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Receptor may play key role in sepsis

Staphylococcus infection
Credit: Bill Branson
Researchers have identified a receptor that may be instrumental in the body’s response to sepsis. And they believe this discovery could be the key to unlocking new treatments for the disease.
The nociceptin receptor activates the chemical nociceptin. Previous research revealed that nociceptin is involved in inflammation; it is known to affect white blood cell function.
This suggests nociceptin has an important role in the body’s response to inflammation and sepsis, according to David Lambert, PhD, of the University of Leicester in the UK, and his colleagues.
The group’s theory, which they have explored in 2 papers published in PLOS ONE, is that nociceptin makes inflammation or sepsis worse. And by blocking the nociceptin system, the symptoms of sepsis could be reduced, which could lead to new treatments.
“We have found that nociceptin, a chemical similar to endorphins produced in the body, is increased in inflammation and sepsis,” said study author Jonathan Thompson, MD, MB ChB, also of the University of Leicester.
“This suggests that drugs which block the nociceptin receptor could dampen the widespread inflammation that occurs in sepsis, and improve outcome. More work is needed, but these drugs are being developed. If they are effective, then we could potentially save many lives.”
In their first paper, the researchers described how they used fluorescent chemistry to find nociceptin receptors on blood vessels with no nerve supply. The team also showed, in a lab model of sepsis, that blocking these receptors has a protective effect.
In the second paper, the researchers recounted their discovery that bloodstream nociceptin levels are elevated in sepsis patients in intensive care. This suggests nociceptin activation might be important in critically ill patients suffering from sepsis.
Dr Lambert and his colleagues noted that sepsis remains a leading cause of admission to intensive care units, with high mortality, costs, and long-term morbidity in those who survive. The incidence of severe sepsis has increased in the last decade, making the discovery of new treatments highly desirable.
“Sepsis is a major health problem . . . that has often been under-recognized,” Dr Thompson said. “It can be rapidly fatal, especially if not diagnosed and treated early, because inflammation can spread and affect many different organs in the body.”
Dr Lambert added, “I am particularly excited by these findings, as they translate many years of laboratory work into a possible target for this disease.”

Staphylococcus infection
Credit: Bill Branson
Researchers have identified a receptor that may be instrumental in the body’s response to sepsis. And they believe this discovery could be the key to unlocking new treatments for the disease.
The nociceptin receptor activates the chemical nociceptin. Previous research revealed that nociceptin is involved in inflammation; it is known to affect white blood cell function.
This suggests nociceptin has an important role in the body’s response to inflammation and sepsis, according to David Lambert, PhD, of the University of Leicester in the UK, and his colleagues.
The group’s theory, which they have explored in 2 papers published in PLOS ONE, is that nociceptin makes inflammation or sepsis worse. And by blocking the nociceptin system, the symptoms of sepsis could be reduced, which could lead to new treatments.
“We have found that nociceptin, a chemical similar to endorphins produced in the body, is increased in inflammation and sepsis,” said study author Jonathan Thompson, MD, MB ChB, also of the University of Leicester.
“This suggests that drugs which block the nociceptin receptor could dampen the widespread inflammation that occurs in sepsis, and improve outcome. More work is needed, but these drugs are being developed. If they are effective, then we could potentially save many lives.”
In their first paper, the researchers described how they used fluorescent chemistry to find nociceptin receptors on blood vessels with no nerve supply. The team also showed, in a lab model of sepsis, that blocking these receptors has a protective effect.
In the second paper, the researchers recounted their discovery that bloodstream nociceptin levels are elevated in sepsis patients in intensive care. This suggests nociceptin activation might be important in critically ill patients suffering from sepsis.
Dr Lambert and his colleagues noted that sepsis remains a leading cause of admission to intensive care units, with high mortality, costs, and long-term morbidity in those who survive. The incidence of severe sepsis has increased in the last decade, making the discovery of new treatments highly desirable.
“Sepsis is a major health problem . . . that has often been under-recognized,” Dr Thompson said. “It can be rapidly fatal, especially if not diagnosed and treated early, because inflammation can spread and affect many different organs in the body.”
Dr Lambert added, “I am particularly excited by these findings, as they translate many years of laboratory work into a possible target for this disease.”

Staphylococcus infection
Credit: Bill Branson
Researchers have identified a receptor that may be instrumental in the body’s response to sepsis. And they believe this discovery could be the key to unlocking new treatments for the disease.
The nociceptin receptor activates the chemical nociceptin. Previous research revealed that nociceptin is involved in inflammation; it is known to affect white blood cell function.
This suggests nociceptin has an important role in the body’s response to inflammation and sepsis, according to David Lambert, PhD, of the University of Leicester in the UK, and his colleagues.
The group’s theory, which they have explored in 2 papers published in PLOS ONE, is that nociceptin makes inflammation or sepsis worse. And by blocking the nociceptin system, the symptoms of sepsis could be reduced, which could lead to new treatments.
“We have found that nociceptin, a chemical similar to endorphins produced in the body, is increased in inflammation and sepsis,” said study author Jonathan Thompson, MD, MB ChB, also of the University of Leicester.
“This suggests that drugs which block the nociceptin receptor could dampen the widespread inflammation that occurs in sepsis, and improve outcome. More work is needed, but these drugs are being developed. If they are effective, then we could potentially save many lives.”
In their first paper, the researchers described how they used fluorescent chemistry to find nociceptin receptors on blood vessels with no nerve supply. The team also showed, in a lab model of sepsis, that blocking these receptors has a protective effect.
In the second paper, the researchers recounted their discovery that bloodstream nociceptin levels are elevated in sepsis patients in intensive care. This suggests nociceptin activation might be important in critically ill patients suffering from sepsis.
Dr Lambert and his colleagues noted that sepsis remains a leading cause of admission to intensive care units, with high mortality, costs, and long-term morbidity in those who survive. The incidence of severe sepsis has increased in the last decade, making the discovery of new treatments highly desirable.
“Sepsis is a major health problem . . . that has often been under-recognized,” Dr Thompson said. “It can be rapidly fatal, especially if not diagnosed and treated early, because inflammation can spread and affect many different organs in the body.”
Dr Lambert added, “I am particularly excited by these findings, as they translate many years of laboratory work into a possible target for this disease.”
Quick Diagnosis Units
Inpatient admissions are a major component of healthcare costs in the United States,[1] where the number of annual inpatient hospital admissions has increased by 15% from 34.3 million in 1993 to 39.5 million in 2006.2 Studies performed predominantly in Europe have shown that inappropriate use of hospital beds exceeds 20% across various specialties.[3] A study by Campbell et al. showed that if given the choice, 60% of physicians would consider an alternative to admission for such patients, if such an option were available, and 70% of patients would prefer not to be admitted for workup.[4] Based on similar findings, various hospitals across the world have tried to make organizational changes to allocate healthcare resources more efficiently. The concept of quick and early diagnosis was first introduced in 1996 by Kendall et al., and it included a hospital unit in the United Kingdom managed by consultants receiving referrals from primary care doctors and led to early diagnostic workup without hospitalization.[5] A more refined version of this concept, a potentially cost‐saving and efficient alternative to inpatient hospitalization for diagnostic purposes, was described by Bosch et al., and named the quick diagnosis unit (QDU).[1]
The basic objectives of QDUs include early diagnosis of potentially severe diseases such as cancer, avoiding unnecessary hospitalization, minimizing hospital morbidity, reducing costs, and improving patient satisfaction. The first described QDU was managed by internists, where patients with specific symptoms such as undiagnosed lumps or masses, anemia, hematuria, or gastrointestinal symptoms could be referred for a diagnostic evaluation. Patients were required to be well enough to travel to the QDU on an outpatient basis, and patients unable to do so were thought to be better suited for hospitalization.[1]
In the present study, we conduct a systematic review, the first one on this subject to our knowledge, of studies that tested established QDUs or similar units in hospital settings. The majority of established units were tested and exist in Europe.[1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] They have been studied in Spain, from where much of these data have been obtained.[1]
METHODS
Study Selection
We searched MEDLINE (January 1946 to November 2012) via OVID and EMBASE (January 1974 to November 2012) via SCOPUS using keywords and Medical Subject Heading terms for quick diagnosis units and rapid diagnosis units. The detailed search strategy can be found in Table 1. A screening of titles and abstracts was done by 2 independent reviewers and followed by full‐text screening. We screened for additional articles by reviewing the bibliography of the articles selected for full‐text screening. We included in our review all studies that (1) were published in any language, (2) focused on the design and implementation of a quick diagnosis unit or a rapid diagnosis unit in a hospital setting, and (3) included at least 2 of the primary outcomes, as described below.
No. | Searches |
---|---|
1 | Quick diagnosis units.mp. |
2 | Quick diagnosis unit.mp. |
3 | (Quick adj diagnosis adj units).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
4 | (Quick adj diagnosis adj unit).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
5 | (Quick adj diagnosis).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
6 | (Diagnosis adj unit).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
7 | (Diagnosis adj units).mp [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
8 | Rapid diagnosis units.mp. |
9 | Rapid diagnosis unit.mp. |
10 | (Rapid adj diagnosis adj units).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
11 | (Rapid adj diagnosis adj unit).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
12 | 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 |
Outcome Measures
Our primary outcome measures were categories of final diagnosis, mean time to final diagnosis in an outpatient setting, inpatient bed‐days per patient saved, and costs saved per patient for QDUs versus in‐hospital stay. Secondary outcomes included disposition of patients after completion of this initial evaluation (whether admitted to the hospital or discharged to clinics) and the patients' care preferences, if available. For cost outcomes, currency exchange rates used for conversion were provided by Citibank National Bank Association, powered by Google online currency converter service (accessed June 16, 2013).
Data Extraction
We extracted data on the specifics of the early diagnostic unit setup including staffing and hours of operation, hospital setting, sources of referral, referring diagnosis, patient population, and the role of the diagnostic units in expediting workup and duration of study. For multiple studies done in the same institution by the same principal author, we used the study with the largest patient population to avoid duplication of data. The primary outcome measures for comparing costs were calculated by different methods and in different currencies by different investigators, which we have attempted to reconcile by using current currency conversion rates. We also evaluated patient preferences (if available) via patient surveys. The data were extracted by 2 independent reviewers, and disagreements were resolved by consensus.
RESULTS
Study Selection
Our literature search initially yielded 2047 publications, out of which 2034 were excluded after title and abstract screening. Thirteen studies were selected for full‐text review, out of which 5 were selected for detailed review based on our inclusion criteria (Figure 1). Three of the studies were in Spanish, and the results were analyzed with the help of a Spanish translator. The other 2 studies were in English.

Study Characteristics
Four studies that were included were descriptive longitudinal studies,[6, 7, 9, 10] and 1 was a retrospective study[8] (Table 2). There were a total of 8895 patients included in all of the studies. All of the studies except 1 described a similar organizational arrangement for the QDU, with 1 internist and 1 registered nurse, administrative support, and the ability to expedite the scheduling of diagnostic tests. The exception was a dedicated lung cancer rapid diagnostic unit (RDU) set up by Sanz‐Santos et al.[9] The study durations ranged from 6 months to 5 years. Patients were referred from local emergency rooms, primary care clinics, and specialty care clinics. The most common reasons for referral were anemia, adenopathy, visceromegaly, febrile syndromes, and incidentally detected masses or nodules on imaging. Two studies included some form of cost analysis,[6, 7] and 3 included patient surveys on satisfaction with patient care.[6, 7, 10]
Author | Methods | Setup of Rapid Diagnosis Units | Sources of Referrals to the Unit | Reasons for Referrals to the Unit | Cases | Duration | Intervention |
---|---|---|---|---|---|---|---|
| |||||||
Bosch et al., 2012 | Prospective descriptive study in 4,170 patients evaluated by a dedicated QDU in a university hospital in Barcelona, Spain, between December 2007 to December 2009 and January 2010 to January 2012. QDU costs compared with costs for randomly selected, retrospectively reviewed hospital admissions for similar diagnosis. Care preferences studied with random surveys. | Quick diagnostic unit consisting of an internist, and a registered nurse. Single consulting room with a family waiting room. Assisted by specialists from other specialties. | Local primary health center (40%), emergency room (56%), other sources (4%). | Anemia, anorexia‐cachexia syndrome, febrile syndrome, adenopathies, abdominal pain, chronic diarrhea, lung abnormalities. | 4,170 | 4 years | Outpatient workup with urgent first visit, preferential scheduling of diagnostic tests and follow‐up until diagnosis is made. |
Capell et al., 2004 | Prospective descriptive study with retrospective controls in 2,748 patients evaluated by a QEDU in a university hospital in Barcelona, Spain, between September 1996 and 2001. QEDU costs compared with costs for randomly selected, retrospectively reviewed hospital admissions for similar diagnosis. Care preferences studied with random surveys. | UDR made up of an internist and a nurse, a consultation and waiting room. | Referrals from emergency rooms (64%), primary care (28.6%), specialty clinics (6.4%). | Abdominal pain (12%), focal neurological symptoms (11.5%), constitutional symptoms (11%), anemia (6%), abnormal chest radiology (5.8%), palpable tumors (5.3%), adenopathies (4.7%), rectal bleeding (4.6%), febrile syndrome (4.6%), hemoptysis (3.5%), others (30%). | 2,748 | 5 years | Preferential scheduling and urgent workup. |
Rubio‐Rivas et al., 2008 | Retrospective, descriptive study for 1,132 patients evaluated by a dedicated RDU in a university hospital in Barcelona, Spain from October 2005 to March 2007. | RDU consisted of an internist, a radiologist, and a nurse. | Local primary health centers (71%), emergency rooms (26%), and others (3%). | FUO, adenopathies, visceromegalies, chronic diarrhea, rectal bleeding, dysphagia, jaundice, hypercalcemia. | 1,132 | 11.5 years | Prioritized scheduling and urgent workup. |
Sanz‐Santos et al., 2010 | Prospective observational study in 678 patients referred to an LC RDU, at a tertiary care center in Barcelona, Spain from October 2005 to September 2009. | An LC‐RDU, with nursing staff, 3 pulmonologists, bronchoscopy suites with EBUS‐TBNA, facilities for mediastinoscopy, CT‐guided FNAC, thoracoscopy, and surgery. | Referrals from specialty clinics (59.4%), primary care (20.2%), and local emergency rooms (20.4%). | Cough, dyspnea, hemoptysis, weight loss, imaging evidence of lung masses. | 678 | 4 years | Specialized outpatient noninvasive and invasive workup. |
Franco‐Hidalgo et al., 2012 | Prospective descriptive study on 167 patients, evaluated by an RDU in a tertiary care hospital in Palencia, Spain between November 2008 and April 2009. Care preferences studied with random surveys. | An RDU run by an internist and nursing staff with administrative support. Has a consulting room and a waiting room. | Referrals from primary care (70.7%), emergency room (21.6%), specialty clinics (7.8%). | Abdominal masses and visceromegalies, chronic diarrhea, dysphagia, ascites, icterus, transaminitis, heart failure, abnormal chest imaging, suspicion of pulmonary TB, or neoplasia, | 167 | 6 months | Early scheduling and urgent specialized workup. |
Outcomes
The most common final diagnosis was malignancy in 18% to 30% of the cases[6, 7, 8, 10] and in 55% of the lung cancer RDU cases[9] (Table 3). The time from initial contact to final diagnosis ranged from 6 to 11 days. Only 3% to 10% of the patients were admitted to the hospital from the QDUs; most patients were discharged to specialty‐care clinics or to primary care centers. Capell et al.[7] estimated that such a unit could save 7 inpatient bed‐days per patient, whereas Rubio‐Rivas et al.[8] estimated that value to be 4.5 bed‐days per patient. Bosch et al.[6] calculated that they saved 8.76 bed‐days per patient.
Author | Final Diagnosis | Time to Diagnosis | Final Disposition | Benefit Analysis | Care Preference Survey | Duration | Intervention |
---|---|---|---|---|---|---|---|
| |||||||
Bosch et al., 2012 | Malignancy (30%), IDA (19%), other benign GI disorders (12%), others (39%). | Mean=8.9 days (cases) (3.13 QDU visits) | Hospital for admission: 3%, primary health centers: 62%, outpatient follow‐up: 35%. | Estimated hospital days saved: mean length of stay 8.76 days. Average cost saved per process (admission to discharge): 2,514.64. | 88% preferred QDU care model over hospital stay. | 4 years | Outpatient workup with urgent first visit, preferential scheduling of diagnostic tests, and follow‐up until diagnosis is made. |
Capell et al., 2004 | Malignancy (15%), GI disorders (24%), neurological disorders (14%). | Mean=5.7 days | Hospital for admission: 7%, primary care: 51%, outpatient hospital follow‐up: 38%,specialty clinics: 4%. | Estimated 7 inpatient bed/days per year during the period of study. Cost saved per encounter: 1,764. | 95% reported high satisfaction with QEDU. | 5 years | Prioritized scheduling and urgent workup. |
Rubio‐Rivas et al., 2008 | Malignancy (18%). | Mean=9 days | Hospital for admission: 10%, outpatient follow‐up: 56%, discharged from follow‐up: 38%. | Hospitalizations avoided: 4.5 bed/days over the study period. Cost analysis not available. | None | 11.5 years | Prioritized scheduling and urgent workup. |
Sanz‐Santos et al., 2010 | Lung cancer (55%). | Mean=11 days | Not available. | No available data on cost analysis or hospitalizations avoided. | None | 4 years | Specialized outpatient noninvasive and invasive workup. |
Franco‐Hidalgo et al., 2012 | Neoplastic (19%), nonmalignant digestive diseases (23%,), infection 13%, and rheumatic (11%). | Mean=8 days | Not available. | No available data on cost analysis or hospitalizations avoided. | 97% reported high/very high satisfaction with the UDR. | 6 months | Early scheduling and urgent specialized workup. |
Two studies included a cost comparison between a conventional inpatient evaluation and a QDU evaluation. Bosch et al.[6] and Capell et al.[7] found an average saving of $3304 (2514) and $2353 (1764) per patient, respectively. Bosch et al.[6] calculated these savings by comparing QDU patients to randomly selected control patients with similar referring complaints, who had reached their final diagnosis during a conventional inpatient evaluation. Capell et al.[7] compared their QDU patient costs to estimated in‐hospital costs for similar diagnoses.
Safety data were reported in detail only by Bosch et al.[6] who showed that 125 (3%) patients who initially were stable for QDU evaluation were referred to the emergency department. A total of 15 patients required admission and 12 died, with an overall mortality for the QDU cohort of 0.3%. Causes of death in this group included sudden unexplained death in 8 patients, pulmonary embolism in 2, aspiration pneumonia in 1, and shock of unknown origin in 1. Capell et al.[7] described a 7% admission rate, and Rubio‐Rivas et al.[8] noted that number to be 10%. No mortality was reported in these 2 studies.
In terms of preference for care, an overwhelming majority (88%) of patients in 1 study[6] preferred the QDU care model over hospitalization, and 95% to 97% of patients in 2 other studies[7, 10] reported very high satisfaction rates.
DISCUSSION
Our systematic review evaluated the effectiveness of QDUs for the diagnostic evaluation of patients with potentially severe disease and showed that such units, where established, are cost‐effective, prevent unnecessary hospitalizations, and diagnose potentially severe diseases, particularly malignant conditions, in a timely manner.
QDUs can evaluate medically stable patients with a variety of complaints such as anemia, lymphadenopathy, undiagnosed lumps and masses, and gastrointestinal symptoms and accelerate the diagnostic evaluation without requiring inpatient hospitalization. Many times patients are admitted to the hospital for a diagnostic evaluation without actual treatment.[12] These patients may not be sick enough to warrant hospitalization and may be able to return to the clinic for an outpatient workup. The QDU approach can complete the evaluation in such patients with the added advantages of saving money and higher patient satisfaction, due to diminished disruption of the patient's daily life.[1, 12] As most primary care physicians are unlikely to provide regular and frequent access for unscheduled care and EDs are more likely to admit patients for diagnostic workup,[2] a QDU approach seems a reasonable alternative for making a quick diagnosis and at the same time avoiding unnecessary hospitalization. Bosch et al. have also evaluated the impact of the QDUs in the diagnosis of specific diseases such as cancer in 169 patients diagnosed at the QDU, and compared them to 53 patients who were diagnosed with cancer during an inpatient evaluation.[11] They found that although QDU patients were significantly younger than hospitalized patients, there was no difference in diagnoses established and the time to diagnosis at the QDU and length of stay in the hospital.
There is a significant cost saving associated with QDUs. The cost savings calculated by Bosch et al. and Capell et al. were for each patient enrolled in this protocol from index encounter to final diagnosis.[6, 7] These 2 studies describe primarily fixed costs saved per patient treated in the QDU versus an inpatient admission. Fixed costs in hospital care include personnel cost, buildings, and equipment, whereas variable costs include medication, test reagents, and disposable supplies.[15] In comparison with the US healthcare system, fixed costs in Europe are considerably lower, and certain variable costs (like medications and procedures) are significantly higher in the United States.[16] This suggests a greater opportunity for healthcare savings for carefully selected patients in the United States, where costs related to inpatient admissions are significantly higher.[16]
Another limitation of our analysis is the paucity of studies on this topic. Many of the publications are from Bosch et al.,[1, 6, 11, 12, 13, 14] a single group in Spain, and these show considerable cost savings, patient satisfaction, and patient safety. However, most of their data are either retrospective or from nonrandomized, prospective cohort studies. The only report describing a similar approach in the United States was by Paschal in the city of New Orleans.[17] After Charity Hospital and the Veterans Affairs Hospital in New Orleans were lost to hurricane Katrina, an urgent care clinic was set up where potentially severe diseases such as cancer, leukemia, and autoimmune and endocrine disorders were diagnosed efficiently, although safety data were not reported.
The reported studies used different study designs and evaluated different primary outcomes. These limitations can be overcome with a well‐designed prospective trial, which could also evaluate the actual impact on patient care, safety, and healthcare savings in the United States.
Safety data were reported in detail only in 1 study,[6] and the rates of admissions were reported by 2 other studies.[7, 8] These suggest that QDUs may be safe for a selected group of patients. Patients evaluated in these units preferred this approach as shown by the overwhelming majority of the patients who chose QDU care over inpatient admissions when patient surveys were performed.
CONCLUSION
In this era of healthcare reform and emphasis on value‐based care, we must optimize the efficiency of our care delivery systems and challenge our preexisting resource‐intensive healthcare models. One source of potential savings is avoiding hospitalizations for purely diagnostic purposes, utilizing quick diagnostic units for patients who are able to return for outpatient evaluations. Such units are established, have been studied in Europe, and our systematic review shows that they are cost‐effective, time‐ and resource‐efficient, and preferred by patients. In our healthcare system, with the high cost of inpatient care, the QDU can yield large savings of healthcare dollars while expediting diagnostic workup, increasing patient satisfaction, and preventing lost productivity from hospital stays. Further exploration and study of alternative care delivery models, such as quick diagnostic units, is required to achieve the goal of cost‐effective high‐quality care for all.
Disclosure: Nothing to report.
- Quick diagnosis units: a potentially useful alternative to conventional hospitalization. Med J Aust. 2009;191:496–498. , , , , .
- The growing role of emergency departments in hospital admissions. N Engl J Med. 2012;5:391–393. , .
- Measuring appropriate use of acute beds. A Systematic review of methods and results. Health Policy. 2000;3:157–184. , , .
- Inappropriate admissions: thoughts of patients and referring doctors. J R Soc Med. 2001;12:628–631. .
- QED: quick and early diagnosis. Lancet. 1996;348:528–529. , , .
- Quick diagnosis units: avoiding referrals from primary care to the ED and hospitalizations. Am J Emerg Med. 2013;31(1):114–123. , , .
- Quick and early diagnostic outpatient unit: an effective and efficient assistential model. Five years experience. Med Clin (Barc). 2004;123(7):247–250. , , , et al.
- Rapid diagnosis unit in a third level hospital. Descriptive study of the first year and a half. Rev Clin Esp. 2008;208(11):561–563. , , , .
- Usefulness of a lung cancer rapid diagnosis specialist clinic. Contribution of ultrasound bronchoscopy. Arch Bronconeumol. 2010;46(12):640–645. , , , et al.
- Rapid diagnosis units or immediate health care clinics in internal medicine. Analysis of the first six months of operation in Palencia (Spain). Semergen. 2012;38(2):126–130. , , , .
- Comparison of quick diagnosis units and conventional hospitalization for the diagnosis of cancer in Spain: a descriptive cohort study. Oncology (Switzerland). 2012;83(5):283–291. , , , , .
- Quick diagnosis units versus hospitalization for the diagnosis of potentially severe diseases in Spain. J Hosp Med. 2012;7(1):41–47. , , , .
- Outpatient quick diagnosis units for the evaluation of suspected severe diseases: an observational study. Clinics. 2011;66(5):737–741. , , , , .
- Quick diagnosis units or conventional hospitalization for the diagnostic evaluation of severe anemia: a paradigm shift in public health systems? Eur J Int Med. 2012;23(2):159–164. , , , et al.
- Distribution of variable vs fixed costs of hospital care. JAMA. 1999;281(7):644–649. , , , et al.
- Explaining high healthcare spending in the united states: an international comparison of supply, utilization, prices and quality. Issue Brief (Commonw Fund). 2012;10:1–14. .
- Launching complex medical workups from an urgent care platform. Ann Int Med. 2012;156:232–233. .
Inpatient admissions are a major component of healthcare costs in the United States,[1] where the number of annual inpatient hospital admissions has increased by 15% from 34.3 million in 1993 to 39.5 million in 2006.2 Studies performed predominantly in Europe have shown that inappropriate use of hospital beds exceeds 20% across various specialties.[3] A study by Campbell et al. showed that if given the choice, 60% of physicians would consider an alternative to admission for such patients, if such an option were available, and 70% of patients would prefer not to be admitted for workup.[4] Based on similar findings, various hospitals across the world have tried to make organizational changes to allocate healthcare resources more efficiently. The concept of quick and early diagnosis was first introduced in 1996 by Kendall et al., and it included a hospital unit in the United Kingdom managed by consultants receiving referrals from primary care doctors and led to early diagnostic workup without hospitalization.[5] A more refined version of this concept, a potentially cost‐saving and efficient alternative to inpatient hospitalization for diagnostic purposes, was described by Bosch et al., and named the quick diagnosis unit (QDU).[1]
The basic objectives of QDUs include early diagnosis of potentially severe diseases such as cancer, avoiding unnecessary hospitalization, minimizing hospital morbidity, reducing costs, and improving patient satisfaction. The first described QDU was managed by internists, where patients with specific symptoms such as undiagnosed lumps or masses, anemia, hematuria, or gastrointestinal symptoms could be referred for a diagnostic evaluation. Patients were required to be well enough to travel to the QDU on an outpatient basis, and patients unable to do so were thought to be better suited for hospitalization.[1]
In the present study, we conduct a systematic review, the first one on this subject to our knowledge, of studies that tested established QDUs or similar units in hospital settings. The majority of established units were tested and exist in Europe.[1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] They have been studied in Spain, from where much of these data have been obtained.[1]
METHODS
Study Selection
We searched MEDLINE (January 1946 to November 2012) via OVID and EMBASE (January 1974 to November 2012) via SCOPUS using keywords and Medical Subject Heading terms for quick diagnosis units and rapid diagnosis units. The detailed search strategy can be found in Table 1. A screening of titles and abstracts was done by 2 independent reviewers and followed by full‐text screening. We screened for additional articles by reviewing the bibliography of the articles selected for full‐text screening. We included in our review all studies that (1) were published in any language, (2) focused on the design and implementation of a quick diagnosis unit or a rapid diagnosis unit in a hospital setting, and (3) included at least 2 of the primary outcomes, as described below.
No. | Searches |
---|---|
1 | Quick diagnosis units.mp. |
2 | Quick diagnosis unit.mp. |
3 | (Quick adj diagnosis adj units).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
4 | (Quick adj diagnosis adj unit).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
5 | (Quick adj diagnosis).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
6 | (Diagnosis adj unit).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
7 | (Diagnosis adj units).mp [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
8 | Rapid diagnosis units.mp. |
9 | Rapid diagnosis unit.mp. |
10 | (Rapid adj diagnosis adj units).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
11 | (Rapid adj diagnosis adj unit).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
12 | 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 |
Outcome Measures
Our primary outcome measures were categories of final diagnosis, mean time to final diagnosis in an outpatient setting, inpatient bed‐days per patient saved, and costs saved per patient for QDUs versus in‐hospital stay. Secondary outcomes included disposition of patients after completion of this initial evaluation (whether admitted to the hospital or discharged to clinics) and the patients' care preferences, if available. For cost outcomes, currency exchange rates used for conversion were provided by Citibank National Bank Association, powered by Google online currency converter service (accessed June 16, 2013).
Data Extraction
We extracted data on the specifics of the early diagnostic unit setup including staffing and hours of operation, hospital setting, sources of referral, referring diagnosis, patient population, and the role of the diagnostic units in expediting workup and duration of study. For multiple studies done in the same institution by the same principal author, we used the study with the largest patient population to avoid duplication of data. The primary outcome measures for comparing costs were calculated by different methods and in different currencies by different investigators, which we have attempted to reconcile by using current currency conversion rates. We also evaluated patient preferences (if available) via patient surveys. The data were extracted by 2 independent reviewers, and disagreements were resolved by consensus.
RESULTS
Study Selection
Our literature search initially yielded 2047 publications, out of which 2034 were excluded after title and abstract screening. Thirteen studies were selected for full‐text review, out of which 5 were selected for detailed review based on our inclusion criteria (Figure 1). Three of the studies were in Spanish, and the results were analyzed with the help of a Spanish translator. The other 2 studies were in English.

Study Characteristics
Four studies that were included were descriptive longitudinal studies,[6, 7, 9, 10] and 1 was a retrospective study[8] (Table 2). There were a total of 8895 patients included in all of the studies. All of the studies except 1 described a similar organizational arrangement for the QDU, with 1 internist and 1 registered nurse, administrative support, and the ability to expedite the scheduling of diagnostic tests. The exception was a dedicated lung cancer rapid diagnostic unit (RDU) set up by Sanz‐Santos et al.[9] The study durations ranged from 6 months to 5 years. Patients were referred from local emergency rooms, primary care clinics, and specialty care clinics. The most common reasons for referral were anemia, adenopathy, visceromegaly, febrile syndromes, and incidentally detected masses or nodules on imaging. Two studies included some form of cost analysis,[6, 7] and 3 included patient surveys on satisfaction with patient care.[6, 7, 10]
Author | Methods | Setup of Rapid Diagnosis Units | Sources of Referrals to the Unit | Reasons for Referrals to the Unit | Cases | Duration | Intervention |
---|---|---|---|---|---|---|---|
| |||||||
Bosch et al., 2012 | Prospective descriptive study in 4,170 patients evaluated by a dedicated QDU in a university hospital in Barcelona, Spain, between December 2007 to December 2009 and January 2010 to January 2012. QDU costs compared with costs for randomly selected, retrospectively reviewed hospital admissions for similar diagnosis. Care preferences studied with random surveys. | Quick diagnostic unit consisting of an internist, and a registered nurse. Single consulting room with a family waiting room. Assisted by specialists from other specialties. | Local primary health center (40%), emergency room (56%), other sources (4%). | Anemia, anorexia‐cachexia syndrome, febrile syndrome, adenopathies, abdominal pain, chronic diarrhea, lung abnormalities. | 4,170 | 4 years | Outpatient workup with urgent first visit, preferential scheduling of diagnostic tests and follow‐up until diagnosis is made. |
Capell et al., 2004 | Prospective descriptive study with retrospective controls in 2,748 patients evaluated by a QEDU in a university hospital in Barcelona, Spain, between September 1996 and 2001. QEDU costs compared with costs for randomly selected, retrospectively reviewed hospital admissions for similar diagnosis. Care preferences studied with random surveys. | UDR made up of an internist and a nurse, a consultation and waiting room. | Referrals from emergency rooms (64%), primary care (28.6%), specialty clinics (6.4%). | Abdominal pain (12%), focal neurological symptoms (11.5%), constitutional symptoms (11%), anemia (6%), abnormal chest radiology (5.8%), palpable tumors (5.3%), adenopathies (4.7%), rectal bleeding (4.6%), febrile syndrome (4.6%), hemoptysis (3.5%), others (30%). | 2,748 | 5 years | Preferential scheduling and urgent workup. |
Rubio‐Rivas et al., 2008 | Retrospective, descriptive study for 1,132 patients evaluated by a dedicated RDU in a university hospital in Barcelona, Spain from October 2005 to March 2007. | RDU consisted of an internist, a radiologist, and a nurse. | Local primary health centers (71%), emergency rooms (26%), and others (3%). | FUO, adenopathies, visceromegalies, chronic diarrhea, rectal bleeding, dysphagia, jaundice, hypercalcemia. | 1,132 | 11.5 years | Prioritized scheduling and urgent workup. |
Sanz‐Santos et al., 2010 | Prospective observational study in 678 patients referred to an LC RDU, at a tertiary care center in Barcelona, Spain from October 2005 to September 2009. | An LC‐RDU, with nursing staff, 3 pulmonologists, bronchoscopy suites with EBUS‐TBNA, facilities for mediastinoscopy, CT‐guided FNAC, thoracoscopy, and surgery. | Referrals from specialty clinics (59.4%), primary care (20.2%), and local emergency rooms (20.4%). | Cough, dyspnea, hemoptysis, weight loss, imaging evidence of lung masses. | 678 | 4 years | Specialized outpatient noninvasive and invasive workup. |
Franco‐Hidalgo et al., 2012 | Prospective descriptive study on 167 patients, evaluated by an RDU in a tertiary care hospital in Palencia, Spain between November 2008 and April 2009. Care preferences studied with random surveys. | An RDU run by an internist and nursing staff with administrative support. Has a consulting room and a waiting room. | Referrals from primary care (70.7%), emergency room (21.6%), specialty clinics (7.8%). | Abdominal masses and visceromegalies, chronic diarrhea, dysphagia, ascites, icterus, transaminitis, heart failure, abnormal chest imaging, suspicion of pulmonary TB, or neoplasia, | 167 | 6 months | Early scheduling and urgent specialized workup. |
Outcomes
The most common final diagnosis was malignancy in 18% to 30% of the cases[6, 7, 8, 10] and in 55% of the lung cancer RDU cases[9] (Table 3). The time from initial contact to final diagnosis ranged from 6 to 11 days. Only 3% to 10% of the patients were admitted to the hospital from the QDUs; most patients were discharged to specialty‐care clinics or to primary care centers. Capell et al.[7] estimated that such a unit could save 7 inpatient bed‐days per patient, whereas Rubio‐Rivas et al.[8] estimated that value to be 4.5 bed‐days per patient. Bosch et al.[6] calculated that they saved 8.76 bed‐days per patient.
Author | Final Diagnosis | Time to Diagnosis | Final Disposition | Benefit Analysis | Care Preference Survey | Duration | Intervention |
---|---|---|---|---|---|---|---|
| |||||||
Bosch et al., 2012 | Malignancy (30%), IDA (19%), other benign GI disorders (12%), others (39%). | Mean=8.9 days (cases) (3.13 QDU visits) | Hospital for admission: 3%, primary health centers: 62%, outpatient follow‐up: 35%. | Estimated hospital days saved: mean length of stay 8.76 days. Average cost saved per process (admission to discharge): 2,514.64. | 88% preferred QDU care model over hospital stay. | 4 years | Outpatient workup with urgent first visit, preferential scheduling of diagnostic tests, and follow‐up until diagnosis is made. |
Capell et al., 2004 | Malignancy (15%), GI disorders (24%), neurological disorders (14%). | Mean=5.7 days | Hospital for admission: 7%, primary care: 51%, outpatient hospital follow‐up: 38%,specialty clinics: 4%. | Estimated 7 inpatient bed/days per year during the period of study. Cost saved per encounter: 1,764. | 95% reported high satisfaction with QEDU. | 5 years | Prioritized scheduling and urgent workup. |
Rubio‐Rivas et al., 2008 | Malignancy (18%). | Mean=9 days | Hospital for admission: 10%, outpatient follow‐up: 56%, discharged from follow‐up: 38%. | Hospitalizations avoided: 4.5 bed/days over the study period. Cost analysis not available. | None | 11.5 years | Prioritized scheduling and urgent workup. |
Sanz‐Santos et al., 2010 | Lung cancer (55%). | Mean=11 days | Not available. | No available data on cost analysis or hospitalizations avoided. | None | 4 years | Specialized outpatient noninvasive and invasive workup. |
Franco‐Hidalgo et al., 2012 | Neoplastic (19%), nonmalignant digestive diseases (23%,), infection 13%, and rheumatic (11%). | Mean=8 days | Not available. | No available data on cost analysis or hospitalizations avoided. | 97% reported high/very high satisfaction with the UDR. | 6 months | Early scheduling and urgent specialized workup. |
Two studies included a cost comparison between a conventional inpatient evaluation and a QDU evaluation. Bosch et al.[6] and Capell et al.[7] found an average saving of $3304 (2514) and $2353 (1764) per patient, respectively. Bosch et al.[6] calculated these savings by comparing QDU patients to randomly selected control patients with similar referring complaints, who had reached their final diagnosis during a conventional inpatient evaluation. Capell et al.[7] compared their QDU patient costs to estimated in‐hospital costs for similar diagnoses.
