways. The first included all patients for which agreement could be determined. For this analysis, if a patient was not prescribed an antibiotic by the resident or attending, there was considered to have been antibiotic agreement. The second analysis included only the patients for whom an antibiotic was started by the inpatient resident or admitting attending.

Finally, RRT activations within the first 24 hours of admission in the 2012 cohort were evaluated to determine whether the RRT could have been prevented by the original admission process. This determination was made via majority opinion of 3 PHM attendings who each independently reviewed the cases.

Statistical Analysis

The distributions of continuous variables (eg, order time difference, LOS) and the ordinal variables (ROM and SOI) were compared using Wilcoxon rank sum tests. 2 tests or Fisher exact tests were used to assess the differences in categorical variables (eg, SOC, gender). All tests were 2‐sided, and the significance level was set at 0.05. Analyses were conducted using the SAS statistical package version 9.3 (SAS Institute Inc., Cary, NC) and SPSS version 21 (IBM/SPSS, Armonk, NY).

RESULTS

The initial search identified 532 admissions. Of these, 140 were excluded (72 were via route other than the ED, 44 were not admitted to PHM, 14 were outside the study period, and 10 did not meet age criteria). Therefore, 182 admissions in the 2011 cohort and 210 admissions in the 2012 cohort were included. For all patients in the 2012 cohort, the correct admission process was followed.

Demographic characteristics between cohorts were similar (Table 1). Data for ROM and SOI were available for 141 (78%) 2011 patients and for 169 (81%) 2012 patients. The distribution of patients over the study months differed between cohorts. Age, gender, ROM, and SOI were not significantly different.

Characteristics of Children Admitted to the Pediatric Hospital Medicine Service
Variable20112012P Value
  • NOTE: Abbreviations: IQR, interquartile range. *Patients admitted between 10:00 pm and 7:00 am.

Male gender, n (%)107 (59)105 (50)0.082
Median age, y (IQR)2 (010)2 (07)0.689
Month admitted, n (%)  0.002
July60 (33)87 (41) 
August57 (31)81 (39) 
September65 (36)42 (20) 
Nighttime admission, n (%)*71 (39)90 (43)0.440
Risk of mortality, n (%)  0.910
1, lowest risk114 (81)138 (82) 
222 (16)23 (14) 
35 (4)6 (4) 
4, highest risk0 (0)2 (1) 
Severity of illness, n (%)  0.095
1, lowest severity60 (43)86 (51) 
254 (38)62 (37) 
325 (18)15 (9) 
4, highest severity2 (1)6 (4) 

The median difference in time from the ED provider admission request to the first inpatient resident order was roughly half as long in 2012 than in 2011 (123 vs 62 minutes, P<0.001) (Table 2). There were 12 cases in which the inpatient order came prior to the ED admission request in 2012 and 2 cases in 2011, and these were excluded from the order time difference analysis. LOS was not significantly different between groups (P=0.348). There were no differences in the frequency of antibiotic changes when all patients were considered or in the subgroup in whom antibiotics were prescribed by either the resident or attending. The number of cases for which the admitting resident's plan was deemed not to have met standard of care were few and not significantly different (P=1). None of these patients experienced harm as a result, and in all cases, SOC was determined to have been provided by the admitting PHM attending. The frequency of RRT calls within the first 24 hours of admission on PHM patients was not significantly different (P=0.114).

Comparison of Patient Cohorts Admitted to Pediatric Hospital Medicine Before and After the Change in Admission Procedure
Variable20112012P Value
  • NOTE: Abbreviations: IQR, interquartile range; RRT, rapid response team.

  • Cases in which the time difference between orders was negative were excluded.

  • P value was calculated using Wilcoxon rank sum test.

Time from admission decision to first inpatient order, min, median (IQR)a123 (70188)62 (30105)<0.001
Length of stay, h, median (IQR)b44 (3167)41 (2271)0.348
Change by attending to resident's antibiotic choice in all patients, n (%)13/182 (7)18/210 (9)0.617
Change by attending to resident's antibiotic choice in patients who received antibiotics, n (%)13/97 (13)18/96 (19)0.312
Resident met standard of care, n (%)180/182 (99)207/210 (99)1
RRT called within first 24 hours, n (%)2/182 (1)8/210 (4)0.114

When only patients admitted during the night in 2011 and 2012 were compared, results were consistent with the overall finding that there was a shorter time to inpatient admission order without a difference in other studied variables (Table 3).

Comparison of Nighttime Admissions Between Cohorts (n=161)
Variable20112012P Value
  • NOTE: Abbreviations: IQR, interquartile range; RRT, rapid response team.

  • Cases in which the time difference between orders was negative were excluded.

  • P value was calculated using Wilcoxon rank sum test.

Time from admission decision to first inpatient order, min, median (IQR)ab90 (40151)42 (1767)0.002
Length of stay, h, median (IQR)b53 (3461)36 (1769)0.307
Change by attending to resident's antibiotic choice in all patients, n (%)7/70 (10)7/88 (8)1
Resident met standard of care, n (%)70/71 (99)88/90 (98)1
RRT called within first 24 hours, n (%)2/71 (3)6/90 (7)0.468

DISCUSSION

The purpose of this study was to evaluate an admission process that removed an ineffective method of attending oversight and allowed residents an opportunity to develop patient care plans prior to attending input. The key change from the original process was removing the step in which the ED provider contacted the PHM attending for new admissions, thus eliminating mandatory inpatient attending input, removing an impediment to workflow, and empowering inpatient pediatric residents to assess new patients and develop management plans. Our data show a reduction in the time difference between the ED admission request and the inpatient resident's first order by more than an hour, indicating a more efficient admission process. Although one might expect that eliminating the act of a phone call would shorten this time by a few minutes, it cannot account for the extent of the difference we found. We postulate that an increased sense of accountability motivated inpatient residents to evaluate and begin management sooner, a topic that requires further exploration.

A more efficient admission process benefits emergency medicine residents and other ED providers as well. It is well documented that ED crowding is associated with decreased quality of care,[11, 12] and ED efficiency is receiving increased attention with newly reportable quality metrics such as Admit Decision Time to Emergency Department Departure Time for Admitted Patients.[13]

Our data do not attenuate the importance of hospitalists in patient care, as evidenced by the fact that PHM attendings continued to frequently amend the residents' antibiotic choicethe only variable we evaluated in terms of change in planand recognized several cases in which the residents' plan did not meet standard of care. Furthermore, attendings continued to be available by phone and pager for guidance and education when needed or requested by the residents. Instead, our data show that removing mandated attending input at the time of admission did not significantly impact major patient outcomes, which may partly be attributable to the general safety of the inpatient pediatric wards.[14, 15] In our study, a comprehensive analysis of patient harm was not possible given the variable list and infrequency with which SOC was not met or RRTs were called. Furthermore, our residency program continues to comply with national pediatric residency requirements for nighttime supervision.[7]

Our PHM division, which had previously allocated 2 hours of attending clinical time per call night, now averages <15 minutes. These data conflict with the current trend in PHM toward more, rather than less, direct attending oversight. Many PHM divisions have moved toward 24/7 in‐house coverage,[5] a situation that often results in shiftwork and multiple handoffs. Removing the in‐house attending overnight would allow for the rapidly growing PHM subspecialty to allocate hospitalists elsewhere depending on their scholarly needs, particularly as divisions seek to become increasingly involved in medical education, research, and hospital leadership.[16, 17] Although one might posit a financial benefit to having in‐house attendings determine the appropriateness of an admission overnight, we identified no case in which the insurance denied an admission.

Safety equivalence of an in‐house to on‐call attending is poorly studied in PHM. However, even in intensive care units, where the majority of morbidity and mortality occur, it is unclear that the presence of an attending, let alone mandating phone calls, positively impacts survival. One prospective trial failed to demonstrate a difference in patient outcomes in the critical care setting when comparing mandated attending in‐house involvement to optional attending availability by phone.[18] Furthermore, several studies have found no association with time of admission and mortality, implying there is no criticality specifically requiring nighttime coverage.[19, 20]

One adult study of nocturnists showed that residents felt they had more contact with attendings who were in‐house than attendings taking home calls.[21] However, when the residents were asked why they did not contact the attending, the only difference between at‐home and in‐house attendings was that for attendings available by phone, residents were less likely to know who to call and were hesitant to wake the attending.