Safety data were reported in detail only by Bosch et al.[6] who showed that 125 (3%) patients who initially were stable for QDU evaluation were referred to the emergency department. A total of 15 patients required admission and 12 died, with an overall mortality for the QDU cohort of 0.3%. Causes of death in this group included sudden unexplained death in 8 patients, pulmonary embolism in 2, aspiration pneumonia in 1, and shock of unknown origin in 1. Capell et al.[7] described a 7% admission rate, and Rubio‐Rivas et al.[8] noted that number to be 10%. No mortality was reported in these 2 studies.
In terms of preference for care, an overwhelming majority (88%) of patients in 1 study[6] preferred the QDU care model over hospitalization, and 95% to 97% of patients in 2 other studies[7, 10] reported very high satisfaction rates.
DISCUSSION
Our systematic review evaluated the effectiveness of QDUs for the diagnostic evaluation of patients with potentially severe disease and showed that such units, where established, are cost‐effective, prevent unnecessary hospitalizations, and diagnose potentially severe diseases, particularly malignant conditions, in a timely manner.
QDUs can evaluate medically stable patients with a variety of complaints such as anemia, lymphadenopathy, undiagnosed lumps and masses, and gastrointestinal symptoms and accelerate the diagnostic evaluation without requiring inpatient hospitalization. Many times patients are admitted to the hospital for a diagnostic evaluation without actual treatment.[12] These patients may not be sick enough to warrant hospitalization and may be able to return to the clinic for an outpatient workup. The QDU approach can complete the evaluation in such patients with the added advantages of saving money and higher patient satisfaction, due to diminished disruption of the patient's daily life.[1, 12] As most primary care physicians are unlikely to provide regular and frequent access for unscheduled care and EDs are more likely to admit patients for diagnostic workup,[2] a QDU approach seems a reasonable alternative for making a quick diagnosis and at the same time avoiding unnecessary hospitalization. Bosch et al. have also evaluated the impact of the QDUs in the diagnosis of specific diseases such as cancer in 169 patients diagnosed at the QDU, and compared them to 53 patients who were diagnosed with cancer during an inpatient evaluation.[11] They found that although QDU patients were significantly younger than hospitalized patients, there was no difference in diagnoses established and the time to diagnosis at the QDU and length of stay in the hospital.
There is a significant cost saving associated with QDUs. The cost savings calculated by Bosch et al. and Capell et al. were for each patient enrolled in this protocol from index encounter to final diagnosis.[6, 7] These 2 studies describe primarily fixed costs saved per patient treated in the QDU versus an inpatient admission. Fixed costs in hospital care include personnel cost, buildings, and equipment, whereas variable costs include medication, test reagents, and disposable supplies.[15] In comparison with the US healthcare system, fixed costs in Europe are considerably lower, and certain variable costs (like medications and procedures) are significantly higher in the United States.[16] This suggests a greater opportunity for healthcare savings for carefully selected patients in the United States, where costs related to inpatient admissions are significantly higher.[16]
Another limitation of our analysis is the paucity of studies on this topic. Many of the publications are from Bosch et al.,[1, 6, 11, 12, 13, 14] a single group in Spain, and these show considerable cost savings, patient satisfaction, and patient safety. However, most of their data are either retrospective or from nonrandomized, prospective cohort studies. The only report describing a similar approach in the United States was by Paschal in the city of New Orleans.[17] After Charity Hospital and the Veterans Affairs Hospital in New Orleans were lost to hurricane Katrina, an urgent care clinic was set up where potentially severe diseases such as cancer, leukemia, and autoimmune and endocrine disorders were diagnosed efficiently, although safety data were not reported.
The reported studies used different study designs and evaluated different primary outcomes. These limitations can be overcome with a well‐designed prospective trial, which could also evaluate the actual impact on patient care, safety, and healthcare savings in the United States.
Safety data were reported in detail only in 1 study,[6] and the rates of admissions were reported by 2 other studies.[7, 8] These suggest that QDUs may be safe for a selected group of patients. Patients evaluated in these units preferred this approach as shown by the overwhelming majority of the patients who chose QDU care over inpatient admissions when patient surveys were performed.
CONCLUSION
In this era of healthcare reform and emphasis on value‐based care, we must optimize the efficiency of our care delivery systems and challenge our preexisting resource‐intensive healthcare models. One source of potential savings is avoiding hospitalizations for purely diagnostic purposes, utilizing quick diagnostic units for patients who are able to return for outpatient evaluations. Such units are established, have been studied in Europe, and our systematic review shows that they are cost‐effective, time‐ and resource‐efficient, and preferred by patients. In our healthcare system, with the high cost of inpatient care, the QDU can yield large savings of healthcare dollars while expediting diagnostic workup, increasing patient satisfaction, and preventing lost productivity from hospital stays. Further exploration and study of alternative care delivery models, such as quick diagnostic units, is required to achieve the goal of cost‐effective high‐quality care for all.
Disclosure: Nothing to report.
Inpatient admissions are a major component of healthcare costs in the United States,[1] where the number of annual inpatient hospital admissions has increased by 15% from 34.3 million in 1993 to 39.5 million in 2006.2 Studies performed predominantly in Europe have shown that inappropriate use of hospital beds exceeds 20% across various specialties.[3] A study by Campbell et al. showed that if given the choice, 60% of physicians would consider an alternative to admission for such patients, if such an option were available, and 70% of patients would prefer not to be admitted for workup.[4] Based on similar findings, various hospitals across the world have tried to make organizational changes to allocate healthcare resources more efficiently. The concept of quick and early diagnosis was first introduced in 1996 by Kendall et al., and it included a hospital unit in the United Kingdom managed by consultants receiving referrals from primary care doctors and led to early diagnostic workup without hospitalization.[5] A more refined version of this concept, a potentially cost‐saving and efficient alternative to inpatient hospitalization for diagnostic purposes, was described by Bosch et al., and named the quick diagnosis unit (QDU).[1]
The basic objectives of QDUs include early diagnosis of potentially severe diseases such as cancer, avoiding unnecessary hospitalization, minimizing hospital morbidity, reducing costs, and improving patient satisfaction. The first described QDU was managed by internists, where patients with specific symptoms such as undiagnosed lumps or masses, anemia, hematuria, or gastrointestinal symptoms could be referred for a diagnostic evaluation. Patients were required to be well enough to travel to the QDU on an outpatient basis, and patients unable to do so were thought to be better suited for hospitalization.[1]
In the present study, we conduct a systematic review, the first one on this subject to our knowledge, of studies that tested established QDUs or similar units in hospital settings. The majority of established units were tested and exist in Europe.[1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] They have been studied in Spain, from where much of these data have been obtained.[1]
METHODS
Study Selection
We searched MEDLINE (January 1946 to November 2012) via OVID and EMBASE (January 1974 to November 2012) via SCOPUS using keywords and Medical Subject Heading terms for quick diagnosis units and rapid diagnosis units. The detailed search strategy can be found in Table 1. A screening of titles and abstracts was done by 2 independent reviewers and followed by full‐text screening. We screened for additional articles by reviewing the bibliography of the articles selected for full‐text screening. We included in our review all studies that (1) were published in any language, (2) focused on the design and implementation of a quick diagnosis unit or a rapid diagnosis unit in a hospital setting, and (3) included at least 2 of the primary outcomes, as described below.
No. | Searches |
---|---|
1 | Quick diagnosis units.mp. |
2 | Quick diagnosis unit.mp. |
3 | (Quick adj diagnosis adj units).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
4 | (Quick adj diagnosis adj unit).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
5 | (Quick adj diagnosis).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
6 | (Diagnosis adj unit).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
7 | (Diagnosis adj units).mp [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
8 | Rapid diagnosis units.mp. |
9 | Rapid diagnosis unit.mp. |
10 | (Rapid adj diagnosis adj units).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
11 | (Rapid adj diagnosis adj unit).mp. [mp=title, abstract, original title, name of substance word, subject heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] |
12 | 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 |
Outcome Measures
Our primary outcome measures were categories of final diagnosis, mean time to final diagnosis in an outpatient setting, inpatient bed‐days per patient saved, and costs saved per patient for QDUs versus in‐hospital stay. Secondary outcomes included disposition of patients after completion of this initial evaluation (whether admitted to the hospital or discharged to clinics) and the patients' care preferences, if available. For cost outcomes, currency exchange rates used for conversion were provided by Citibank National Bank Association, powered by Google online currency converter service (accessed June 16, 2013).
Data Extraction
We extracted data on the specifics of the early diagnostic unit setup including staffing and hours of operation, hospital setting, sources of referral, referring diagnosis, patient population, and the role of the diagnostic units in expediting workup and duration of study. For multiple studies done in the same institution by the same principal author, we used the study with the largest patient population to avoid duplication of data. The primary outcome measures for comparing costs were calculated by different methods and in different currencies by different investigators, which we have attempted to reconcile by using current currency conversion rates. We also evaluated patient preferences (if available) via patient surveys. The data were extracted by 2 independent reviewers, and disagreements were resolved by consensus.
RESULTS
Study Selection
Our literature search initially yielded 2047 publications, out of which 2034 were excluded after title and abstract screening. Thirteen studies were selected for full‐text review, out of which 5 were selected for detailed review based on our inclusion criteria (Figure 1). Three of the studies were in Spanish, and the results were analyzed with the help of a Spanish translator. The other 2 studies were in English.

Study Characteristics
Four studies that were included were descriptive longitudinal studies,[6, 7, 9, 10] and 1 was a retrospective study[8] (Table 2). There were a total of 8895 patients included in all of the studies. All of the studies except 1 described a similar organizational arrangement for the QDU, with 1 internist and 1 registered nurse, administrative support, and the ability to expedite the scheduling of diagnostic tests. The exception was a dedicated lung cancer rapid diagnostic unit (RDU) set up by Sanz‐Santos et al.[9] The study durations ranged from 6 months to 5 years. Patients were referred from local emergency rooms, primary care clinics, and specialty care clinics. The most common reasons for referral were anemia, adenopathy, visceromegaly, febrile syndromes, and incidentally detected masses or nodules on imaging. Two studies included some form of cost analysis,[6, 7] and 3 included patient surveys on satisfaction with patient care.[6, 7, 10]
Author | Methods | Setup of Rapid Diagnosis Units | Sources of Referrals to the Unit | Reasons for Referrals to the Unit | Cases | Duration | Intervention |
---|---|---|---|---|---|---|---|
| |||||||
Bosch et al., 2012 | Prospective descriptive study in 4,170 patients evaluated by a dedicated QDU in a university hospital in Barcelona, Spain, between December 2007 to December 2009 and January 2010 to January 2012. QDU costs compared with costs for randomly selected, retrospectively reviewed hospital admissions for similar diagnosis. Care preferences studied with random surveys. | Quick diagnostic unit consisting of an internist, and a registered nurse. Single consulting room with a family waiting room. Assisted by specialists from other specialties. | Local primary health center (40%), emergency room (56%), other sources (4%). | Anemia, anorexia‐cachexia syndrome, febrile syndrome, adenopathies, abdominal pain, chronic diarrhea, lung abnormalities. | 4,170 | 4 years | Outpatient workup with urgent first visit, preferential scheduling of diagnostic tests and follow‐up until diagnosis is made. |
Capell et al., 2004 | Prospective descriptive study with retrospective controls in 2,748 patients evaluated by a QEDU in a university hospital in Barcelona, Spain, between September 1996 and 2001. QEDU costs compared with costs for randomly selected, retrospectively reviewed hospital admissions for similar diagnosis. Care preferences studied with random surveys. | UDR made up of an internist and a nurse, a consultation and waiting room. | Referrals from emergency rooms (64%), primary care (28.6%), specialty clinics (6.4%). | Abdominal pain (12%), focal neurological symptoms (11.5%), constitutional symptoms (11%), anemia (6%), abnormal chest radiology (5.8%), palpable tumors (5.3%), adenopathies (4.7%), rectal bleeding (4.6%), febrile syndrome (4.6%), hemoptysis (3.5%), others (30%). | 2,748 | 5 years | Preferential scheduling and urgent workup. |
Rubio‐Rivas et al., 2008 | Retrospective, descriptive study for 1,132 patients evaluated by a dedicated RDU in a university hospital in Barcelona, Spain from October 2005 to March 2007. | RDU consisted of an internist, a radiologist, and a nurse. | Local primary health centers (71%), emergency rooms (26%), and others (3%). | FUO, adenopathies, visceromegalies, chronic diarrhea, rectal bleeding, dysphagia, jaundice, hypercalcemia. | 1,132 | 11.5 years | Prioritized scheduling and urgent workup. |
Sanz‐Santos et al., 2010 | Prospective observational study in 678 patients referred to an LC RDU, at a tertiary care center in Barcelona, Spain from October 2005 to September 2009. | An LC‐RDU, with nursing staff, 3 pulmonologists, bronchoscopy suites with EBUS‐TBNA, facilities for mediastinoscopy, CT‐guided FNAC, thoracoscopy, and surgery. | Referrals from specialty clinics (59.4%), primary care (20.2%), and local emergency rooms (20.4%). | Cough, dyspnea, hemoptysis, weight loss, imaging evidence of lung masses. | 678 | 4 years | Specialized outpatient noninvasive and invasive workup. |
Franco‐Hidalgo et al., 2012 | Prospective descriptive study on 167 patients, evaluated by an RDU in a tertiary care hospital in Palencia, Spain between November 2008 and April 2009. Care preferences studied with random surveys. | An RDU run by an internist and nursing staff with administrative support. Has a consulting room and a waiting room. | Referrals from primary care (70.7%), emergency room (21.6%), specialty clinics (7.8%). | Abdominal masses and visceromegalies, chronic diarrhea, dysphagia, ascites, icterus, transaminitis, heart failure, abnormal chest imaging, suspicion of pulmonary TB, or neoplasia, | 167 | 6 months | Early scheduling and urgent specialized workup. |
Outcomes
The most common final diagnosis was malignancy in 18% to 30% of the cases[6, 7, 8, 10] and in 55% of the lung cancer RDU cases[9] (Table 3). The time from initial contact to final diagnosis ranged from 6 to 11 days. Only 3% to 10% of the patients were admitted to the hospital from the QDUs; most patients were discharged to specialty‐care clinics or to primary care centers. Capell et al.[7] estimated that such a unit could save 7 inpatient bed‐days per patient, whereas Rubio‐Rivas et al.[8] estimated that value to be 4.5 bed‐days per patient. Bosch et al.[6] calculated that they saved 8.76 bed‐days per patient.
Author | Final Diagnosis | Time to Diagnosis | Final Disposition | Benefit Analysis | Care Preference Survey | Duration | Intervention |
---|---|---|---|---|---|---|---|
| |||||||
Bosch et al., 2012 | Malignancy (30%), IDA (19%), other benign GI disorders (12%), others (39%). | Mean=8.9 days (cases) (3.13 QDU visits) | Hospital for admission: 3%, primary health centers: 62%, outpatient follow‐up: 35%. | Estimated hospital days saved: mean length of stay 8.76 days. Average cost saved per process (admission to discharge): 2,514.64. | 88% preferred QDU care model over hospital stay. | 4 years | Outpatient workup with urgent first visit, preferential scheduling of diagnostic tests, and follow‐up until diagnosis is made. |
Capell et al., 2004 | Malignancy (15%), GI disorders (24%), neurological disorders (14%). | Mean=5.7 days | Hospital for admission: 7%, primary care: 51%, outpatient hospital follow‐up: 38%,specialty clinics: 4%. | Estimated 7 inpatient bed/days per year during the period of study. Cost saved per encounter: 1,764. | 95% reported high satisfaction with QEDU. | 5 years | Prioritized scheduling and urgent workup. |
Rubio‐Rivas et al., 2008 | Malignancy (18%). | Mean=9 days | Hospital for admission: 10%, outpatient follow‐up: 56%, discharged from follow‐up: 38%. | Hospitalizations avoided: 4.5 bed/days over the study period. Cost analysis not available. | None | 11.5 years | Prioritized scheduling and urgent workup. |
Sanz‐Santos et al., 2010 | Lung cancer (55%). | Mean=11 days | Not available. | No available data on cost analysis or hospitalizations avoided. | None | 4 years | Specialized outpatient noninvasive and invasive workup. |
Franco‐Hidalgo et al., 2012 | Neoplastic (19%), nonmalignant digestive diseases (23%,), infection 13%, and rheumatic (11%). | Mean=8 days | Not available. | No available data on cost analysis or hospitalizations avoided. | 97% reported high/very high satisfaction with the UDR. | 6 months | Early scheduling and urgent specialized workup. |
Two studies included a cost comparison between a conventional inpatient evaluation and a QDU evaluation. Bosch et al.[6] and Capell et al.[7] found an average saving of $3304 (2514) and $2353 (1764) per patient, respectively. Bosch et al.[6] calculated these savings by comparing QDU patients to randomly selected control patients with similar referring complaints, who had reached their final diagnosis during a conventional inpatient evaluation. Capell et al.[7] compared their QDU patient costs to estimated in‐hospital costs for similar diagnoses.
Safety data were reported in detail only by Bosch et al.[6] who showed that 125 (3%) patients who initially were stable for QDU evaluation were referred to the emergency department. A total of 15 patients required admission and 12 died, with an overall mortality for the QDU cohort of 0.3%. Causes of death in this group included sudden unexplained death in 8 patients, pulmonary embolism in 2, aspiration pneumonia in 1, and shock of unknown origin in 1. Capell et al.[7] described a 7% admission rate, and Rubio‐Rivas et al.[8] noted that number to be 10%. No mortality was reported in these 2 studies.
In terms of preference for care, an overwhelming majority (88%) of patients in 1 study[6] preferred the QDU care model over hospitalization, and 95% to 97% of patients in 2 other studies[7, 10] reported very high satisfaction rates.
DISCUSSION
Our systematic review evaluated the effectiveness of QDUs for the diagnostic evaluation of patients with potentially severe disease and showed that such units, where established, are cost‐effective, prevent unnecessary hospitalizations, and diagnose potentially severe diseases, particularly malignant conditions, in a timely manner.
QDUs can evaluate medically stable patients with a variety of complaints such as anemia, lymphadenopathy, undiagnosed lumps and masses, and gastrointestinal symptoms and accelerate the diagnostic evaluation without requiring inpatient hospitalization. Many times patients are admitted to the hospital for a diagnostic evaluation without actual treatment.[12] These patients may not be sick enough to warrant hospitalization and may be able to return to the clinic for an outpatient workup. The QDU approach can complete the evaluation in such patients with the added advantages of saving money and higher patient satisfaction, due to diminished disruption of the patient's daily life.[1, 12] As most primary care physicians are unlikely to provide regular and frequent access for unscheduled care and EDs are more likely to admit patients for diagnostic workup,[2] a QDU approach seems a reasonable alternative for making a quick diagnosis and at the same time avoiding unnecessary hospitalization. Bosch et al. have also evaluated the impact of the QDUs in the diagnosis of specific diseases such as cancer in 169 patients diagnosed at the QDU, and compared them to 53 patients who were diagnosed with cancer during an inpatient evaluation.[11] They found that although QDU patients were significantly younger than hospitalized patients, there was no difference in diagnoses established and the time to diagnosis at the QDU and length of stay in the hospital.
There is a significant cost saving associated with QDUs. The cost savings calculated by Bosch et al. and Capell et al. were for each patient enrolled in this protocol from index encounter to final diagnosis.[6, 7] These 2 studies describe primarily fixed costs saved per patient treated in the QDU versus an inpatient admission. Fixed costs in hospital care include personnel cost, buildings, and equipment, whereas variable costs include medication, test reagents, and disposable supplies.[15] In comparison with the US healthcare system, fixed costs in Europe are considerably lower, and certain variable costs (like medications and procedures) are significantly higher in the United States.[16] This suggests a greater opportunity for healthcare savings for carefully selected patients in the United States, where costs related to inpatient admissions are significantly higher.[16]
Another limitation of our analysis is the paucity of studies on this topic. Many of the publications are from Bosch et al.,[1, 6, 11, 12, 13, 14] a single group in Spain, and these show considerable cost savings, patient satisfaction, and patient safety. However, most of their data are either retrospective or from nonrandomized, prospective cohort studies. The only report describing a similar approach in the United States was by Paschal in the city of New Orleans.[17] After Charity Hospital and the Veterans Affairs Hospital in New Orleans were lost to hurricane Katrina, an urgent care clinic was set up where potentially severe diseases such as cancer, leukemia, and autoimmune and endocrine disorders were diagnosed efficiently, although safety data were not reported.
The reported studies used different study designs and evaluated different primary outcomes. These limitations can be overcome with a well‐designed prospective trial, which could also evaluate the actual impact on patient care, safety, and healthcare savings in the United States.
Safety data were reported in detail only in 1 study,[6] and the rates of admissions were reported by 2 other studies.[7, 8] These suggest that QDUs may be safe for a selected group of patients. Patients evaluated in these units preferred this approach as shown by the overwhelming majority of the patients who chose QDU care over inpatient admissions when patient surveys were performed.
CONCLUSION
In this era of healthcare reform and emphasis on value‐based care, we must optimize the efficiency of our care delivery systems and challenge our preexisting resource‐intensive healthcare models. One source of potential savings is avoiding hospitalizations for purely diagnostic purposes, utilizing quick diagnostic units for patients who are able to return for outpatient evaluations. Such units are established, have been studied in Europe, and our systematic review shows that they are cost‐effective, time‐ and resource‐efficient, and preferred by patients. In our healthcare system, with the high cost of inpatient care, the QDU can yield large savings of healthcare dollars while expediting diagnostic workup, increasing patient satisfaction, and preventing lost productivity from hospital stays. Further exploration and study of alternative care delivery models, such as quick diagnostic units, is required to achieve the goal of cost‐effective high‐quality care for all.
Disclosure: Nothing to report.
- Quick diagnosis units: a potentially useful alternative to conventional hospitalization. Med J Aust. 2009;191:496–498. , , , , .
- The growing role of emergency departments in hospital admissions. N Engl J Med. 2012;5:391–393. , .
- Measuring appropriate use of acute beds. A Systematic review of methods and results. Health Policy. 2000;3:157–184. , , .
- Inappropriate admissions: thoughts of patients and referring doctors. J R Soc Med. 2001;12:628–631. .
- QED: quick and early diagnosis. Lancet. 1996;348:528–529. , , .
- Quick diagnosis units: avoiding referrals from primary care to the ED and hospitalizations. Am J Emerg Med. 2013;31(1):114–123. , , .
- Quick and early diagnostic outpatient unit: an effective and efficient assistential model. Five years experience. Med Clin (Barc). 2004;123(7):247–250. , , , et al.
- Rapid diagnosis unit in a third level hospital. Descriptive study of the first year and a half. Rev Clin Esp. 2008;208(11):561–563. , , , .
- Usefulness of a lung cancer rapid diagnosis specialist clinic. Contribution of ultrasound bronchoscopy. Arch Bronconeumol. 2010;46(12):640–645. , , , et al.
- Rapid diagnosis units or immediate health care clinics in internal medicine. Analysis of the first six months of operation in Palencia (Spain). Semergen. 2012;38(2):126–130. , , , .
- Comparison of quick diagnosis units and conventional hospitalization for the diagnosis of cancer in Spain: a descriptive cohort study. Oncology (Switzerland). 2012;83(5):283–291. , , , , .
- Quick diagnosis units versus hospitalization for the diagnosis of potentially severe diseases in Spain. J Hosp Med. 2012;7(1):41–47. , , , .
- Outpatient quick diagnosis units for the evaluation of suspected severe diseases: an observational study. Clinics. 2011;66(5):737–741. , , , , .
- Quick diagnosis units or conventional hospitalization for the diagnostic evaluation of severe anemia: a paradigm shift in public health systems? Eur J Int Med. 2012;23(2):159–164. , , , et al.
- Distribution of variable vs fixed costs of hospital care. JAMA. 1999;281(7):644–649. , , , et al.
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- Usefulness of a lung cancer rapid diagnosis specialist clinic. Contribution of ultrasound bronchoscopy. Arch Bronconeumol. 2010;46(12):640–645. , , , et al.
- Rapid diagnosis units or immediate health care clinics in internal medicine. Analysis of the first six months of operation in Palencia (Spain). Semergen. 2012;38(2):126–130. , , , .
- Comparison of quick diagnosis units and conventional hospitalization for the diagnosis of cancer in Spain: a descriptive cohort study. Oncology (Switzerland). 2012;83(5):283–291. , , , , .
- Quick diagnosis units versus hospitalization for the diagnosis of potentially severe diseases in Spain. J Hosp Med. 2012;7(1):41–47. , , , .
- Outpatient quick diagnosis units for the evaluation of suspected severe diseases: an observational study. Clinics. 2011;66(5):737–741. , , , , .
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- Launching complex medical workups from an urgent care platform. Ann Int Med. 2012;156:232–233. .
Vascular Catheter and Lumen Adequacy
Catheter‐related bloodstream infections (CRBSIs) are among the most common forms of hospital‐acquired infection and increase both length of stay and cost of hospitalization.[1, 2] Notable efforts are being devoted to reduce the rate of CRBSI, usually by implementing a bundle of measures.[3, 4, 5] One measure focuses on reducing to a minimum the exposure to vascular catheters.[3] In addition, various studies have shown that multilumen central venous catheters (CVCs) are associated with a higher risk of CRBSI than are single‐lumen catheters.[6, 7, 8, 9, 10] Accordingly, the Healthcare Infection Control Practices Advisory Committee (HICPAC) guideline recommends that clinicians use a CVC with the minimum number of ports or lumens essential for the management of the patient.[3]
Despite the fact that most CRBSIs occur in conventional wards,[1, 11] only a few studies have been focused on the potential magnitude of reducing the number of unnecessary vascular catheters and catheter lumens in the noncritical‐care setting.[12, 13, 14, 15, 16] The adequacy of vascular‐catheter use has been predominantly assessed for nontunneled CVCs.[14, 16, 17, 18] An institutional program aimed at reducing the overall rate of CRBSI should also include other sources, such as conventional peripheral venous catheters (PVCs), peripherally inserted central catheters (PICCs), and arterial catheters (ACs).[3] The need to extend surveillance to other types of catheters has been identified by the Infectious Diseases Society of America (IDSA) as an unresolved issue.[19]
We sought to investigate the rate and appropriateness of use of vascular catheters in the entire population of inpatients at a tertiary‐care center on a single day, as well as the adequacy of the number of catheter lumens harbored by each patient, by using a set of preestablished objective criteria.
METHODS
Setting and Study Population
We performed a 1‐day cross‐sectional study in March 2012 at the University Hospital 12 de Octubre in Madrid, Spain, a 1368‐bed tertiary‐care institution with a catchment area of 412,930 inhabitants in 2011 and 5 different adult intensive care units (ICUs; medical, trauma, coronary, general surgery, and cardiac surgery). In 2009, our center joined a national program aimed at implementing a catheter‐care bundle in adult ICUs with the intention of achieving zero incidence of CVC‐related bloodstream infections. This bundle consisted of a number of evidence‐based practices[4] (eg, avoiding the femoral site if possible and removing unnecessary CVCs).[20]
Study Design and Data Collection
All inpatient beds were reviewed, even if they were unoccupied on the day of the survey. The only exclusions were pediatric wards and the hospital facility for imprisoned patients. All inpatients with 1 vascular catheters in place on the day of survey were subsequently included. We analyzed ACs, PVCs, and the following types of CVCs: nontunneled (temporary) catheters, skin‐tunneled catheters (Hickman type), totally implantable catheters (Port‐A‐Cath), Swan‐Ganz thermodilution catheters, dialysis catheters (Shaldon type), and PICCs.
The following data were abstracted using a standardized sheet from each patient's medical and nursing records and by direct inspection: basic patient demographics; type of ward (medical/surgical [hereinafter, conventional wards] or ICU); type of vascular catheter; anatomic site of catheter insertion; medical or nursing team responsible for catheter placement; catheter insertion‐site dressing regimen; overall number of vascular catheters per patient; and overall number of venous or arterial catheter lumens per patient (resulting from adding up all the lumens present in each patient, including 3‐way stopcocks and noncoring needles in Port‐A‐Cath devices; each of the inflow ports in 3‐way stopcocks attached to a vascular catheter was counted as a separate lumen). Those patients who were not in their wards on the day of survey for any reason (eg, an ongoing surgical procedure) were excluded.
The current indication to maintain ongoing catheterization was recorded by means of the following variables: overall number of intravenous (IV) medications administered during the previous 24 hours; type of medication (antimicrobial therapy, fluid therapy, vasoactive and inotropic drugs, chemotherapy, blood products, or others [eg, analgesics or diuretics]); type of IV administration regimen; and other indications for catheter use (need for monitoring hemodynamic status, renal replacement therapy, or need for preemptive vascular access in patients expected to be at risk of hemodynamic deterioration potentially requiring fluid resuscitation or inotropic support over the next days [eg, septic shock, acute decompensation of heart failure, or gastrointestinal bleeding within the previous week]).[21] After thorough scrutiny of prescription orders, we assigned each medication to one of 3 different IV administration regimens: (1) rapid infusion (over <1 hour); (2) infusion over 1 to 24 hours; and (3) continuous infusion over a 24‐hour period. In doubtful cases, nursing staff was directly asked about the regimen of infusion.
No formal informed consent was obtained from the participants, as the present study was strictly observational and part of the institutional quality initiatives. The local Clinical Research Ethics Committee approved the study protocol.
Assessment Criteria
The adequacy of use of vascular catheters and catheter lumens was assessed by one of 4 researchers not associated with day‐to‐day patient care by using a set of a priori determined criteria. To determine appropriateness, a maximum theoretical number of vascular lumens was assigned to each specific indication for catheterization (Table 1). We considered that all IV medications administered by rapid infusion could be delivered consecutively through 1 single catheter lumen. Those medications administered by infusion over 1 to 24 hours, or by continuous infusion over a 24‐hour period, would require an exclusive lumen. The nature of the infusate, the potential incompatibility between infused drugs, and the method of infusion (gravity drip or pump) were not taken into account in this assignment process. Hemodynamic monitoring and renal replacement therapy also required an exclusive catheter lumen. When the vascular catheterization was retained only for preemptive reasons, we considered as justified the use of a maximum of 2 single‐lumen catheters.
Indication | No. of Catheter Lumens Deemed Necessary |
---|---|
| |
Administration of IV medications | |
Rapid infusion over <1 hour | 1 common lumen (for all medications) |
Infusion over 124 hours | 1 exclusive lumen (for each medication) |
Continuous infusion over a 24‐hour period | 1 exclusive lumen (for each medication) |
Hemodynamic monitoring | 1 exclusive lumen |
Renal replacement therapy | 1 exclusive lumen |
Preemptive catheterization (ie, patients at risk of hemodynamic deterioration over the next days) | Maximum of 2 single‐lumen catheters |
Appropriateness of the Use of Vascular Catheters
The presence of a conventional PVC or a nontunneled (temporary) CVC was considered justifiable if 1 of its lumens was indicated according to the above‐mentioned criteria. We applied the principle that the requirements of catheter lumens should be met by keeping the number of catheters as low as possible. For instance, if a given patient had 2 catheters with an overall number of 3 lumens (ie, 1 single‐lumen catheter and 1 double‐lumen catheter), and only 2 catheter lumens were actually deemed necessary, we considered that the overall number of catheters was inappropriate. In view of their particular characteristics, the following types of catheters were by definition deemed to be appropriate: Swan‐Ganz catheters and ACs (as nearly exclusively used for hemodynamic monitoring in critically ill patients), dialysis catheters (as solely used for this specific purpose), and PICCs and nontemporary CVCs (as most of them had been placed prior to the current hospitalization episode for the periodic administration of chemotherapy or domiciliary parenteral nutrition). Because no IV medications are delivered through Swan‐Ganz catheters, ACs, and dialysis catheters, we did not take into account the presence of these devices when assessing the appropriateness of other vascular catheters present in a given patient.
Appropriateness of the Use of Catheter Lumens
First, we added up all the catheter lumens present in each patient (regardless of the type of device), and then we established the theoretical number of catheter lumens that the patient would have actually required, according to the above‐mentioned criteria. The difference between both figures gave the number of unnecessary catheter lumens. Only the potentially removable lumens were included in this analysis (those of PVCs and nontunneled CVCs, as well as each of the noncoring needles inserted in Port‐A‐Cath catheters). The lumens of skin‐tunneled CVCs, Swan‐Ganz catheters, and PICCs were considered nonremovable and, therefore, always justified. We excluded ACs from this specific analysis.
Statistical Analysis
Quantitative data were shown as the meanstandard deviation, whereas qualitative variables were expressed as absolute and relative frequencies with 95% confidence intervals (CIs). Categorical and continuous variables were compared using [2] and unpaired Student t tests, respectively. We calculated 3 different ratios: patients with 1 inappropriate catheter to overall number of inpatients; patients with 1 inappropriate catheter to patients with 1 vascular catheter in place on the day of survey; and overall number of unnecessary catheter lumens to overall number of catheter lumens. All the significance tests were 2‐tailed. Statistics were performed using SPSS version 15.0 (SPSS Inc, Chicago, IL).
RESULTS
Out of 1134 reviewed inpatient beds, 834 (73.5%) were occupied on the day of the survey. The mean age of the included patients was 64.518.8 years, and 415 (49.8%) were male. Of these patients, 575 (68.9%) had 1 vascular catheter in place. The proportion of patients with a vascular catheter was significantly higher in ICUs compared with conventional wards (100% vs 66.7%, P<0.0001; Table 2). The overall numbers of vascular catheters and catheter lumens analyzed were 703 and 1448, respectively. Regarding the type of device, 567 (80.6%) were PVCs, 111 (15.8%) were CVCs (including 65 nontunneled CVCs, 16 dialysis catheters, 15 PICCs, 7 skin‐tunneled CVCs, 5 totally implantable CVCs, and 3 Swan‐Ganz catheters), and 25 (3.5%) were ACs. The distribution according to hospital ward and anatomic site of insertion is detailed in Table 2. The use of CVCs and ACs was higher in ICUs (42.0% and 28.4% of all catheters in place, respectively) compared with conventional wards (12.0% and 0.0%, P<0.0001). The use of the subclavian vein insertion site was more common in medical wards (65.7% of all CVCs, excluding PICCs) than in surgical wards or ICUs (26.9%, P=0.0002). Most of the catheters had been inserted by nursing staff members (75.2%), followed by anesthesia physicians (13.4%) and critical‐care medicine physicians (4.8%). An opaque gauze or transparent polyurethane insertion‐site dressing was present in 378 (53.8%) and 319 (45.3%) catheters, respectively, with no significant differences according to the type of device or hospital ward.