This study had several limitations. First, we could not effectively blind reviewers; a salient point given that the reviewers benefited from the new system with a reduced nighttime workload. We attempted to minimize this bias by employing multiple independent evaluations followed by group consensus whenever possible. Second, even though we had 3 hospitalists independently review each 2012 RRT to determine whether it was preventable by the prior system, this task was prone to retrospective bias. Third, there was a significant difference in the month of admission between cohorts. Rather than biasing toward our observed time difference, the fact that more patients were admitted in July 2012the beginning of the academic yearmay have decreased our observed difference given that residents were less experienced. Forth, this study used certain measurable outcomes as proxies for quality of care and patient harm and was likely underpowered to truly detect a difference in some of the more infrequent variables. Furthermore, we did not evaluate other potential harms, such as cost. Fifth, we did not evaluate whether or not the new process changed ED provider behavior (ie, an ED provider may wait longer to request admission overnight given that the PHM attending is not mandated to provide input until the morning). Finally, although LOS was used as a balancing measure, it would likely have taken major events or omissions during the admission process to cause it to change significantly, and therefore the lack of statistical difference in this metric does not necessarily imply that more subtle aspects of care were the same between groups. We also chose not to include readmission rate for this reason, as any change could not conclusively be attributed to the new admission process.

CONCLUSION

Increasing resident autonomy by removing mandated input during PHM admissions makes the process more efficient and results in no significant changes to major patient outcomes. These data may be used by rapidly growing PHM divisions to redefine faculty clinical responsibilities, particularly at night.

ACKNOWLEDGMENTS

Disclosures: This project was supported by the University of Rochester CTSA award number UL1 TR000042 from the National Center for Advancing Translational Sciences of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors report no conflicts of interest.

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References
  1. Accreditation Council for Graduate Medical Education Task Force on Quality Care and Professionalism. The ACGME 2011 duty hour standards: enhancing quality of care, supervision, and resident professional development. Accreditation Council for Graduate Medical Education, Chicago, IL; 2011. Available at: http://www.acgme.org/acgmeweb/Portals/0/PDFs/jgme‐monograph[1].pdf. Last accessed on December 18, 2013.
  2. Moonesinghe SR, Lowery J, Shahi N, Millen A, Beard JD. Impact of reduction in working hours for doctors in training on postgraduate medical education and patients' outcomes: systemic review. BMJ. 2011;342:d1580.
  3. Feig BA, Hasso AN. ACGME 2011 duty‐hour guidelines: consequences expected by radiology residency directors and chief residents. J Am Coll Radiol. 2012;9(11):820827.
  4. Chiong W. Justifying patient risks associated with medical education. JAMA. 2007;298(9):10461048.
  5. Gosdin C, Simmons J, Yau C, Sucharew H, Carlson D, Paciorkowski N. Survey of academic pediatric hospitalist programs in the U.S.: organizational, administrative and financial factors. J Hosp Med. 2013;8(6):285291.
  6. Oshimura J, Sperring J, Bauer BD, Rauch DA. Inpatient staffing within pediatric residency programs: work hour restrictions and the evolving role of the pediatric hospitalist. J Hosp Med. 2012;7(4):299303.
  7. ACGME Program Requirements for Graduate Medical Education in Pediatrics. ACGME Approved: September 30, 2012; Effective: July 1, 2013. Available at: http://www.acgme.org/acgmeweb/Portals/0/PFAssets/2013‐PR‐FAQ‐PIF/320_pediatrics_07012013.pdf. Accessed September 17, 2013.
  8. Farnan JM, Petty LA, Georgitis E, et al. A systematic review: the effect of clinical supervision on patient and residency education outcomes. Acad Med. 2012;87(4):428442.
  9. Kerlin MP, Halpern SD. Twenty‐four‐hour intensivist staffing in teaching hospitals: tension between safety today and safety tomorrow. Chest. 2012;141(5):13151320.
  10. Fred HL. Medical education on the brink: 62 years of front‐line observations and opinions. Tex Heart Inst J. 2012;39(3):322329.
  11. Pines JM, Hollander JE. Emergency department crowding is associated with poor care for patients with severe pain. Ann Emerg Med. 2008;51:67.
  12. Bernstein SL, Aronsky D, Duseja R, et al. The effect of emergency department crowding on clinically oriented outcomes. Acad Emerg Med. 2009;16(1):110.
  13. The Specifications Manual for National Hospital Inpatient Quality Measures. A Collaboration of the Centers for Medicare 128(1):7278.
  14. Sharek PJ, Parast LM, Leong K, et al. Effect of a rapid response team on hospital‐wide mortality and code rates outside the ICU in a Children's Hospital. JAMA. 2007;298(19):22672274.
  15. Section on Hospital Medicine. Guiding principles for Pediatric Hospital Medicine programs. Pediatrics. 2013;132(4):782786. SHM fact sheet: about hospital medicine. http://www.hospitalmedicine.org/AM/Template.cfm?Section=Media_Kit42(5):120126.
  16. Kerlin MP, Small DS, Cooney E, et al. A randomized trial of nighttime physician staffing in an intensive care unit. N Engl J Med. 2013;368(23):22012209.
  17. Cavallazzi R, Marik PE, Hirani A, Pachinburavan M, Vasu TS, Leiby BE. Association between time of admission to the ICU and mortality: a systematic review and meta‐analysis. Chest. 2010;138(1):6875.
  18. Numa A, Williams G, Awad J, Duffy B. After‐hours admissions are not associated with increased risk‐adjusted mortality in pediatric intensive care. Intensive Care Med. 2008;34(1):148151.
  19. Haber LA, Lau CY, Sharpe BA, Arora VM, Farnan JM, Ranji SR. Effects of increased overnight supervision on resident education, decision‐making, and autonomy. J Hosp Med. 2012;7(8):606610.
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Maintaining high‐quality patient care, optimizing patient safety, and providing adequate trainee supervision has been an area of debate in medical education recently, and many physicians remain concerned that excessive regulation and duty hour restrictions may prevent residents from obtaining sufficient experience and developing an appropriate sense of autonomy.[1, 2, 3, 4] However, pediatric hospital medicine (PHM) has seen dramatic increases in evening and nighttime in‐house attending coverage, and the trend is expected to continue.[5, 6] Whether it be for financial, educational, or patient‐centered reasons, increased in‐house attending coverage at an academic medical setting, almost by definition, increases direct resident supervision.[7]

Increased supervision may result in better educational outcomes,[8] but many forces, such as night float systems and electronic medical records (EMRs), pull residents away from the bedside, leaving them with fewer opportunities to make decisions and a reduced sense of personal responsibility and patient ownership. Experiential learning is of great value in medical training, and without this, residents may exit their training with less confidence and competence, only rarely having been able to make important medical decisions on their own.[9, 10]

Counter to the shift toward increased supervision, we recently amended our process for pediatric admissions to the PHM service by transitioning from mandatory to on‐demand attending input during the admissions process. We hypothesized that this would improve its efficiency by encouraging residents to develop an increased sense of patient ownership and would not significantly impact patient care.

METHODS

Setting

This cohort study was conducted at the Golisano Children's Hospital (GCH) at the University of Rochester in Rochester, New York. The pediatric residency program at this tertiary care center includes 48 pediatric residents and 21 medicinepediatric residents. The PHM division, comprised of 8 pediatric hospitalists, provides care to approximately one‐third of the children with medical illnesses admitted to GCH. During the daytime, PHM attendings provide in‐house supervision for 2 resident teams, each consisting of a senior resident and 2 interns. At night, PHM attendings take calls from home. Residents are encouraged to contact attendings, available by cell phone and pager, with questions or concerns regarding patient care. The institutional review board of the University of Rochester Medical Center approved this study and informed consent was waived.

Process Change

Prior to the change, a pediatric emergency department (ED) provider at GCH directly contacted the PHM attending for all admissions to the PHM service (Figure 1). If the PHM attending accepted the admission, the ED provider then notified the pediatric admitting officer (PAO), a third‐year pediatric or fourth‐year medicinepediatric resident, who either performed or delegated the admission duties (eg, history and physical exam, admission orders).