No. of Patients | ||||
---|---|---|---|---|
Overall, N=834 | Medical Wards, n=498 | Surgical Wards, n=279 | ICUs, n=57 | |
No. of Catheters | ||||
Overall, N=703 | Medical Wards, n=391 | Surgical Wards, n=224 | ICUs, n=88 | |
| ||||
No. of catheters in place, n (%) | 259 (31.1) | 168 (33.7) | 91 (32.6) | 0 (0.0) |
1 | 575 (68.9) | 330 (66.3) | 188 (67.4) | 57 (100.0)a |
1 | 477 (57.2) | 299 (60.0) | 158 (56.6) | 20 (35.1) |
2 | 72 (8.6) | 26 (5.2) | 24 (8.6) | 22 (38.6) |
3 | 22 (2.6) | 5 (1.0) | 6 (2.2) | 11 (19.3) |
4 | 4 (0.5) | 0 (0.0) | 0 (0.0) | 4 (7.0) |
Type of catheter, n (%) | ||||
PVC | 567 (80.6) | 345 (88.2) | 196 (87.5) | 26 (29.6)a |
CVC | 111 (15.8) | 46 (11.8) | 28 (12.5) | 37 (42.0)a |
AC | 25 (3.6) | 0 (0.0) | 0 (0.0) | 25 (28.4)a |
Insertion site, n (%)b | ||||
PVCs | ||||
Hand and forearm | 425 (74.9) | 245 (71.0) | 156 (79.6) | 24 (92.3) |
Antecubital fossa | 105 (18.5) | 73 (21.2) | 31 (15.8) | 1 (3.8) |
Arm | 36 (6.3) | 26 (7.5) | 9 (4.6) | 1 (3.8) |
Lower extremity | 1 (0.2) | 1 (0.3) | 0 (0.0) | 0 (0.0) |
CVCs | ||||
Arm (PICC) | 13 (11.7) | 11 (23.9) | 2 (7.1) | 0 (0.0) |
Subclavian vein | 40 (36.0) | 23 (50.0) | 7 (25.0) | 10 (27.0) |
IJ vein | 47 (42.3) | 9 (19.6) | 18 (64.3) | 20 (54.1) |
Femoral vein | 11 (9.9) | 3 (6.5) | 1 (3.6) | 7 (18.9) |
ACs | ||||
Upper extremity | 19 (76.0) | 0 (0.0) | 0 (0.0) | 19 (76.0) |
Lower extremity | 6 (24.0) | 0 (0.0) | 0 (0.0) | 6 (24.0) |
No. of lumens per catheter, meanSD | ||||
All nonarterial catheters | 2.060.82 | 1.880.57 | 1.980.56 | 2.981.42c |
PVCs | 1.860.45 | 1.810.44 | 1.880.39 | 2.120.66c |
CVCs | 3.091.39 | 2.450.99 | 2.680.98 | 4.021.40c |
After excluding ACs, we found an overall mean number of 2.060.82 lumens per catheter (1.860.45 per PVC and 3.091.39 per CVC), with significant differences between ICUs and conventional wards (P<0.0001; Table 2). There was a mean of 0.860.57 3‐way stopcocks per catheter. The mean number of concurrent IV medications per patient was 2.82.7 (ranging from 2.32.1 in those with a single catheter to 10.52.6 in those with 4 catheters). The most commonly administered medications were antimicrobials (46.6% of patients with a vascular catheter), fluid therapy (33.4%), chemotherapy (2.3%), and vasoactive and inotropic drugs (1.0%). According to the administration regimen, 455 (79.1%), 30 (5.2%), and 182 (31.6%) patients were receiving medications by rapid infusion, infusion over 124 hours, or continuous infusion over a 24‐hour period, respectively. In 57 patients (9.9%), the catheter was used only as preemptive vascular access. No apparent indication for the use of a vascular catheter was found in 63 patients (10.9%).
Based on our criteria, 126 out of 834 inpatients (15.1%, 95% CI: 12.817.7) had 1 inappropriate catheter, with significant differences between conventional wards and ICUs (13.2% vs 26.3%, P=0.014). This prevalence rate increases to 21.9% (95% CI: 18.725.5) when only patients with 1 vascular catheter in place were analyzed.
Focusing on the number of catheter lumens, 631 out of 1448 (43.6%, 95% CI: 41.046.1) were considered unnecessary. There was a nonsignificant trend toward a higher rate of unnecessary lumens in conventional wards compared with ICUs (44.8% vs 39.4%, P=0.086; Table 3). Because some centers have policies requiring all inpatients to harbor 1 PVC in place throughout the entire hospitalization period, we performed a first sensitivity analysis in which we assumed that having a single functional vascular lumen was appropriate in all cases, regardless of any other criteria. Under this assumption, only 248 lumens (17.1%, 95% CI: 15.319.2) could be regarded as unnecessary. We conducted a second sensitivity analysis by including in the rate denominator only those catheter lumens potentially removable (eg, PVCs, nontunneled CVCs, and noncoring needles inserted in Port‐A‐Cath catheters). By applying this method, 48.6% of lumens (631 out of 1298, 95% CI: 45.951.3) could be considered inappropriate.
Rate | Overall | Medical Wards | Surgical Wards | ICUs |
---|---|---|---|---|
| ||||
Patients with 1 inappropriate catheter/overall no. of inpatients, n (%) | 126/834 (15.1) | 66/498 (13.2) | 45/279 (16.1) | 15/57 (26.3)a |
Patients with 1 inappropriate catheter/patients with 1 vascular catheter, n (%) | 126/575 (21.9) | 66/330 (20.0) | 45/188 (23.9) | 15/57 (26.3) |
No. of unnecessary vascular catheter lumens/overall no. of vascular catheter lumens, n (%) | 631/1448 (43.6) | 298/684 (43.6) | 207/444 (46.6) | 126/320 (39.4) b |
DISCUSSION
In this cross‐sectional survey, we found that 1 out of every 5 (20%) adult inpatients with a vascular catheter in place in our tertiary‐care center had an inappropriate number of catheters. This figure increased to 43.6% when the number of catheter lumens was analyzed (or 17.1% if we assumed that all patients should have at least 1 vascular access during their hospitalization period solely on the basis of preemptive reasons). Such rates of unnecessary catheter use throughout an entire institution offer an opportunity for improvement in clinical practice and, eventually, for reducing catheter‐related morbidity.
Other authors have also assessed the adequacy of CVC use in either ICU[16, 17] or non‐ICU settings.[14, 15, 16, 18, 21] A recent hospital‐wide survey found that 4.8% of catheter‐days were unnecessary, with a higher proportion in conventional wards than in the ICU,[16] mirroring the results from previous studies.[14] Another prospective study, limited to conventional wards, reported that almost half of the patients had 1 day with inappropriate vascular‐device use; age, total number of catheters used, and duration of catheterization were significantly associated with this event.[21] On the contrary, compliance with the criteria drawn up by the HICPAC and the Infusion Nurses Society for PICC use was found to be high overall in a medium‐sized community hospital.[22] Interestingly, we found a differential pattern in the adequacy of catheter use between hospital areas in function of the variable analyzed: number of inappropriate vascular catheters (higher rate in ICUs) or number of unnecessary vascular lumens (higher rate in conventional wards). Although our assessment criteria may partially account for such differences (ie, drug infusions for >1 hour justified the use of an exclusive catheter lumen), this finding raises the question of whether future interventions should be aimed at modifying specific catheter practices according to the type of hospital ward.
In contrast to the amount of literature on CVC, there is a scarcity of studies evaluating the appropriateness of PVC use in clinical practice. Lederle et al. reported that 17% of patients admitted in conventional wards of a university hospital had an idle PVC, with 20% of patient‐days of catheter exposure considered unnecessary.[12] The same authors subsequently demonstrated a significant decrease in these figures by implementing a multidisciplinary quality‐improvement intervention.[13] A previous cross‐sectional survey in our center revealed a PVC use rate as high as 46.2% among non‐critically ill adult inpatients.[23] Phlebitis is a common complication of PVC use, occurring in about 7% of inpatients and usually leading to catheter removal and replacement.[24] Although at a much lower incidence, peripheral catheterization also represents a non‐negligible source of CRBSI.[25] In our institution, in which a recently implemented specific bundle has resulted in a clear improvement in CVC care,[4, 20] about 60% of CRBSI occurring during the first 3 months of 2013 were due to PVCs (unpublished data). Therefore, this type of device should be routinely included in future surveys seeking to investigate the local epidemiology of catheter use at each institution. In that sense, it should be noted that a recent clinical trial showed no benefit of routine third‐day replacement vs clinically indicated replacement for phlebitis or CRBSI.[24]
Our study suggests that the daily review of the need for maintaining the vascular catheter should take into account the number of vascular lumens, as >40% of them were deemed unnecessary. To our knowledge, this area for potential intervention has not been addressed in previous surveys. Numerous studies have long demonstrated that the use of double‐lumen or triple‐lumen CVCs is associated with a higher rate of CRBSI than single‐lumen devices.[6, 7, 8] Even though a meta‐analysis concluded that this relationship diminishes when only high‐quality studies were included,[9] a more recent prospective study reported a hazard ratio for infection of 4.4 for each additional lumen.[10] However, it might be argued against our decision to count each inflow port in multiway stopcocks as a separate vascular lumen. Because the present survey was ultimately aimed at identifying opportunities to reduce the risk of CRBSI by decreasing catheter exposure, such an approach was chosen to properly capture and quantify every single potential source of infection in catheterized patients. We hypothesize that the use of 3‐lumen stopcocks could involve an increased number of manipulations, thus jeopardizing the integrity of the insertion‐site dressing and subsequently favoring the intraluminal bacterial colonization of the common catheter. Although the current guidelines do not provide specific recommendations regarding the number of lumens in devices other than CVCs,[3, 19] the potential benefit of reducing this figure to the minimum in PVCs and ACs should also be assumed. In our opinion, specific efforts have to be focused on improving the use of 3‐way stopcocks, as we found a mean of 1.86 lumens per PVC and >3 lumens per CVC in our study. Maybe the need for 3‐way stopcocks should be reassessed on a daily basis in a similar way as that recommended for temporary CVCs.[3, 19] By eliminating unnecessary vascular lumens, the risk of CRBSI could be diminished without compromising the availability of vascular access for preemptive purposes.
The present surveillance also provides an accurate insight into the real‐life vascular catheter practices in a hospital‐wide setting, in contrast with most of the previous studies, which have been conducted in specific wards or units.[13, 15, 17] One relevant finding was the relatively low use of the subclavian vein site for central venous access (only 40.8% of all CVCs inserted), with significant differences between medical wards and the remaining hospital areas. Various studies have shown that the subclavian site is associated with a lower risk of infectious and thrombotic complications.[4, 26, 27, 28] Therefore, the HICPAC and IDSA guidelines strongly recommend using a subclavian site, rather than a jugular or a femoral site, to minimize infection risk for nontunneled CVCs.[3, 19] Nevertheless, recent studies have suggested that internal jugular and femoral sites could be acceptable when a subclavian approach is not feasible, particularly if chlorhexidine‐impregnated dressings are used and catheters are left in place for <4 days.[29]
The current study has a number of inherent limitations; the most significant is its cross‐sectional design, which precludes direct comparison of the rates of catheter use with other prospective cohort surveys.[14, 16, 21] In addition, we were not able to assess the changing dynamics of catheter use over time. In other words, the lack of use of a given device on the day of the survey does not necessarily imply inappropriateness. The criteria used to determine appropriateness of vascular catheterization were consensus opinion and not evidence‐based, a weakness shared by previous studies,[21] as current guidelines only address the indications for certain devices (ie, HICPAC and Infusion Nurses Society recommendations for PICC use).[3, 30] Although we have attempted to be liberal in accepting indications for catheter use (ie, preemptive access in patients deemed at risk of hemodynamic instability), some misclassification bias cannot be ruled out. In evaluating the adequacy of catheter lumens, we did not take into account the simultaneous delivery of incompatible infusateswhich must be infused through separate linesor other relevant variables (eg, nursing availability). Because the aim of our study was simply to determine whether a patient had an appropriate number of vascular catheters and vascular lumens in overall terms, all vascular lumens in each subject were individually counted and added regardless of the nature of the device, and therefore we were not able to disaggregate the adequacy rate by specific catheter types. Finally, the generalizability of the results may be hampered by their single‐center nature, and this limitation applies particularly to institutions with different policies than ours regarding preemptive vascular catheterization (ie, those requiring that all inpatients have at least 1 vascular lumen at any time during hospitalization).
On the other hand, some strengths of this survey merit consideration, namely its comprehensive design, capturing the entire population of adult inpatients in different hospital areas and every type of vascular catheter. Moreover, we addressed the adequacy of maintaining catheterization not only in terms of idle catheters in place, but also in terms of unnecessary lumens. In conclusion, there remains room for improvement in daily practice regarding the prompt removal of vascular catheters and vascular lumens that are no longer medically necessary. Further educational efforts among physicians and nursing staff should be targeted toward achieving this simple but effective measure to reduce the incidence of CRBSI.
Disclosures: M.F.R. holds a research‐training contract Ro Hortega from the Spanish Ministry of Economy and Competitiveness (Instituto de Salud Carlos III; grant no. CM11/00187). F.L.M. is partially supported by a grant from the Research Intensification Programme in the National Health Care System (I3SNS) from the Spanish Ministry of Economy and Competitiveness (Instituto de Salud Carlos III; grant no. INT11/174). All authors report no conflicts of interest relevant to this article. This study was partially presented at the 52nd Annual Interscience Congress on Antimicrobial Agents and Chemotherapy (ICAAC), September 912, 2012, San Francisco, California.
- VINCat Program. Laboratory‐based surveillance of hospital‐acquired catheter‐related bloodstream infections in Catalonia: results of the VINCat Program (2007–2010). Enferm Infecc Microbiol Clin. 2012;30(suppl 3):13–19. , , , ;
- ICU‐Bacteremia Study Group. Outcomes of primary and catheter‐related bacteremia: a cohort and case‐control study in critically ill patients. Am J Respir Crit Care Med. 2001;163:1584–1590. , ;
- Healthcare Infection Control Practices Advisory Committee. Guidelines for the prevention of intravascular catheter‐related infections. Clin Infect Dis. 2011;52:e162–e193. , , , et al;
- An intervention to decrease catheter‐related bloodstream infections in the ICU [published correction appears in N Engl J Med. 2007;356:2660]. N Engl J Med. 2006;355:2725–2732. , , , et al.
- Prevention Epicenter Program. A multicenter intervention to prevent catheter‐associated bloodstream infections. Infect Control Hosp Epidemiol. 2006;27:662–669. , , , et al;
- Infection rate for single‐lumen v triple‐lumen subclavian catheters. Infect Control Hosp Epidemiol. 1988;9:154–158. , , .
- Increased infection rate in double‐lumen versus single‐lumen Hickman catheters in cancer patients. South Med J. 1990;83:34–36. , , , , .
- Use of triple‐lumen subclavian catheters for administration of total parenteral nutrition. JPEN J Parenter Enteral Nutr. 1992;16:403–407. , , , , .
- Rates of infection for single‐lumen versus multilumen central venous catheters: a meta‐analysis. Crit Care Med. 2003;31:2385–2390. , , , , .
- Multilumen central venous catheters increase risk for catheter‐related bloodstream infection: prospective surveillance study. Infection. 2008;36:322–327. , , , et al.
- Preventing catheter‐related bloodstream infections outside the intensive care unit: expanding prevention to new settings. Clin Infect Dis. 2010;51:335–341. , , .
- The idle intravenous catheter. Ann Intern Med. 1992;116:737–738. , , , .
- Reduction of unnecessary intravenous catheter use: internal medicine house staff participate in a successful quality improvement project. Arch Intern Med. 1994;154:1829–1832. , , , .
- Unnecessary use of central venous catheters: the need to look outside the intensive care unit. Infect Control Hosp Epidemiol. 2004;25:266–268. , , , , , .
- Prospective cohort study of central venous catheters among internal medicine ward patients. Am J Infect Control. 2006;34:636–641. , , , , , .
- Hospital‐wide survey of the use of central venous catheters. J Hosp Infect. 2011;77:304–308. , , , et al.
- Evaluation of unnecessary central venous catheters in critically ill patients: a prospective observational study. Can J Anaesth. 2010;57:830–835. , , .
- Temporary central venous catheter utilization patterns in a large tertiary care center: tracking the “idle central venous catheter.” Infect Control Hosp Epidemiol. 2012;33:50–57. , , , et al.
- Strategies to prevent central line‐associated bloodstream infections in acute care hospitals. Infect Control Hosp Epidemiol. 2008;29(suppl 1):S22–S30. , , , et al.
- Spanish Ministry of Health, Social Services and Equality. Quality Agency of the National Health Service. Bacteriemia Zero Project. Available at: http://www.seguridaddelpaciente.es/index.php/lang‐en/projects/financiacion‐estudios/bacteriemia‐zero‐project.html. Accessed June 4, 2013.
- Inappropriate intravascular device use: a prospective study. J Hosp Infect. 2011;78:128–132. , , , , .
- Peripherally inserted central catheter: compliance with evidence‐based indications for insertion in an inpatient setting. J Infus Nurs. 2013;36:291–296. , .
- Use and abuse of intravenous catheters in conventional hospital wards [article in Spanish]. An Med Interna. 2006;23:475–477. , , , et al.
- Routine versus clinically indicated replacement of peripheral intravenous catheters: a randomised controlled equivalence trial. Lancet. 2012;380:1066–1074. , , , et al.
- Clinical epidemiology and outcomes of peripheral venous catheter–related bloodstream infections at a university‐affiliated hospital. J Hosp Infect. 2007;67:22–29. , , , et al.
- Risk of infection due to central venous catheters: effect of site of placement and catheter type. Infect Control Hosp Epidemiol. 1998;19:842–845. , , , .
- Complications of femoral and subclavian venous catheterization in critically ill patients: a randomized controlled trial. JAMA. 2001;286:700–707. , , , et al.
- Central venous access sites for the prevention of venous thrombosis, stenosis and infection in patients requiring long‐term intravenous therapy. Cochrane Database Syst Rev. 2007;(3):CD004084. , .
- Jugular vs. femoral short‐term catheterization and risk of infection in ICU patients: causal analysis of 2 randomized trials. Am J Respir Crit Care Med. 2013;188:1232–1239. , , , et al.
- Infusion Nurses Society. Infusion nursing standards of practice. J Infus Nurs. 2011;34(1 suppl):S38.
Catheter‐related bloodstream infections (CRBSIs) are among the most common forms of hospital‐acquired infection and increase both length of stay and cost of hospitalization.[1, 2] Notable efforts are being devoted to reduce the rate of CRBSI, usually by implementing a bundle of measures.[3, 4, 5] One measure focuses on reducing to a minimum the exposure to vascular catheters.[3] In addition, various studies have shown that multilumen central venous catheters (CVCs) are associated with a higher risk of CRBSI than are single‐lumen catheters.[6, 7, 8, 9, 10] Accordingly, the Healthcare Infection Control Practices Advisory Committee (HICPAC) guideline recommends that clinicians use a CVC with the minimum number of ports or lumens essential for the management of the patient.[3]
Despite the fact that most CRBSIs occur in conventional wards,[1, 11] only a few studies have been focused on the potential magnitude of reducing the number of unnecessary vascular catheters and catheter lumens in the noncritical‐care setting.[12, 13, 14, 15, 16] The adequacy of vascular‐catheter use has been predominantly assessed for nontunneled CVCs.[14, 16, 17, 18] An institutional program aimed at reducing the overall rate of CRBSI should also include other sources, such as conventional peripheral venous catheters (PVCs), peripherally inserted central catheters (PICCs), and arterial catheters (ACs).[3] The need to extend surveillance to other types of catheters has been identified by the Infectious Diseases Society of America (IDSA) as an unresolved issue.[19]
We sought to investigate the rate and appropriateness of use of vascular catheters in the entire population of inpatients at a tertiary‐care center on a single day, as well as the adequacy of the number of catheter lumens harbored by each patient, by using a set of preestablished objective criteria.
METHODS
Setting and Study Population
We performed a 1‐day cross‐sectional study in March 2012 at the University Hospital 12 de Octubre in Madrid, Spain, a 1368‐bed tertiary‐care institution with a catchment area of 412,930 inhabitants in 2011 and 5 different adult intensive care units (ICUs; medical, trauma, coronary, general surgery, and cardiac surgery). In 2009, our center joined a national program aimed at implementing a catheter‐care bundle in adult ICUs with the intention of achieving zero incidence of CVC‐related bloodstream infections. This bundle consisted of a number of evidence‐based practices[4] (eg, avoiding the femoral site if possible and removing unnecessary CVCs).[20]
Study Design and Data Collection
All inpatient beds were reviewed, even if they were unoccupied on the day of the survey. The only exclusions were pediatric wards and the hospital facility for imprisoned patients. All inpatients with 1 vascular catheters in place on the day of survey were subsequently included. We analyzed ACs, PVCs, and the following types of CVCs: nontunneled (temporary) catheters, skin‐tunneled catheters (Hickman type), totally implantable catheters (Port‐A‐Cath), Swan‐Ganz thermodilution catheters, dialysis catheters (Shaldon type), and PICCs.
The following data were abstracted using a standardized sheet from each patient's medical and nursing records and by direct inspection: basic patient demographics; type of ward (medical/surgical [hereinafter, conventional wards] or ICU); type of vascular catheter; anatomic site of catheter insertion; medical or nursing team responsible for catheter placement; catheter insertion‐site dressing regimen; overall number of vascular catheters per patient; and overall number of venous or arterial catheter lumens per patient (resulting from adding up all the lumens present in each patient, including 3‐way stopcocks and noncoring needles in Port‐A‐Cath devices; each of the inflow ports in 3‐way stopcocks attached to a vascular catheter was counted as a separate lumen). Those patients who were not in their wards on the day of survey for any reason (eg, an ongoing surgical procedure) were excluded.
The current indication to maintain ongoing catheterization was recorded by means of the following variables: overall number of intravenous (IV) medications administered during the previous 24 hours; type of medication (antimicrobial therapy, fluid therapy, vasoactive and inotropic drugs, chemotherapy, blood products, or others [eg, analgesics or diuretics]); type of IV administration regimen; and other indications for catheter use (need for monitoring hemodynamic status, renal replacement therapy, or need for preemptive vascular access in patients expected to be at risk of hemodynamic deterioration potentially requiring fluid resuscitation or inotropic support over the next days [eg, septic shock, acute decompensation of heart failure, or gastrointestinal bleeding within the previous week]).[21] After thorough scrutiny of prescription orders, we assigned each medication to one of 3 different IV administration regimens: (1) rapid infusion (over <1 hour); (2) infusion over 1 to 24 hours; and (3) continuous infusion over a 24‐hour period. In doubtful cases, nursing staff was directly asked about the regimen of infusion.
No formal informed consent was obtained from the participants, as the present study was strictly observational and part of the institutional quality initiatives. The local Clinical Research Ethics Committee approved the study protocol.
Assessment Criteria
The adequacy of use of vascular catheters and catheter lumens was assessed by one of 4 researchers not associated with day‐to‐day patient care by using a set of a priori determined criteria. To determine appropriateness, a maximum theoretical number of vascular lumens was assigned to each specific indication for catheterization (Table 1). We considered that all IV medications administered by rapid infusion could be delivered consecutively through 1 single catheter lumen. Those medications administered by infusion over 1 to 24 hours, or by continuous infusion over a 24‐hour period, would require an exclusive lumen. The nature of the infusate, the potential incompatibility between infused drugs, and the method of infusion (gravity drip or pump) were not taken into account in this assignment process. Hemodynamic monitoring and renal replacement therapy also required an exclusive catheter lumen. When the vascular catheterization was retained only for preemptive reasons, we considered as justified the use of a maximum of 2 single‐lumen catheters.
Indication | No. of Catheter Lumens Deemed Necessary |
---|---|
| |
Administration of IV medications | |
Rapid infusion over <1 hour | 1 common lumen (for all medications) |
Infusion over 124 hours | 1 exclusive lumen (for each medication) |
Continuous infusion over a 24‐hour period | 1 exclusive lumen (for each medication) |
Hemodynamic monitoring | 1 exclusive lumen |
Renal replacement therapy | 1 exclusive lumen |
Preemptive catheterization (ie, patients at risk of hemodynamic deterioration over the next days) | Maximum of 2 single‐lumen catheters |
Appropriateness of the Use of Vascular Catheters
The presence of a conventional PVC or a nontunneled (temporary) CVC was considered justifiable if 1 of its lumens was indicated according to the above‐mentioned criteria. We applied the principle that the requirements of catheter lumens should be met by keeping the number of catheters as low as possible. For instance, if a given patient had 2 catheters with an overall number of 3 lumens (ie, 1 single‐lumen catheter and 1 double‐lumen catheter), and only 2 catheter lumens were actually deemed necessary, we considered that the overall number of catheters was inappropriate. In view of their particular characteristics, the following types of catheters were by definition deemed to be appropriate: Swan‐Ganz catheters and ACs (as nearly exclusively used for hemodynamic monitoring in critically ill patients), dialysis catheters (as solely used for this specific purpose), and PICCs and nontemporary CVCs (as most of them had been placed prior to the current hospitalization episode for the periodic administration of chemotherapy or domiciliary parenteral nutrition). Because no IV medications are delivered through Swan‐Ganz catheters, ACs, and dialysis catheters, we did not take into account the presence of these devices when assessing the appropriateness of other vascular catheters present in a given patient.
Appropriateness of the Use of Catheter Lumens
First, we added up all the catheter lumens present in each patient (regardless of the type of device), and then we established the theoretical number of catheter lumens that the patient would have actually required, according to the above‐mentioned criteria. The difference between both figures gave the number of unnecessary catheter lumens. Only the potentially removable lumens were included in this analysis (those of PVCs and nontunneled CVCs, as well as each of the noncoring needles inserted in Port‐A‐Cath catheters). The lumens of skin‐tunneled CVCs, Swan‐Ganz catheters, and PICCs were considered nonremovable and, therefore, always justified. We excluded ACs from this specific analysis.
Statistical Analysis
Quantitative data were shown as the meanstandard deviation, whereas qualitative variables were expressed as absolute and relative frequencies with 95% confidence intervals (CIs). Categorical and continuous variables were compared using [2] and unpaired Student t tests, respectively. We calculated 3 different ratios: patients with 1 inappropriate catheter to overall number of inpatients; patients with 1 inappropriate catheter to patients with 1 vascular catheter in place on the day of survey; and overall number of unnecessary catheter lumens to overall number of catheter lumens. All the significance tests were 2‐tailed. Statistics were performed using SPSS version 15.0 (SPSS Inc, Chicago, IL).
RESULTS
Out of 1134 reviewed inpatient beds, 834 (73.5%) were occupied on the day of the survey. The mean age of the included patients was 64.518.8 years, and 415 (49.8%) were male. Of these patients, 575 (68.9%) had 1 vascular catheter in place. The proportion of patients with a vascular catheter was significantly higher in ICUs compared with conventional wards (100% vs 66.7%, P<0.0001; Table 2). The overall numbers of vascular catheters and catheter lumens analyzed were 703 and 1448, respectively. Regarding the type of device, 567 (80.6%) were PVCs, 111 (15.8%) were CVCs (including 65 nontunneled CVCs, 16 dialysis catheters, 15 PICCs, 7 skin‐tunneled CVCs, 5 totally implantable CVCs, and 3 Swan‐Ganz catheters), and 25 (3.5%) were ACs. The distribution according to hospital ward and anatomic site of insertion is detailed in Table 2. The use of CVCs and ACs was higher in ICUs (42.0% and 28.4% of all catheters in place, respectively) compared with conventional wards (12.0% and 0.0%, P<0.0001). The use of the subclavian vein insertion site was more common in medical wards (65.7% of all CVCs, excluding PICCs) than in surgical wards or ICUs (26.9%, P=0.0002). Most of the catheters had been inserted by nursing staff members (75.2%), followed by anesthesia physicians (13.4%) and critical‐care medicine physicians (4.8%). An opaque gauze or transparent polyurethane insertion‐site dressing was present in 378 (53.8%) and 319 (45.3%) catheters, respectively, with no significant differences according to the type of device or hospital ward.
No. of Patients | ||||
---|---|---|---|---|
Overall, N=834 | Medical Wards, n=498 | Surgical Wards, n=279 | ICUs, n=57 | |
No. of Catheters | ||||
Overall, N=703 | Medical Wards, n=391 | Surgical Wards, n=224 | ICUs, n=88 | |
| ||||
No. of catheters in place, n (%) | 259 (31.1) | 168 (33.7) | 91 (32.6) | 0 (0.0) |
1 | 575 (68.9) | 330 (66.3) | 188 (67.4) | 57 (100.0)a |
1 | 477 (57.2) | 299 (60.0) | 158 (56.6) | 20 (35.1) |
2 | 72 (8.6) | 26 (5.2) | 24 (8.6) | 22 (38.6) |
3 | 22 (2.6) | 5 (1.0) | 6 (2.2) | 11 (19.3) |
4 | 4 (0.5) | 0 (0.0) | 0 (0.0) | 4 (7.0) |
Type of catheter, n (%) | ||||
PVC | 567 (80.6) | 345 (88.2) | 196 (87.5) | 26 (29.6)a |
CVC | 111 (15.8) | 46 (11.8) | 28 (12.5) | 37 (42.0)a |
AC | 25 (3.6) | 0 (0.0) | 0 (0.0) | 25 (28.4)a |
Insertion site, n (%)b | ||||
PVCs | ||||
Hand and forearm | 425 (74.9) | 245 (71.0) | 156 (79.6) | 24 (92.3) |
Antecubital fossa | 105 (18.5) | 73 (21.2) | 31 (15.8) | 1 (3.8) |
Arm | 36 (6.3) | 26 (7.5) | 9 (4.6) | 1 (3.8) |
Lower extremity | 1 (0.2) | 1 (0.3) | 0 (0.0) | 0 (0.0) |
CVCs | ||||
Arm (PICC) | 13 (11.7) | 11 (23.9) | 2 (7.1) | 0 (0.0) |
Subclavian vein | 40 (36.0) | 23 (50.0) | 7 (25.0) | 10 (27.0) |
IJ vein | 47 (42.3) | 9 (19.6) | 18 (64.3) | 20 (54.1) |
Femoral vein | 11 (9.9) | 3 (6.5) | 1 (3.6) | 7 (18.9) |
ACs | ||||
Upper extremity | 19 (76.0) | 0 (0.0) | 0 (0.0) | 19 (76.0) |
Lower extremity | 6 (24.0) | 0 (0.0) | 0 (0.0) | 6 (24.0) |
No. of lumens per catheter, meanSD | ||||
All nonarterial catheters | 2.060.82 | 1.880.57 | 1.980.56 | 2.981.42c |
PVCs | 1.860.45 | 1.810.44 | 1.880.39 | 2.120.66c |
CVCs | 3.091.39 | 2.450.99 | 2.680.98 | 4.021.40c |
After excluding ACs, we found an overall mean number of 2.060.82 lumens per catheter (1.860.45 per PVC and 3.091.39 per CVC), with significant differences between ICUs and conventional wards (P<0.0001; Table 2). There was a mean of 0.860.57 3‐way stopcocks per catheter. The mean number of concurrent IV medications per patient was 2.82.7 (ranging from 2.32.1 in those with a single catheter to 10.52.6 in those with 4 catheters). The most commonly administered medications were antimicrobials (46.6% of patients with a vascular catheter), fluid therapy (33.4%), chemotherapy (2.3%), and vasoactive and inotropic drugs (1.0%). According to the administration regimen, 455 (79.1%), 30 (5.2%), and 182 (31.6%) patients were receiving medications by rapid infusion, infusion over 124 hours, or continuous infusion over a 24‐hour period, respectively. In 57 patients (9.9%), the catheter was used only as preemptive vascular access. No apparent indication for the use of a vascular catheter was found in 63 patients (10.9%).
Based on our criteria, 126 out of 834 inpatients (15.1%, 95% CI: 12.817.7) had 1 inappropriate catheter, with significant differences between conventional wards and ICUs (13.2% vs 26.3%, P=0.014). This prevalence rate increases to 21.9% (95% CI: 18.725.5) when only patients with 1 vascular catheter in place were analyzed.
Focusing on the number of catheter lumens, 631 out of 1448 (43.6%, 95% CI: 41.046.1) were considered unnecessary. There was a nonsignificant trend toward a higher rate of unnecessary lumens in conventional wards compared with ICUs (44.8% vs 39.4%, P=0.086; Table 3). Because some centers have policies requiring all inpatients to harbor 1 PVC in place throughout the entire hospitalization period, we performed a first sensitivity analysis in which we assumed that having a single functional vascular lumen was appropriate in all cases, regardless of any other criteria. Under this assumption, only 248 lumens (17.1%, 95% CI: 15.319.2) could be regarded as unnecessary. We conducted a second sensitivity analysis by including in the rate denominator only those catheter lumens potentially removable (eg, PVCs, nontunneled CVCs, and noncoring needles inserted in Port‐A‐Cath catheters). By applying this method, 48.6% of lumens (631 out of 1298, 95% CI: 45.951.3) could be considered inappropriate.
Rate | Overall | Medical Wards | Surgical Wards | ICUs |
---|---|---|---|---|
| ||||
Patients with 1 inappropriate catheter/overall no. of inpatients, n (%) | 126/834 (15.1) | 66/498 (13.2) | 45/279 (16.1) | 15/57 (26.3)a |
Patients with 1 inappropriate catheter/patients with 1 vascular catheter, n (%) | 126/575 (21.9) | 66/330 (20.0) | 45/188 (23.9) | 15/57 (26.3) |
No. of unnecessary vascular catheter lumens/overall no. of vascular catheter lumens, n (%) | 631/1448 (43.6) | 298/684 (43.6) | 207/444 (46.6) | 126/320 (39.4) b |
DISCUSSION
In this cross‐sectional survey, we found that 1 out of every 5 (20%) adult inpatients with a vascular catheter in place in our tertiary‐care center had an inappropriate number of catheters. This figure increased to 43.6% when the number of catheter lumens was analyzed (or 17.1% if we assumed that all patients should have at least 1 vascular access during their hospitalization period solely on the basis of preemptive reasons). Such rates of unnecessary catheter use throughout an entire institution offer an opportunity for improvement in clinical practice and, eventually, for reducing catheter‐related morbidity.
Other authors have also assessed the adequacy of CVC use in either ICU[16, 17] or non‐ICU settings.[14, 15, 16, 18, 21] A recent hospital‐wide survey found that 4.8% of catheter‐days were unnecessary, with a higher proportion in conventional wards than in the ICU,[16] mirroring the results from previous studies.[14] Another prospective study, limited to conventional wards, reported that almost half of the patients had 1 day with inappropriate vascular‐device use; age, total number of catheters used, and duration of catheterization were significantly associated with this event.[21] On the contrary, compliance with the criteria drawn up by the HICPAC and the Infusion Nurses Society for PICC use was found to be high overall in a medium‐sized community hospital.[22] Interestingly, we found a differential pattern in the adequacy of catheter use between hospital areas in function of the variable analyzed: number of inappropriate vascular catheters (higher rate in ICUs) or number of unnecessary vascular lumens (higher rate in conventional wards). Although our assessment criteria may partially account for such differences (ie, drug infusions for >1 hour justified the use of an exclusive catheter lumen), this finding raises the question of whether future interventions should be aimed at modifying specific catheter practices according to the type of hospital ward.