Figure 1
Admission process for patients admitted to PHM service in pre‐ and post‐intervention cohorts. Abbreviations: ED, emergency department; PAO, pediatric admitting officer; PHM, pediatric hospital medicine.

On June 18, 2012, a new process for pediatric admissions was implemented (Figure 1). The ED provider now called the PAO, and not the attending, to discuss an admission to the PHM service. The PAO was empowered to accept the patient on behalf of the PHM attending, and perform or delegate the admission duties. During daytime hours (7:00 am5:00 pm), the PAO was expected to alert the PHM attending of the admission to allow the attending to see the patient on the day of admission. The PHM attending discussed the case with the admitting resident after the resident had an opportunity to assess the patient and formulate a management plan. During evening hours (5:00 pm10:00 pm), the admitting resident was expected to contact the PHM attending on call after evaluating the patient and developing a plan. Overnight (10:00 pm7:00 am), the PAO was given discretion as to whether she/he needed to contact the PHM attending on call; the PHM service attending then saw the patient in the morning. Residents were strongly encouraged to call the PHM attending with any questions or concerns or if they did not feel an admission was appropriate to the PHM service.

Study Population

The study population included all patients <19 years of age admitted to the PHM service from the ED. The pre‐ and post‐intervention cohorts included patients admitted from July 1, 2011 to September 30, 2011 and July 1, 2012 to September 30, 2012, respectively. These dates were chosen because residents are least experienced in the summer months, and hence we would predict the greatest disparity during this time. Patients who were directly admitted via transport from an outside facility, office or from home, or who were transferred from another service within GCH were excluded. Patients were identified from administrative databases.

Data Collection

Date and time of admission, severity of illness (SOI) scores, and risk of mortality (ROM) scores were obtained from the administrative dataset. The EMR was then used to extract the following variables: gender; date and time of the ED provider's admission request and first inpatient resident order; date and time of patient discharge, defined as the time the after‐visit summary was finalized by an inpatient provider; and the number of rapid response team (RRT) activations within 24 hours of the first inpatient resident order. The order time difference was calculated by subtracting the date and time of the ED provider admission request from the first inpatient order. Cases in which the order time difference was negative were excluded from the order time analysis due to the possibility that some extenuating circumstance for these patients, not related to the admission process, caused the early inpatient order. Length of stay (LOS) was calculated as the difference between the date and time of ED admission request and date and time of patient discharge.

The first 24 hours of each admission were reviewed independently by 3 PHM attending investigators. Neither reviewer evaluated a chart for which he had cosigned the admission note. Charts were assessed to determine whether a reasonable standard of care (SOC) was provided by the inpatient resident during admission. For instances in which SOC was not felt to have been provided by the resident, the chart was reviewed by the second investigator. If there was disagreement between the 2 investigators, a third PHM attending was used to determine the majority opinion. Due to the nature of data collected, it was not possible to blind reviewers.

PHM attending investigators also assessed how often the inpatient resident's antibiotic choice was changed by the admitting PHM attending. This evaluation excluded topical antibiotics and antibiotics not related to the admitting diagnosis (eg, continuation of outpatient antibiotics for otitis media). A change in antibiotics was defined as a change in class or a change within classes, initiation, or discontinuation of an antibiotic by the attending. Switching the route of administration was considered a change if it was not done as part of the transition to discharge. Antibiotic choice was considered in agreement if a change was made by the PHM attending based on new patient information that was not available to the admitting inpatient resident if it could be reasonably concluded that the attending would have otherwise agreed with the original choice. If this determination could not be made, the antibiotic agreement was classified as unknown. Data regarding antibiotic agreement were analyzed in 2 ways. The first included all patients for which agreement could be determined. For this analysis, if a patient was not prescribed an antibiotic by the resident or attending, there was considered to have been antibiotic agreement. The second analysis included only the patients for whom an antibiotic was started by the inpatient resident or admitting attending.

Finally, RRT activations within the first 24 hours of admission in the 2012 cohort were evaluated to determine whether the RRT could have been prevented by the original admission process. This determination was made via majority opinion of 3 PHM attendings who each independently reviewed the cases.

Statistical Analysis

The distributions of continuous variables (eg, order time difference, LOS) and the ordinal variables (ROM and SOI) were compared using Wilcoxon rank sum tests. 2 tests or Fisher exact tests were used to assess the differences in categorical variables (eg, SOC, gender). All tests were 2‐sided, and the significance level was set at 0.05. Analyses were conducted using the SAS statistical package version 9.3 (SAS Institute Inc., Cary, NC) and SPSS version 21 (IBM/SPSS, Armonk, NY).

RESULTS

The initial search identified 532 admissions. Of these, 140 were excluded (72 were via route other than the ED, 44 were not admitted to PHM, 14 were outside the study period, and 10 did not meet age criteria). Therefore, 182 admissions in the 2011 cohort and 210 admissions in the 2012 cohort were included. For all patients in the 2012 cohort, the correct admission process was followed.

Demographic characteristics between cohorts were similar (Table 1). Data for ROM and SOI were available for 141 (78%) 2011 patients and for 169 (81%) 2012 patients. The distribution of patients over the study months differed between cohorts. Age, gender, ROM, and SOI were not significantly different.

Characteristics of Children Admitted to the Pediatric Hospital Medicine Service
Variable20112012P Value
  • NOTE: Abbreviations: IQR, interquartile range. *Patients admitted between 10:00 pm and 7:00 am.

Male gender, n (%)107 (59)105 (50)0.082
Median age, y (IQR)2 (010)2 (07)0.689
Month admitted, n (%)  0.002
July60 (33)87 (41) 
August57 (31)81 (39) 
September65 (36)42 (20) 
Nighttime admission, n (%)*71 (39)90 (43)0.440
Risk of mortality, n (%)  0.910
1, lowest risk114 (81)138 (82) 
222 (16)23 (14) 
35 (4)6 (4) 
4, highest risk0 (0)2 (1) 
Severity of illness, n (%)  0.095
1, lowest severity60 (43)86 (51) 
254 (38)62 (37) 
325 (18)15 (9) 
4, highest severity2 (1)6 (4) 

The median difference in time from the ED provider admission request to the first inpatient resident order was roughly half as long in 2012 than in 2011 (123 vs 62 minutes, P<0.001) (Table 2). There were 12 cases in which the inpatient order came prior to the ED admission request in 2012 and 2 cases in 2011, and these were excluded from the order time difference analysis. LOS was not significantly different between groups (P=0.348). There were no differences in the frequency of antibiotic changes when all patients were considered or in the subgroup in whom antibiotics were prescribed by either the resident or attending. The number of cases for which the admitting resident's plan was deemed not to have met standard of care were few and not significantly different (P=1). None of these patients experienced harm as a result, and in all cases, SOC was determined to have been provided by the admitting PHM attending. The frequency of RRT calls within the first 24 hours of admission on PHM patients was not significantly different (P=0.114).

Comparison of Patient Cohorts Admitted to Pediatric Hospital Medicine Before and After the Change in Admission Procedure
Variable20112012P Value
  • NOTE: Abbreviations: IQR, interquartile range; RRT, rapid response team.

  • Cases in which the time difference between orders was negative were excluded.

  • P value was calculated using Wilcoxon rank sum test.

Time from admission decision to first inpatient order, min, median (IQR)a123 (70188)62 (30105)<0.001
Length of stay, h, median (IQR)b44 (3167)41 (2271)0.348
Change by attending to resident's antibiotic choice in all patients, n (%)13/182 (7)18/210 (9)0.617
Change by attending to resident's antibiotic choice in patients who received antibiotics, n (%)13/97 (13)18/96 (19)0.312
Resident met standard of care, n (%)180/182 (99)207/210 (99)1
RRT called within first 24 hours, n (%)2/182 (1)8/210 (4)0.114

When only patients admitted during the night in 2011 and 2012 were compared, results were consistent with the overall finding that there was a shorter time to inpatient admission order without a difference in other studied variables (Table 3).

Comparison of Nighttime Admissions Between Cohorts (n=161)
Variable20112012P Value
  • NOTE: Abbreviations: IQR, interquartile range; RRT, rapid response team.

  • Cases in which the time difference between orders was negative were excluded.

  • P value was calculated using Wilcoxon rank sum test.

Time from admission decision to first inpatient order, min, median (IQR)ab90 (40151)42 (1767)0.002
Length of stay, h, median (IQR)b53 (3461)36 (1769)0.307
Change by attending to resident's antibiotic choice in all patients, n (%)7/70 (10)7/88 (8)1
Resident met standard of care, n (%)70/71 (99)88/90 (98)1
RRT called within first 24 hours, n (%)2/71 (3)6/90 (7)0.468

DISCUSSION

The purpose of this study was to evaluate an admission process that removed an ineffective method of attending oversight and allowed residents an opportunity to develop patient care plans prior to attending input. The key change from the original process was removing the step in which the ED provider contacted the PHM attending for new admissions, thus eliminating mandatory inpatient attending input, removing an impediment to workflow, and empowering inpatient pediatric residents to assess new patients and develop management plans. Our data show a reduction in the time difference between the ED admission request and the inpatient resident's first order by more than an hour, indicating a more efficient admission process. Although one might expect that eliminating the act of a phone call would shorten this time by a few minutes, it cannot account for the extent of the difference we found. We postulate that an increased sense of accountability motivated inpatient residents to evaluate and begin management sooner, a topic that requires further exploration.

A more efficient admission process benefits emergency medicine residents and other ED providers as well. It is well documented that ED crowding is associated with decreased quality of care,[11, 12] and ED efficiency is receiving increased attention with newly reportable quality metrics such as Admit Decision Time to Emergency Department Departure Time for Admitted Patients.[13]

Our data do not attenuate the importance of hospitalists in patient care, as evidenced by the fact that PHM attendings continued to frequently amend the residents' antibiotic choicethe only variable we evaluated in terms of change in planand recognized several cases in which the residents' plan did not meet standard of care. Furthermore, attendings continued to be available by phone and pager for guidance and education when needed or requested by the residents. Instead, our data show that removing mandated attending input at the time of admission did not significantly impact major patient outcomes, which may partly be attributable to the general safety of the inpatient pediatric wards.[14, 15] In our study, a comprehensive analysis of patient harm was not possible given the variable list and infrequency with which SOC was not met or RRTs were called. Furthermore, our residency program continues to comply with national pediatric residency requirements for nighttime supervision.[7]

Our PHM division, which had previously allocated 2 hours of attending clinical time per call night, now averages <15 minutes. These data conflict with the current trend in PHM toward more, rather than less, direct attending oversight. Many PHM divisions have moved toward 24/7 in‐house coverage,[5] a situation that often results in shiftwork and multiple handoffs. Removing the in‐house attending overnight would allow for the rapidly growing PHM subspecialty to allocate hospitalists elsewhere depending on their scholarly needs, particularly as divisions seek to become increasingly involved in medical education, research, and hospital leadership.[16, 17] Although one might posit a financial benefit to having in‐house attendings determine the appropriateness of an admission overnight, we identified no case in which the insurance denied an admission.

Safety equivalence of an in‐house to on‐call attending is poorly studied in PHM. However, even in intensive care units, where the majority of morbidity and mortality occur, it is unclear that the presence of an attending, let alone mandating phone calls, positively impacts survival. One prospective trial failed to demonstrate a difference in patient outcomes in the critical care setting when comparing mandated attending in‐house involvement to optional attending availability by phone.[18] Furthermore, several studies have found no association with time of admission and mortality, implying there is no criticality specifically requiring nighttime coverage.[19, 20]

One adult study of nocturnists showed that residents felt they had more contact with attendings who were in‐house than attendings taking home calls.[21] However, when the residents were asked why they did not contact the attending, the only difference between at‐home and in‐house attendings was that for attendings available by phone, residents were less likely to know who to call and were hesitant to wake the attending.

This study had several limitations. First, we could not effectively blind reviewers; a salient point given that the reviewers benefited from the new system with a reduced nighttime workload. We attempted to minimize this bias by employing multiple independent evaluations followed by group consensus whenever possible. Second, even though we had 3 hospitalists independently review each 2012 RRT to determine whether it was preventable by the prior system, this task was prone to retrospective bias. Third, there was a significant difference in the month of admission between cohorts. Rather than biasing toward our observed time difference, the fact that more patients were admitted in July 2012the beginning of the academic yearmay have decreased our observed difference given that residents were less experienced. Forth, this study used certain measurable outcomes as proxies for quality of care and patient harm and was likely underpowered to truly detect a difference in some of the more infrequent variables. Furthermore, we did not evaluate other potential harms, such as cost. Fifth, we did not evaluate whether or not the new process changed ED provider behavior (ie, an ED provider may wait longer to request admission overnight given that the PHM attending is not mandated to provide input until the morning). Finally, although LOS was used as a balancing measure, it would likely have taken major events or omissions during the admission process to cause it to change significantly, and therefore the lack of statistical difference in this metric does not necessarily imply that more subtle aspects of care were the same between groups. We also chose not to include readmission rate for this reason, as any change could not conclusively be attributed to the new admission process.

CONCLUSION

Increasing resident autonomy by removing mandated input during PHM admissions makes the process more efficient and results in no significant changes to major patient outcomes. These data may be used by rapidly growing PHM divisions to redefine faculty clinical responsibilities, particularly at night.

ACKNOWLEDGMENTS

Disclosures: This project was supported by the University of Rochester CTSA award number UL1 TR000042 from the National Center for Advancing Translational Sciences of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors report no conflicts of interest.

Maintaining high‐quality patient care, optimizing patient safety, and providing adequate trainee supervision has been an area of debate in medical education recently, and many physicians remain concerned that excessive regulation and duty hour restrictions may prevent residents from obtaining sufficient experience and developing an appropriate sense of autonomy.[1, 2, 3, 4] However, pediatric hospital medicine (PHM) has seen dramatic increases in evening and nighttime in‐house attending coverage, and the trend is expected to continue.[5, 6] Whether it be for financial, educational, or patient‐centered reasons, increased in‐house attending coverage at an academic medical setting, almost by definition, increases direct resident supervision.[7]

Increased supervision may result in better educational outcomes,[8] but many forces, such as night float systems and electronic medical records (EMRs), pull residents away from the bedside, leaving them with fewer opportunities to make decisions and a reduced sense of personal responsibility and patient ownership. Experiential learning is of great value in medical training, and without this, residents may exit their training with less confidence and competence, only rarely having been able to make important medical decisions on their own.[9, 10]

Counter to the shift toward increased supervision, we recently amended our process for pediatric admissions to the PHM service by transitioning from mandatory to on‐demand attending input during the admissions process. We hypothesized that this would improve its efficiency by encouraging residents to develop an increased sense of patient ownership and would not significantly impact patient care.

METHODS

Setting

This cohort study was conducted at the Golisano Children's Hospital (GCH) at the University of Rochester in Rochester, New York. The pediatric residency program at this tertiary care center includes 48 pediatric residents and 21 medicinepediatric residents. The PHM division, comprised of 8 pediatric hospitalists, provides care to approximately one‐third of the children with medical illnesses admitted to GCH. During the daytime, PHM attendings provide in‐house supervision for 2 resident teams, each consisting of a senior resident and 2 interns. At night, PHM attendings take calls from home. Residents are encouraged to contact attendings, available by cell phone and pager, with questions or concerns regarding patient care. The institutional review board of the University of Rochester Medical Center approved this study and informed consent was waived.

Process Change

Prior to the change, a pediatric emergency department (ED) provider at GCH directly contacted the PHM attending for all admissions to the PHM service (Figure 1). If the PHM attending accepted the admission, the ED provider then notified the pediatric admitting officer (PAO), a third‐year pediatric or fourth‐year medicinepediatric resident, who either performed or delegated the admission duties (eg, history and physical exam, admission orders).

Figure 1
Admission process for patients admitted to PHM service in pre‐ and post‐intervention cohorts. Abbreviations: ED, emergency department; PAO, pediatric admitting officer; PHM, pediatric hospital medicine.