In contrast to the amount of literature on CVC, there is a scarcity of studies evaluating the appropriateness of PVC use in clinical practice. Lederle et al. reported that 17% of patients admitted in conventional wards of a university hospital had an idle PVC, with 20% of patient‐days of catheter exposure considered unnecessary.[12] The same authors subsequently demonstrated a significant decrease in these figures by implementing a multidisciplinary quality‐improvement intervention.[13] A previous cross‐sectional survey in our center revealed a PVC use rate as high as 46.2% among non‐critically ill adult inpatients.[23] Phlebitis is a common complication of PVC use, occurring in about 7% of inpatients and usually leading to catheter removal and replacement.[24] Although at a much lower incidence, peripheral catheterization also represents a non‐negligible source of CRBSI.[25] In our institution, in which a recently implemented specific bundle has resulted in a clear improvement in CVC care,[4, 20] about 60% of CRBSI occurring during the first 3 months of 2013 were due to PVCs (unpublished data). Therefore, this type of device should be routinely included in future surveys seeking to investigate the local epidemiology of catheter use at each institution. In that sense, it should be noted that a recent clinical trial showed no benefit of routine third‐day replacement vs clinically indicated replacement for phlebitis or CRBSI.[24]
Our study suggests that the daily review of the need for maintaining the vascular catheter should take into account the number of vascular lumens, as >40% of them were deemed unnecessary. To our knowledge, this area for potential intervention has not been addressed in previous surveys. Numerous studies have long demonstrated that the use of double‐lumen or triple‐lumen CVCs is associated with a higher rate of CRBSI than single‐lumen devices.[6, 7, 8] Even though a meta‐analysis concluded that this relationship diminishes when only high‐quality studies were included,[9] a more recent prospective study reported a hazard ratio for infection of 4.4 for each additional lumen.[10] However, it might be argued against our decision to count each inflow port in multiway stopcocks as a separate vascular lumen. Because the present survey was ultimately aimed at identifying opportunities to reduce the risk of CRBSI by decreasing catheter exposure, such an approach was chosen to properly capture and quantify every single potential source of infection in catheterized patients. We hypothesize that the use of 3‐lumen stopcocks could involve an increased number of manipulations, thus jeopardizing the integrity of the insertion‐site dressing and subsequently favoring the intraluminal bacterial colonization of the common catheter. Although the current guidelines do not provide specific recommendations regarding the number of lumens in devices other than CVCs,[3, 19] the potential benefit of reducing this figure to the minimum in PVCs and ACs should also be assumed. In our opinion, specific efforts have to be focused on improving the use of 3‐way stopcocks, as we found a mean of 1.86 lumens per PVC and >3 lumens per CVC in our study. Maybe the need for 3‐way stopcocks should be reassessed on a daily basis in a similar way as that recommended for temporary CVCs.[3, 19] By eliminating unnecessary vascular lumens, the risk of CRBSI could be diminished without compromising the availability of vascular access for preemptive purposes.
The present surveillance also provides an accurate insight into the real‐life vascular catheter practices in a hospital‐wide setting, in contrast with most of the previous studies, which have been conducted in specific wards or units.[13, 15, 17] One relevant finding was the relatively low use of the subclavian vein site for central venous access (only 40.8% of all CVCs inserted), with significant differences between medical wards and the remaining hospital areas. Various studies have shown that the subclavian site is associated with a lower risk of infectious and thrombotic complications.[4, 26, 27, 28] Therefore, the HICPAC and IDSA guidelines strongly recommend using a subclavian site, rather than a jugular or a femoral site, to minimize infection risk for nontunneled CVCs.[3, 19] Nevertheless, recent studies have suggested that internal jugular and femoral sites could be acceptable when a subclavian approach is not feasible, particularly if chlorhexidine‐impregnated dressings are used and catheters are left in place for <4 days.[29]
The current study has a number of inherent limitations; the most significant is its cross‐sectional design, which precludes direct comparison of the rates of catheter use with other prospective cohort surveys.[14, 16, 21] In addition, we were not able to assess the changing dynamics of catheter use over time. In other words, the lack of use of a given device on the day of the survey does not necessarily imply inappropriateness. The criteria used to determine appropriateness of vascular catheterization were consensus opinion and not evidence‐based, a weakness shared by previous studies,[21] as current guidelines only address the indications for certain devices (ie, HICPAC and Infusion Nurses Society recommendations for PICC use).[3, 30] Although we have attempted to be liberal in accepting indications for catheter use (ie, preemptive access in patients deemed at risk of hemodynamic instability), some misclassification bias cannot be ruled out. In evaluating the adequacy of catheter lumens, we did not take into account the simultaneous delivery of incompatible infusateswhich must be infused through separate linesor other relevant variables (eg, nursing availability). Because the aim of our study was simply to determine whether a patient had an appropriate number of vascular catheters and vascular lumens in overall terms, all vascular lumens in each subject were individually counted and added regardless of the nature of the device, and therefore we were not able to disaggregate the adequacy rate by specific catheter types. Finally, the generalizability of the results may be hampered by their single‐center nature, and this limitation applies particularly to institutions with different policies than ours regarding preemptive vascular catheterization (ie, those requiring that all inpatients have at least 1 vascular lumen at any time during hospitalization).
On the other hand, some strengths of this survey merit consideration, namely its comprehensive design, capturing the entire population of adult inpatients in different hospital areas and every type of vascular catheter. Moreover, we addressed the adequacy of maintaining catheterization not only in terms of idle catheters in place, but also in terms of unnecessary lumens. In conclusion, there remains room for improvement in daily practice regarding the prompt removal of vascular catheters and vascular lumens that are no longer medically necessary. Further educational efforts among physicians and nursing staff should be targeted toward achieving this simple but effective measure to reduce the incidence of CRBSI.
Disclosures: M.F.R. holds a research‐training contract Ro Hortega from the Spanish Ministry of Economy and Competitiveness (Instituto de Salud Carlos III; grant no. CM11/00187). F.L.M. is partially supported by a grant from the Research Intensification Programme in the National Health Care System (I3SNS) from the Spanish Ministry of Economy and Competitiveness (Instituto de Salud Carlos III; grant no. INT11/174). All authors report no conflicts of interest relevant to this article. This study was partially presented at the 52nd Annual Interscience Congress on Antimicrobial Agents and Chemotherapy (ICAAC), September 912, 2012, San Francisco, California.
Catheter‐related bloodstream infections (CRBSIs) are among the most common forms of hospital‐acquired infection and increase both length of stay and cost of hospitalization.[1, 2] Notable efforts are being devoted to reduce the rate of CRBSI, usually by implementing a bundle of measures.[3, 4, 5] One measure focuses on reducing to a minimum the exposure to vascular catheters.[3] In addition, various studies have shown that multilumen central venous catheters (CVCs) are associated with a higher risk of CRBSI than are single‐lumen catheters.[6, 7, 8, 9, 10] Accordingly, the Healthcare Infection Control Practices Advisory Committee (HICPAC) guideline recommends that clinicians use a CVC with the minimum number of ports or lumens essential for the management of the patient.[3]
Despite the fact that most CRBSIs occur in conventional wards,[1, 11] only a few studies have been focused on the potential magnitude of reducing the number of unnecessary vascular catheters and catheter lumens in the noncritical‐care setting.[12, 13, 14, 15, 16] The adequacy of vascular‐catheter use has been predominantly assessed for nontunneled CVCs.[14, 16, 17, 18] An institutional program aimed at reducing the overall rate of CRBSI should also include other sources, such as conventional peripheral venous catheters (PVCs), peripherally inserted central catheters (PICCs), and arterial catheters (ACs).[3] The need to extend surveillance to other types of catheters has been identified by the Infectious Diseases Society of America (IDSA) as an unresolved issue.[19]
We sought to investigate the rate and appropriateness of use of vascular catheters in the entire population of inpatients at a tertiary‐care center on a single day, as well as the adequacy of the number of catheter lumens harbored by each patient, by using a set of preestablished objective criteria.
METHODS
Setting and Study Population
We performed a 1‐day cross‐sectional study in March 2012 at the University Hospital 12 de Octubre in Madrid, Spain, a 1368‐bed tertiary‐care institution with a catchment area of 412,930 inhabitants in 2011 and 5 different adult intensive care units (ICUs; medical, trauma, coronary, general surgery, and cardiac surgery). In 2009, our center joined a national program aimed at implementing a catheter‐care bundle in adult ICUs with the intention of achieving zero incidence of CVC‐related bloodstream infections. This bundle consisted of a number of evidence‐based practices[4] (eg, avoiding the femoral site if possible and removing unnecessary CVCs).[20]
Study Design and Data Collection
All inpatient beds were reviewed, even if they were unoccupied on the day of the survey. The only exclusions were pediatric wards and the hospital facility for imprisoned patients. All inpatients with 1 vascular catheters in place on the day of survey were subsequently included. We analyzed ACs, PVCs, and the following types of CVCs: nontunneled (temporary) catheters, skin‐tunneled catheters (Hickman type), totally implantable catheters (Port‐A‐Cath), Swan‐Ganz thermodilution catheters, dialysis catheters (Shaldon type), and PICCs.
The following data were abstracted using a standardized sheet from each patient's medical and nursing records and by direct inspection: basic patient demographics; type of ward (medical/surgical [hereinafter, conventional wards] or ICU); type of vascular catheter; anatomic site of catheter insertion; medical or nursing team responsible for catheter placement; catheter insertion‐site dressing regimen; overall number of vascular catheters per patient; and overall number of venous or arterial catheter lumens per patient (resulting from adding up all the lumens present in each patient, including 3‐way stopcocks and noncoring needles in Port‐A‐Cath devices; each of the inflow ports in 3‐way stopcocks attached to a vascular catheter was counted as a separate lumen). Those patients who were not in their wards on the day of survey for any reason (eg, an ongoing surgical procedure) were excluded.
The current indication to maintain ongoing catheterization was recorded by means of the following variables: overall number of intravenous (IV) medications administered during the previous 24 hours; type of medication (antimicrobial therapy, fluid therapy, vasoactive and inotropic drugs, chemotherapy, blood products, or others [eg, analgesics or diuretics]); type of IV administration regimen; and other indications for catheter use (need for monitoring hemodynamic status, renal replacement therapy, or need for preemptive vascular access in patients expected to be at risk of hemodynamic deterioration potentially requiring fluid resuscitation or inotropic support over the next days [eg, septic shock, acute decompensation of heart failure, or gastrointestinal bleeding within the previous week]).[21] After thorough scrutiny of prescription orders, we assigned each medication to one of 3 different IV administration regimens: (1) rapid infusion (over <1 hour); (2) infusion over 1 to 24 hours; and (3) continuous infusion over a 24‐hour period. In doubtful cases, nursing staff was directly asked about the regimen of infusion.
No formal informed consent was obtained from the participants, as the present study was strictly observational and part of the institutional quality initiatives. The local Clinical Research Ethics Committee approved the study protocol.
Assessment Criteria
The adequacy of use of vascular catheters and catheter lumens was assessed by one of 4 researchers not associated with day‐to‐day patient care by using a set of a priori determined criteria. To determine appropriateness, a maximum theoretical number of vascular lumens was assigned to each specific indication for catheterization (Table 1). We considered that all IV medications administered by rapid infusion could be delivered consecutively through 1 single catheter lumen. Those medications administered by infusion over 1 to 24 hours, or by continuous infusion over a 24‐hour period, would require an exclusive lumen. The nature of the infusate, the potential incompatibility between infused drugs, and the method of infusion (gravity drip or pump) were not taken into account in this assignment process. Hemodynamic monitoring and renal replacement therapy also required an exclusive catheter lumen. When the vascular catheterization was retained only for preemptive reasons, we considered as justified the use of a maximum of 2 single‐lumen catheters.
Indication | No. of Catheter Lumens Deemed Necessary |
---|---|
| |
Administration of IV medications | |
Rapid infusion over <1 hour | 1 common lumen (for all medications) |
Infusion over 124 hours | 1 exclusive lumen (for each medication) |
Continuous infusion over a 24‐hour period | 1 exclusive lumen (for each medication) |
Hemodynamic monitoring | 1 exclusive lumen |
Renal replacement therapy | 1 exclusive lumen |
Preemptive catheterization (ie, patients at risk of hemodynamic deterioration over the next days) | Maximum of 2 single‐lumen catheters |
Appropriateness of the Use of Vascular Catheters
The presence of a conventional PVC or a nontunneled (temporary) CVC was considered justifiable if 1 of its lumens was indicated according to the above‐mentioned criteria. We applied the principle that the requirements of catheter lumens should be met by keeping the number of catheters as low as possible. For instance, if a given patient had 2 catheters with an overall number of 3 lumens (ie, 1 single‐lumen catheter and 1 double‐lumen catheter), and only 2 catheter lumens were actually deemed necessary, we considered that the overall number of catheters was inappropriate. In view of their particular characteristics, the following types of catheters were by definition deemed to be appropriate: Swan‐Ganz catheters and ACs (as nearly exclusively used for hemodynamic monitoring in critically ill patients), dialysis catheters (as solely used for this specific purpose), and PICCs and nontemporary CVCs (as most of them had been placed prior to the current hospitalization episode for the periodic administration of chemotherapy or domiciliary parenteral nutrition). Because no IV medications are delivered through Swan‐Ganz catheters, ACs, and dialysis catheters, we did not take into account the presence of these devices when assessing the appropriateness of other vascular catheters present in a given patient.
Appropriateness of the Use of Catheter Lumens
First, we added up all the catheter lumens present in each patient (regardless of the type of device), and then we established the theoretical number of catheter lumens that the patient would have actually required, according to the above‐mentioned criteria. The difference between both figures gave the number of unnecessary catheter lumens. Only the potentially removable lumens were included in this analysis (those of PVCs and nontunneled CVCs, as well as each of the noncoring needles inserted in Port‐A‐Cath catheters). The lumens of skin‐tunneled CVCs, Swan‐Ganz catheters, and PICCs were considered nonremovable and, therefore, always justified. We excluded ACs from this specific analysis.
Statistical Analysis
Quantitative data were shown as the meanstandard deviation, whereas qualitative variables were expressed as absolute and relative frequencies with 95% confidence intervals (CIs). Categorical and continuous variables were compared using [2] and unpaired Student t tests, respectively. We calculated 3 different ratios: patients with 1 inappropriate catheter to overall number of inpatients; patients with 1 inappropriate catheter to patients with 1 vascular catheter in place on the day of survey; and overall number of unnecessary catheter lumens to overall number of catheter lumens. All the significance tests were 2‐tailed. Statistics were performed using SPSS version 15.0 (SPSS Inc, Chicago, IL).
RESULTS
Out of 1134 reviewed inpatient beds, 834 (73.5%) were occupied on the day of the survey. The mean age of the included patients was 64.518.8 years, and 415 (49.8%) were male. Of these patients, 575 (68.9%) had 1 vascular catheter in place. The proportion of patients with a vascular catheter was significantly higher in ICUs compared with conventional wards (100% vs 66.7%, P<0.0001; Table 2). The overall numbers of vascular catheters and catheter lumens analyzed were 703 and 1448, respectively. Regarding the type of device, 567 (80.6%) were PVCs, 111 (15.8%) were CVCs (including 65 nontunneled CVCs, 16 dialysis catheters, 15 PICCs, 7 skin‐tunneled CVCs, 5 totally implantable CVCs, and 3 Swan‐Ganz catheters), and 25 (3.5%) were ACs. The distribution according to hospital ward and anatomic site of insertion is detailed in Table 2. The use of CVCs and ACs was higher in ICUs (42.0% and 28.4% of all catheters in place, respectively) compared with conventional wards (12.0% and 0.0%, P<0.0001). The use of the subclavian vein insertion site was more common in medical wards (65.7% of all CVCs, excluding PICCs) than in surgical wards or ICUs (26.9%, P=0.0002). Most of the catheters had been inserted by nursing staff members (75.2%), followed by anesthesia physicians (13.4%) and critical‐care medicine physicians (4.8%). An opaque gauze or transparent polyurethane insertion‐site dressing was present in 378 (53.8%) and 319 (45.3%) catheters, respectively, with no significant differences according to the type of device or hospital ward.
No. of Patients | ||||
---|---|---|---|---|
Overall, N=834 | Medical Wards, n=498 | Surgical Wards, n=279 | ICUs, n=57 | |
No. of Catheters | ||||
Overall, N=703 | Medical Wards, n=391 | Surgical Wards, n=224 | ICUs, n=88 | |
| ||||
No. of catheters in place, n (%) | 259 (31.1) | 168 (33.7) | 91 (32.6) | 0 (0.0) |
1 | 575 (68.9) | 330 (66.3) | 188 (67.4) | 57 (100.0)a |
1 | 477 (57.2) | 299 (60.0) | 158 (56.6) | 20 (35.1) |
2 | 72 (8.6) | 26 (5.2) | 24 (8.6) | 22 (38.6) |
3 | 22 (2.6) | 5 (1.0) | 6 (2.2) | 11 (19.3) |
4 | 4 (0.5) | 0 (0.0) | 0 (0.0) | 4 (7.0) |
Type of catheter, n (%) | ||||
PVC | 567 (80.6) | 345 (88.2) | 196 (87.5) | 26 (29.6)a |
CVC | 111 (15.8) | 46 (11.8) | 28 (12.5) | 37 (42.0)a |
AC | 25 (3.6) | 0 (0.0) | 0 (0.0) | 25 (28.4)a |
Insertion site, n (%)b | ||||
PVCs | ||||
Hand and forearm | 425 (74.9) | 245 (71.0) | 156 (79.6) | 24 (92.3) |
Antecubital fossa | 105 (18.5) | 73 (21.2) | 31 (15.8) | 1 (3.8) |
Arm | 36 (6.3) | 26 (7.5) | 9 (4.6) | 1 (3.8) |
Lower extremity | 1 (0.2) | 1 (0.3) | 0 (0.0) | 0 (0.0) |
CVCs | ||||
Arm (PICC) | 13 (11.7) | 11 (23.9) | 2 (7.1) | 0 (0.0) |
Subclavian vein | 40 (36.0) | 23 (50.0) | 7 (25.0) | 10 (27.0) |
IJ vein | 47 (42.3) | 9 (19.6) | 18 (64.3) | 20 (54.1) |
Femoral vein | 11 (9.9) | 3 (6.5) | 1 (3.6) | 7 (18.9) |
ACs | ||||
Upper extremity | 19 (76.0) | 0 (0.0) | 0 (0.0) | 19 (76.0) |
Lower extremity | 6 (24.0) | 0 (0.0) | 0 (0.0) | 6 (24.0) |
No. of lumens per catheter, meanSD | ||||
All nonarterial catheters | 2.060.82 | 1.880.57 | 1.980.56 | 2.981.42c |
PVCs | 1.860.45 | 1.810.44 | 1.880.39 | 2.120.66c |
CVCs | 3.091.39 | 2.450.99 | 2.680.98 | 4.021.40c |
After excluding ACs, we found an overall mean number of 2.060.82 lumens per catheter (1.860.45 per PVC and 3.091.39 per CVC), with significant differences between ICUs and conventional wards (P<0.0001; Table 2). There was a mean of 0.860.57 3‐way stopcocks per catheter. The mean number of concurrent IV medications per patient was 2.82.7 (ranging from 2.32.1 in those with a single catheter to 10.52.6 in those with 4 catheters). The most commonly administered medications were antimicrobials (46.6% of patients with a vascular catheter), fluid therapy (33.4%), chemotherapy (2.3%), and vasoactive and inotropic drugs (1.0%). According to the administration regimen, 455 (79.1%), 30 (5.2%), and 182 (31.6%) patients were receiving medications by rapid infusion, infusion over 124 hours, or continuous infusion over a 24‐hour period, respectively. In 57 patients (9.9%), the catheter was used only as preemptive vascular access. No apparent indication for the use of a vascular catheter was found in 63 patients (10.9%).
Based on our criteria, 126 out of 834 inpatients (15.1%, 95% CI: 12.817.7) had 1 inappropriate catheter, with significant differences between conventional wards and ICUs (13.2% vs 26.3%, P=0.014). This prevalence rate increases to 21.9% (95% CI: 18.725.5) when only patients with 1 vascular catheter in place were analyzed.
Focusing on the number of catheter lumens, 631 out of 1448 (43.6%, 95% CI: 41.046.1) were considered unnecessary. There was a nonsignificant trend toward a higher rate of unnecessary lumens in conventional wards compared with ICUs (44.8% vs 39.4%, P=0.086; Table 3). Because some centers have policies requiring all inpatients to harbor 1 PVC in place throughout the entire hospitalization period, we performed a first sensitivity analysis in which we assumed that having a single functional vascular lumen was appropriate in all cases, regardless of any other criteria. Under this assumption, only 248 lumens (17.1%, 95% CI: 15.319.2) could be regarded as unnecessary. We conducted a second sensitivity analysis by including in the rate denominator only those catheter lumens potentially removable (eg, PVCs, nontunneled CVCs, and noncoring needles inserted in Port‐A‐Cath catheters). By applying this method, 48.6% of lumens (631 out of 1298, 95% CI: 45.951.3) could be considered inappropriate.
Rate | Overall | Medical Wards | Surgical Wards | ICUs |
---|---|---|---|---|
| ||||
Patients with 1 inappropriate catheter/overall no. of inpatients, n (%) | 126/834 (15.1) | 66/498 (13.2) | 45/279 (16.1) | 15/57 (26.3)a |
Patients with 1 inappropriate catheter/patients with 1 vascular catheter, n (%) | 126/575 (21.9) | 66/330 (20.0) | 45/188 (23.9) | 15/57 (26.3) |
No. of unnecessary vascular catheter lumens/overall no. of vascular catheter lumens, n (%) | 631/1448 (43.6) | 298/684 (43.6) | 207/444 (46.6) | 126/320 (39.4) b |
DISCUSSION
In this cross‐sectional survey, we found that 1 out of every 5 (20%) adult inpatients with a vascular catheter in place in our tertiary‐care center had an inappropriate number of catheters. This figure increased to 43.6% when the number of catheter lumens was analyzed (or 17.1% if we assumed that all patients should have at least 1 vascular access during their hospitalization period solely on the basis of preemptive reasons). Such rates of unnecessary catheter use throughout an entire institution offer an opportunity for improvement in clinical practice and, eventually, for reducing catheter‐related morbidity.
Other authors have also assessed the adequacy of CVC use in either ICU[16, 17] or non‐ICU settings.[14, 15, 16, 18, 21] A recent hospital‐wide survey found that 4.8% of catheter‐days were unnecessary, with a higher proportion in conventional wards than in the ICU,[16] mirroring the results from previous studies.[14] Another prospective study, limited to conventional wards, reported that almost half of the patients had 1 day with inappropriate vascular‐device use; age, total number of catheters used, and duration of catheterization were significantly associated with this event.[21] On the contrary, compliance with the criteria drawn up by the HICPAC and the Infusion Nurses Society for PICC use was found to be high overall in a medium‐sized community hospital.[22] Interestingly, we found a differential pattern in the adequacy of catheter use between hospital areas in function of the variable analyzed: number of inappropriate vascular catheters (higher rate in ICUs) or number of unnecessary vascular lumens (higher rate in conventional wards). Although our assessment criteria may partially account for such differences (ie, drug infusions for >1 hour justified the use of an exclusive catheter lumen), this finding raises the question of whether future interventions should be aimed at modifying specific catheter practices according to the type of hospital ward.
In contrast to the amount of literature on CVC, there is a scarcity of studies evaluating the appropriateness of PVC use in clinical practice. Lederle et al. reported that 17% of patients admitted in conventional wards of a university hospital had an idle PVC, with 20% of patient‐days of catheter exposure considered unnecessary.[12] The same authors subsequently demonstrated a significant decrease in these figures by implementing a multidisciplinary quality‐improvement intervention.[13] A previous cross‐sectional survey in our center revealed a PVC use rate as high as 46.2% among non‐critically ill adult inpatients.[23] Phlebitis is a common complication of PVC use, occurring in about 7% of inpatients and usually leading to catheter removal and replacement.[24] Although at a much lower incidence, peripheral catheterization also represents a non‐negligible source of CRBSI.[25] In our institution, in which a recently implemented specific bundle has resulted in a clear improvement in CVC care,[4, 20] about 60% of CRBSI occurring during the first 3 months of 2013 were due to PVCs (unpublished data). Therefore, this type of device should be routinely included in future surveys seeking to investigate the local epidemiology of catheter use at each institution. In that sense, it should be noted that a recent clinical trial showed no benefit of routine third‐day replacement vs clinically indicated replacement for phlebitis or CRBSI.[24]
Our study suggests that the daily review of the need for maintaining the vascular catheter should take into account the number of vascular lumens, as >40% of them were deemed unnecessary. To our knowledge, this area for potential intervention has not been addressed in previous surveys. Numerous studies have long demonstrated that the use of double‐lumen or triple‐lumen CVCs is associated with a higher rate of CRBSI than single‐lumen devices.[6, 7, 8] Even though a meta‐analysis concluded that this relationship diminishes when only high‐quality studies were included,[9] a more recent prospective study reported a hazard ratio for infection of 4.4 for each additional lumen.[10] However, it might be argued against our decision to count each inflow port in multiway stopcocks as a separate vascular lumen. Because the present survey was ultimately aimed at identifying opportunities to reduce the risk of CRBSI by decreasing catheter exposure, such an approach was chosen to properly capture and quantify every single potential source of infection in catheterized patients. We hypothesize that the use of 3‐lumen stopcocks could involve an increased number of manipulations, thus jeopardizing the integrity of the insertion‐site dressing and subsequently favoring the intraluminal bacterial colonization of the common catheter. Although the current guidelines do not provide specific recommendations regarding the number of lumens in devices other than CVCs,[3, 19] the potential benefit of reducing this figure to the minimum in PVCs and ACs should also be assumed. In our opinion, specific efforts have to be focused on improving the use of 3‐way stopcocks, as we found a mean of 1.86 lumens per PVC and >3 lumens per CVC in our study. Maybe the need for 3‐way stopcocks should be reassessed on a daily basis in a similar way as that recommended for temporary CVCs.[3, 19] By eliminating unnecessary vascular lumens, the risk of CRBSI could be diminished without compromising the availability of vascular access for preemptive purposes.
The present surveillance also provides an accurate insight into the real‐life vascular catheter practices in a hospital‐wide setting, in contrast with most of the previous studies, which have been conducted in specific wards or units.[13, 15, 17] One relevant finding was the relatively low use of the subclavian vein site for central venous access (only 40.8% of all CVCs inserted), with significant differences between medical wards and the remaining hospital areas. Various studies have shown that the subclavian site is associated with a lower risk of infectious and thrombotic complications.[4, 26, 27, 28] Therefore, the HICPAC and IDSA guidelines strongly recommend using a subclavian site, rather than a jugular or a femoral site, to minimize infection risk for nontunneled CVCs.[3, 19] Nevertheless, recent studies have suggested that internal jugular and femoral sites could be acceptable when a subclavian approach is not feasible, particularly if chlorhexidine‐impregnated dressings are used and catheters are left in place for <4 days.[29]
The current study has a number of inherent limitations; the most significant is its cross‐sectional design, which precludes direct comparison of the rates of catheter use with other prospective cohort surveys.[14, 16, 21] In addition, we were not able to assess the changing dynamics of catheter use over time. In other words, the lack of use of a given device on the day of the survey does not necessarily imply inappropriateness. The criteria used to determine appropriateness of vascular catheterization were consensus opinion and not evidence‐based, a weakness shared by previous studies,[21] as current guidelines only address the indications for certain devices (ie, HICPAC and Infusion Nurses Society recommendations for PICC use).[3, 30] Although we have attempted to be liberal in accepting indications for catheter use (ie, preemptive access in patients deemed at risk of hemodynamic instability), some misclassification bias cannot be ruled out. In evaluating the adequacy of catheter lumens, we did not take into account the simultaneous delivery of incompatible infusateswhich must be infused through separate linesor other relevant variables (eg, nursing availability). Because the aim of our study was simply to determine whether a patient had an appropriate number of vascular catheters and vascular lumens in overall terms, all vascular lumens in each subject were individually counted and added regardless of the nature of the device, and therefore we were not able to disaggregate the adequacy rate by specific catheter types. Finally, the generalizability of the results may be hampered by their single‐center nature, and this limitation applies particularly to institutions with different policies than ours regarding preemptive vascular catheterization (ie, those requiring that all inpatients have at least 1 vascular lumen at any time during hospitalization).
On the other hand, some strengths of this survey merit consideration, namely its comprehensive design, capturing the entire population of adult inpatients in different hospital areas and every type of vascular catheter. Moreover, we addressed the adequacy of maintaining catheterization not only in terms of idle catheters in place, but also in terms of unnecessary lumens. In conclusion, there remains room for improvement in daily practice regarding the prompt removal of vascular catheters and vascular lumens that are no longer medically necessary. Further educational efforts among physicians and nursing staff should be targeted toward achieving this simple but effective measure to reduce the incidence of CRBSI.
Disclosures: M.F.R. holds a research‐training contract Ro Hortega from the Spanish Ministry of Economy and Competitiveness (Instituto de Salud Carlos III; grant no. CM11/00187). F.L.M. is partially supported by a grant from the Research Intensification Programme in the National Health Care System (I3SNS) from the Spanish Ministry of Economy and Competitiveness (Instituto de Salud Carlos III; grant no. INT11/174). All authors report no conflicts of interest relevant to this article. This study was partially presented at the 52nd Annual Interscience Congress on Antimicrobial Agents and Chemotherapy (ICAAC), September 912, 2012, San Francisco, California.
- VINCat Program. Laboratory‐based surveillance of hospital‐acquired catheter‐related bloodstream infections in Catalonia: results of the VINCat Program (2007–2010). Enferm Infecc Microbiol Clin. 2012;30(suppl 3):13–19. , , , ;
- ICU‐Bacteremia Study Group. Outcomes of primary and catheter‐related bacteremia: a cohort and case‐control study in critically ill patients. Am J Respir Crit Care Med. 2001;163:1584–1590. , ;
- Healthcare Infection Control Practices Advisory Committee. Guidelines for the prevention of intravascular catheter‐related infections. Clin Infect Dis. 2011;52:e162–e193. , , , et al;
- An intervention to decrease catheter‐related bloodstream infections in the ICU [published correction appears in N Engl J Med. 2007;356:2660]. N Engl J Med. 2006;355:2725–2732. , , , et al.
- Prevention Epicenter Program. A multicenter intervention to prevent catheter‐associated bloodstream infections. Infect Control Hosp Epidemiol. 2006;27:662–669. , , , et al;
- Infection rate for single‐lumen v triple‐lumen subclavian catheters. Infect Control Hosp Epidemiol. 1988;9:154–158. , , .
- Increased infection rate in double‐lumen versus single‐lumen Hickman catheters in cancer patients. South Med J. 1990;83:34–36. , , , , .
- Use of triple‐lumen subclavian catheters for administration of total parenteral nutrition. JPEN J Parenter Enteral Nutr. 1992;16:403–407. , , , , .
- Rates of infection for single‐lumen versus multilumen central venous catheters: a meta‐analysis. Crit Care Med. 2003;31:2385–2390. , , , , .
- Multilumen central venous catheters increase risk for catheter‐related bloodstream infection: prospective surveillance study. Infection. 2008;36:322–327. , , , et al.
- Preventing catheter‐related bloodstream infections outside the intensive care unit: expanding prevention to new settings. Clin Infect Dis. 2010;51:335–341. , , .
- The idle intravenous catheter. Ann Intern Med. 1992;116:737–738. , , , .
- Reduction of unnecessary intravenous catheter use: internal medicine house staff participate in a successful quality improvement project. Arch Intern Med. 1994;154:1829–1832. , , , .
- Unnecessary use of central venous catheters: the need to look outside the intensive care unit. Infect Control Hosp Epidemiol. 2004;25:266–268. , , , , , .
- Prospective cohort study of central venous catheters among internal medicine ward patients. Am J Infect Control. 2006;34:636–641. , , , , , .
- Hospital‐wide survey of the use of central venous catheters. J Hosp Infect. 2011;77:304–308. , , , et al.
- Evaluation of unnecessary central venous catheters in critically ill patients: a prospective observational study. Can J Anaesth. 2010;57:830–835. , , .
- Temporary central venous catheter utilization patterns in a large tertiary care center: tracking the “idle central venous catheter.” Infect Control Hosp Epidemiol. 2012;33:50–57. , , , et al.
- Strategies to prevent central line‐associated bloodstream infections in acute care hospitals. Infect Control Hosp Epidemiol. 2008;29(suppl 1):S22–S30. , , , et al.
- Spanish Ministry of Health, Social Services and Equality. Quality Agency of the National Health Service. Bacteriemia Zero Project. Available at: http://www.seguridaddelpaciente.es/index.php/lang‐en/projects/financiacion‐estudios/bacteriemia‐zero‐project.html. Accessed June 4, 2013.
- Inappropriate intravascular device use: a prospective study. J Hosp Infect. 2011;78:128–132. , , , , .
- Peripherally inserted central catheter: compliance with evidence‐based indications for insertion in an inpatient setting. J Infus Nurs. 2013;36:291–296. , .
- Use and abuse of intravenous catheters in conventional hospital wards [article in Spanish]. An Med Interna. 2006;23:475–477. , , , et al.
- Routine versus clinically indicated replacement of peripheral intravenous catheters: a randomised controlled equivalence trial. Lancet. 2012;380:1066–1074. , , , et al.
- Clinical epidemiology and outcomes of peripheral venous catheter–related bloodstream infections at a university‐affiliated hospital. J Hosp Infect. 2007;67:22–29. , , , et al.
- Risk of infection due to central venous catheters: effect of site of placement and catheter type. Infect Control Hosp Epidemiol. 1998;19:842–845. , , , .
- Complications of femoral and subclavian venous catheterization in critically ill patients: a randomized controlled trial. JAMA. 2001;286:700–707. , , , et al.
- Central venous access sites for the prevention of venous thrombosis, stenosis and infection in patients requiring long‐term intravenous therapy. Cochrane Database Syst Rev. 2007;(3):CD004084. , .
- Jugular vs. femoral short‐term catheterization and risk of infection in ICU patients: causal analysis of 2 randomized trials. Am J Respir Crit Care Med. 2013;188:1232–1239. , , , et al.
- Infusion Nurses Society. Infusion nursing standards of practice. J Infus Nurs. 2011;34(1 suppl):S38.
- VINCat Program. Laboratory‐based surveillance of hospital‐acquired catheter‐related bloodstream infections in Catalonia: results of the VINCat Program (2007–2010). Enferm Infecc Microbiol Clin. 2012;30(suppl 3):13–19. , , , ;
- ICU‐Bacteremia Study Group. Outcomes of primary and catheter‐related bacteremia: a cohort and case‐control study in critically ill patients. Am J Respir Crit Care Med. 2001;163:1584–1590. , ;
- Healthcare Infection Control Practices Advisory Committee. Guidelines for the prevention of intravascular catheter‐related infections. Clin Infect Dis. 2011;52:e162–e193. , , , et al;
- An intervention to decrease catheter‐related bloodstream infections in the ICU [published correction appears in N Engl J Med. 2007;356:2660]. N Engl J Med. 2006;355:2725–2732. , , , et al.
- Prevention Epicenter Program. A multicenter intervention to prevent catheter‐associated bloodstream infections. Infect Control Hosp Epidemiol. 2006;27:662–669. , , , et al;
- Infection rate for single‐lumen v triple‐lumen subclavian catheters. Infect Control Hosp Epidemiol. 1988;9:154–158. , , .
- Increased infection rate in double‐lumen versus single‐lumen Hickman catheters in cancer patients. South Med J. 1990;83:34–36. , , , , .
- Use of triple‐lumen subclavian catheters for administration of total parenteral nutrition. JPEN J Parenter Enteral Nutr. 1992;16:403–407. , , , , .
- Rates of infection for single‐lumen versus multilumen central venous catheters: a meta‐analysis. Crit Care Med. 2003;31:2385–2390. , , , , .
- Multilumen central venous catheters increase risk for catheter‐related bloodstream infection: prospective surveillance study. Infection. 2008;36:322–327. , , , et al.
- Preventing catheter‐related bloodstream infections outside the intensive care unit: expanding prevention to new settings. Clin Infect Dis. 2010;51:335–341. , , .
- The idle intravenous catheter. Ann Intern Med. 1992;116:737–738. , , , .
- Reduction of unnecessary intravenous catheter use: internal medicine house staff participate in a successful quality improvement project. Arch Intern Med. 1994;154:1829–1832. , , , .
- Unnecessary use of central venous catheters: the need to look outside the intensive care unit. Infect Control Hosp Epidemiol. 2004;25:266–268. , , , , , .
- Prospective cohort study of central venous catheters among internal medicine ward patients. Am J Infect Control. 2006;34:636–641. , , , , , .
- Hospital‐wide survey of the use of central venous catheters. J Hosp Infect. 2011;77:304–308. , , , et al.
- Evaluation of unnecessary central venous catheters in critically ill patients: a prospective observational study. Can J Anaesth. 2010;57:830–835. , , .
- Temporary central venous catheter utilization patterns in a large tertiary care center: tracking the “idle central venous catheter.” Infect Control Hosp Epidemiol. 2012;33:50–57. , , , et al.
- Strategies to prevent central line‐associated bloodstream infections in acute care hospitals. Infect Control Hosp Epidemiol. 2008;29(suppl 1):S22–S30. , , , et al.
- Spanish Ministry of Health, Social Services and Equality. Quality Agency of the National Health Service. Bacteriemia Zero Project. Available at: http://www.seguridaddelpaciente.es/index.php/lang‐en/projects/financiacion‐estudios/bacteriemia‐zero‐project.html. Accessed June 4, 2013.
- Inappropriate intravascular device use: a prospective study. J Hosp Infect. 2011;78:128–132. , , , , .
- Peripherally inserted central catheter: compliance with evidence‐based indications for insertion in an inpatient setting. J Infus Nurs. 2013;36:291–296. , .
- Use and abuse of intravenous catheters in conventional hospital wards [article in Spanish]. An Med Interna. 2006;23:475–477. , , , et al.
- Routine versus clinically indicated replacement of peripheral intravenous catheters: a randomised controlled equivalence trial. Lancet. 2012;380:1066–1074. , , , et al.
- Clinical epidemiology and outcomes of peripheral venous catheter–related bloodstream infections at a university‐affiliated hospital. J Hosp Infect. 2007;67:22–29. , , , et al.
- Risk of infection due to central venous catheters: effect of site of placement and catheter type. Infect Control Hosp Epidemiol. 1998;19:842–845. , , , .
- Complications of femoral and subclavian venous catheterization in critically ill patients: a randomized controlled trial. JAMA. 2001;286:700–707. , , , et al.
- Central venous access sites for the prevention of venous thrombosis, stenosis and infection in patients requiring long‐term intravenous therapy. Cochrane Database Syst Rev. 2007;(3):CD004084. , .
- Jugular vs. femoral short‐term catheterization and risk of infection in ICU patients: causal analysis of 2 randomized trials. Am J Respir Crit Care Med. 2013;188:1232–1239. , , , et al.
- Infusion Nurses Society. Infusion nursing standards of practice. J Infus Nurs. 2011;34(1 suppl):S38.
© 2013 Society of Hospital Medicine
Lichen Nitidus
Consensus Recommendations From the American Acne & Rosacea Society on the Management of Rosacea, Part 2: A Status Report on Topical Agents
Analysis of Multiple In‐Hospital CPR
Cardiopulmonary resuscitation (CPR) is a potentially lifesaving intervention associated with intense resource utilization and poor outcomes.[1, 2, 3] CPR is the default intervention for hospitalized patients in cardiopulmonary arrest in the United States. The most common measure of successful in‐hospital CPR reported in the literature is survival to (hospital) discharge, with most estimates between 13% and 37%.[3, 4, 5, 6] Poor rates of survival to discharge may be explained by use of CPR in patients for whom it was not originally intended, such as the very elderly with multiple illnesses or the terminally ill.[7, 8] Use of CPR in patients unlikely to benefit may be due to a physician's inability to estimate the probability of survival, desire to offer hope to patients, fear of litigation, and poor communication with patients about goals of care.[7, 8, 9, 10]
The general public has overly optimistic expectations about CPR; surveys have reported perceived survival after CPR of up to 90%.[11, 12, 13] Although objective information substantially affects patient preferences for resuscitation,[14] prognosis is rarely discussed during code status encounters[15, 16]; physician estimates of prognosis also are often inaccurate.[9, 17] With a scarcity of data describing the characteristics of patients undergoing multiple CPR attempts, and their outcomes, patients and their families could have false expectations about the likely outcomes from multiple CPR attempts, because physician counsel is not well‐informed.
In this study, we examine the epidemiology of in‐hospital CPR recipients stratified by the number of occurrences of CPR during a single hospitalization, along with their outcomes. We hypothesize that recipients of multiple CPR during a single hospitalization are an epidemiologically distinct group compared with those who receive CPR once during their hospitalization, and that their outcomes are worse.
METHODS
Data Source
We used unweighted data for the years 2000 to 2009 from the Healthcare Cost and Utilization ProjectNationwide Inpatient Sample (HCUP‐NIS). The NIS is the largest all‐payer inpatient‐care database in the United States, containing nationally representative information regarding up to 8 million hospital stays per year. Each year, NIS data consist of a 20% stratified sample of hospital discharges involving up to 1100 nonfederal hospitals from up to 44 states. The NIS utilizes International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes to capture up to 25 diagnoses and 15 procedures associated with the index hospitalization.[18]
Demographic, Clinical, and Hospital Characteristics of Cardiopulmonary Resuscitation Recipients
Adults (age 18 years) who underwent CPR (ICD‐9 procedure code 99.60) during their hospitalization were abstracted; this ICD‐9 code has been used previously to explore CPR epidemiology and outcomes.[3, 19, 20] Patients were divided into 2 groups, those who had 1 CPR attempt and those who had multiple (>1) CPR attempts, based on the number of times the ICD‐9 code for CPR was included in their hospitalization data. Patients who had cardiopulmonary arrest (ICD‐9 code 427.5 or 799.1) as a presenting diagnosis were excluded, as these indicate an out‐of‐hospital event.
Demographic variables included patient age, sex, race, median household income as defined annually in the NIS dataset, insurance status, admission source (skilled nursing facility or not; emergency room vs not), and type (elective vs nonelective; trauma vs nontrauma). Clinical variables included patient comorbidity as assessed by using the enhanced Charlson Comorbidity Index (CCI).[21] Rates of in‐hospital dialysis (ICD‐9 codes 39.95, V451, V561), tracheostomy (ICD‐9 codes 31.1, 31.2), in‐hospital neurologic compromise (coma, ICD‐9 code 780.01; semi‐coma, ICD‐9 code 780.09; persistent vegetative state, ICD‐9 code 780.03; anoxic brain injury, ICD‐9 code 348.1; and brain damage, ICD‐9 code 997.01), ventilator support (ICD‐9 code 967.02); and artificial nutrition (total parenteral nutrition, ICD‐9 code 99.15; enteral infusion of nutritional substances, ICD‐9 code 96.6) were assessed as potential indicators of clinical debilitation and/or intense healthcare resource utilization. Hospital variables were region in the United States (Northeast, Midwest, West, and South), location (urban vs nonurban), teaching status, and bed size (small, medium, and large), as defined annually in the NIS.[18]
Outcomes
Outcomes of interest were survival to discharge, discharge disposition, and cost of hospitalization.
Statistical Analysis
Sensitivity analyses were done to validate the use of the number of occurrences of CPR code 99.60 as a marker of multiple CPR, as well the association between multiple CPR and outcome. We computed the interval (in days) between the first and last CPR such that a result would not be computed if either value were missing. We found that 80.2% of patients who had CPR multiple times also had valid interval data between the first and last CPR. This was slightly higher than the 75.9% of patients with 1 CPR code who also had valid data for the interval (in days) between admission and CPR, indicating the reliability of using the number of CPR codes as a marker of multiple CPR attempts.
Bivariate analyses comparing characteristics and outcomes of interest for recipients of 1 CPR versus multiple CPR were performed using the [2] test for categorical variables and Student t test for continuous variables; differences in age and CCI score (analyzed as continuous variables) were assessed using the Mann‐Whitney U test because the distribution of data for these was not normal. Hospital length of stay and cost were natural log transformed to normalize distribution. Cost was calculated using HCUP‐NISadjusted, hospital‐specific cost‐to‐charge ratios; costs were adjusted for inflation, converting all costs to year 2009 dollar values using rates from the US Bureau of Labor Statistics.[22] Cost‐to‐charge ratios were first made available in the NIS datasets in year 2001; therefore, data for the year 2000 were excluded from all cost analyses. The aggregate cost of hospitalization at a population‐level was estimated using the discharge weight variable included in the NIS.
Separate multivariate logistic regression models were constructed to assess (1) factors independently associated with occurrence of multiple CPR, and (2) whether multiple CPR is independently associated with survival to discharge. Generalized estimating equations were used to account for hospital clustering. Odds ratios (OR) with 95% confidence intervals (CI) were computed for the final multivariate models. All P values <0.05 were considered significant; all tests were 2‐sided.
Data management and analysis were performed using SAS statistical software, version 9.3 (SAS Institute Inc, Cary, NC), and SPSS for Windows, version 18.0 (SPSS Inc, Chicago, IL). The HCUP‐NIS is a public database with no personally identifying information. This study was deemed exempt from institutional review board approval at our institution.
RESULTS
Of a total of 65,308,185 adults hospitalized between the years 2000 and 2009, there were 166,519 CPR recipients, yielding a CPR incidence of 2.5 per 1000 hospitalizations. Among CPR recipients, 96.6% (n=166,899) had 1 CPR and 3.4% (n=5620) had multiple CPR during their hospitalization (range, 111 CPR). When further stratified, 3% had 2 CPR attempts (n=4949) and 0.4% (n=671) had 3 CPR attempts.
Compared with patients who had 1 CPR, those who had multiple CPR were more often younger (median age, 71 vs 67 years), nonwhite, and in a low‐income quartile (all P<0.001; Table 1). Rates of admission from a nursing facility (3.3% for the 1‐CPR group vs 3.1% for the multiple‐CPR group, P=0.65) or as a trauma (0.3% for the 1‐CPR group and 0.4% for the multiple‐CPR group, P=0.34) were similar.
Characteristic | 1 CPR (n=160,899), % | Multiple CPRs (n=5,620), % | P Value |
---|---|---|---|
| |||
Sex, F | 45.6 | 47.2 | 0.02 |
Age, y, <65 | 37.3 | 42.5 | <0.001 |
Race | <0.001 | ||
White | 65.8 | 58.7 | |
Black | 18.7 | 21.6 | |
Other | 15.5 | 19.8 | |
Income quartile | <0.001 | ||
Low | 24.1 | 27.8 | |
Medium‐low | 24.9 | 24.7 | |
Medium | 23.2 | 22.9 | |
High | 25.2 | 22.2 | |
Unknown | 2.5 | 2.4 | |
Insurance | <0.001 | ||
Medicare | 65.1 | 61.8 | |
Medicaid | 9.4 | 12.4 | |
Private | 18.4 | 17.7 | |
Other | 7.1 | 8.1 | |
Admission source, ER | 67.9 | 72.0 | <0.001 |
Admission type, elective | 10.0 | 7.1 | <0.001 |
Patients who had multiple CPR had slightly higher mean CCI scores (2.7 vs 2.6, P=0.02). They had higher rates of neurologic compromise and aggressive interventions; they were also more commonly treated in nonteaching hospitals, and in the western region of the United States (Table 2). After multivariate analysis, several patient, clinical, and hospital factors were independently associated with occurrence of multiple CPR (Figure 1).
Characteristic | 1 CPR (n=160,899), % | Multiple CPRs (n=5,620), % | P Value |
---|---|---|---|
| |||
Clinical | |||
Charlson score 4 | 25.4 | 27.2 | 0.002 |
MI | 24.9 | 28.5 | <0.001 |
CHF | 38.3 | 43.3 | <0.001 |
Cerebrovascular event | 8.5 | 7.1 | <0.001 |
Metastatic malignancy | 10.6 | 8.7 | <0.001 |
COPD | 26.0 | 26.0 | 0.945 |
Neurologic impairment | 13.8 | 21.1 | <0.001 |
Supplemental nutrition | 7.2 | 8.3 | 0.002 |
Mechanical ventilator | 57.4 | 83.1 | <0.001 |
Cardiac surgery | 2.6 | 2.0 | 0.007 |
Hospital | |||
Location, urban | 90.1 | 92.1 | <0.001 |
Teaching status, no | 58.0 | 64.5 | <0.001 |
Region | <0.001 | ||
Northeast | 19.0 | 15.2 | |
Midwest | 18.6 | 15.7 | |
South | 37.4 | 37.1 | |
West | 25.0 | 32.0 | |
Bed size | 0.715 | ||
Small | 10.2 | 9.8 | |
Medium | 25.5 | 25.3 | |
Large | 64.3 | 64.9 |

In bivariate analysis of survival, patients who had multiple CPR had lower rates of survival to discharge (11.3% vs 23.4%, P<0.001). Results were similar (11.6% for multiple CPR vs 22.5% for 1 CPR, P<0.001) when all patients who had CPR but did not have valid timing data were excluded in sensitivity analyses. Further stratification showed that survival to discharge decreased by >40% for each increase in CPR attempt (23.4%, 11.9%, and 6.7% for 1, 2, and 3 CPR attempts, respectively, P<0.001; Figure 2). After adjustment, multiple CPR versus 1 CPR during a hospitalization was independently associated with a lower likelihood of survival to discharge (adjusted OR: 0.41, 95% CI: 0.37‐0.44, P<0.001; Table 3).

Characteristica | OR | 95% CI | P Value | |
---|---|---|---|---|
Lower | Upper | |||
| ||||
Demographic | ||||
Age <65 years | 1.339 | 1.304 | 1.375 | <0.001 |
Sex, F | 1.128 | 1.099 | 1.157 | <0.001 |
Race, nonwhite | 0.781 | 0.758 | 0.804 | <0.001 |
Low income quartile | 0.887 | 0.858 | 0.915 | <0.001 |
Year of admission | 1.051 | 1.046 | 1.056 | <0.001 |
Clinical | ||||
Multiple CPR | 0.406 | 0.371 | 0.445 | <0.001 |
CCI score | 0.939 | 0.933 | 0.944 | <0.001 |
Cardiac surgery | 1.785 | 1.720 | 1.853 | <0.001 |
Hospital | ||||
Region, Midwest | 1.472 | 1.405 | 1.543 | <0.001 |
Region, South | 1.262 | 1.218 | 1.309 | 0.008 |
Region, West | 1.452 | 1.398 | 1.509 | <0.001 |
Location, urban | 0.876 | 0.837 | 0.917 | <0.001 |
Survivors with multiple CPR were less likely to be discharged home compared with survivors with 1 CPR (19.3% vs 29.9%, respectively, P<0.001); 1 in 15 survivors of multiple CPR were discharged to a hospice (6.8%) versus 1 in 23 1‐CPR survivors (4.3%; P=0.002). Mean length of stay was 5.8 versus 5.5 days for patients who had multiple CPR versus 1 CPR, respectively (P<0.001), and 16.0 versus 10.5 days for discharged survivors of multiple CPR versus 1 CPR (P<0.001). The average cost per day of hospitalization was higher for recipients of multiple CPR versus 1 CPR ($4484.60 vs $3581.40, P<0.001). The aggregate cost of hospitalization for 1‐time CPR recipients doubled between the years 2001 and 2009 (from $1.3 billion to $2.9 billion); that of recipients of multiple CPR attempts quadrupled in the same time frame (from $38.6 million to $160.7 million).
DISCUSSION
A number of studies have investigated the epidemiology of patients in whom CPR is attempted.[2, 3, 5, 20, 23, 24] Several pre‐, intra‐, and post‐resuscitation factors have been shown to affect the survival of resuscitated patients.[6, 7, 25, 26] To our knowledge, neither the epidemiology of hospitalized patients in whom resuscitation is attempted multiple times nor the prognostic value of multiple CPR attempts has been investigated. In this study, we found that multiple resuscitations are more commonly performed on younger, generally sicker patients; their outcomes are significantly compromised compared with patients who are resuscitated once during their hospitalization.
There was a steep decline in survival based on the number of resuscitation events. In multivariate analysis, patients who had multiple CPR were 2.5‐fold less likely to survive their hospitalization; survivors of multiple CPR also were more likely to be discharged to a hospice. Overall, this is indicative of clinical deterioration and prolongation of dying should a patient suffer multiple cardiopulmonary arrests during a hospitalization. The robust inverse relationship between multiple CPR and survival to discharge has implications for the development of prognostic models of outcomes following CPR, as previously designed prediction models of CPR outcomes such as the Cardiac Arrest Survival Post‐Resuscitation In‐hospital (CASPRI) score,[25] Pre‐Arrest Morbidity (PAM) score,[27] and Prognosis After Resuscitation (PAR) score[28] do not include multiple resuscitations as a variable of interest.
In‐hospital factors were found to be more important than patient factors, such as comorbidities or race, in determining the likelihood of multiple CPR attempts. Hospital teaching status and region remained significantly associated with likelihood of multiple CPR attempts. This is in agreement with studies that have described demographic and regional variation in utilization of do‐not‐resuscitate orders.[29, 30] These findings suggest substantial heterogeneity in the clinical culture and hospital practices across the United States regarding preemptive discussions about resuscitation. This means that where a patient receives care is a significant determinant of their probability of undergoing multiple CPR.
It is known that older patients are more likely to have advance directive orders[30, 31] and possibly document their wishes with regard to further resuscitation efforts. There also may be an inclination toward more aggressive care for younger adults compared with those of an advanced age. Uncertainty about a patient's goals of care likely feeds into an increased possibility of multiple resuscitation attempts; this may explain why neurologic compromise and being on ventilator support were independently associated with likelihood of multiple CPR, as these patients often have lost their ability to actively participate in decision‐making. The results of this study highlight the importance of engaging patients with a plausible risk of cardiopulmonary arrest about their goals for care and advance directives in a timely manner, regardless of age.
We found that the care of patients who undergo multiple resuscitations is associated with a higher cost of hospitalization than for patients in whom resuscitation is attempted once during their hospitalization. In addition, there was an exponential increase in aggregate cost over time for multiple CPR recipients compared with 1‐time CPR recipients. In a prior study, Ebell and Kruse showed an exponential inverse relationship between cost per surviving patient and rate of survival to discharge.[32] Considering that 93.3% of patients who had 3 resuscitation attempts died during their hospitalization, and that hospital‐level factors appear to play a significant role in likelihood of multiple CPR, consensus guidelines regarding the appropriateness of 3 resuscitation attempts during a single hospitalization may be relevant to aid the care of these patients.
Although the NIS is well‐validated,[18] there are some limitations. Whereas CPR incidence in this study (2.5 per 1000 hospitalizations) is within estimates (15 arrests per 1000 hospitalizations) reported in previous studies,[3, 5] potential undercoding of multiple CPR may explain why the multiple‐CPR rate in this study is lower than re‐arrest estimates provided in published studies.[2, 33] Indeed, accurate calculation of re‐arrest rates requires data on do‐not‐resuscitate orders instituted after successful resuscitation, which are not provided in the NIS. Information on patient‐provider discussions about CPR or prognosis is not included. Data regarding the underlying cause and type of arrest rhythm, rates of return to spontaneous circulation, length of code, patient location, critical‐care resources and length of critical‐care stay, availability of rapid‐response/code teams, time to defibrillation, use of therapeutic hypothermia, adherence to resuscitation guidelines, quality of CPR, and long‐term follow‐up are not included in the database. Presenting rhythms were not assessed, as there are no ICD‐9 codes for asystole and pulseless electrical activity. The NIS is de‐identified; therefore, chart review to assess the validity of codes is impossible. However, our sensitivity analyses indicate the reliability of using the number of occurrences of the CPR code as a marker of multiple CPR. The strength of our study lies in the use of data that provide a population‐level insight into the epidemiology of patients resuscitated multiple times during their hospitalization, and their outcomes.
Decision‐making about CPR is at the center of a complex debate that incorporates often divergent clinical, economic, ethical, and personal issues. As debate continues regarding when to not resuscitate,[34, 35, 36, 37] studies that explore the public perspective of survival thresholds for the provision of multiple resuscitations will be crucial. As competition for finite healthcare dollars escalates, stratified analyses of the cost implications of resuscitation care are essential. Studies are needed to examine the impact of a history of successful resuscitation in a previous hospitalization on outcomes following CPR in a subsequent hospitalization. Overall, our study fills an important knowledge gap in resuscitation practice and outcomes in the United States and highlights the importance of discussing resuscitation options between a patient and his or her family on hospital admission and, if needed, again after the first successful resuscitation attempt.
Disclosure
Nothing to report.
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- Cardiopulmonary resuscitation of adults in the hospital: a report of 14,720 cardiac arrests from the national registry of cardiopulmonary resuscitation. Resuscitation. 2003;58:297–308. , , , et al.
- Epidemiologic study of in‐hospital cardiopulmonary resuscitation in the elderly. N Engl J Med. 2009;361:22–31. , , , et al.
- National Registry of Cardiopulmonary Resuscitation Investigators. Survival from in‐hospital cardiac arrest during nights and weekends. JAMA. 2008;299:785–792. , , , et al;
- In‐hospital cardiac arrest: incidence, prognosis and possible measures to improve survival. Intensive Care Med. 2007;33:237–245. , , , .
- Predictors of survival following in‐hospital adult cardiopulmonary resuscitation. CMAJ. 2002;167:343–348. , , , .
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- Cardiopulmonary resuscitation in older people—a review. Rev Clin Gerontol. 2010;20:20–29. , .
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- Physicians' confidence in discussing do not resuscitate orders with patients and surrogates. J Med Ethics. 2008;34:96–101. , , .
- How misconceptions among elderly patients regarding survival outcomes of inpatient cardiopulmonary resuscitation affect do‐not‐resuscitate orders. J Am Osteopath Assoc. 2006;106:402–404. , .
- Cardiopulmonary resuscitation on television—miracles and misinformation. N Engl J Med. 1996;334:1578–1582. , , .
- Public expectations of survival following cardiopulmonary resuscitation. Acad Emerg Med. 2000;7:48–53. , , .
- The influence of the probability of survival on patients' preferences regarding cardiopulmonary resuscitation. N Engl J Med. 1994;330:545–549. , , , et al.
- Code status discussions between attending hospitalist physicians and medical patients at hospital admission. J Gen Intern Med. 2011;26:359–366. , , , , .
- Hospital do‐not‐resuscitate orders: why they have failed and how to fix them. J Gen Intern Med. 2011;26:791–797. , , .
- The inability of physicians to predict the outcome of in‐hospital resuscitation. J Gen Intern Med. 1996;11:16–22. , , , .
- Healthcare Cost and Utilization Project. Overview of the Nationwide Inpatient Sample. http://www.hcup‐us.ahrq.gov/nisoverview.jsp. Accessed June 24, 2013.
- Long‐term outcomes in elderly survivors of in‐hospital cardiac arrest. N Engl J Med. 2013;368:1019–1026. , , , et al.
- Epidemiology and outcomes of in‐hospital cardiopulmonary resuscitation in the United States, 2000–2009. Resuscitation. 2013;84:1255–1260. , , .
- Coding algorithms for defining comorbidities in ICD‐9‐CM and ICD‐10 administrative data. Med Care. 2005;43:1130–1139. , , , et al.
- US Department of Labor, Bureau of Labor Statistics. Inflation calculator. http://www.bls.gov/data/inflation_calculator.htm. Accessed June 24, 2013.
- Part 4: CPR overview. 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2010;122:S676–S684. , , , et al.
- Choices of seriously ill patients about cardiopulmonary resuscitation: correlates and outcomes. Am J Med. 1996;100:128–137. , , , et al.
- A validated prediction tool for initial survivors of in‐hospital cardiac arrest. Arch Intern Med. 2012;172:947–953. , , , et al.
- Pre‐resuscitation factors associated with mortality in 49,130 cases of in‐hospital cardiac arrest: a report from the national registry for cardiopulmonary resuscitation. Resuscitation. 2010;81:302–311. , , , .
- Pre‐arrest morbidity and other correlates of survival after in‐hospital cardiopulmonary arrest. Am J Med. 1989;87:28–34. , , , .
- Prediction of failure to survive following in‐hospital cardiopulmonary resuscitation: comparison of two predictive instruments. Resuscitation. 1994;28:21–25. , .
- Regional and institutional variation in the initiation of early do‐not‐resuscitate orders. Arch Intern Med. 2005;165:1705–1712. , .
- Epidemiology of do‐not‐resuscitate orders: disparity by age, diagnosis, gender, race, and functional impairment. Arch Intern Med. 1995;155:2056–2062. , , , et al.
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- A proposed model for the cost of cardiopulmonary resuscitation. Med Care. 1994;32:640–649. , .
- Predictors of cardiopulmonary arrest outcome in a comprehensive cancer center intensive care unit. Scand J Trauma Resusc Emerg Med. 2013; 21:18. , , .
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- Should there be a choice for cardiopulmonary resuscitation when death is expected? Revisiting an old idea whose time is yet to come. J Palliat Med. 2002;5:107–116. .
- Clinical model for ethical cardiopulmonary resuscitation decision‐making. Intern Med J. 2013;43:77–83. .
- Avoiding the futility of resuscitation. Resuscitation. 2001;50:161–166. , , .
Cardiopulmonary resuscitation (CPR) is a potentially lifesaving intervention associated with intense resource utilization and poor outcomes.[1, 2, 3] CPR is the default intervention for hospitalized patients in cardiopulmonary arrest in the United States. The most common measure of successful in‐hospital CPR reported in the literature is survival to (hospital) discharge, with most estimates between 13% and 37%.[3, 4, 5, 6] Poor rates of survival to discharge may be explained by use of CPR in patients for whom it was not originally intended, such as the very elderly with multiple illnesses or the terminally ill.[7, 8] Use of CPR in patients unlikely to benefit may be due to a physician's inability to estimate the probability of survival, desire to offer hope to patients, fear of litigation, and poor communication with patients about goals of care.[7, 8, 9, 10]
The general public has overly optimistic expectations about CPR; surveys have reported perceived survival after CPR of up to 90%.[11, 12, 13] Although objective information substantially affects patient preferences for resuscitation,[14] prognosis is rarely discussed during code status encounters[15, 16]; physician estimates of prognosis also are often inaccurate.[9, 17] With a scarcity of data describing the characteristics of patients undergoing multiple CPR attempts, and their outcomes, patients and their families could have false expectations about the likely outcomes from multiple CPR attempts, because physician counsel is not well‐informed.
In this study, we examine the epidemiology of in‐hospital CPR recipients stratified by the number of occurrences of CPR during a single hospitalization, along with their outcomes. We hypothesize that recipients of multiple CPR during a single hospitalization are an epidemiologically distinct group compared with those who receive CPR once during their hospitalization, and that their outcomes are worse.
METHODS
Data Source
We used unweighted data for the years 2000 to 2009 from the Healthcare Cost and Utilization ProjectNationwide Inpatient Sample (HCUP‐NIS). The NIS is the largest all‐payer inpatient‐care database in the United States, containing nationally representative information regarding up to 8 million hospital stays per year. Each year, NIS data consist of a 20% stratified sample of hospital discharges involving up to 1100 nonfederal hospitals from up to 44 states. The NIS utilizes International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes to capture up to 25 diagnoses and 15 procedures associated with the index hospitalization.[18]
Demographic, Clinical, and Hospital Characteristics of Cardiopulmonary Resuscitation Recipients
Adults (age 18 years) who underwent CPR (ICD‐9 procedure code 99.60) during their hospitalization were abstracted; this ICD‐9 code has been used previously to explore CPR epidemiology and outcomes.[3, 19, 20] Patients were divided into 2 groups, those who had 1 CPR attempt and those who had multiple (>1) CPR attempts, based on the number of times the ICD‐9 code for CPR was included in their hospitalization data. Patients who had cardiopulmonary arrest (ICD‐9 code 427.5 or 799.1) as a presenting diagnosis were excluded, as these indicate an out‐of‐hospital event.
Demographic variables included patient age, sex, race, median household income as defined annually in the NIS dataset, insurance status, admission source (skilled nursing facility or not; emergency room vs not), and type (elective vs nonelective; trauma vs nontrauma). Clinical variables included patient comorbidity as assessed by using the enhanced Charlson Comorbidity Index (CCI).[21] Rates of in‐hospital dialysis (ICD‐9 codes 39.95, V451, V561), tracheostomy (ICD‐9 codes 31.1, 31.2), in‐hospital neurologic compromise (coma, ICD‐9 code 780.01; semi‐coma, ICD‐9 code 780.09; persistent vegetative state, ICD‐9 code 780.03; anoxic brain injury, ICD‐9 code 348.1; and brain damage, ICD‐9 code 997.01), ventilator support (ICD‐9 code 967.02); and artificial nutrition (total parenteral nutrition, ICD‐9 code 99.15; enteral infusion of nutritional substances, ICD‐9 code 96.6) were assessed as potential indicators of clinical debilitation and/or intense healthcare resource utilization. Hospital variables were region in the United States (Northeast, Midwest, West, and South), location (urban vs nonurban), teaching status, and bed size (small, medium, and large), as defined annually in the NIS.[18]
Outcomes
Outcomes of interest were survival to discharge, discharge disposition, and cost of hospitalization.
Statistical Analysis
Sensitivity analyses were done to validate the use of the number of occurrences of CPR code 99.60 as a marker of multiple CPR, as well the association between multiple CPR and outcome. We computed the interval (in days) between the first and last CPR such that a result would not be computed if either value were missing. We found that 80.2% of patients who had CPR multiple times also had valid interval data between the first and last CPR. This was slightly higher than the 75.9% of patients with 1 CPR code who also had valid data for the interval (in days) between admission and CPR, indicating the reliability of using the number of CPR codes as a marker of multiple CPR attempts.
Bivariate analyses comparing characteristics and outcomes of interest for recipients of 1 CPR versus multiple CPR were performed using the [2] test for categorical variables and Student t test for continuous variables; differences in age and CCI score (analyzed as continuous variables) were assessed using the Mann‐Whitney U test because the distribution of data for these was not normal. Hospital length of stay and cost were natural log transformed to normalize distribution. Cost was calculated using HCUP‐NISadjusted, hospital‐specific cost‐to‐charge ratios; costs were adjusted for inflation, converting all costs to year 2009 dollar values using rates from the US Bureau of Labor Statistics.[22] Cost‐to‐charge ratios were first made available in the NIS datasets in year 2001; therefore, data for the year 2000 were excluded from all cost analyses. The aggregate cost of hospitalization at a population‐level was estimated using the discharge weight variable included in the NIS.
Separate multivariate logistic regression models were constructed to assess (1) factors independently associated with occurrence of multiple CPR, and (2) whether multiple CPR is independently associated with survival to discharge. Generalized estimating equations were used to account for hospital clustering. Odds ratios (OR) with 95% confidence intervals (CI) were computed for the final multivariate models. All P values <0.05 were considered significant; all tests were 2‐sided.
Data management and analysis were performed using SAS statistical software, version 9.3 (SAS Institute Inc, Cary, NC), and SPSS for Windows, version 18.0 (SPSS Inc, Chicago, IL). The HCUP‐NIS is a public database with no personally identifying information. This study was deemed exempt from institutional review board approval at our institution.
RESULTS
Of a total of 65,308,185 adults hospitalized between the years 2000 and 2009, there were 166,519 CPR recipients, yielding a CPR incidence of 2.5 per 1000 hospitalizations. Among CPR recipients, 96.6% (n=166,899) had 1 CPR and 3.4% (n=5620) had multiple CPR during their hospitalization (range, 111 CPR). When further stratified, 3% had 2 CPR attempts (n=4949) and 0.4% (n=671) had 3 CPR attempts.
Compared with patients who had 1 CPR, those who had multiple CPR were more often younger (median age, 71 vs 67 years), nonwhite, and in a low‐income quartile (all P<0.001; Table 1). Rates of admission from a nursing facility (3.3% for the 1‐CPR group vs 3.1% for the multiple‐CPR group, P=0.65) or as a trauma (0.3% for the 1‐CPR group and 0.4% for the multiple‐CPR group, P=0.34) were similar.
Characteristic | 1 CPR (n=160,899), % | Multiple CPRs (n=5,620), % | P Value |
---|---|---|---|
| |||
Sex, F | 45.6 | 47.2 | 0.02 |
Age, y, <65 | 37.3 | 42.5 | <0.001 |
Race | <0.001 | ||
White | 65.8 | 58.7 | |
Black | 18.7 | 21.6 | |
Other | 15.5 | 19.8 | |
Income quartile | <0.001 | ||
Low | 24.1 | 27.8 | |
Medium‐low | 24.9 | 24.7 | |
Medium | 23.2 | 22.9 | |
High | 25.2 | 22.2 | |
Unknown | 2.5 | 2.4 | |
Insurance | <0.001 | ||
Medicare | 65.1 | 61.8 | |
Medicaid | 9.4 | 12.4 | |
Private | 18.4 | 17.7 | |
Other | 7.1 | 8.1 | |
Admission source, ER | 67.9 | 72.0 | <0.001 |
Admission type, elective | 10.0 | 7.1 | <0.001 |
Patients who had multiple CPR had slightly higher mean CCI scores (2.7 vs 2.6, P=0.02). They had higher rates of neurologic compromise and aggressive interventions; they were also more commonly treated in nonteaching hospitals, and in the western region of the United States (Table 2). After multivariate analysis, several patient, clinical, and hospital factors were independently associated with occurrence of multiple CPR (Figure 1).
Characteristic | 1 CPR (n=160,899), % | Multiple CPRs (n=5,620), % | P Value |
---|---|---|---|
| |||
Clinical | |||
Charlson score 4 | 25.4 | 27.2 | 0.002 |
MI | 24.9 | 28.5 | <0.001 |
CHF | 38.3 | 43.3 | <0.001 |
Cerebrovascular event | 8.5 | 7.1 | <0.001 |
Metastatic malignancy | 10.6 | 8.7 | <0.001 |
COPD | 26.0 | 26.0 | 0.945 |
Neurologic impairment | 13.8 | 21.1 | <0.001 |
Supplemental nutrition | 7.2 | 8.3 | 0.002 |
Mechanical ventilator | 57.4 | 83.1 | <0.001 |
Cardiac surgery | 2.6 | 2.0 | 0.007 |
Hospital | |||
Location, urban | 90.1 | 92.1 | <0.001 |
Teaching status, no | 58.0 | 64.5 | <0.001 |
Region | <0.001 | ||
Northeast | 19.0 | 15.2 | |
Midwest | 18.6 | 15.7 | |
South | 37.4 | 37.1 | |
West | 25.0 | 32.0 | |
Bed size | 0.715 | ||
Small | 10.2 | 9.8 | |
Medium | 25.5 | 25.3 | |
Large | 64.3 | 64.9 |

In bivariate analysis of survival, patients who had multiple CPR had lower rates of survival to discharge (11.3% vs 23.4%, P<0.001). Results were similar (11.6% for multiple CPR vs 22.5% for 1 CPR, P<0.001) when all patients who had CPR but did not have valid timing data were excluded in sensitivity analyses. Further stratification showed that survival to discharge decreased by >40% for each increase in CPR attempt (23.4%, 11.9%, and 6.7% for 1, 2, and 3 CPR attempts, respectively, P<0.001; Figure 2). After adjustment, multiple CPR versus 1 CPR during a hospitalization was independently associated with a lower likelihood of survival to discharge (adjusted OR: 0.41, 95% CI: 0.37‐0.44, P<0.001; Table 3).

Characteristica | OR | 95% CI | P Value | |
---|---|---|---|---|
Lower | Upper | |||
| ||||
Demographic | ||||
Age <65 years | 1.339 | 1.304 | 1.375 | <0.001 |
Sex, F | 1.128 | 1.099 | 1.157 | <0.001 |
Race, nonwhite | 0.781 | 0.758 | 0.804 | <0.001 |
Low income quartile | 0.887 | 0.858 | 0.915 | <0.001 |
Year of admission | 1.051 | 1.046 | 1.056 | <0.001 |
Clinical | ||||
Multiple CPR | 0.406 | 0.371 | 0.445 | <0.001 |
CCI score | 0.939 | 0.933 | 0.944 | <0.001 |
Cardiac surgery | 1.785 | 1.720 | 1.853 | <0.001 |
Hospital | ||||
Region, Midwest | 1.472 | 1.405 | 1.543 | <0.001 |
Region, South | 1.262 | 1.218 | 1.309 | 0.008 |
Region, West | 1.452 | 1.398 | 1.509 | <0.001 |
Location, urban | 0.876 | 0.837 | 0.917 | <0.001 |
Survivors with multiple CPR were less likely to be discharged home compared with survivors with 1 CPR (19.3% vs 29.9%, respectively, P<0.001); 1 in 15 survivors of multiple CPR were discharged to a hospice (6.8%) versus 1 in 23 1‐CPR survivors (4.3%; P=0.002). Mean length of stay was 5.8 versus 5.5 days for patients who had multiple CPR versus 1 CPR, respectively (P<0.001), and 16.0 versus 10.5 days for discharged survivors of multiple CPR versus 1 CPR (P<0.001). The average cost per day of hospitalization was higher for recipients of multiple CPR versus 1 CPR ($4484.60 vs $3581.40, P<0.001). The aggregate cost of hospitalization for 1‐time CPR recipients doubled between the years 2001 and 2009 (from $1.3 billion to $2.9 billion); that of recipients of multiple CPR attempts quadrupled in the same time frame (from $38.6 million to $160.7 million).