On June 18, 2012, a new process for pediatric admissions was implemented (Figure 1). The ED provider now called the PAO, and not the attending, to discuss an admission to the PHM service. The PAO was empowered to accept the patient on behalf of the PHM attending, and perform or delegate the admission duties. During daytime hours (7:00 am5:00 pm), the PAO was expected to alert the PHM attending of the admission to allow the attending to see the patient on the day of admission. The PHM attending discussed the case with the admitting resident after the resident had an opportunity to assess the patient and formulate a management plan. During evening hours (5:00 pm10:00 pm), the admitting resident was expected to contact the PHM attending on call after evaluating the patient and developing a plan. Overnight (10:00 pm7:00 am), the PAO was given discretion as to whether she/he needed to contact the PHM attending on call; the PHM service attending then saw the patient in the morning. Residents were strongly encouraged to call the PHM attending with any questions or concerns or if they did not feel an admission was appropriate to the PHM service.

Study Population

The study population included all patients <19 years of age admitted to the PHM service from the ED. The pre‐ and post‐intervention cohorts included patients admitted from July 1, 2011 to September 30, 2011 and July 1, 2012 to September 30, 2012, respectively. These dates were chosen because residents are least experienced in the summer months, and hence we would predict the greatest disparity during this time. Patients who were directly admitted via transport from an outside facility, office or from home, or who were transferred from another service within GCH were excluded. Patients were identified from administrative databases.

Data Collection

Date and time of admission, severity of illness (SOI) scores, and risk of mortality (ROM) scores were obtained from the administrative dataset. The EMR was then used to extract the following variables: gender; date and time of the ED provider's admission request and first inpatient resident order; date and time of patient discharge, defined as the time the after‐visit summary was finalized by an inpatient provider; and the number of rapid response team (RRT) activations within 24 hours of the first inpatient resident order. The order time difference was calculated by subtracting the date and time of the ED provider admission request from the first inpatient order. Cases in which the order time difference was negative were excluded from the order time analysis due to the possibility that some extenuating circumstance for these patients, not related to the admission process, caused the early inpatient order. Length of stay (LOS) was calculated as the difference between the date and time of ED admission request and date and time of patient discharge.

The first 24 hours of each admission were reviewed independently by 3 PHM attending investigators. Neither reviewer evaluated a chart for which he had cosigned the admission note. Charts were assessed to determine whether a reasonable standard of care (SOC) was provided by the inpatient resident during admission. For instances in which SOC was not felt to have been provided by the resident, the chart was reviewed by the second investigator. If there was disagreement between the 2 investigators, a third PHM attending was used to determine the majority opinion. Due to the nature of data collected, it was not possible to blind reviewers.

PHM attending investigators also assessed how often the inpatient resident's antibiotic choice was changed by the admitting PHM attending. This evaluation excluded topical antibiotics and antibiotics not related to the admitting diagnosis (eg, continuation of outpatient antibiotics for otitis media). A change in antibiotics was defined as a change in class or a change within classes, initiation, or discontinuation of an antibiotic by the attending. Switching the route of administration was considered a change if it was not done as part of the transition to discharge. Antibiotic choice was considered in agreement if a change was made by the PHM attending based on new patient information that was not available to the admitting inpatient resident if it could be reasonably concluded that the attending would have otherwise agreed with the original choice. If this determination could not be made, the antibiotic agreement was classified as unknown. Data regarding antibiotic agreement were analyzed in 2 ways. The first included all patients for which agreement could be determined. For this analysis, if a patient was not prescribed an antibiotic by the resident or attending, there was considered to have been antibiotic agreement. The second analysis included only the patients for whom an antibiotic was started by the inpatient resident or admitting attending.

Finally, RRT activations within the first 24 hours of admission in the 2012 cohort were evaluated to determine whether the RRT could have been prevented by the original admission process. This determination was made via majority opinion of 3 PHM attendings who each independently reviewed the cases.

Statistical Analysis

The distributions of continuous variables (eg, order time difference, LOS) and the ordinal variables (ROM and SOI) were compared using Wilcoxon rank sum tests. 2 tests or Fisher exact tests were used to assess the differences in categorical variables (eg, SOC, gender). All tests were 2‐sided, and the significance level was set at 0.05. Analyses were conducted using the SAS statistical package version 9.3 (SAS Institute Inc., Cary, NC) and SPSS version 21 (IBM/SPSS, Armonk, NY).

RESULTS

The initial search identified 532 admissions. Of these, 140 were excluded (72 were via route other than the ED, 44 were not admitted to PHM, 14 were outside the study period, and 10 did not meet age criteria). Therefore, 182 admissions in the 2011 cohort and 210 admissions in the 2012 cohort were included. For all patients in the 2012 cohort, the correct admission process was followed.

Demographic characteristics between cohorts were similar (Table 1). Data for ROM and SOI were available for 141 (78%) 2011 patients and for 169 (81%) 2012 patients. The distribution of patients over the study months differed between cohorts. Age, gender, ROM, and SOI were not significantly different.

Characteristics of Children Admitted to the Pediatric Hospital Medicine Service
Variable20112012P Value
  • NOTE: Abbreviations: IQR, interquartile range. *Patients admitted between 10:00 pm and 7:00 am.

Male gender, n (%)107 (59)105 (50)0.082
Median age, y (IQR)2 (010)2 (07)0.689
Month admitted, n (%)  0.002
July60 (33)87 (41) 
August57 (31)81 (39) 
September65 (36)42 (20) 
Nighttime admission, n (%)*71 (39)90 (43)0.440
Risk of mortality, n (%)  0.910
1, lowest risk114 (81)138 (82) 
222 (16)23 (14) 
35 (4)6 (4) 
4, highest risk0 (0)2 (1) 
Severity of illness, n (%)  0.095
1, lowest severity60 (43)86 (51) 
254 (38)62 (37) 
325 (18)15 (9) 
4, highest severity2 (1)6 (4) 

The median difference in time from the ED provider admission request to the first inpatient resident order was roughly half as long in 2012 than in 2011 (123 vs 62 minutes, P<0.001) (Table 2). There were 12 cases in which the inpatient order came prior to the ED admission request in 2012 and 2 cases in 2011, and these were excluded from the order time difference analysis. LOS was not significantly different between groups (P=0.348). There were no differences in the frequency of antibiotic changes when all patients were considered or in the subgroup in whom antibiotics were prescribed by either the resident or attending. The number of cases for which the admitting resident's plan was deemed not to have met standard of care were few and not significantly different (P=1). None of these patients experienced harm as a result, and in all cases, SOC was determined to have been provided by the admitting PHM attending. The frequency of RRT calls within the first 24 hours of admission on PHM patients was not significantly different (P=0.114).

Comparison of Patient Cohorts Admitted to Pediatric Hospital Medicine Before and After the Change in Admission Procedure
Variable20112012P Value
  • NOTE: Abbreviations: IQR, interquartile range; RRT, rapid response team.

  • Cases in which the time difference between orders was negative were excluded.

  • P value was calculated using Wilcoxon rank sum test.

Time from admission decision to first inpatient order, min, median (IQR)a123 (70188)62 (30105)<0.001
Length of stay, h, median (IQR)b44 (3167)41 (2271)0.348
Change by attending to resident's antibiotic choice in all patients, n (%)13/182 (7)18/210 (9)0.617
Change by attending to resident's antibiotic choice in patients who received antibiotics, n (%)13/97 (13)18/96 (19)0.312
Resident met standard of care, n (%)180/182 (99)207/210 (99)1
RRT called within first 24 hours, n (%)2/182 (1)8/210 (4)0.114

When only patients admitted during the night in 2011 and 2012 were compared, results were consistent with the overall finding that there was a shorter time to inpatient admission order without a difference in other studied variables (Table 3).

Comparison of Nighttime Admissions Between Cohorts (n=161)
Variable20112012P Value
  • NOTE: Abbreviations: IQR, interquartile range; RRT, rapid response team.

  • Cases in which the time difference between orders was negative were excluded.

  • P value was calculated using Wilcoxon rank sum test.

Time from admission decision to first inpatient order, min, median (IQR)ab90 (40151)42 (1767)0.002
Length of stay, h, median (IQR)b53 (3461)36 (1769)0.307
Change by attending to resident's antibiotic choice in all patients, n (%)7/70 (10)7/88 (8)1
Resident met standard of care, n (%)70/71 (99)88/90 (98)1
RRT called within first 24 hours, n (%)2/71 (3)6/90 (7)0.468

DISCUSSION

The purpose of this study was to evaluate an admission process that removed an ineffective method of attending oversight and allowed residents an opportunity to develop patient care plans prior to attending input. The key change from the original process was removing the step in which the ED provider contacted the PHM attending for new admissions, thus eliminating mandatory inpatient attending input, removing an impediment to workflow, and empowering inpatient pediatric residents to assess new patients and develop management plans. Our data show a reduction in the time difference between the ED admission request and the inpatient resident's first order by more than an hour, indicating a more efficient admission process. Although one might expect that eliminating the act of a phone call would shorten this time by a few minutes, it cannot account for the extent of the difference we found. We postulate that an increased sense of accountability motivated inpatient residents to evaluate and begin management sooner, a topic that requires further exploration.

A more efficient admission process benefits emergency medicine residents and other ED providers as well. It is well documented that ED crowding is associated with decreased quality of care,[11, 12] and ED efficiency is receiving increased attention with newly reportable quality metrics such as Admit Decision Time to Emergency Department Departure Time for Admitted Patients.[13]

Our data do not attenuate the importance of hospitalists in patient care, as evidenced by the fact that PHM attendings continued to frequently amend the residents' antibiotic choicethe only variable we evaluated in terms of change in planand recognized several cases in which the residents' plan did not meet standard of care. Furthermore, attendings continued to be available by phone and pager for guidance and education when needed or requested by the residents. Instead, our data show that removing mandated attending input at the time of admission did not significantly impact major patient outcomes, which may partly be attributable to the general safety of the inpatient pediatric wards.[14, 15] In our study, a comprehensive analysis of patient harm was not possible given the variable list and infrequency with which SOC was not met or RRTs were called. Furthermore, our residency program continues to comply with national pediatric residency requirements for nighttime supervision.[7]

Our PHM division, which had previously allocated 2 hours of attending clinical time per call night, now averages <15 minutes. These data conflict with the current trend in PHM toward more, rather than less, direct attending oversight. Many PHM divisions have moved toward 24/7 in‐house coverage,[5] a situation that often results in shiftwork and multiple handoffs. Removing the in‐house attending overnight would allow for the rapidly growing PHM subspecialty to allocate hospitalists elsewhere depending on their scholarly needs, particularly as divisions seek to become increasingly involved in medical education, research, and hospital leadership.[16, 17] Although one might posit a financial benefit to having in‐house attendings determine the appropriateness of an admission overnight, we identified no case in which the insurance denied an admission.

Safety equivalence of an in‐house to on‐call attending is poorly studied in PHM. However, even in intensive care units, where the majority of morbidity and mortality occur, it is unclear that the presence of an attending, let alone mandating phone calls, positively impacts survival. One prospective trial failed to demonstrate a difference in patient outcomes in the critical care setting when comparing mandated attending in‐house involvement to optional attending availability by phone.[18] Furthermore, several studies have found no association with time of admission and mortality, implying there is no criticality specifically requiring nighttime coverage.[19, 20]

One adult study of nocturnists showed that residents felt they had more contact with attendings who were in‐house than attendings taking home calls.[21] However, when the residents were asked why they did not contact the attending, the only difference between at‐home and in‐house attendings was that for attendings available by phone, residents were less likely to know who to call and were hesitant to wake the attending.

This study had several limitations. First, we could not effectively blind reviewers; a salient point given that the reviewers benefited from the new system with a reduced nighttime workload. We attempted to minimize this bias by employing multiple independent evaluations followed by group consensus whenever possible. Second, even though we had 3 hospitalists independently review each 2012 RRT to determine whether it was preventable by the prior system, this task was prone to retrospective bias. Third, there was a significant difference in the month of admission between cohorts. Rather than biasing toward our observed time difference, the fact that more patients were admitted in July 2012the beginning of the academic yearmay have decreased our observed difference given that residents were less experienced. Forth, this study used certain measurable outcomes as proxies for quality of care and patient harm and was likely underpowered to truly detect a difference in some of the more infrequent variables. Furthermore, we did not evaluate other potential harms, such as cost. Fifth, we did not evaluate whether or not the new process changed ED provider behavior (ie, an ED provider may wait longer to request admission overnight given that the PHM attending is not mandated to provide input until the morning). Finally, although LOS was used as a balancing measure, it would likely have taken major events or omissions during the admission process to cause it to change significantly, and therefore the lack of statistical difference in this metric does not necessarily imply that more subtle aspects of care were the same between groups. We also chose not to include readmission rate for this reason, as any change could not conclusively be attributed to the new admission process.

CONCLUSION

Increasing resident autonomy by removing mandated input during PHM admissions makes the process more efficient and results in no significant changes to major patient outcomes. These data may be used by rapidly growing PHM divisions to redefine faculty clinical responsibilities, particularly at night.

ACKNOWLEDGMENTS

Disclosures: This project was supported by the University of Rochester CTSA award number UL1 TR000042 from the National Center for Advancing Translational Sciences of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors report no conflicts of interest.