DISCUSSION
A number of studies have investigated the epidemiology of patients in whom CPR is attempted.[2, 3, 5, 20, 23, 24] Several pre‐, intra‐, and post‐resuscitation factors have been shown to affect the survival of resuscitated patients.[6, 7, 25, 26] To our knowledge, neither the epidemiology of hospitalized patients in whom resuscitation is attempted multiple times nor the prognostic value of multiple CPR attempts has been investigated. In this study, we found that multiple resuscitations are more commonly performed on younger, generally sicker patients; their outcomes are significantly compromised compared with patients who are resuscitated once during their hospitalization.
There was a steep decline in survival based on the number of resuscitation events. In multivariate analysis, patients who had multiple CPR were 2.5‐fold less likely to survive their hospitalization; survivors of multiple CPR also were more likely to be discharged to a hospice. Overall, this is indicative of clinical deterioration and prolongation of dying should a patient suffer multiple cardiopulmonary arrests during a hospitalization. The robust inverse relationship between multiple CPR and survival to discharge has implications for the development of prognostic models of outcomes following CPR, as previously designed prediction models of CPR outcomes such as the Cardiac Arrest Survival Post‐Resuscitation In‐hospital (CASPRI) score,[25] Pre‐Arrest Morbidity (PAM) score,[27] and Prognosis After Resuscitation (PAR) score[28] do not include multiple resuscitations as a variable of interest.
In‐hospital factors were found to be more important than patient factors, such as comorbidities or race, in determining the likelihood of multiple CPR attempts. Hospital teaching status and region remained significantly associated with likelihood of multiple CPR attempts. This is in agreement with studies that have described demographic and regional variation in utilization of do‐not‐resuscitate orders.[29, 30] These findings suggest substantial heterogeneity in the clinical culture and hospital practices across the United States regarding preemptive discussions about resuscitation. This means that where a patient receives care is a significant determinant of their probability of undergoing multiple CPR.
It is known that older patients are more likely to have advance directive orders[30, 31] and possibly document their wishes with regard to further resuscitation efforts. There also may be an inclination toward more aggressive care for younger adults compared with those of an advanced age. Uncertainty about a patient's goals of care likely feeds into an increased possibility of multiple resuscitation attempts; this may explain why neurologic compromise and being on ventilator support were independently associated with likelihood of multiple CPR, as these patients often have lost their ability to actively participate in decision‐making. The results of this study highlight the importance of engaging patients with a plausible risk of cardiopulmonary arrest about their goals for care and advance directives in a timely manner, regardless of age.
We found that the care of patients who undergo multiple resuscitations is associated with a higher cost of hospitalization than for patients in whom resuscitation is attempted once during their hospitalization. In addition, there was an exponential increase in aggregate cost over time for multiple CPR recipients compared with 1‐time CPR recipients. In a prior study, Ebell and Kruse showed an exponential inverse relationship between cost per surviving patient and rate of survival to discharge.[32] Considering that 93.3% of patients who had 3 resuscitation attempts died during their hospitalization, and that hospital‐level factors appear to play a significant role in likelihood of multiple CPR, consensus guidelines regarding the appropriateness of 3 resuscitation attempts during a single hospitalization may be relevant to aid the care of these patients.
Although the NIS is well‐validated,[18] there are some limitations. Whereas CPR incidence in this study (2.5 per 1000 hospitalizations) is within estimates (15 arrests per 1000 hospitalizations) reported in previous studies,[3, 5] potential undercoding of multiple CPR may explain why the multiple‐CPR rate in this study is lower than re‐arrest estimates provided in published studies.[2, 33] Indeed, accurate calculation of re‐arrest rates requires data on do‐not‐resuscitate orders instituted after successful resuscitation, which are not provided in the NIS. Information on patient‐provider discussions about CPR or prognosis is not included. Data regarding the underlying cause and type of arrest rhythm, rates of return to spontaneous circulation, length of code, patient location, critical‐care resources and length of critical‐care stay, availability of rapid‐response/code teams, time to defibrillation, use of therapeutic hypothermia, adherence to resuscitation guidelines, quality of CPR, and long‐term follow‐up are not included in the database. Presenting rhythms were not assessed, as there are no ICD‐9 codes for asystole and pulseless electrical activity. The NIS is de‐identified; therefore, chart review to assess the validity of codes is impossible. However, our sensitivity analyses indicate the reliability of using the number of occurrences of the CPR code as a marker of multiple CPR. The strength of our study lies in the use of data that provide a population‐level insight into the epidemiology of patients resuscitated multiple times during their hospitalization, and their outcomes.
Decision‐making about CPR is at the center of a complex debate that incorporates often divergent clinical, economic, ethical, and personal issues. As debate continues regarding when to not resuscitate,[34, 35, 36, 37] studies that explore the public perspective of survival thresholds for the provision of multiple resuscitations will be crucial. As competition for finite healthcare dollars escalates, stratified analyses of the cost implications of resuscitation care are essential. Studies are needed to examine the impact of a history of successful resuscitation in a previous hospitalization on outcomes following CPR in a subsequent hospitalization. Overall, our study fills an important knowledge gap in resuscitation practice and outcomes in the United States and highlights the importance of discussing resuscitation options between a patient and his or her family on hospital admission and, if needed, again after the first successful resuscitation attempt.
Disclosure
Nothing to report.
Cardiopulmonary resuscitation (CPR) is a potentially lifesaving intervention associated with intense resource utilization and poor outcomes.[1, 2, 3] CPR is the default intervention for hospitalized patients in cardiopulmonary arrest in the United States. The most common measure of successful in‐hospital CPR reported in the literature is survival to (hospital) discharge, with most estimates between 13% and 37%.[3, 4, 5, 6] Poor rates of survival to discharge may be explained by use of CPR in patients for whom it was not originally intended, such as the very elderly with multiple illnesses or the terminally ill.[7, 8] Use of CPR in patients unlikely to benefit may be due to a physician's inability to estimate the probability of survival, desire to offer hope to patients, fear of litigation, and poor communication with patients about goals of care.[7, 8, 9, 10]
The general public has overly optimistic expectations about CPR; surveys have reported perceived survival after CPR of up to 90%.[11, 12, 13] Although objective information substantially affects patient preferences for resuscitation,[14] prognosis is rarely discussed during code status encounters[15, 16]; physician estimates of prognosis also are often inaccurate.[9, 17] With a scarcity of data describing the characteristics of patients undergoing multiple CPR attempts, and their outcomes, patients and their families could have false expectations about the likely outcomes from multiple CPR attempts, because physician counsel is not well‐informed.
In this study, we examine the epidemiology of in‐hospital CPR recipients stratified by the number of occurrences of CPR during a single hospitalization, along with their outcomes. We hypothesize that recipients of multiple CPR during a single hospitalization are an epidemiologically distinct group compared with those who receive CPR once during their hospitalization, and that their outcomes are worse.
METHODS
Data Source
We used unweighted data for the years 2000 to 2009 from the Healthcare Cost and Utilization ProjectNationwide Inpatient Sample (HCUP‐NIS). The NIS is the largest all‐payer inpatient‐care database in the United States, containing nationally representative information regarding up to 8 million hospital stays per year. Each year, NIS data consist of a 20% stratified sample of hospital discharges involving up to 1100 nonfederal hospitals from up to 44 states. The NIS utilizes International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes to capture up to 25 diagnoses and 15 procedures associated with the index hospitalization.[18]
Demographic, Clinical, and Hospital Characteristics of Cardiopulmonary Resuscitation Recipients
Adults (age 18 years) who underwent CPR (ICD‐9 procedure code 99.60) during their hospitalization were abstracted; this ICD‐9 code has been used previously to explore CPR epidemiology and outcomes.[3, 19, 20] Patients were divided into 2 groups, those who had 1 CPR attempt and those who had multiple (>1) CPR attempts, based on the number of times the ICD‐9 code for CPR was included in their hospitalization data. Patients who had cardiopulmonary arrest (ICD‐9 code 427.5 or 799.1) as a presenting diagnosis were excluded, as these indicate an out‐of‐hospital event.
Demographic variables included patient age, sex, race, median household income as defined annually in the NIS dataset, insurance status, admission source (skilled nursing facility or not; emergency room vs not), and type (elective vs nonelective; trauma vs nontrauma). Clinical variables included patient comorbidity as assessed by using the enhanced Charlson Comorbidity Index (CCI).[21] Rates of in‐hospital dialysis (ICD‐9 codes 39.95, V451, V561), tracheostomy (ICD‐9 codes 31.1, 31.2), in‐hospital neurologic compromise (coma, ICD‐9 code 780.01; semi‐coma, ICD‐9 code 780.09; persistent vegetative state, ICD‐9 code 780.03; anoxic brain injury, ICD‐9 code 348.1; and brain damage, ICD‐9 code 997.01), ventilator support (ICD‐9 code 967.02); and artificial nutrition (total parenteral nutrition, ICD‐9 code 99.15; enteral infusion of nutritional substances, ICD‐9 code 96.6) were assessed as potential indicators of clinical debilitation and/or intense healthcare resource utilization. Hospital variables were region in the United States (Northeast, Midwest, West, and South), location (urban vs nonurban), teaching status, and bed size (small, medium, and large), as defined annually in the NIS.[18]
Outcomes
Outcomes of interest were survival to discharge, discharge disposition, and cost of hospitalization.
Statistical Analysis
Sensitivity analyses were done to validate the use of the number of occurrences of CPR code 99.60 as a marker of multiple CPR, as well the association between multiple CPR and outcome. We computed the interval (in days) between the first and last CPR such that a result would not be computed if either value were missing. We found that 80.2% of patients who had CPR multiple times also had valid interval data between the first and last CPR. This was slightly higher than the 75.9% of patients with 1 CPR code who also had valid data for the interval (in days) between admission and CPR, indicating the reliability of using the number of CPR codes as a marker of multiple CPR attempts.
Bivariate analyses comparing characteristics and outcomes of interest for recipients of 1 CPR versus multiple CPR were performed using the [2] test for categorical variables and Student t test for continuous variables; differences in age and CCI score (analyzed as continuous variables) were assessed using the Mann‐Whitney U test because the distribution of data for these was not normal. Hospital length of stay and cost were natural log transformed to normalize distribution. Cost was calculated using HCUP‐NISadjusted, hospital‐specific cost‐to‐charge ratios; costs were adjusted for inflation, converting all costs to year 2009 dollar values using rates from the US Bureau of Labor Statistics.[22] Cost‐to‐charge ratios were first made available in the NIS datasets in year 2001; therefore, data for the year 2000 were excluded from all cost analyses. The aggregate cost of hospitalization at a population‐level was estimated using the discharge weight variable included in the NIS.
Separate multivariate logistic regression models were constructed to assess (1) factors independently associated with occurrence of multiple CPR, and (2) whether multiple CPR is independently associated with survival to discharge. Generalized estimating equations were used to account for hospital clustering. Odds ratios (OR) with 95% confidence intervals (CI) were computed for the final multivariate models. All P values <0.05 were considered significant; all tests were 2‐sided.
Data management and analysis were performed using SAS statistical software, version 9.3 (SAS Institute Inc, Cary, NC), and SPSS for Windows, version 18.0 (SPSS Inc, Chicago, IL). The HCUP‐NIS is a public database with no personally identifying information. This study was deemed exempt from institutional review board approval at our institution.
RESULTS
Of a total of 65,308,185 adults hospitalized between the years 2000 and 2009, there were 166,519 CPR recipients, yielding a CPR incidence of 2.5 per 1000 hospitalizations. Among CPR recipients, 96.6% (n=166,899) had 1 CPR and 3.4% (n=5620) had multiple CPR during their hospitalization (range, 111 CPR). When further stratified, 3% had 2 CPR attempts (n=4949) and 0.4% (n=671) had 3 CPR attempts.
Compared with patients who had 1 CPR, those who had multiple CPR were more often younger (median age, 71 vs 67 years), nonwhite, and in a low‐income quartile (all P<0.001; Table 1). Rates of admission from a nursing facility (3.3% for the 1‐CPR group vs 3.1% for the multiple‐CPR group, P=0.65) or as a trauma (0.3% for the 1‐CPR group and 0.4% for the multiple‐CPR group, P=0.34) were similar.
Characteristic | 1 CPR (n=160,899), % | Multiple CPRs (n=5,620), % | P Value |
---|---|---|---|
| |||
Sex, F | 45.6 | 47.2 | 0.02 |
Age, y, <65 | 37.3 | 42.5 | <0.001 |
Race | <0.001 | ||
White | 65.8 | 58.7 | |
Black | 18.7 | 21.6 | |
Other | 15.5 | 19.8 | |
Income quartile | <0.001 | ||
Low | 24.1 | 27.8 | |
Medium‐low | 24.9 | 24.7 | |
Medium | 23.2 | 22.9 | |
High | 25.2 | 22.2 | |
Unknown | 2.5 | 2.4 | |
Insurance | <0.001 | ||
Medicare | 65.1 | 61.8 | |
Medicaid | 9.4 | 12.4 | |
Private | 18.4 | 17.7 | |
Other | 7.1 | 8.1 | |
Admission source, ER | 67.9 | 72.0 | <0.001 |
Admission type, elective | 10.0 | 7.1 | <0.001 |
Patients who had multiple CPR had slightly higher mean CCI scores (2.7 vs 2.6, P=0.02). They had higher rates of neurologic compromise and aggressive interventions; they were also more commonly treated in nonteaching hospitals, and in the western region of the United States (Table 2). After multivariate analysis, several patient, clinical, and hospital factors were independently associated with occurrence of multiple CPR (Figure 1).
Characteristic | 1 CPR (n=160,899), % | Multiple CPRs (n=5,620), % | P Value |
---|---|---|---|
| |||
Clinical | |||
Charlson score 4 | 25.4 | 27.2 | 0.002 |
MI | 24.9 | 28.5 | <0.001 |
CHF | 38.3 | 43.3 | <0.001 |
Cerebrovascular event | 8.5 | 7.1 | <0.001 |
Metastatic malignancy | 10.6 | 8.7 | <0.001 |
COPD | 26.0 | 26.0 | 0.945 |
Neurologic impairment | 13.8 | 21.1 | <0.001 |
Supplemental nutrition | 7.2 | 8.3 | 0.002 |
Mechanical ventilator | 57.4 | 83.1 | <0.001 |
Cardiac surgery | 2.6 | 2.0 | 0.007 |
Hospital | |||
Location, urban | 90.1 | 92.1 | <0.001 |
Teaching status, no | 58.0 | 64.5 | <0.001 |
Region | <0.001 | ||
Northeast | 19.0 | 15.2 | |
Midwest | 18.6 | 15.7 | |
South | 37.4 | 37.1 | |
West | 25.0 | 32.0 | |
Bed size | 0.715 | ||
Small | 10.2 | 9.8 | |
Medium | 25.5 | 25.3 | |
Large | 64.3 | 64.9 |

In bivariate analysis of survival, patients who had multiple CPR had lower rates of survival to discharge (11.3% vs 23.4%, P<0.001). Results were similar (11.6% for multiple CPR vs 22.5% for 1 CPR, P<0.001) when all patients who had CPR but did not have valid timing data were excluded in sensitivity analyses. Further stratification showed that survival to discharge decreased by >40% for each increase in CPR attempt (23.4%, 11.9%, and 6.7% for 1, 2, and 3 CPR attempts, respectively, P<0.001; Figure 2). After adjustment, multiple CPR versus 1 CPR during a hospitalization was independently associated with a lower likelihood of survival to discharge (adjusted OR: 0.41, 95% CI: 0.37‐0.44, P<0.001; Table 3).

Characteristica | OR | 95% CI | P Value | |
---|---|---|---|---|
Lower | Upper | |||
| ||||
Demographic | ||||
Age <65 years | 1.339 | 1.304 | 1.375 | <0.001 |
Sex, F | 1.128 | 1.099 | 1.157 | <0.001 |
Race, nonwhite | 0.781 | 0.758 | 0.804 | <0.001 |
Low income quartile | 0.887 | 0.858 | 0.915 | <0.001 |
Year of admission | 1.051 | 1.046 | 1.056 | <0.001 |
Clinical | ||||
Multiple CPR | 0.406 | 0.371 | 0.445 | <0.001 |
CCI score | 0.939 | 0.933 | 0.944 | <0.001 |
Cardiac surgery | 1.785 | 1.720 | 1.853 | <0.001 |
Hospital | ||||
Region, Midwest | 1.472 | 1.405 | 1.543 | <0.001 |
Region, South | 1.262 | 1.218 | 1.309 | 0.008 |
Region, West | 1.452 | 1.398 | 1.509 | <0.001 |
Location, urban | 0.876 | 0.837 | 0.917 | <0.001 |
Survivors with multiple CPR were less likely to be discharged home compared with survivors with 1 CPR (19.3% vs 29.9%, respectively, P<0.001); 1 in 15 survivors of multiple CPR were discharged to a hospice (6.8%) versus 1 in 23 1‐CPR survivors (4.3%; P=0.002). Mean length of stay was 5.8 versus 5.5 days for patients who had multiple CPR versus 1 CPR, respectively (P<0.001), and 16.0 versus 10.5 days for discharged survivors of multiple CPR versus 1 CPR (P<0.001). The average cost per day of hospitalization was higher for recipients of multiple CPR versus 1 CPR ($4484.60 vs $3581.40, P<0.001). The aggregate cost of hospitalization for 1‐time CPR recipients doubled between the years 2001 and 2009 (from $1.3 billion to $2.9 billion); that of recipients of multiple CPR attempts quadrupled in the same time frame (from $38.6 million to $160.7 million).
DISCUSSION
A number of studies have investigated the epidemiology of patients in whom CPR is attempted.[2, 3, 5, 20, 23, 24] Several pre‐, intra‐, and post‐resuscitation factors have been shown to affect the survival of resuscitated patients.[6, 7, 25, 26] To our knowledge, neither the epidemiology of hospitalized patients in whom resuscitation is attempted multiple times nor the prognostic value of multiple CPR attempts has been investigated. In this study, we found that multiple resuscitations are more commonly performed on younger, generally sicker patients; their outcomes are significantly compromised compared with patients who are resuscitated once during their hospitalization.
There was a steep decline in survival based on the number of resuscitation events. In multivariate analysis, patients who had multiple CPR were 2.5‐fold less likely to survive their hospitalization; survivors of multiple CPR also were more likely to be discharged to a hospice. Overall, this is indicative of clinical deterioration and prolongation of dying should a patient suffer multiple cardiopulmonary arrests during a hospitalization. The robust inverse relationship between multiple CPR and survival to discharge has implications for the development of prognostic models of outcomes following CPR, as previously designed prediction models of CPR outcomes such as the Cardiac Arrest Survival Post‐Resuscitation In‐hospital (CASPRI) score,[25] Pre‐Arrest Morbidity (PAM) score,[27] and Prognosis After Resuscitation (PAR) score[28] do not include multiple resuscitations as a variable of interest.
In‐hospital factors were found to be more important than patient factors, such as comorbidities or race, in determining the likelihood of multiple CPR attempts. Hospital teaching status and region remained significantly associated with likelihood of multiple CPR attempts. This is in agreement with studies that have described demographic and regional variation in utilization of do‐not‐resuscitate orders.[29, 30] These findings suggest substantial heterogeneity in the clinical culture and hospital practices across the United States regarding preemptive discussions about resuscitation. This means that where a patient receives care is a significant determinant of their probability of undergoing multiple CPR.
It is known that older patients are more likely to have advance directive orders[30, 31] and possibly document their wishes with regard to further resuscitation efforts. There also may be an inclination toward more aggressive care for younger adults compared with those of an advanced age. Uncertainty about a patient's goals of care likely feeds into an increased possibility of multiple resuscitation attempts; this may explain why neurologic compromise and being on ventilator support were independently associated with likelihood of multiple CPR, as these patients often have lost their ability to actively participate in decision‐making. The results of this study highlight the importance of engaging patients with a plausible risk of cardiopulmonary arrest about their goals for care and advance directives in a timely manner, regardless of age.
We found that the care of patients who undergo multiple resuscitations is associated with a higher cost of hospitalization than for patients in whom resuscitation is attempted once during their hospitalization. In addition, there was an exponential increase in aggregate cost over time for multiple CPR recipients compared with 1‐time CPR recipients. In a prior study, Ebell and Kruse showed an exponential inverse relationship between cost per surviving patient and rate of survival to discharge.[32] Considering that 93.3% of patients who had 3 resuscitation attempts died during their hospitalization, and that hospital‐level factors appear to play a significant role in likelihood of multiple CPR, consensus guidelines regarding the appropriateness of 3 resuscitation attempts during a single hospitalization may be relevant to aid the care of these patients.
Although the NIS is well‐validated,[18] there are some limitations. Whereas CPR incidence in this study (2.5 per 1000 hospitalizations) is within estimates (15 arrests per 1000 hospitalizations) reported in previous studies,[3, 5] potential undercoding of multiple CPR may explain why the multiple‐CPR rate in this study is lower than re‐arrest estimates provided in published studies.[2, 33] Indeed, accurate calculation of re‐arrest rates requires data on do‐not‐resuscitate orders instituted after successful resuscitation, which are not provided in the NIS. Information on patient‐provider discussions about CPR or prognosis is not included. Data regarding the underlying cause and type of arrest rhythm, rates of return to spontaneous circulation, length of code, patient location, critical‐care resources and length of critical‐care stay, availability of rapid‐response/code teams, time to defibrillation, use of therapeutic hypothermia, adherence to resuscitation guidelines, quality of CPR, and long‐term follow‐up are not included in the database. Presenting rhythms were not assessed, as there are no ICD‐9 codes for asystole and pulseless electrical activity. The NIS is de‐identified; therefore, chart review to assess the validity of codes is impossible. However, our sensitivity analyses indicate the reliability of using the number of occurrences of the CPR code as a marker of multiple CPR. The strength of our study lies in the use of data that provide a population‐level insight into the epidemiology of patients resuscitated multiple times during their hospitalization, and their outcomes.
Decision‐making about CPR is at the center of a complex debate that incorporates often divergent clinical, economic, ethical, and personal issues. As debate continues regarding when to not resuscitate,[34, 35, 36, 37] studies that explore the public perspective of survival thresholds for the provision of multiple resuscitations will be crucial. As competition for finite healthcare dollars escalates, stratified analyses of the cost implications of resuscitation care are essential. Studies are needed to examine the impact of a history of successful resuscitation in a previous hospitalization on outcomes following CPR in a subsequent hospitalization. Overall, our study fills an important knowledge gap in resuscitation practice and outcomes in the United States and highlights the importance of discussing resuscitation options between a patient and his or her family on hospital admission and, if needed, again after the first successful resuscitation attempt.
Disclosure
Nothing to report.
- Trends in inpatient treatment intensity among Medicare beneficiaries at the end of life. Health Serv Res. 2004;39:363–376. , , , .
- Cardiopulmonary resuscitation of adults in the hospital: a report of 14,720 cardiac arrests from the national registry of cardiopulmonary resuscitation. Resuscitation. 2003;58:297–308. , , , et al.
- Epidemiologic study of in‐hospital cardiopulmonary resuscitation in the elderly. N Engl J Med. 2009;361:22–31. , , , et al.
- National Registry of Cardiopulmonary Resuscitation Investigators. Survival from in‐hospital cardiac arrest during nights and weekends. JAMA. 2008;299:785–792. , , , et al;
- In‐hospital cardiac arrest: incidence, prognosis and possible measures to improve survival. Intensive Care Med. 2007;33:237–245. , , , .
- Predictors of survival following in‐hospital adult cardiopulmonary resuscitation. CMAJ. 2002;167:343–348. , , , .
- Pre‐arrest predictors of failure to survive after in‐hospital cardiopulmonary resuscitation: a meta‐analysis. Fam Pract. 2011;28:505–515. , .
- Cardiopulmonary resuscitation in older people—a review. Rev Clin Gerontol. 2010;20:20–29. , .
- Extent and determinants of error in doctors' prognoses in terminally ill patients: prospective cohort study. BMJ. 2000;320:469–472. , .
- Physicians' confidence in discussing do not resuscitate orders with patients and surrogates. J Med Ethics. 2008;34:96–101. , , .
- How misconceptions among elderly patients regarding survival outcomes of inpatient cardiopulmonary resuscitation affect do‐not‐resuscitate orders. J Am Osteopath Assoc. 2006;106:402–404. , .
- Cardiopulmonary resuscitation on television—miracles and misinformation. N Engl J Med. 1996;334:1578–1582. , , .
- Public expectations of survival following cardiopulmonary resuscitation. Acad Emerg Med. 2000;7:48–53. , , .
- The influence of the probability of survival on patients' preferences regarding cardiopulmonary resuscitation. N Engl J Med. 1994;330:545–549. , , , et al.
- Code status discussions between attending hospitalist physicians and medical patients at hospital admission. J Gen Intern Med. 2011;26:359–366. , , , , .
- Hospital do‐not‐resuscitate orders: why they have failed and how to fix them. J Gen Intern Med. 2011;26:791–797. , , .
- The inability of physicians to predict the outcome of in‐hospital resuscitation. J Gen Intern Med. 1996;11:16–22. , , , .
- Healthcare Cost and Utilization Project. Overview of the Nationwide Inpatient Sample. http://www.hcup‐us.ahrq.gov/nisoverview.jsp. Accessed June 24, 2013.
- Long‐term outcomes in elderly survivors of in‐hospital cardiac arrest. N Engl J Med. 2013;368:1019–1026. , , , et al.
- Epidemiology and outcomes of in‐hospital cardiopulmonary resuscitation in the United States, 2000–2009. Resuscitation. 2013;84:1255–1260. , , .
- Coding algorithms for defining comorbidities in ICD‐9‐CM and ICD‐10 administrative data. Med Care. 2005;43:1130–1139. , , , et al.
- US Department of Labor, Bureau of Labor Statistics. Inflation calculator. http://www.bls.gov/data/inflation_calculator.htm. Accessed June 24, 2013.
- Part 4: CPR overview. 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2010;122:S676–S684. , , , et al.
- Choices of seriously ill patients about cardiopulmonary resuscitation: correlates and outcomes. Am J Med. 1996;100:128–137. , , , et al.
- A validated prediction tool for initial survivors of in‐hospital cardiac arrest. Arch Intern Med. 2012;172:947–953. , , , et al.
- Pre‐resuscitation factors associated with mortality in 49,130 cases of in‐hospital cardiac arrest: a report from the national registry for cardiopulmonary resuscitation. Resuscitation. 2010;81:302–311. , , , .
- Pre‐arrest morbidity and other correlates of survival after in‐hospital cardiopulmonary arrest. Am J Med. 1989;87:28–34. , , , .
- Prediction of failure to survive following in‐hospital cardiopulmonary resuscitation: comparison of two predictive instruments. Resuscitation. 1994;28:21–25. , .
- Regional and institutional variation in the initiation of early do‐not‐resuscitate orders. Arch Intern Med. 2005;165:1705–1712. , .
- Epidemiology of do‐not‐resuscitate orders: disparity by age, diagnosis, gender, race, and functional impairment. Arch Intern Med. 1995;155:2056–2062. , , , et al.
- Patients' understanding of advance directives and cardiopulmonary resuscitation. J Crit Care. 2005;20:26–34. , , , , , .
- A proposed model for the cost of cardiopulmonary resuscitation. Med Care. 1994;32:640–649. , .
- Predictors of cardiopulmonary arrest outcome in a comprehensive cancer center intensive care unit. Scand J Trauma Resusc Emerg Med. 2013; 21:18. , , .
- A critic's assessment of our approach to cardiac arrest. N Engl J Med. 2011;364:374–375. .
- Should there be a choice for cardiopulmonary resuscitation when death is expected? Revisiting an old idea whose time is yet to come. J Palliat Med. 2002;5:107–116. .
- Clinical model for ethical cardiopulmonary resuscitation decision‐making. Intern Med J. 2013;43:77–83. .
- Avoiding the futility of resuscitation. Resuscitation. 2001;50:161–166. , , .
- Trends in inpatient treatment intensity among Medicare beneficiaries at the end of life. Health Serv Res. 2004;39:363–376. , , , .
- Cardiopulmonary resuscitation of adults in the hospital: a report of 14,720 cardiac arrests from the national registry of cardiopulmonary resuscitation. Resuscitation. 2003;58:297–308. , , , et al.
- Epidemiologic study of in‐hospital cardiopulmonary resuscitation in the elderly. N Engl J Med. 2009;361:22–31. , , , et al.
- National Registry of Cardiopulmonary Resuscitation Investigators. Survival from in‐hospital cardiac arrest during nights and weekends. JAMA. 2008;299:785–792. , , , et al;
- In‐hospital cardiac arrest: incidence, prognosis and possible measures to improve survival. Intensive Care Med. 2007;33:237–245. , , , .
- Predictors of survival following in‐hospital adult cardiopulmonary resuscitation. CMAJ. 2002;167:343–348. , , , .
- Pre‐arrest predictors of failure to survive after in‐hospital cardiopulmonary resuscitation: a meta‐analysis. Fam Pract. 2011;28:505–515. , .
- Cardiopulmonary resuscitation in older people—a review. Rev Clin Gerontol. 2010;20:20–29. , .
- Extent and determinants of error in doctors' prognoses in terminally ill patients: prospective cohort study. BMJ. 2000;320:469–472. , .
- Physicians' confidence in discussing do not resuscitate orders with patients and surrogates. J Med Ethics. 2008;34:96–101. , , .
- How misconceptions among elderly patients regarding survival outcomes of inpatient cardiopulmonary resuscitation affect do‐not‐resuscitate orders. J Am Osteopath Assoc. 2006;106:402–404. , .
- Cardiopulmonary resuscitation on television—miracles and misinformation. N Engl J Med. 1996;334:1578–1582. , , .
- Public expectations of survival following cardiopulmonary resuscitation. Acad Emerg Med. 2000;7:48–53. , , .
- The influence of the probability of survival on patients' preferences regarding cardiopulmonary resuscitation. N Engl J Med. 1994;330:545–549. , , , et al.
- Code status discussions between attending hospitalist physicians and medical patients at hospital admission. J Gen Intern Med. 2011;26:359–366. , , , , .
- Hospital do‐not‐resuscitate orders: why they have failed and how to fix them. J Gen Intern Med. 2011;26:791–797. , , .
- The inability of physicians to predict the outcome of in‐hospital resuscitation. J Gen Intern Med. 1996;11:16–22. , , , .
- Healthcare Cost and Utilization Project. Overview of the Nationwide Inpatient Sample. http://www.hcup‐us.ahrq.gov/nisoverview.jsp. Accessed June 24, 2013.
- Long‐term outcomes in elderly survivors of in‐hospital cardiac arrest. N Engl J Med. 2013;368:1019–1026. , , , et al.
- Epidemiology and outcomes of in‐hospital cardiopulmonary resuscitation in the United States, 2000–2009. Resuscitation. 2013;84:1255–1260. , , .
- Coding algorithms for defining comorbidities in ICD‐9‐CM and ICD‐10 administrative data. Med Care. 2005;43:1130–1139. , , , et al.
- US Department of Labor, Bureau of Labor Statistics. Inflation calculator. http://www.bls.gov/data/inflation_calculator.htm. Accessed June 24, 2013.
- Part 4: CPR overview. 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2010;122:S676–S684. , , , et al.
- Choices of seriously ill patients about cardiopulmonary resuscitation: correlates and outcomes. Am J Med. 1996;100:128–137. , , , et al.
- A validated prediction tool for initial survivors of in‐hospital cardiac arrest. Arch Intern Med. 2012;172:947–953. , , , et al.
- Pre‐resuscitation factors associated with mortality in 49,130 cases of in‐hospital cardiac arrest: a report from the national registry for cardiopulmonary resuscitation. Resuscitation. 2010;81:302–311. , , , .
- Pre‐arrest morbidity and other correlates of survival after in‐hospital cardiopulmonary arrest. Am J Med. 1989;87:28–34. , , , .
- Prediction of failure to survive following in‐hospital cardiopulmonary resuscitation: comparison of two predictive instruments. Resuscitation. 1994;28:21–25. , .
- Regional and institutional variation in the initiation of early do‐not‐resuscitate orders. Arch Intern Med. 2005;165:1705–1712. , .
- Epidemiology of do‐not‐resuscitate orders: disparity by age, diagnosis, gender, race, and functional impairment. Arch Intern Med. 1995;155:2056–2062. , , , et al.
- Patients' understanding of advance directives and cardiopulmonary resuscitation. J Crit Care. 2005;20:26–34. , , , , , .
- A proposed model for the cost of cardiopulmonary resuscitation. Med Care. 1994;32:640–649. , .
- Predictors of cardiopulmonary arrest outcome in a comprehensive cancer center intensive care unit. Scand J Trauma Resusc Emerg Med. 2013; 21:18. , , .
- A critic's assessment of our approach to cardiac arrest. N Engl J Med. 2011;364:374–375. .
- Should there be a choice for cardiopulmonary resuscitation when death is expected? Revisiting an old idea whose time is yet to come. J Palliat Med. 2002;5:107–116. .
- Clinical model for ethical cardiopulmonary resuscitation decision‐making. Intern Med J. 2013;43:77–83. .
- Avoiding the futility of resuscitation. Resuscitation. 2001;50:161–166. , , .
© 2013 Society of Hospital Medicine
Time to Introduce Yourself to Patients
At the core of a good physician is mastery of critical communication skills. Good communication establishes rapport and can also heal patients. As communication is an essential ingredient of good physicianship, the recipe starts with a fundamental staplethe physician introduction. The physician introduction is step 2 of Kahn's etiquette‐based medicine checklist to promote good doctoring.[1] Although such rudimentary communication skills are cemented in kindergarten, sadly, more training is needed for doctors. In a recent Journal of Hospital Medicine study, interns failed to introduce themselves in 3 out of 5 inpatient encounters.[2]
Despite waning introductions, increasing importance is being placed on hospitalized patient's knowledge of their treating physician's name and role for patient safety. The Transitions of Care Consensus Policy Statement endorsed by 6 medical societies, including the Society of Hospital Medicine, recommend patients know who their treating physician is while caring for them at every step across the continuum, including hospitalization.[3] The Accreditation Council for Graduate Medical Education requires that patients be informed of who the supervising physician is and understand the roles of any trainees in their care.[4] Last, the death of young Lewis Blackman in South Carolina resulted in state legislation requiring clear identification of physicians and their roles for patients.[5] Given these recommendations, tools to remind physicians to introduce themselves and explain their role to patients are worth consideration. In this issue of the Journal of Hospital Medicine, the effectiveness of 2 interventions using physician photo tools is described.[6, 7]
Even though both studies advance our knowledge on the effectiveness of such interventions, nonrandom variable uptake by physicians represents a major common hurdle. Physician workload, competing priorities, and time pressures prevent physicians from distributing such tools. Consistent adopters of the cards likely already introduce themselves regularly. Interestingly, physicians likely withhold the cards from patients they perceive as unsatisfied, who ironically have the most to gain. System changes, such as increasing handoffs and transient coverage with resident duty hours, can also hamper tool effectiveness through the introduction of more physicians to remember, inherently decreasing the ability of patients to identify their treating physicians.[8]
Patient factors also affect the success of such interventions. Interestingly, patients' baseline ability to identify their physician ranged from 11% to 51% in these studies. Such differences can be readily attributed to previous disparities noted by age, race, gender, and education level in patient recall of their physician.[8] Future work should target interventions for these subgroups, while also accounting for the high prevalence of low health literacy, memory impairment, sleep loss, and poor vision among inpatients, all of which can hamper such interventions.[9, 10]
Although neither intervention improved overall patient satisfaction, patient satisfaction is influenced by a variety of factors unrelated to physician care, such as nursing or the environment. Given the inherent ceiling effect in patient satisfaction metrics, both studies were underpowered to show minor differences. It is also worth noting that complex social interventions depend on their context. Although some patients may enjoy receiving the cards, others may feel that it is not critical to their patient satisfaction. Using a realist evaluation would ask patients what they thought of the cards and why.[11] Like one of the authors, we noted that patients do like the cards, suggesting the problem is not the cards but the metrics of evaluation.[12]
In addition to robust evaluation metrics, future interventions should incorporate patient‐centered approaches to empower patients to ask their doctors about their name and role. With the request coming from patients, doctors are much more likely to comply. Using lessons from marketing and advertising, the hospital is full of artifacts, such as white boards, wristbands, remote controls, and monitors, that can be repurposed to advertise the doctor's name to the patient. Future advances can exploit new mobile technologies and repurpose old ones, such as the hospital television, to remind patients of their care team and other critical information. Regardless of what the future may bring, let's face itintroducing yourself properly to your patients is always good medicine.
- Etiquette‐based medicine. N Engl J Med. 2008;358(19):1988–1989. .
- Do internal medicine interns practice etiquette‐based communication? A critical look at the inpatient encounter. J Hosp Med. 2013;8(11):631–634. , , , et al.
- Transitions of Care Consensus policy statement: American College of Physicians, Society of General Internal Medicine, Society of Hospital Medicine, American Geriatrics Society, American College of Emergency Physicians, and Society for Academic Emergency Medicine. J Hosp Med. 2009;4(6):364–370. , , , et al.
- ACGME Common Program Requirements. Available at: http://www.acgme.org/acgmeweb/Portals/0/PFAssets/ProgramRequirements/CPRs2013.pdf. Accessed November 12, 2013.
- The Informed Patient. Patients Get Power of Fast Response. Available at: http://online.wsj.com/news/articles/SB10001424052970204047504574384591232799668. Accessed November 12, 2013. .
- The impact of facecards on patients' knowledge, satisfaction, trust, and agreement with hospital physicians: a pilot study. J Hosp Med. 2014;9(3):137–141. , , .
- Effect of a face sheet tool on medical team provider identification and family satisfaction. J Hosp Med. 2014;9(3):186–188. , , .
- Ability of hospitalized patients to identify their in‐hospital physicians. Arch Intern Med. 2009;169(2):199–201. , , , , , .
- Noise and sleep among adult medical inpatients: far from a quiet night. Arch Intern Med. 2012;172(1):68–70. , , , , .
- More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18(suppl 1):197–204. , , , , .
- Realist evaluation as a framework for the assessment of teaching about the improvement of care. J Nurs Educ. 2009;48(12):661–667. , .
- Improving inpatients' identification of their doctors: use of FACE cards. Jt Comm J Qual Patient Saf. 2009;35(12):613–619. , , , et al.
At the core of a good physician is mastery of critical communication skills. Good communication establishes rapport and can also heal patients. As communication is an essential ingredient of good physicianship, the recipe starts with a fundamental staplethe physician introduction. The physician introduction is step 2 of Kahn's etiquette‐based medicine checklist to promote good doctoring.[1] Although such rudimentary communication skills are cemented in kindergarten, sadly, more training is needed for doctors. In a recent Journal of Hospital Medicine study, interns failed to introduce themselves in 3 out of 5 inpatient encounters.[2]
Despite waning introductions, increasing importance is being placed on hospitalized patient's knowledge of their treating physician's name and role for patient safety. The Transitions of Care Consensus Policy Statement endorsed by 6 medical societies, including the Society of Hospital Medicine, recommend patients know who their treating physician is while caring for them at every step across the continuum, including hospitalization.[3] The Accreditation Council for Graduate Medical Education requires that patients be informed of who the supervising physician is and understand the roles of any trainees in their care.[4] Last, the death of young Lewis Blackman in South Carolina resulted in state legislation requiring clear identification of physicians and their roles for patients.[5] Given these recommendations, tools to remind physicians to introduce themselves and explain their role to patients are worth consideration. In this issue of the Journal of Hospital Medicine, the effectiveness of 2 interventions using physician photo tools is described.[6, 7]
Even though both studies advance our knowledge on the effectiveness of such interventions, nonrandom variable uptake by physicians represents a major common hurdle. Physician workload, competing priorities, and time pressures prevent physicians from distributing such tools. Consistent adopters of the cards likely already introduce themselves regularly. Interestingly, physicians likely withhold the cards from patients they perceive as unsatisfied, who ironically have the most to gain. System changes, such as increasing handoffs and transient coverage with resident duty hours, can also hamper tool effectiveness through the introduction of more physicians to remember, inherently decreasing the ability of patients to identify their treating physicians.[8]
Patient factors also affect the success of such interventions. Interestingly, patients' baseline ability to identify their physician ranged from 11% to 51% in these studies. Such differences can be readily attributed to previous disparities noted by age, race, gender, and education level in patient recall of their physician.[8] Future work should target interventions for these subgroups, while also accounting for the high prevalence of low health literacy, memory impairment, sleep loss, and poor vision among inpatients, all of which can hamper such interventions.[9, 10]
Although neither intervention improved overall patient satisfaction, patient satisfaction is influenced by a variety of factors unrelated to physician care, such as nursing or the environment. Given the inherent ceiling effect in patient satisfaction metrics, both studies were underpowered to show minor differences. It is also worth noting that complex social interventions depend on their context. Although some patients may enjoy receiving the cards, others may feel that it is not critical to their patient satisfaction. Using a realist evaluation would ask patients what they thought of the cards and why.[11] Like one of the authors, we noted that patients do like the cards, suggesting the problem is not the cards but the metrics of evaluation.[12]
In addition to robust evaluation metrics, future interventions should incorporate patient‐centered approaches to empower patients to ask their doctors about their name and role. With the request coming from patients, doctors are much more likely to comply. Using lessons from marketing and advertising, the hospital is full of artifacts, such as white boards, wristbands, remote controls, and monitors, that can be repurposed to advertise the doctor's name to the patient. Future advances can exploit new mobile technologies and repurpose old ones, such as the hospital television, to remind patients of their care team and other critical information. Regardless of what the future may bring, let's face itintroducing yourself properly to your patients is always good medicine.
At the core of a good physician is mastery of critical communication skills. Good communication establishes rapport and can also heal patients. As communication is an essential ingredient of good physicianship, the recipe starts with a fundamental staplethe physician introduction. The physician introduction is step 2 of Kahn's etiquette‐based medicine checklist to promote good doctoring.[1] Although such rudimentary communication skills are cemented in kindergarten, sadly, more training is needed for doctors. In a recent Journal of Hospital Medicine study, interns failed to introduce themselves in 3 out of 5 inpatient encounters.[2]
Despite waning introductions, increasing importance is being placed on hospitalized patient's knowledge of their treating physician's name and role for patient safety. The Transitions of Care Consensus Policy Statement endorsed by 6 medical societies, including the Society of Hospital Medicine, recommend patients know who their treating physician is while caring for them at every step across the continuum, including hospitalization.[3] The Accreditation Council for Graduate Medical Education requires that patients be informed of who the supervising physician is and understand the roles of any trainees in their care.[4] Last, the death of young Lewis Blackman in South Carolina resulted in state legislation requiring clear identification of physicians and their roles for patients.[5] Given these recommendations, tools to remind physicians to introduce themselves and explain their role to patients are worth consideration. In this issue of the Journal of Hospital Medicine, the effectiveness of 2 interventions using physician photo tools is described.[6, 7]
Even though both studies advance our knowledge on the effectiveness of such interventions, nonrandom variable uptake by physicians represents a major common hurdle. Physician workload, competing priorities, and time pressures prevent physicians from distributing such tools. Consistent adopters of the cards likely already introduce themselves regularly. Interestingly, physicians likely withhold the cards from patients they perceive as unsatisfied, who ironically have the most to gain. System changes, such as increasing handoffs and transient coverage with resident duty hours, can also hamper tool effectiveness through the introduction of more physicians to remember, inherently decreasing the ability of patients to identify their treating physicians.[8]
Patient factors also affect the success of such interventions. Interestingly, patients' baseline ability to identify their physician ranged from 11% to 51% in these studies. Such differences can be readily attributed to previous disparities noted by age, race, gender, and education level in patient recall of their physician.[8] Future work should target interventions for these subgroups, while also accounting for the high prevalence of low health literacy, memory impairment, sleep loss, and poor vision among inpatients, all of which can hamper such interventions.[9, 10]
Although neither intervention improved overall patient satisfaction, patient satisfaction is influenced by a variety of factors unrelated to physician care, such as nursing or the environment. Given the inherent ceiling effect in patient satisfaction metrics, both studies were underpowered to show minor differences. It is also worth noting that complex social interventions depend on their context. Although some patients may enjoy receiving the cards, others may feel that it is not critical to their patient satisfaction. Using a realist evaluation would ask patients what they thought of the cards and why.[11] Like one of the authors, we noted that patients do like the cards, suggesting the problem is not the cards but the metrics of evaluation.[12]
In addition to robust evaluation metrics, future interventions should incorporate patient‐centered approaches to empower patients to ask their doctors about their name and role. With the request coming from patients, doctors are much more likely to comply. Using lessons from marketing and advertising, the hospital is full of artifacts, such as white boards, wristbands, remote controls, and monitors, that can be repurposed to advertise the doctor's name to the patient. Future advances can exploit new mobile technologies and repurpose old ones, such as the hospital television, to remind patients of their care team and other critical information. Regardless of what the future may bring, let's face itintroducing yourself properly to your patients is always good medicine.
- Etiquette‐based medicine. N Engl J Med. 2008;358(19):1988–1989. .
- Do internal medicine interns practice etiquette‐based communication? A critical look at the inpatient encounter. J Hosp Med. 2013;8(11):631–634. , , , et al.
- Transitions of Care Consensus policy statement: American College of Physicians, Society of General Internal Medicine, Society of Hospital Medicine, American Geriatrics Society, American College of Emergency Physicians, and Society for Academic Emergency Medicine. J Hosp Med. 2009;4(6):364–370. , , , et al.
- ACGME Common Program Requirements. Available at: http://www.acgme.org/acgmeweb/Portals/0/PFAssets/ProgramRequirements/CPRs2013.pdf. Accessed November 12, 2013.
- The Informed Patient. Patients Get Power of Fast Response. Available at: http://online.wsj.com/news/articles/SB10001424052970204047504574384591232799668. Accessed November 12, 2013. .
- The impact of facecards on patients' knowledge, satisfaction, trust, and agreement with hospital physicians: a pilot study. J Hosp Med. 2014;9(3):137–141. , , .
- Effect of a face sheet tool on medical team provider identification and family satisfaction. J Hosp Med. 2014;9(3):186–188. , , .
- Ability of hospitalized patients to identify their in‐hospital physicians. Arch Intern Med. 2009;169(2):199–201. , , , , , .
- Noise and sleep among adult medical inpatients: far from a quiet night. Arch Intern Med. 2012;172(1):68–70. , , , , .
- More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18(suppl 1):197–204. , , , , .
- Realist evaluation as a framework for the assessment of teaching about the improvement of care. J Nurs Educ. 2009;48(12):661–667. , .
- Improving inpatients' identification of their doctors: use of FACE cards. Jt Comm J Qual Patient Saf. 2009;35(12):613–619. , , , et al.
- Etiquette‐based medicine. N Engl J Med. 2008;358(19):1988–1989. .
- Do internal medicine interns practice etiquette‐based communication? A critical look at the inpatient encounter. J Hosp Med. 2013;8(11):631–634. , , , et al.
- Transitions of Care Consensus policy statement: American College of Physicians, Society of General Internal Medicine, Society of Hospital Medicine, American Geriatrics Society, American College of Emergency Physicians, and Society for Academic Emergency Medicine. J Hosp Med. 2009;4(6):364–370. , , , et al.
- ACGME Common Program Requirements. Available at: http://www.acgme.org/acgmeweb/Portals/0/PFAssets/ProgramRequirements/CPRs2013.pdf. Accessed November 12, 2013.
- The Informed Patient. Patients Get Power of Fast Response. Available at: http://online.wsj.com/news/articles/SB10001424052970204047504574384591232799668. Accessed November 12, 2013. .
- The impact of facecards on patients' knowledge, satisfaction, trust, and agreement with hospital physicians: a pilot study. J Hosp Med. 2014;9(3):137–141. , , .
- Effect of a face sheet tool on medical team provider identification and family satisfaction. J Hosp Med. 2014;9(3):186–188. , , .
- Ability of hospitalized patients to identify their in‐hospital physicians. Arch Intern Med. 2009;169(2):199–201. , , , , , .
- Noise and sleep among adult medical inpatients: far from a quiet night. Arch Intern Med. 2012;172(1):68–70. , , , , .
- More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18(suppl 1):197–204. , , , , .
- Realist evaluation as a framework for the assessment of teaching about the improvement of care. J Nurs Educ. 2009;48(12):661–667. , .
- Improving inpatients' identification of their doctors: use of FACE cards. Jt Comm J Qual Patient Saf. 2009;35(12):613–619. , , , et al.
Prior Opioid use Among Veterans
Recent trends show a marked increase in outpatient use of chronic opioid therapy (COT) for chronic noncancer pain (CNCP)[1, 2] without decreases in reported CNCP,[3] raising concerns about the efficacy and risk‐to‐benefit ratio of opioids in this population.[4, 5, 6, 7, 8] Increasing rates of outpatient use likely are accompanied by increasing rates of opioid exposure among patients admitted to the hospital. To our knowledge there are no published data regarding the prevalence of COT during the months preceding hospitalization.
Opioid use has been linked to increased emergency room utilization[9, 10] and emergency hospitalization,[11] but associations between opioid use and inpatient metrics (eg, mortality, readmission) have not been explored. Furthermore, lack of knowledge about the prevalence of opioid use prior to hospitalization may impede efforts to improve inpatient pain management and satisfaction with care. Although there is reason to expect that strategies to safely and effectively treat acute pain during the inpatient stay differ between opioid‐nave patients and opioid‐exposed patients, evidence regarding treatment strategies is limited.[12, 13, 14] Opioid pain medications are associated with hospital adverse events, with both prior opioid exposure and lack of opioid use as proposed risk factors.[15] A better understanding of the prevalence and characteristics of hospitalized COT patients is fundamental to future work to achieve safer and more effective inpatient pain management.
The primary purpose of this study was to determine the prevalence of prior COT among hospitalized medical patients. Additionally, we aimed to characterize inpatients with occasional and chronic opioid therapy prior to admission in comparison to opioid‐nave inpatients, as differences between these groups may suggest directions for further investigation into the distinct needs or challenges of hospitalized opioid‐exposed patients.
METHODS
We used inpatient and outpatient administrative data from the Department of Veterans Affairs (VA) Healthcare System. The primary data source to identify acute medical admissions was the VA Patient Treatment File, a national administrative database of all inpatient admissions, including patient demographic characteristics, primary and secondary diagnoses (using International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM], codes), and hospitalization characteristics. Outpatient pharmacy data were from the VA Pharmacy Prescription Data Files. The VA Vital Status Files provided dates of death.
We identified all first acute medical admissions to 129 VA hospitals during fiscal years (FYs) 2009 to 2011 (October 2009September 2011). We defined first admissions as the initial medical hospitalization occurring following a minimum 365‐day hospitalization‐free period. Patients were required to demonstrate pharmacy use by receipt of any outpatient medication from the VA on 2 separate occasions within 270 days preceding the first admission, to avoid misclassification of patients who routinely obtained medications only from a non‐VA provider. Patients admitted from extended care facilities were excluded.
We grouped patients by opioid‐use status based on outpatient prescription records: (1) no opioid use, defined as no opioid prescriptions in the 6 months prior to hospitalization; (2) occasional opioid use, defined as patients who received any opioid prescription during the 6 months prior but did not meet definition of chronic use; and (3) chronic opioid therapy, defined as 90 or more days' supply of opioids received within 6 months preceding hospitalization. We did not specify continuous prescribing. Opioids included in the definition were codeine, dihydrocodeine, fentanyl (mucosal and topical), hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, pentazocine, propoxyphene, tapentadol, and tramadol.[16, 17]
We compared groups by demographic variables including age, sex, race, income, rural vs urban residence (determined from Rural‐Urban Commuting Area codes), region based on hospital location; overall comorbidity using the Charlson Comorbidity Index (CCI);[18] and 10 selected conditions to characterize comorbidity (see Supporting Information, Appendix A, in the online version of this article). These 10 conditions were chosen based on probable associations with chronic opioid use or high prevalence among hospitalized veterans.[9, 19, 20]
We used a CNCP definition based on ICD‐9‐CM codes.[9] This definition did not include episodic conditions such as migraine[2] or a measure of pain intensity.[21] All conditions were determined from diagnoses coded during any encounter in the year prior to hospitalization, exclusive of the first (ie, index) admission. We also determined the frequency of palliative care use, defined as presence of ICD‐9‐CM code V667 during index hospitalization or within the past year. Patients with palliative care use (n=3070) were excluded from further analyses.
We compared opioid use groups by baseline characteristics using the [2] statistic to determine if the distribution was nonrandom. We used analysis of variance to compare hospital length of stay between groups. We used the [2] statistic to compare rates of 4 outcomes of interest: intensive care unit (ICU) admission during the index hospitalization, discharge disposition other than home, 30‐day readmission rate, and in‐hospital or 30‐day mortality.
To assess the association between opioid‐use status and the 4 outcomes of interest, we constructed 2 multivariable regression models; the first was adjusted only for admission diagnosis using the Clinical Classification Software (CCS),[22] and the second was adjusted for demographics, CCI, and the 10 selected comorbidities in addition to admission diagnosis.
The authors had full access to and take full responsibility for the integrity of the data. All analyses were conducted using SAS statistical software version 9.2 (SAS Institute, Cary, NC). The study was approved by the University of Iowa institutional review board and the Iowa City VA Health Care System Research and Development Committee.
RESULTS
Patient Demographics
Demographic characteristics of patients differed by opioid‐use group (Table 1). Hospitalized patients who received COT in the 6 months prior to admission tended to be younger than their comparators, more often female, white, have a rural residence, and live in the South or West.
Variables | No Opioids, n=66,899 (54.5%) | Occasional Opioids, n=24,093 (19.6%) | Chronic Opioids, n=31,802 (25.9%) |
---|---|---|---|
| |||
Age, y, mean (SD) | 68.7 (12.8) | 66.5 (12.7) | 64.5 (11.5) |
Age, n (%) | |||
59 (reference) | 15,170 (22.7) | 6,703 (27.8) | 10,334 (32.5) |
6065 | 15,076 (22.5) | 5,973 (24.8) | 8,983 (28.3) |
6677 | 17,226 (25.8) | 5,871 (24.4) | 7,453 (23.4) |
78 | 19,427 (29.0) | 5,546 (23.0) | 5,032 (15.8) |
Male, n (%) | 64,673 (96.7) | 22,964 (95.3) | 30,200 (95.0) |
Race, n (%) | |||
White | 48,888 (73.1) | 17,358 (72.1) | 25,087 (78.9) |
Black | 14,480 (21.6) | 5,553 (23.1) | 5,089 (16.0) |
Other | 1,172 (1.8) | 450 (1.9) | 645 (2.0) |
Unknown | 2,359 (3.5) | 732 (3.0) | 981 (3.1) |
Income $20,000, n (%) | 40,414 (60.4) | 14,105 (58.5) | 18,945 (59.6) |
Rural residence, n (%) | 16,697 (25.0) | 6,277 (26.1) | 9,356 (29.4) |
Region, n (%) | |||
Northeast | 15,053 (22.5) | 4,437 (18.4) | 5,231 (16.5) |
South | 24,083 (36.0) | 9,390 (39.0) | 12,720 (40.0) |
Midwest | 16,000 (23.9) | 5,714 (23.7) | 7,762 (24.4) |
West | 11,763 (17.6) | 4,552 (18.9) | 6,089 (19.2) |
Charlson Comorbidity Index, mean (SD) | 2.3 (2.0) | 2.6 (2.3) | 2.7 (2.3) |
Comorbidities, n (%) | |||
Cancer (not metastatic) | 11,818 (17.7) | 5,549 (23.0) | 6,874 (21.6) |
Metastatic cancer | 866 (1.3) | 733 (3.0) | 1,104 (3.5) |
Chronic pain | 25,748 (38.5) | 14,811 (61.5) | 23,894 (75.1) |
COPD | 20,750 (31.0) | 7,876 (32.7) | 12,117 (38.1) |
Diabetes, complicated | 10,917 (16.3) | 4,620 (19.2) | 6,304 (19.8) |
Heart failure | 14,267 (21.3) | 5,035 (20.9) | 6,501 (20.4) |
Renal disease | 11,311 (16.9) | 4,586 (19.0) | 4,981 (15.7) |
Dementia | 2,180 (3.3) | 459 (1.9) | 453 (1.4) |
Mental health other than PTSD | 33,390 (49.9) | 13,657 (56.7) | 20,726 (65.2) |
PTSD | 7,216 (10.8) | 3,607 (15.0) | 5,938 (18.7) |
Palliative care use, n (%) | 1,407 (2.1) | 639 (2.7) | 1,024 (3.2) |
Prevalence of Opioid Use
Among the cohort (N=122,794) of hospitalized veterans, 66,899 (54.5%) received no opioids from the VA during the 6‐month period prior to hospitalization; 31,802 (25.9%) received COT in the 6 months prior to admission. An additional 24,093 (19.6%) had occasional opioid therapy (Table 1). A total of 257,623 opioid prescriptions were provided to patients in the 6‐month period prior to their index hospitalization. Of these, 100,379 (39.0%) were for hydrocodone, 48,584 (18.9%) for oxycodone, 36,658 (14.2%) for tramadol, and 35,471 (13.8%) for morphine. These 4 medications accounted for 85.8% of total opioid prescriptions (see Supporting Information, Appendix B, in the online version of this article).
Among the COT group, 3610 (11.4%) received opioids 90 days, 10,110 (31.8%) received opioids between 91 and 179 days, and 18,082 (56.9%) patients received opioids 180 days in the prior 6 months (see Supporting Information, Appendix C, in the online version of this article).
Among the subset of patients with cancer (metastatic and nonmetastatic, n=26,944), 29.6% were prescribed COT, and 23.3% had occasional opioid use. Among the subset of patients with CNCP (n=64,453), 37.1% were prescribed COT, and 23.0% had occasional opioid use.
Comorbid Conditions
Compared to patients not receiving opioids, a larger proportion of patients receiving both occasional and chronic opioids had diagnoses of cancer and of CNCP. Diagnoses more common in COT patients included chronic obstructive pulmonary disease (COPD), complicated diabetes, post‐traumatic stress disorder (PTSD), and other mental health disorders. In contrast, COT patients were less likely than no‐opioid and occasional opioid patients to have heart failure (HF), renal disease, and dementia. Palliative care was used by 2.1% of patients in the no‐opioid group, and 3.2% of patients in the COT group (Table 1). Renal disease was most common among the occasional‐use group.
Unadjusted Hospitalization Outcomes
Unadjusted hospitalization outcomes differed between opioid‐exposure groups (Table 2). Patients receiving occasional or chronic opioids had shorter length of stay and lower rates of non‐home discharge than did patients without any opioid use. The rate of death during hospitalization or within 30 days did not differ between groups. The occasional‐use and COT groups had higher 30‐day readmission rates than did the no‐use group.
No Opioids, n=65,492 | Occasional Opioids, n=23,454 | Chronic Opioids, n=30,778 | P | |
---|---|---|---|---|
| ||||
Hospital length of stay, d, mean (SD) | 4.7 (5.1) | 4.5 (4.8) | 4.5 (4.8) | 0.0003 |
ICU stay, n (%) | 10,281 (15.7) | 3,299 (14.1) | 4,570 (14.9) | <0.0001 |
Non‐home discharge, n (%) | 2,944 (4.5) | 997 (4.3) | 1,233 (4.0) | 0.0020 |
30‐day readmission, n (%) | 9,023 (13.8) | 3,629 (15.5) | 4,773 (15.5) | <0.0001 |
Death during hospitalization or within 30 days, n (%) | 2,532 (3.9) | 863 (3.7) | 1,191 (3.9) | 0.4057 |
Multivariable Models
In the fully adjusted multivariable models, opioid exposure (in the form of either chronic or occasional use) had no significant association with ICU stay during index admission or non‐home discharge (Table 3). Both the occasional‐opioid use and COT groups were more likely to experience 30‐day hospital readmission, a relationship that remained consistent across the partially and fully adjusted models. The occasional‐opioid use group saw no increased mortality risk. In the model adjusted only for admission diagnosis, COT was not associated with increased mortality risk. When additionally adjusted for demographic variables, CCI, and selected comorbidities, however, COT was associated with increased risk of death during hospitalization or within 30 days (odds ratio: 1.19, 90% confidence interval: 1.10‐1.29).
Occasional Opioid Use | Chronic Opioid Therapy | |||
---|---|---|---|---|
Model 1, OR (95% CI) | Model 2, OR (95% CI) | Model 1, OR (95% CI) | Model 2, OR (95% CI) | |
| ||||
ICU stay | 0.94 (0.90‐0.99) | 0.95 (0.91‐1.00) | 1.00 (0.96‐1.04) | 1.01 (0.97‐1.05) |
Non‐home discharge | 0.92 (0.85‐0.99) | 0.97 (0.90‐1.05) | 0.85 (0.80‐0.92) | 0.95 (0.88‐1.03) |
30‐day readmission | 1.14 (1.09‐1.19) | 1.14 (1.09‐1.19) | 1.14 (1.10‐1.19) | 1.15 (1.10‐1.20) |
Death during hospitalization or within 30 days | 0.96 (0.88‐1.04) | 1.04 (0.95‐1.13) | 0.96 (0.90‐1.04) | 1.19 (1.10‐1.29) |
DISCUSSION
This observational study is, to our knowledge, the first to report prevalence of and characteristics associated with prior opioid use among hospitalized medical patients. The prevalence of any opioid use and of COT was substantially higher in this hospitalized cohort than reported in outpatient settings. The prevalence of any opioid use during 1 year (FY 2009) among all veterans with VA primary care use was 26.1%.[23] A study of incident prescribing rates among veterans with new diagnoses of noncancer‐related pain demonstrated 11% received an opioid prescription within 1 year.[24] Using a definition of 90 consecutive prescription days to define COT, Dobscha et al.[25] found that 5% of veterans with persistent elevated pain intensity and no previous opioid prescriptions subsequently received COT within 12 months. The high prevalence we found likely reflects cumulative effects of incident use as well as an increased symptom burden in a population defined by need for medical hospitalization.
Although a veteran population may not be generalizable to a nonveteran setting, we do note prior studies reporting prevalence of any opioid use in outpatient cohorts (in 2000 and 2005) of between 18% and 30%, with higher rates among women and patients over 65 years of age.[1, 2]
Our work was purposefully inclusive of cancer patients so that we might assess the degree to which cancer diagnoses accounted for prior opioid use in hospitalized patients. Surprisingly, the rate of COT for patients with cancer was lower than that for patients with CNCP, perhaps reflecting that a cancer condition defined in administrative data may not constitute a pain‐causing disease.
Recognition of the prevalence of opioid therapy is important as we work to understand and improve safety, satisfaction, utilization, and long‐term health outcomes associated with hospitalization. Our finding that over half of medical inpatients have preexisting CNCP diagnoses, and a not entirely overlapping proportion has prior opioid exposure, implies a need for future work to refine expectations and strategies for inpatient management, potentially tailored to prior opioid use and presence of CNCP.
A recent Joint Commission sentinel event alert[26] highlights opioid adverse events in the hospital and identifies both lack of previous opioid therapy and prior opioid therapy as factors increasing risk. ICU admission during the hospital stay may reflect adverse events such as opioid‐induced respiratory depression; in our study, patients with no opioid use prior to admission were more likely to have an ICU stay, although the effect was small. One might speculate that clinicians, accustomed to treating pain in opioid‐exposed patients, are using inappropriately large starting dosages of narcotics for inpatients without first assessing prior opioid exposure. Another possible explanation is that patients on COT are admitted to the hospital with less severe illness, potentially reflecting functional, social, or access limitations that compromise ability to manage illness in the outpatient setting. More detailed comparison of illness severity is beyond the scope of the present work.
Patient satisfaction with pain management is reflected in 2 of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) questions, and is publically reported.[27] HCAHPS results also figure in the formula for the Centers for Medicare and Medicaid Services value‐based purchasing.[28] Preadmission pain is predictive of postoperative pain[29, 30] and may shape patient expectations; how preadmission opioid use modulates nonsurgical pain and satisfaction with management in the medical inpatient remains to be studied. The high prevalence of prior COT underscores the importance of understanding characteristics of patients on COT, and potential differences and disparities in pain management, when designing interventions to augment patient satisfaction with pain management.
Although the age distribution and patterns of comorbidities differed between the opioid‐use groups, opioid therapy remained a small but significant predictor of hospital readmission; this association was independent of CNCP diagnosis. Functional outcomes are recognized as important measures of efficacy of outpatient pain management strategies,[31] with some evidence that opioids are associated with worse functioning.[32, 33] Functional limitations, as well as inadequately or inappropriately treated pain, may drive both admissions and readmissions. Alternately, COT may be a marker for unmeasured factors that increase a patient's risk of returning to the hospital. Further work is needed to elucidate the relationship between COT and healthcare utilization associated with the inpatient stay.
Our finding that patients on COT have an increased mortality risk is concerning, given the rapid expansion in use of these medications. Although pain is increasingly prevalent toward end of life,[34] we did not observe an association between either CNCP (data not shown) or occasional opioid use and mortality. COT may complicate chronic disease through adverse drug effects including respiratory depression, apnea, or endocrine or immune alteration. Complex chronically ill patients with conditions such as COPD, HF, or diabetes may be particularly susceptible to these effects. Incident use of morphine is associated with increased mortality in acute coronary syndrome and HF[35, 36]: we are not aware of any work describing the relationship between prior opioid use and incident use during hospitalization in medical patients.
Limitations
Our work focuses on hospitalized veterans, a population that remains predominately male, limiting generalizability of the findings. Rates of mental health diagnoses and PTSD, associated with CNCP and COT,[24, 37] are higher in this population than would be expected in a general hospitalized population. Because our outcomes included readmission, and our definition of opioid exposure was designed to reflect outpatient prescribing, we included only patients without recent hospitalization. Therefore, our results may not be generalizable to patients with frequent and recurring hospitalization.
Our definition of opioid exposure depended on pharmacy dispensing records; we are not able to confirm if veterans were taking the medications as prescribed. Further, we were not able to capture data on opioids prescribed by non‐VA providers, which may have led to underestimation of prevalence.
Our definitions of COT and CNCP are imperfect, and should be noted when comparing to other studies. Because we did not specify continuous 90‐day prescribing, we may have misclassified occasional opioid therapy as COT in comparison to other authors. That continuous prescribing is equivalent to continuous use assumes that patients take medications exactly as prescribed. We used occasional opioid therapy as a comparison group, and detailed the distribution of days prescribed among the COT group (see Supporting Information, Appendix C, in the online version of this article), to augment interpretability of these results. Our CNCP diagnosis was less inclusive than others,[2] as we omitted episodic pain (eg, migraine and sprains) and human immunodeficiency virus‐related pain. As COT for CNCP conditions lacks a robust evidence base,[38] defining pain diagnoses using administrative data to reflect conditions for which COT is used in a guideline‐concordant way remains difficult.
Last, differences observed between opioid‐use groups may be due to an unmeasured confounder not captured by the variables we included. Specifically, we did not include other long‐term outpatient medications in our models. It is possible that COT is part of a larger context of inappropriate prescribing, rather than a single‐medication effect on outcomes studied.
CONCLUSION
Nearly 1 in 4 hospitalized veterans has current or recent COT at the time of hospital admission for nonsurgical conditions; nearly half have been prescribed any opioids. Practitioners designing interventions to improve pain management in the inpatient setting should account for prior opioid use. Patients who are on COT prior to hospitalization differ in age and comorbidities from their counterparts who are not on COT. Further elucidation of differences between opioid‐use groups may help providers address care needs during the transition to posthospitalization care. CNCP diagnoses and chronic opioid exposure are different entities and cannot serve as proxies in administrative data. Additional work on utilization and outcomes in specific patient populations may improve our understanding of the long‐term health effects of chronic opioid therapy.
Disclosures: Dr. Mosher is supported by the Veterans Administration (VA) Quality Scholars Fellowship, Office of Academic Affiliations, Department of Veterans Affairs. Dr. Cram is supported by a K24 award from NIAMS (AR062133) at the National Institutes of Health. The preliminary results of this article were presented at the Society of General Internal Medicine Annual Meeting in Denver, Colordao, April 2013. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. Data are available to researchers with VA accreditation, the statistical code and the protocol are available to interested readers by contacting Dr. Mosher. The authors report no conflict of interest in regard to this study.
- Age and gender trends in long‐term opioid analgesic use for noncancer pain. Am J Public Health. 2010;100:2541–2547. , , , et al.
- Trends in use of opioids for non‐cancer pain conditions 2000–2005 in commercial and Medicaid insurance plans: the TROUP study. Pain. 2008;138:440–449. , , , , , .
- Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education. Relieving pain in America: a blueprint for transforming prevention, care, education, and research. Washington, DC: National Academies Press; 2011.
- Long‐term opioid therapy reconsidered. Ann Intern Med. 2011;155:325–328. , , , .
- What are we treating with long‐term opioid therapy? Arch Intern Med. 2012;172:433–434. , .
- Opioids for chronic noncancer pain: a meta‐analysis of effectiveness and side effects. CMAJ. 2006;174:1589–1594. , , , .
- Opioids in chronic non‐cancer pain: systematic review of efficacy and safety. Pain. 2004;112:372–380. , , , .
- A systematic review of randomized trials of long‐term opioid management for chronic non‐cancer pain. Pain Physician. 2011;14:91–121. , , , et al.
- Rates of adverse events of long‐acting opioids in a state Medicaid program. Ann Pharmacother. 2007;41:921–928. , , , , , .
- Emergency department visits among recipients of chronic opioid therapy. Arch Intern Med. 2010;170:1425–1432. , , , et al.
- Emergency hospitalizations for adverse drug events in older Americans. N Engl J Med. 2011;365:2002–2012. , , , .
- Assessment and management of acute pain in adult medical inpatients: a systematic review. Pain Med. 2009;10:1183–1199. , .
- Acute pain management in opioid‐tolerant patients: a growing challenge. Anaesth Intensive Care. 2011;39:804–823. , , , .
- Acute pain management of the chronic pain patient on opiates: a survey of caregivers at University of Washington Medical Center. Clin J Pain. 1994;10:133–138. , , , .
- The Joint Commission and the FDA take steps to curb adverse events related to the use and misuse of opioid drugs. ED Manag. 2012;24:112–116.
- Tramadol. CMAJ. 2013;185:E352. , .
- Prescription opioid abuse in the United Kingdom. Br J Clin Pharmacol. 2013;76:823–824. , , , , .
- Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47:1245–1251. , , , .
- Bringing the war back home: mental health disorders among 103,788 US veterans returning from Iraq and Afghanistan seen at Department of Veterans Affairs facilities. Arch Intern Med. 2007;167:476–482. , , , , .
- Coding algorithms for defining comorbidities in ICD‐9‐CM and ICD‐10 administrative data. Med Care. 2005;43:1130–1139. , , , et al.
- Sex Differences in the medical care of VA patients with chronic non‐cancer pain [published online ahead of print June 26, 2013]. Pain Med. doi: 10.1111/pme.12177. , , , , , .
- Agency for Healthcare Research and Quality. Clinical Classifications Software (CCS) for ICD‐9‐CM. Available at: http://www.hcup‐us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed October 17, 2013.
- Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration. Med Care. 2013;51:368–373. , , , et al.
- Association of mental health disorders with prescription opioids and high‐risk opioid use in US veterans of Iraq and Afghanistan. JAMA. 2012;307:940–947. , , , et al.
- Correlates of prescription opioid initiation and long‐term opioid use in veterans with persistent pain. Clin J Pain. 2013;29:102–108. , , , , .
- Safe use of opioids in hospitals. Sentinel Event Alert. 2012;49:1–5.
- Centers for Medicare (2):2–9.
- The risk of severe postoperative pain: modification and validation of a clinical prediction rule. Anesth Analg. 2008;107:1330–1339. , , , , , .