References
  1. Accreditation Council for Graduate Medical Education Task Force on Quality Care and Professionalism. The ACGME 2011 duty hour standards: enhancing quality of care, supervision, and resident professional development. Accreditation Council for Graduate Medical Education, Chicago, IL; 2011. Available at: http://www.acgme.org/acgmeweb/Portals/0/PDFs/jgme‐monograph[1].pdf. Last accessed on December 18, 2013.
  2. Moonesinghe SR, Lowery J, Shahi N, Millen A, Beard JD. Impact of reduction in working hours for doctors in training on postgraduate medical education and patients' outcomes: systemic review. BMJ. 2011;342:d1580.
  3. Feig BA, Hasso AN. ACGME 2011 duty‐hour guidelines: consequences expected by radiology residency directors and chief residents. J Am Coll Radiol. 2012;9(11):820827.
  4. Chiong W. Justifying patient risks associated with medical education. JAMA. 2007;298(9):10461048.
  5. Gosdin C, Simmons J, Yau C, Sucharew H, Carlson D, Paciorkowski N. Survey of academic pediatric hospitalist programs in the U.S.: organizational, administrative and financial factors. J Hosp Med. 2013;8(6):285291.
  6. Oshimura J, Sperring J, Bauer BD, Rauch DA. Inpatient staffing within pediatric residency programs: work hour restrictions and the evolving role of the pediatric hospitalist. J Hosp Med. 2012;7(4):299303.
  7. ACGME Program Requirements for Graduate Medical Education in Pediatrics. ACGME Approved: September 30, 2012; Effective: July 1, 2013. Available at: http://www.acgme.org/acgmeweb/Portals/0/PFAssets/2013‐PR‐FAQ‐PIF/320_pediatrics_07012013.pdf. Accessed September 17, 2013.
  8. Farnan JM, Petty LA, Georgitis E, et al. A systematic review: the effect of clinical supervision on patient and residency education outcomes. Acad Med. 2012;87(4):428442.
  9. Kerlin MP, Halpern SD. Twenty‐four‐hour intensivist staffing in teaching hospitals: tension between safety today and safety tomorrow. Chest. 2012;141(5):13151320.
  10. Fred HL. Medical education on the brink: 62 years of front‐line observations and opinions. Tex Heart Inst J. 2012;39(3):322329.
  11. Pines JM, Hollander JE. Emergency department crowding is associated with poor care for patients with severe pain. Ann Emerg Med. 2008;51:67.
  12. Bernstein SL, Aronsky D, Duseja R, et al. The effect of emergency department crowding on clinically oriented outcomes. Acad Emerg Med. 2009;16(1):110.
  13. The Specifications Manual for National Hospital Inpatient Quality Measures. A Collaboration of the Centers for Medicare 128(1):7278.
  14. Sharek PJ, Parast LM, Leong K, et al. Effect of a rapid response team on hospital‐wide mortality and code rates outside the ICU in a Children's Hospital. JAMA. 2007;298(19):22672274.
  15. Section on Hospital Medicine. Guiding principles for Pediatric Hospital Medicine programs. Pediatrics. 2013;132(4):782786. SHM fact sheet: about hospital medicine. http://www.hospitalmedicine.org/AM/Template.cfm?Section=Media_Kit42(5):120126.
  16. Kerlin MP, Small DS, Cooney E, et al. A randomized trial of nighttime physician staffing in an intensive care unit. N Engl J Med. 2013;368(23):22012209.
  17. Cavallazzi R, Marik PE, Hirani A, Pachinburavan M, Vasu TS, Leiby BE. Association between time of admission to the ICU and mortality: a systematic review and meta‐analysis. Chest. 2010;138(1):6875.
  18. Numa A, Williams G, Awad J, Duffy B. After‐hours admissions are not associated with increased risk‐adjusted mortality in pediatric intensive care. Intensive Care Med. 2008;34(1):148151.
  19. Haber LA, Lau CY, Sharpe BA, Arora VM, Farnan JM, Ranji SR. Effects of increased overnight supervision on resident education, decision‐making, and autonomy. J Hosp Med. 2012;7(8):606610.
References
  1. Accreditation Council for Graduate Medical Education Task Force on Quality Care and Professionalism. The ACGME 2011 duty hour standards: enhancing quality of care, supervision, and resident professional development. Accreditation Council for Graduate Medical Education, Chicago, IL; 2011. Available at: http://www.acgme.org/acgmeweb/Portals/0/PDFs/jgme‐monograph[1].pdf. Last accessed on December 18, 2013.
  2. Moonesinghe SR, Lowery J, Shahi N, Millen A, Beard JD. Impact of reduction in working hours for doctors in training on postgraduate medical education and patients' outcomes: systemic review. BMJ. 2011;342:d1580.
  3. Feig BA, Hasso AN. ACGME 2011 duty‐hour guidelines: consequences expected by radiology residency directors and chief residents. J Am Coll Radiol. 2012;9(11):820827.
  4. Chiong W. Justifying patient risks associated with medical education. JAMA. 2007;298(9):10461048.
  5. Gosdin C, Simmons J, Yau C, Sucharew H, Carlson D, Paciorkowski N. Survey of academic pediatric hospitalist programs in the U.S.: organizational, administrative and financial factors. J Hosp Med. 2013;8(6):285291.
  6. Oshimura J, Sperring J, Bauer BD, Rauch DA. Inpatient staffing within pediatric residency programs: work hour restrictions and the evolving role of the pediatric hospitalist. J Hosp Med. 2012;7(4):299303.
  7. ACGME Program Requirements for Graduate Medical Education in Pediatrics. ACGME Approved: September 30, 2012; Effective: July 1, 2013. Available at: http://www.acgme.org/acgmeweb/Portals/0/PFAssets/2013‐PR‐FAQ‐PIF/320_pediatrics_07012013.pdf. Accessed September 17, 2013.
  8. Farnan JM, Petty LA, Georgitis E, et al. A systematic review: the effect of clinical supervision on patient and residency education outcomes. Acad Med. 2012;87(4):428442.
  9. Kerlin MP, Halpern SD. Twenty‐four‐hour intensivist staffing in teaching hospitals: tension between safety today and safety tomorrow. Chest. 2012;141(5):13151320.
  10. Fred HL. Medical education on the brink: 62 years of front‐line observations and opinions. Tex Heart Inst J. 2012;39(3):322329.
  11. Pines JM, Hollander JE. Emergency department crowding is associated with poor care for patients with severe pain. Ann Emerg Med. 2008;51:67.
  12. Bernstein SL, Aronsky D, Duseja R, et al. The effect of emergency department crowding on clinically oriented outcomes. Acad Emerg Med. 2009;16(1):110.
  13. The Specifications Manual for National Hospital Inpatient Quality Measures. A Collaboration of the Centers for Medicare 128(1):7278.
  14. Sharek PJ, Parast LM, Leong K, et al. Effect of a rapid response team on hospital‐wide mortality and code rates outside the ICU in a Children's Hospital. JAMA. 2007;298(19):22672274.
  15. Section on Hospital Medicine. Guiding principles for Pediatric Hospital Medicine programs. Pediatrics. 2013;132(4):782786. SHM fact sheet: about hospital medicine. http://www.hospitalmedicine.org/AM/Template.cfm?Section=Media_Kit42(5):120126.
  16. Kerlin MP, Small DS, Cooney E, et al. A randomized trial of nighttime physician staffing in an intensive care unit. N Engl J Med. 2013;368(23):22012209.
  17. Cavallazzi R, Marik PE, Hirani A, Pachinburavan M, Vasu TS, Leiby BE. Association between time of admission to the ICU and mortality: a systematic review and meta‐analysis. Chest. 2010;138(1):6875.
  18. Numa A, Williams G, Awad J, Duffy B. After‐hours admissions are not associated with increased risk‐adjusted mortality in pediatric intensive care. Intensive Care Med. 2008;34(1):148151.
  19. Haber LA, Lau CY, Sharpe BA, Arora VM, Farnan JM, Ranji SR. Effects of increased overnight supervision on resident education, decision‐making, and autonomy. J Hosp Med. 2012;7(8):606610.
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Journal of Hospital Medicine - 9(2)
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Journal of Hospital Medicine - 9(2)
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Tempering pediatric hospitalist supervision of residents improves admission process efficiency without decreasing quality of care
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Tempering pediatric hospitalist supervision of residents improves admission process efficiency without decreasing quality of care
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© 2013 Society of Hospital Medicine

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Address for correspondence and reprint requests: Eric Biondi, MD, Department of Pediatrics, University of Rochester Medical Center, 601 Elmwood Ave., Box 667, Rochester, NY 14620; Telephone: 585‐276‐4113; Fax: 585‐276‐1128; E‐mail: [email protected]
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Bringing CME to the Bedside

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Returning hospitalists to their formative training environment for CME: The UCSF Hospitalist Mini‐College

Hospitalists, and physicians in general, recognize the need for continuing medical education (CME) to update their knowledge and skills to provide the best possible care for patients. Interactive and personalized learning activities provide the most effective approaches for maintaining or improving physician competency.[1, 2] Despite guidelines that recommend a shift of CME from the traditional large lecture format to case‐based and highly interactive learning techniques,[3] this has been challenging to achieve in practice.

In this issue of the Journal of Hospital Medicine, Sehgal and collaborators at the University of California, San Francisco (UCSF) report innovative and highly appealing CME activity that provides a short, focused experience for the practicing hospitalist seeking to update his or her skills.[4] The UCSF Hospitalist Mini‐College (UHMC) embraced the principles for creation of effective CME by conducting needs assessment from community hospitalists and constructing a program that provides focused, interactive, small‐group, intensive experiences and then evaluating the experience to improve subsequent iterations of the Mini‐College. The UHMC immerses participants in a relatively intense experience that includes close interaction with prominent faculty, hands‐on bedside experiences, practical skills, and attendance at sessions (resident report, morbidity and mortality conferences) that are part of every resident trainee's experience. Participants would be linked to their previous learning activities. As the authors point out, there may be a powerful stimulus to learning when practicing physicians return to the milieu of training environments. This observation deserves further investigation.

The report does not provide evidence that participation in the Mini‐College improved patient outcomes or physician performance in practice; these outcome measures remain elusive and an aspirational goal in medical education research. However, experienced clinician educators have come to recognize and adopt effective interventions that simply make sense in the same fashion that it makes sense to use a parachute when jumping out of an airplane in flight.[5] The UHMC makes sense. The medical education literature is replete with articles describing educational innovations and their 1‐ to 2‐year outcomes, leaving the reader wondering about sustainability. It is reassuring that Sehgal et al. report 5 years of experience with the UHMC, and that the program has consistently had a waiting list of hospitalists who want to participate despite the expense. Although it requires patience on the part of educational innovators, this report helps set a standard for reporting enduring innovation in the education arena.

The article provides a description that is sufficiently detailed for other academic medical centers to replicate the intervention or to effectively adapt the principles of the intervention for the needs of their local hospitalist community. The authors should be congratulated for sharing the details of their program and for sharing powerful comments by participants. For hospitalist medical educators interested in sharing details of effective and sustained innovations, publication of this article emphasizes the Journal of Hospital Medicine's interest in disseminating these important projects.

In summary, the report on the UHMC model challenges all of us in academic hospital medicine to think creatively about how to provide effective, engaging, and exciting learning opportunities beyond the years of medical school and residency training.

References
  1. Bloom BS. Effects of continuing medical education on improving physician clinical care and patient health: a review of systematic reviews. Int J Technol Assess Health Care. 2005;21:380385.
  2. Dorman T, Miller BM. Continuing medical education: the link between physician learning and health care outcomes. Acad Med. 2011;86:1339.
  3. Davis N, Davis D, Bloch R. Continuing medical education: AMEE Education Guide No. 35. Med Teach. 2008;30:652666.
  4. Sehgal NL, Wachter RM, Vidyarthi AR. Bringing continuing medical education to the bedside: The University of California, San Francisco hospitalist mini‐college. J HospMed. 2014;9:129134.
  5. Smith GC, Pell JP. Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials. BMJ. 2003;327:14591461.
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Issue
Journal of Hospital Medicine - 9(2)
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135-135
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Hospitalists, and physicians in general, recognize the need for continuing medical education (CME) to update their knowledge and skills to provide the best possible care for patients. Interactive and personalized learning activities provide the most effective approaches for maintaining or improving physician competency.[1, 2] Despite guidelines that recommend a shift of CME from the traditional large lecture format to case‐based and highly interactive learning techniques,[3] this has been challenging to achieve in practice.

In this issue of the Journal of Hospital Medicine, Sehgal and collaborators at the University of California, San Francisco (UCSF) report innovative and highly appealing CME activity that provides a short, focused experience for the practicing hospitalist seeking to update his or her skills.[4] The UCSF Hospitalist Mini‐College (UHMC) embraced the principles for creation of effective CME by conducting needs assessment from community hospitalists and constructing a program that provides focused, interactive, small‐group, intensive experiences and then evaluating the experience to improve subsequent iterations of the Mini‐College. The UHMC immerses participants in a relatively intense experience that includes close interaction with prominent faculty, hands‐on bedside experiences, practical skills, and attendance at sessions (resident report, morbidity and mortality conferences) that are part of every resident trainee's experience. Participants would be linked to their previous learning activities. As the authors point out, there may be a powerful stimulus to learning when practicing physicians return to the milieu of training environments. This observation deserves further investigation.

The report does not provide evidence that participation in the Mini‐College improved patient outcomes or physician performance in practice; these outcome measures remain elusive and an aspirational goal in medical education research. However, experienced clinician educators have come to recognize and adopt effective interventions that simply make sense in the same fashion that it makes sense to use a parachute when jumping out of an airplane in flight.[5] The UHMC makes sense. The medical education literature is replete with articles describing educational innovations and their 1‐ to 2‐year outcomes, leaving the reader wondering about sustainability. It is reassuring that Sehgal et al. report 5 years of experience with the UHMC, and that the program has consistently had a waiting list of hospitalists who want to participate despite the expense. Although it requires patience on the part of educational innovators, this report helps set a standard for reporting enduring innovation in the education arena.

The article provides a description that is sufficiently detailed for other academic medical centers to replicate the intervention or to effectively adapt the principles of the intervention for the needs of their local hospitalist community. The authors should be congratulated for sharing the details of their program and for sharing powerful comments by participants. For hospitalist medical educators interested in sharing details of effective and sustained innovations, publication of this article emphasizes the Journal of Hospital Medicine's interest in disseminating these important projects.

In summary, the report on the UHMC model challenges all of us in academic hospital medicine to think creatively about how to provide effective, engaging, and exciting learning opportunities beyond the years of medical school and residency training.

Hospitalists, and physicians in general, recognize the need for continuing medical education (CME) to update their knowledge and skills to provide the best possible care for patients. Interactive and personalized learning activities provide the most effective approaches for maintaining or improving physician competency.[1, 2] Despite guidelines that recommend a shift of CME from the traditional large lecture format to case‐based and highly interactive learning techniques,[3] this has been challenging to achieve in practice.

In this issue of the Journal of Hospital Medicine, Sehgal and collaborators at the University of California, San Francisco (UCSF) report innovative and highly appealing CME activity that provides a short, focused experience for the practicing hospitalist seeking to update his or her skills.[4] The UCSF Hospitalist Mini‐College (UHMC) embraced the principles for creation of effective CME by conducting needs assessment from community hospitalists and constructing a program that provides focused, interactive, small‐group, intensive experiences and then evaluating the experience to improve subsequent iterations of the Mini‐College. The UHMC immerses participants in a relatively intense experience that includes close interaction with prominent faculty, hands‐on bedside experiences, practical skills, and attendance at sessions (resident report, morbidity and mortality conferences) that are part of every resident trainee's experience. Participants would be linked to their previous learning activities. As the authors point out, there may be a powerful stimulus to learning when practicing physicians return to the milieu of training environments. This observation deserves further investigation.

The report does not provide evidence that participation in the Mini‐College improved patient outcomes or physician performance in practice; these outcome measures remain elusive and an aspirational goal in medical education research. However, experienced clinician educators have come to recognize and adopt effective interventions that simply make sense in the same fashion that it makes sense to use a parachute when jumping out of an airplane in flight.[5] The UHMC makes sense. The medical education literature is replete with articles describing educational innovations and their 1‐ to 2‐year outcomes, leaving the reader wondering about sustainability. It is reassuring that Sehgal et al. report 5 years of experience with the UHMC, and that the program has consistently had a waiting list of hospitalists who want to participate despite the expense. Although it requires patience on the part of educational innovators, this report helps set a standard for reporting enduring innovation in the education arena.

The article provides a description that is sufficiently detailed for other academic medical centers to replicate the intervention or to effectively adapt the principles of the intervention for the needs of their local hospitalist community. The authors should be congratulated for sharing the details of their program and for sharing powerful comments by participants. For hospitalist medical educators interested in sharing details of effective and sustained innovations, publication of this article emphasizes the Journal of Hospital Medicine's interest in disseminating these important projects.

In summary, the report on the UHMC model challenges all of us in academic hospital medicine to think creatively about how to provide effective, engaging, and exciting learning opportunities beyond the years of medical school and residency training.

References
  1. Bloom BS. Effects of continuing medical education on improving physician clinical care and patient health: a review of systematic reviews. Int J Technol Assess Health Care. 2005;21:380385.
  2. Dorman T, Miller BM. Continuing medical education: the link between physician learning and health care outcomes. Acad Med. 2011;86:1339.
  3. Davis N, Davis D, Bloch R. Continuing medical education: AMEE Education Guide No. 35. Med Teach. 2008;30:652666.
  4. Sehgal NL, Wachter RM, Vidyarthi AR. Bringing continuing medical education to the bedside: The University of California, San Francisco hospitalist mini‐college. J HospMed. 2014;9:129134.
  5. Smith GC, Pell JP. Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials. BMJ. 2003;327:14591461.
References
  1. Bloom BS. Effects of continuing medical education on improving physician clinical care and patient health: a review of systematic reviews. Int J Technol Assess Health Care. 2005;21:380385.
  2. Dorman T, Miller BM. Continuing medical education: the link between physician learning and health care outcomes. Acad Med. 2011;86:1339.
  3. Davis N, Davis D, Bloch R. Continuing medical education: AMEE Education Guide No. 35. Med Teach. 2008;30:652666.
  4. Sehgal NL, Wachter RM, Vidyarthi AR. Bringing continuing medical education to the bedside: The University of California, San Francisco hospitalist mini‐college. J HospMed. 2014;9:129134.
  5. Smith GC, Pell JP. Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials. BMJ. 2003;327:14591461.
Issue
Journal of Hospital Medicine - 9(2)
Issue
Journal of Hospital Medicine - 9(2)
Page Number
135-135
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135-135
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Returning hospitalists to their formative training environment for CME: The UCSF Hospitalist Mini‐College
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Returning hospitalists to their formative training environment for CME: The UCSF Hospitalist Mini‐College
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Address for correspondence and reprint requests: Daniel Hunt, MD, Inpatient Clinician Educator Service, Department of Medicine, Massachusetts General Hospital, 50 Staniford Street, Suite 503B, Boston, MA 02114; Telephone: 617‐643‐0581; Fax: 617‐643‐0660; E‐mail: [email protected]
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