- Preoperative predictors of moderate to intense acute postoperative pain in patients undergoing abdominal surgery. Acta Anaesthesiol Scand. 2002;46:1265–1271. , , , et al.
- Successful and unsuccessful outcomes with long‐term opioid therapy: a survey of physicians' opinions. J Palliat Med. 2006;9:50–56. , , , , , .
- Opioid use among low back pain patients in primary care: is opioid prescription associated with disability at 6‐month follow‐up? Pain. 2013;154:1038–1044. , , , .
- Disability Risk Identification Study Cohort. Early opioid prescription and subsequent disability among workers with back injuries: the Disability Risk Identification Study Cohort. Spine (Phila Pa 1976). 2008;33:199–204. , , , , ;
- The epidemiology of pain during the last 2 years of life. Ann Intern Med. 2010;153:563–569. , , , et al.
- Association of intravenous morphine use and outcomes in acute coronary syndromes: results from the CRUSADE Quality Improvement Initiative. Am Heart J. 2005;149:1043–1049. , , , et al.
- Use of intravenous morphine for acute decompensated heart failure in patients with and without acute coronary syndromes. Acute Card Care. 2011;13:76–80. , , , et al.
- VA mental health services utilization in Iraq and Afghanistan veterans in the first year of receiving new mental health diagnoses. J Trauma Stress. 2010;23:5–16. , , , et al.
- Long‐term opioid management for chronic noncancer pain. Cochrane Database Syst Rev. 2010;(1):CD006605. , , , et al.
Recent trends show a marked increase in outpatient use of chronic opioid therapy (COT) for chronic noncancer pain (CNCP)[1, 2] without decreases in reported CNCP,[3] raising concerns about the efficacy and risk‐to‐benefit ratio of opioids in this population.[4, 5, 6, 7, 8] Increasing rates of outpatient use likely are accompanied by increasing rates of opioid exposure among patients admitted to the hospital. To our knowledge there are no published data regarding the prevalence of COT during the months preceding hospitalization.
Opioid use has been linked to increased emergency room utilization[9, 10] and emergency hospitalization,[11] but associations between opioid use and inpatient metrics (eg, mortality, readmission) have not been explored. Furthermore, lack of knowledge about the prevalence of opioid use prior to hospitalization may impede efforts to improve inpatient pain management and satisfaction with care. Although there is reason to expect that strategies to safely and effectively treat acute pain during the inpatient stay differ between opioid‐nave patients and opioid‐exposed patients, evidence regarding treatment strategies is limited.[12, 13, 14] Opioid pain medications are associated with hospital adverse events, with both prior opioid exposure and lack of opioid use as proposed risk factors.[15] A better understanding of the prevalence and characteristics of hospitalized COT patients is fundamental to future work to achieve safer and more effective inpatient pain management.
The primary purpose of this study was to determine the prevalence of prior COT among hospitalized medical patients. Additionally, we aimed to characterize inpatients with occasional and chronic opioid therapy prior to admission in comparison to opioid‐nave inpatients, as differences between these groups may suggest directions for further investigation into the distinct needs or challenges of hospitalized opioid‐exposed patients.
METHODS
We used inpatient and outpatient administrative data from the Department of Veterans Affairs (VA) Healthcare System. The primary data source to identify acute medical admissions was the VA Patient Treatment File, a national administrative database of all inpatient admissions, including patient demographic characteristics, primary and secondary diagnoses (using International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM], codes), and hospitalization characteristics. Outpatient pharmacy data were from the VA Pharmacy Prescription Data Files. The VA Vital Status Files provided dates of death.
We identified all first acute medical admissions to 129 VA hospitals during fiscal years (FYs) 2009 to 2011 (October 2009September 2011). We defined first admissions as the initial medical hospitalization occurring following a minimum 365‐day hospitalization‐free period. Patients were required to demonstrate pharmacy use by receipt of any outpatient medication from the VA on 2 separate occasions within 270 days preceding the first admission, to avoid misclassification of patients who routinely obtained medications only from a non‐VA provider. Patients admitted from extended care facilities were excluded.
We grouped patients by opioid‐use status based on outpatient prescription records: (1) no opioid use, defined as no opioid prescriptions in the 6 months prior to hospitalization; (2) occasional opioid use, defined as patients who received any opioid prescription during the 6 months prior but did not meet definition of chronic use; and (3) chronic opioid therapy, defined as 90 or more days' supply of opioids received within 6 months preceding hospitalization. We did not specify continuous prescribing. Opioids included in the definition were codeine, dihydrocodeine, fentanyl (mucosal and topical), hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, pentazocine, propoxyphene, tapentadol, and tramadol.[16, 17]
We compared groups by demographic variables including age, sex, race, income, rural vs urban residence (determined from Rural‐Urban Commuting Area codes), region based on hospital location; overall comorbidity using the Charlson Comorbidity Index (CCI);[18] and 10 selected conditions to characterize comorbidity (see Supporting Information, Appendix A, in the online version of this article). These 10 conditions were chosen based on probable associations with chronic opioid use or high prevalence among hospitalized veterans.[9, 19, 20]
We used a CNCP definition based on ICD‐9‐CM codes.[9] This definition did not include episodic conditions such as migraine[2] or a measure of pain intensity.[21] All conditions were determined from diagnoses coded during any encounter in the year prior to hospitalization, exclusive of the first (ie, index) admission. We also determined the frequency of palliative care use, defined as presence of ICD‐9‐CM code V667 during index hospitalization or within the past year. Patients with palliative care use (n=3070) were excluded from further analyses.
We compared opioid use groups by baseline characteristics using the [2] statistic to determine if the distribution was nonrandom. We used analysis of variance to compare hospital length of stay between groups. We used the [2] statistic to compare rates of 4 outcomes of interest: intensive care unit (ICU) admission during the index hospitalization, discharge disposition other than home, 30‐day readmission rate, and in‐hospital or 30‐day mortality.
To assess the association between opioid‐use status and the 4 outcomes of interest, we constructed 2 multivariable regression models; the first was adjusted only for admission diagnosis using the Clinical Classification Software (CCS),[22] and the second was adjusted for demographics, CCI, and the 10 selected comorbidities in addition to admission diagnosis.
The authors had full access to and take full responsibility for the integrity of the data. All analyses were conducted using SAS statistical software version 9.2 (SAS Institute, Cary, NC). The study was approved by the University of Iowa institutional review board and the Iowa City VA Health Care System Research and Development Committee.
RESULTS
Patient Demographics
Demographic characteristics of patients differed by opioid‐use group (Table 1). Hospitalized patients who received COT in the 6 months prior to admission tended to be younger than their comparators, more often female, white, have a rural residence, and live in the South or West.
Variables | No Opioids, n=66,899 (54.5%) | Occasional Opioids, n=24,093 (19.6%) | Chronic Opioids, n=31,802 (25.9%) |
---|---|---|---|
| |||
Age, y, mean (SD) | 68.7 (12.8) | 66.5 (12.7) | 64.5 (11.5) |
Age, n (%) | |||
59 (reference) | 15,170 (22.7) | 6,703 (27.8) | 10,334 (32.5) |
6065 | 15,076 (22.5) | 5,973 (24.8) | 8,983 (28.3) |
6677 | 17,226 (25.8) | 5,871 (24.4) | 7,453 (23.4) |
78 | 19,427 (29.0) | 5,546 (23.0) | 5,032 (15.8) |
Male, n (%) | 64,673 (96.7) | 22,964 (95.3) | 30,200 (95.0) |
Race, n (%) | |||
White | 48,888 (73.1) | 17,358 (72.1) | 25,087 (78.9) |
Black | 14,480 (21.6) | 5,553 (23.1) | 5,089 (16.0) |
Other | 1,172 (1.8) | 450 (1.9) | 645 (2.0) |
Unknown | 2,359 (3.5) | 732 (3.0) | 981 (3.1) |
Income $20,000, n (%) | 40,414 (60.4) | 14,105 (58.5) | 18,945 (59.6) |
Rural residence, n (%) | 16,697 (25.0) | 6,277 (26.1) | 9,356 (29.4) |
Region, n (%) | |||
Northeast | 15,053 (22.5) | 4,437 (18.4) | 5,231 (16.5) |
South | 24,083 (36.0) | 9,390 (39.0) | 12,720 (40.0) |
Midwest | 16,000 (23.9) | 5,714 (23.7) | 7,762 (24.4) |
West | 11,763 (17.6) | 4,552 (18.9) | 6,089 (19.2) |
Charlson Comorbidity Index, mean (SD) | 2.3 (2.0) | 2.6 (2.3) | 2.7 (2.3) |
Comorbidities, n (%) | |||
Cancer (not metastatic) | 11,818 (17.7) | 5,549 (23.0) | 6,874 (21.6) |
Metastatic cancer | 866 (1.3) | 733 (3.0) | 1,104 (3.5) |
Chronic pain | 25,748 (38.5) | 14,811 (61.5) | 23,894 (75.1) |
COPD | 20,750 (31.0) | 7,876 (32.7) | 12,117 (38.1) |
Diabetes, complicated | 10,917 (16.3) | 4,620 (19.2) | 6,304 (19.8) |
Heart failure | 14,267 (21.3) | 5,035 (20.9) | 6,501 (20.4) |
Renal disease | 11,311 (16.9) | 4,586 (19.0) | 4,981 (15.7) |
Dementia | 2,180 (3.3) | 459 (1.9) | 453 (1.4) |
Mental health other than PTSD | 33,390 (49.9) | 13,657 (56.7) | 20,726 (65.2) |
PTSD | 7,216 (10.8) | 3,607 (15.0) | 5,938 (18.7) |
Palliative care use, n (%) | 1,407 (2.1) | 639 (2.7) | 1,024 (3.2) |
Prevalence of Opioid Use
Among the cohort (N=122,794) of hospitalized veterans, 66,899 (54.5%) received no opioids from the VA during the 6‐month period prior to hospitalization; 31,802 (25.9%) received COT in the 6 months prior to admission. An additional 24,093 (19.6%) had occasional opioid therapy (Table 1). A total of 257,623 opioid prescriptions were provided to patients in the 6‐month period prior to their index hospitalization. Of these, 100,379 (39.0%) were for hydrocodone, 48,584 (18.9%) for oxycodone, 36,658 (14.2%) for tramadol, and 35,471 (13.8%) for morphine. These 4 medications accounted for 85.8% of total opioid prescriptions (see Supporting Information, Appendix B, in the online version of this article).
Among the COT group, 3610 (11.4%) received opioids 90 days, 10,110 (31.8%) received opioids between 91 and 179 days, and 18,082 (56.9%) patients received opioids 180 days in the prior 6 months (see Supporting Information, Appendix C, in the online version of this article).
Among the subset of patients with cancer (metastatic and nonmetastatic, n=26,944), 29.6% were prescribed COT, and 23.3% had occasional opioid use. Among the subset of patients with CNCP (n=64,453), 37.1% were prescribed COT, and 23.0% had occasional opioid use.
Comorbid Conditions
Compared to patients not receiving opioids, a larger proportion of patients receiving both occasional and chronic opioids had diagnoses of cancer and of CNCP. Diagnoses more common in COT patients included chronic obstructive pulmonary disease (COPD), complicated diabetes, post‐traumatic stress disorder (PTSD), and other mental health disorders. In contrast, COT patients were less likely than no‐opioid and occasional opioid patients to have heart failure (HF), renal disease, and dementia. Palliative care was used by 2.1% of patients in the no‐opioid group, and 3.2% of patients in the COT group (Table 1). Renal disease was most common among the occasional‐use group.
Unadjusted Hospitalization Outcomes
Unadjusted hospitalization outcomes differed between opioid‐exposure groups (Table 2). Patients receiving occasional or chronic opioids had shorter length of stay and lower rates of non‐home discharge than did patients without any opioid use. The rate of death during hospitalization or within 30 days did not differ between groups. The occasional‐use and COT groups had higher 30‐day readmission rates than did the no‐use group.
No Opioids, n=65,492 | Occasional Opioids, n=23,454 | Chronic Opioids, n=30,778 | P | |
---|---|---|---|---|
| ||||
Hospital length of stay, d, mean (SD) | 4.7 (5.1) | 4.5 (4.8) | 4.5 (4.8) | 0.0003 |
ICU stay, n (%) | 10,281 (15.7) | 3,299 (14.1) | 4,570 (14.9) | <0.0001 |
Non‐home discharge, n (%) | 2,944 (4.5) | 997 (4.3) | 1,233 (4.0) | 0.0020 |
30‐day readmission, n (%) | 9,023 (13.8) | 3,629 (15.5) | 4,773 (15.5) | <0.0001 |
Death during hospitalization or within 30 days, n (%) | 2,532 (3.9) | 863 (3.7) | 1,191 (3.9) | 0.4057 |
Multivariable Models
In the fully adjusted multivariable models, opioid exposure (in the form of either chronic or occasional use) had no significant association with ICU stay during index admission or non‐home discharge (Table 3). Both the occasional‐opioid use and COT groups were more likely to experience 30‐day hospital readmission, a relationship that remained consistent across the partially and fully adjusted models. The occasional‐opioid use group saw no increased mortality risk. In the model adjusted only for admission diagnosis, COT was not associated with increased mortality risk. When additionally adjusted for demographic variables, CCI, and selected comorbidities, however, COT was associated with increased risk of death during hospitalization or within 30 days (odds ratio: 1.19, 90% confidence interval: 1.10‐1.29).
Occasional Opioid Use | Chronic Opioid Therapy | |||
---|---|---|---|---|
Model 1, OR (95% CI) | Model 2, OR (95% CI) | Model 1, OR (95% CI) | Model 2, OR (95% CI) | |
| ||||
ICU stay | 0.94 (0.90‐0.99) | 0.95 (0.91‐1.00) | 1.00 (0.96‐1.04) | 1.01 (0.97‐1.05) |
Non‐home discharge | 0.92 (0.85‐0.99) | 0.97 (0.90‐1.05) | 0.85 (0.80‐0.92) | 0.95 (0.88‐1.03) |
30‐day readmission | 1.14 (1.09‐1.19) | 1.14 (1.09‐1.19) | 1.14 (1.10‐1.19) | 1.15 (1.10‐1.20) |
Death during hospitalization or within 30 days | 0.96 (0.88‐1.04) | 1.04 (0.95‐1.13) | 0.96 (0.90‐1.04) | 1.19 (1.10‐1.29) |
DISCUSSION
This observational study is, to our knowledge, the first to report prevalence of and characteristics associated with prior opioid use among hospitalized medical patients. The prevalence of any opioid use and of COT was substantially higher in this hospitalized cohort than reported in outpatient settings. The prevalence of any opioid use during 1 year (FY 2009) among all veterans with VA primary care use was 26.1%.[23] A study of incident prescribing rates among veterans with new diagnoses of noncancer‐related pain demonstrated 11% received an opioid prescription within 1 year.[24] Using a definition of 90 consecutive prescription days to define COT, Dobscha et al.[25] found that 5% of veterans with persistent elevated pain intensity and no previous opioid prescriptions subsequently received COT within 12 months. The high prevalence we found likely reflects cumulative effects of incident use as well as an increased symptom burden in a population defined by need for medical hospitalization.
Although a veteran population may not be generalizable to a nonveteran setting, we do note prior studies reporting prevalence of any opioid use in outpatient cohorts (in 2000 and 2005) of between 18% and 30%, with higher rates among women and patients over 65 years of age.[1, 2]
Our work was purposefully inclusive of cancer patients so that we might assess the degree to which cancer diagnoses accounted for prior opioid use in hospitalized patients. Surprisingly, the rate of COT for patients with cancer was lower than that for patients with CNCP, perhaps reflecting that a cancer condition defined in administrative data may not constitute a pain‐causing disease.
Recognition of the prevalence of opioid therapy is important as we work to understand and improve safety, satisfaction, utilization, and long‐term health outcomes associated with hospitalization. Our finding that over half of medical inpatients have preexisting CNCP diagnoses, and a not entirely overlapping proportion has prior opioid exposure, implies a need for future work to refine expectations and strategies for inpatient management, potentially tailored to prior opioid use and presence of CNCP.
A recent Joint Commission sentinel event alert[26] highlights opioid adverse events in the hospital and identifies both lack of previous opioid therapy and prior opioid therapy as factors increasing risk. ICU admission during the hospital stay may reflect adverse events such as opioid‐induced respiratory depression; in our study, patients with no opioid use prior to admission were more likely to have an ICU stay, although the effect was small. One might speculate that clinicians, accustomed to treating pain in opioid‐exposed patients, are using inappropriately large starting dosages of narcotics for inpatients without first assessing prior opioid exposure. Another possible explanation is that patients on COT are admitted to the hospital with less severe illness, potentially reflecting functional, social, or access limitations that compromise ability to manage illness in the outpatient setting. More detailed comparison of illness severity is beyond the scope of the present work.
Patient satisfaction with pain management is reflected in 2 of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) questions, and is publically reported.[27] HCAHPS results also figure in the formula for the Centers for Medicare and Medicaid Services value‐based purchasing.[28] Preadmission pain is predictive of postoperative pain[29, 30] and may shape patient expectations; how preadmission opioid use modulates nonsurgical pain and satisfaction with management in the medical inpatient remains to be studied. The high prevalence of prior COT underscores the importance of understanding characteristics of patients on COT, and potential differences and disparities in pain management, when designing interventions to augment patient satisfaction with pain management.
Although the age distribution and patterns of comorbidities differed between the opioid‐use groups, opioid therapy remained a small but significant predictor of hospital readmission; this association was independent of CNCP diagnosis. Functional outcomes are recognized as important measures of efficacy of outpatient pain management strategies,[31] with some evidence that opioids are associated with worse functioning.[32, 33] Functional limitations, as well as inadequately or inappropriately treated pain, may drive both admissions and readmissions. Alternately, COT may be a marker for unmeasured factors that increase a patient's risk of returning to the hospital. Further work is needed to elucidate the relationship between COT and healthcare utilization associated with the inpatient stay.
Our finding that patients on COT have an increased mortality risk is concerning, given the rapid expansion in use of these medications. Although pain is increasingly prevalent toward end of life,[34] we did not observe an association between either CNCP (data not shown) or occasional opioid use and mortality. COT may complicate chronic disease through adverse drug effects including respiratory depression, apnea, or endocrine or immune alteration. Complex chronically ill patients with conditions such as COPD, HF, or diabetes may be particularly susceptible to these effects. Incident use of morphine is associated with increased mortality in acute coronary syndrome and HF[35, 36]: we are not aware of any work describing the relationship between prior opioid use and incident use during hospitalization in medical patients.
Limitations
Our work focuses on hospitalized veterans, a population that remains predominately male, limiting generalizability of the findings. Rates of mental health diagnoses and PTSD, associated with CNCP and COT,[24, 37] are higher in this population than would be expected in a general hospitalized population. Because our outcomes included readmission, and our definition of opioid exposure was designed to reflect outpatient prescribing, we included only patients without recent hospitalization. Therefore, our results may not be generalizable to patients with frequent and recurring hospitalization.
Our definition of opioid exposure depended on pharmacy dispensing records; we are not able to confirm if veterans were taking the medications as prescribed. Further, we were not able to capture data on opioids prescribed by non‐VA providers, which may have led to underestimation of prevalence.
Our definitions of COT and CNCP are imperfect, and should be noted when comparing to other studies. Because we did not specify continuous 90‐day prescribing, we may have misclassified occasional opioid therapy as COT in comparison to other authors. That continuous prescribing is equivalent to continuous use assumes that patients take medications exactly as prescribed. We used occasional opioid therapy as a comparison group, and detailed the distribution of days prescribed among the COT group (see Supporting Information, Appendix C, in the online version of this article), to augment interpretability of these results. Our CNCP diagnosis was less inclusive than others,[2] as we omitted episodic pain (eg, migraine and sprains) and human immunodeficiency virus‐related pain. As COT for CNCP conditions lacks a robust evidence base,[38] defining pain diagnoses using administrative data to reflect conditions for which COT is used in a guideline‐concordant way remains difficult.
Last, differences observed between opioid‐use groups may be due to an unmeasured confounder not captured by the variables we included. Specifically, we did not include other long‐term outpatient medications in our models. It is possible that COT is part of a larger context of inappropriate prescribing, rather than a single‐medication effect on outcomes studied.
CONCLUSION
Nearly 1 in 4 hospitalized veterans has current or recent COT at the time of hospital admission for nonsurgical conditions; nearly half have been prescribed any opioids. Practitioners designing interventions to improve pain management in the inpatient setting should account for prior opioid use. Patients who are on COT prior to hospitalization differ in age and comorbidities from their counterparts who are not on COT. Further elucidation of differences between opioid‐use groups may help providers address care needs during the transition to posthospitalization care. CNCP diagnoses and chronic opioid exposure are different entities and cannot serve as proxies in administrative data. Additional work on utilization and outcomes in specific patient populations may improve our understanding of the long‐term health effects of chronic opioid therapy.
Disclosures: Dr. Mosher is supported by the Veterans Administration (VA) Quality Scholars Fellowship, Office of Academic Affiliations, Department of Veterans Affairs. Dr. Cram is supported by a K24 award from NIAMS (AR062133) at the National Institutes of Health. The preliminary results of this article were presented at the Society of General Internal Medicine Annual Meeting in Denver, Colordao, April 2013. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. Data are available to researchers with VA accreditation, the statistical code and the protocol are available to interested readers by contacting Dr. Mosher. The authors report no conflict of interest in regard to this study.
Recent trends show a marked increase in outpatient use of chronic opioid therapy (COT) for chronic noncancer pain (CNCP)[1, 2] without decreases in reported CNCP,[3] raising concerns about the efficacy and risk‐to‐benefit ratio of opioids in this population.[4, 5, 6, 7, 8] Increasing rates of outpatient use likely are accompanied by increasing rates of opioid exposure among patients admitted to the hospital. To our knowledge there are no published data regarding the prevalence of COT during the months preceding hospitalization.
Opioid use has been linked to increased emergency room utilization[9, 10] and emergency hospitalization,[11] but associations between opioid use and inpatient metrics (eg, mortality, readmission) have not been explored. Furthermore, lack of knowledge about the prevalence of opioid use prior to hospitalization may impede efforts to improve inpatient pain management and satisfaction with care. Although there is reason to expect that strategies to safely and effectively treat acute pain during the inpatient stay differ between opioid‐nave patients and opioid‐exposed patients, evidence regarding treatment strategies is limited.[12, 13, 14] Opioid pain medications are associated with hospital adverse events, with both prior opioid exposure and lack of opioid use as proposed risk factors.[15] A better understanding of the prevalence and characteristics of hospitalized COT patients is fundamental to future work to achieve safer and more effective inpatient pain management.
The primary purpose of this study was to determine the prevalence of prior COT among hospitalized medical patients. Additionally, we aimed to characterize inpatients with occasional and chronic opioid therapy prior to admission in comparison to opioid‐nave inpatients, as differences between these groups may suggest directions for further investigation into the distinct needs or challenges of hospitalized opioid‐exposed patients.
METHODS
We used inpatient and outpatient administrative data from the Department of Veterans Affairs (VA) Healthcare System. The primary data source to identify acute medical admissions was the VA Patient Treatment File, a national administrative database of all inpatient admissions, including patient demographic characteristics, primary and secondary diagnoses (using International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM], codes), and hospitalization characteristics. Outpatient pharmacy data were from the VA Pharmacy Prescription Data Files. The VA Vital Status Files provided dates of death.
We identified all first acute medical admissions to 129 VA hospitals during fiscal years (FYs) 2009 to 2011 (October 2009September 2011). We defined first admissions as the initial medical hospitalization occurring following a minimum 365‐day hospitalization‐free period. Patients were required to demonstrate pharmacy use by receipt of any outpatient medication from the VA on 2 separate occasions within 270 days preceding the first admission, to avoid misclassification of patients who routinely obtained medications only from a non‐VA provider. Patients admitted from extended care facilities were excluded.
We grouped patients by opioid‐use status based on outpatient prescription records: (1) no opioid use, defined as no opioid prescriptions in the 6 months prior to hospitalization; (2) occasional opioid use, defined as patients who received any opioid prescription during the 6 months prior but did not meet definition of chronic use; and (3) chronic opioid therapy, defined as 90 or more days' supply of opioids received within 6 months preceding hospitalization. We did not specify continuous prescribing. Opioids included in the definition were codeine, dihydrocodeine, fentanyl (mucosal and topical), hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, pentazocine, propoxyphene, tapentadol, and tramadol.[16, 17]
We compared groups by demographic variables including age, sex, race, income, rural vs urban residence (determined from Rural‐Urban Commuting Area codes), region based on hospital location; overall comorbidity using the Charlson Comorbidity Index (CCI);[18] and 10 selected conditions to characterize comorbidity (see Supporting Information, Appendix A, in the online version of this article). These 10 conditions were chosen based on probable associations with chronic opioid use or high prevalence among hospitalized veterans.[9, 19, 20]
We used a CNCP definition based on ICD‐9‐CM codes.[9] This definition did not include episodic conditions such as migraine[2] or a measure of pain intensity.[21] All conditions were determined from diagnoses coded during any encounter in the year prior to hospitalization, exclusive of the first (ie, index) admission. We also determined the frequency of palliative care use, defined as presence of ICD‐9‐CM code V667 during index hospitalization or within the past year. Patients with palliative care use (n=3070) were excluded from further analyses.
We compared opioid use groups by baseline characteristics using the [2] statistic to determine if the distribution was nonrandom. We used analysis of variance to compare hospital length of stay between groups. We used the [2] statistic to compare rates of 4 outcomes of interest: intensive care unit (ICU) admission during the index hospitalization, discharge disposition other than home, 30‐day readmission rate, and in‐hospital or 30‐day mortality.
To assess the association between opioid‐use status and the 4 outcomes of interest, we constructed 2 multivariable regression models; the first was adjusted only for admission diagnosis using the Clinical Classification Software (CCS),[22] and the second was adjusted for demographics, CCI, and the 10 selected comorbidities in addition to admission diagnosis.
The authors had full access to and take full responsibility for the integrity of the data. All analyses were conducted using SAS statistical software version 9.2 (SAS Institute, Cary, NC). The study was approved by the University of Iowa institutional review board and the Iowa City VA Health Care System Research and Development Committee.
RESULTS
Patient Demographics
Demographic characteristics of patients differed by opioid‐use group (Table 1). Hospitalized patients who received COT in the 6 months prior to admission tended to be younger than their comparators, more often female, white, have a rural residence, and live in the South or West.
Variables | No Opioids, n=66,899 (54.5%) | Occasional Opioids, n=24,093 (19.6%) | Chronic Opioids, n=31,802 (25.9%) |
---|---|---|---|
| |||
Age, y, mean (SD) | 68.7 (12.8) | 66.5 (12.7) | 64.5 (11.5) |
Age, n (%) | |||
59 (reference) | 15,170 (22.7) | 6,703 (27.8) | 10,334 (32.5) |
6065 | 15,076 (22.5) | 5,973 (24.8) | 8,983 (28.3) |
6677 | 17,226 (25.8) | 5,871 (24.4) | 7,453 (23.4) |
78 | 19,427 (29.0) | 5,546 (23.0) | 5,032 (15.8) |
Male, n (%) | 64,673 (96.7) | 22,964 (95.3) | 30,200 (95.0) |
Race, n (%) | |||
White | 48,888 (73.1) | 17,358 (72.1) | 25,087 (78.9) |
Black | 14,480 (21.6) | 5,553 (23.1) | 5,089 (16.0) |
Other | 1,172 (1.8) | 450 (1.9) | 645 (2.0) |
Unknown | 2,359 (3.5) | 732 (3.0) | 981 (3.1) |
Income $20,000, n (%) | 40,414 (60.4) | 14,105 (58.5) | 18,945 (59.6) |
Rural residence, n (%) | 16,697 (25.0) | 6,277 (26.1) | 9,356 (29.4) |
Region, n (%) | |||
Northeast | 15,053 (22.5) | 4,437 (18.4) | 5,231 (16.5) |
South | 24,083 (36.0) | 9,390 (39.0) | 12,720 (40.0) |
Midwest | 16,000 (23.9) | 5,714 (23.7) | 7,762 (24.4) |
West | 11,763 (17.6) | 4,552 (18.9) | 6,089 (19.2) |
Charlson Comorbidity Index, mean (SD) | 2.3 (2.0) | 2.6 (2.3) | 2.7 (2.3) |
Comorbidities, n (%) | |||
Cancer (not metastatic) | 11,818 (17.7) | 5,549 (23.0) | 6,874 (21.6) |
Metastatic cancer | 866 (1.3) | 733 (3.0) | 1,104 (3.5) |
Chronic pain | 25,748 (38.5) | 14,811 (61.5) | 23,894 (75.1) |
COPD | 20,750 (31.0) | 7,876 (32.7) | 12,117 (38.1) |
Diabetes, complicated | 10,917 (16.3) | 4,620 (19.2) | 6,304 (19.8) |
Heart failure | 14,267 (21.3) | 5,035 (20.9) | 6,501 (20.4) |
Renal disease | 11,311 (16.9) | 4,586 (19.0) | 4,981 (15.7) |
Dementia | 2,180 (3.3) | 459 (1.9) | 453 (1.4) |
Mental health other than PTSD | 33,390 (49.9) | 13,657 (56.7) | 20,726 (65.2) |
PTSD | 7,216 (10.8) | 3,607 (15.0) | 5,938 (18.7) |
Palliative care use, n (%) | 1,407 (2.1) | 639 (2.7) | 1,024 (3.2) |
Prevalence of Opioid Use
Among the cohort (N=122,794) of hospitalized veterans, 66,899 (54.5%) received no opioids from the VA during the 6‐month period prior to hospitalization; 31,802 (25.9%) received COT in the 6 months prior to admission. An additional 24,093 (19.6%) had occasional opioid therapy (Table 1). A total of 257,623 opioid prescriptions were provided to patients in the 6‐month period prior to their index hospitalization. Of these, 100,379 (39.0%) were for hydrocodone, 48,584 (18.9%) for oxycodone, 36,658 (14.2%) for tramadol, and 35,471 (13.8%) for morphine. These 4 medications accounted for 85.8% of total opioid prescriptions (see Supporting Information, Appendix B, in the online version of this article).
Among the COT group, 3610 (11.4%) received opioids 90 days, 10,110 (31.8%) received opioids between 91 and 179 days, and 18,082 (56.9%) patients received opioids 180 days in the prior 6 months (see Supporting Information, Appendix C, in the online version of this article).
Among the subset of patients with cancer (metastatic and nonmetastatic, n=26,944), 29.6% were prescribed COT, and 23.3% had occasional opioid use. Among the subset of patients with CNCP (n=64,453), 37.1% were prescribed COT, and 23.0% had occasional opioid use.
Comorbid Conditions
Compared to patients not receiving opioids, a larger proportion of patients receiving both occasional and chronic opioids had diagnoses of cancer and of CNCP. Diagnoses more common in COT patients included chronic obstructive pulmonary disease (COPD), complicated diabetes, post‐traumatic stress disorder (PTSD), and other mental health disorders. In contrast, COT patients were less likely than no‐opioid and occasional opioid patients to have heart failure (HF), renal disease, and dementia. Palliative care was used by 2.1% of patients in the no‐opioid group, and 3.2% of patients in the COT group (Table 1). Renal disease was most common among the occasional‐use group.
Unadjusted Hospitalization Outcomes
Unadjusted hospitalization outcomes differed between opioid‐exposure groups (Table 2). Patients receiving occasional or chronic opioids had shorter length of stay and lower rates of non‐home discharge than did patients without any opioid use. The rate of death during hospitalization or within 30 days did not differ between groups. The occasional‐use and COT groups had higher 30‐day readmission rates than did the no‐use group.
No Opioids, n=65,492 | Occasional Opioids, n=23,454 | Chronic Opioids, n=30,778 | P | |
---|---|---|---|---|
| ||||
Hospital length of stay, d, mean (SD) | 4.7 (5.1) | 4.5 (4.8) | 4.5 (4.8) | 0.0003 |
ICU stay, n (%) | 10,281 (15.7) | 3,299 (14.1) | 4,570 (14.9) | <0.0001 |
Non‐home discharge, n (%) | 2,944 (4.5) | 997 (4.3) | 1,233 (4.0) | 0.0020 |
30‐day readmission, n (%) | 9,023 (13.8) | 3,629 (15.5) | 4,773 (15.5) | <0.0001 |
Death during hospitalization or within 30 days, n (%) | 2,532 (3.9) | 863 (3.7) | 1,191 (3.9) | 0.4057 |
Multivariable Models
In the fully adjusted multivariable models, opioid exposure (in the form of either chronic or occasional use) had no significant association with ICU stay during index admission or non‐home discharge (Table 3). Both the occasional‐opioid use and COT groups were more likely to experience 30‐day hospital readmission, a relationship that remained consistent across the partially and fully adjusted models. The occasional‐opioid use group saw no increased mortality risk. In the model adjusted only for admission diagnosis, COT was not associated with increased mortality risk. When additionally adjusted for demographic variables, CCI, and selected comorbidities, however, COT was associated with increased risk of death during hospitalization or within 30 days (odds ratio: 1.19, 90% confidence interval: 1.10‐1.29).
Occasional Opioid Use | Chronic Opioid Therapy | |||
---|---|---|---|---|
Model 1, OR (95% CI) | Model 2, OR (95% CI) | Model 1, OR (95% CI) | Model 2, OR (95% CI) | |
| ||||
ICU stay | 0.94 (0.90‐0.99) | 0.95 (0.91‐1.00) | 1.00 (0.96‐1.04) | 1.01 (0.97‐1.05) |
Non‐home discharge | 0.92 (0.85‐0.99) | 0.97 (0.90‐1.05) | 0.85 (0.80‐0.92) | 0.95 (0.88‐1.03) |
30‐day readmission | 1.14 (1.09‐1.19) | 1.14 (1.09‐1.19) | 1.14 (1.10‐1.19) | 1.15 (1.10‐1.20) |
Death during hospitalization or within 30 days | 0.96 (0.88‐1.04) | 1.04 (0.95‐1.13) | 0.96 (0.90‐1.04) | 1.19 (1.10‐1.29) |
DISCUSSION
This observational study is, to our knowledge, the first to report prevalence of and characteristics associated with prior opioid use among hospitalized medical patients. The prevalence of any opioid use and of COT was substantially higher in this hospitalized cohort than reported in outpatient settings. The prevalence of any opioid use during 1 year (FY 2009) among all veterans with VA primary care use was 26.1%.[23] A study of incident prescribing rates among veterans with new diagnoses of noncancer‐related pain demonstrated 11% received an opioid prescription within 1 year.[24] Using a definition of 90 consecutive prescription days to define COT, Dobscha et al.[25] found that 5% of veterans with persistent elevated pain intensity and no previous opioid prescriptions subsequently received COT within 12 months. The high prevalence we found likely reflects cumulative effects of incident use as well as an increased symptom burden in a population defined by need for medical hospitalization.
Although a veteran population may not be generalizable to a nonveteran setting, we do note prior studies reporting prevalence of any opioid use in outpatient cohorts (in 2000 and 2005) of between 18% and 30%, with higher rates among women and patients over 65 years of age.[1, 2]
Our work was purposefully inclusive of cancer patients so that we might assess the degree to which cancer diagnoses accounted for prior opioid use in hospitalized patients. Surprisingly, the rate of COT for patients with cancer was lower than that for patients with CNCP, perhaps reflecting that a cancer condition defined in administrative data may not constitute a pain‐causing disease.
Recognition of the prevalence of opioid therapy is important as we work to understand and improve safety, satisfaction, utilization, and long‐term health outcomes associated with hospitalization. Our finding that over half of medical inpatients have preexisting CNCP diagnoses, and a not entirely overlapping proportion has prior opioid exposure, implies a need for future work to refine expectations and strategies for inpatient management, potentially tailored to prior opioid use and presence of CNCP.
A recent Joint Commission sentinel event alert[26] highlights opioid adverse events in the hospital and identifies both lack of previous opioid therapy and prior opioid therapy as factors increasing risk. ICU admission during the hospital stay may reflect adverse events such as opioid‐induced respiratory depression; in our study, patients with no opioid use prior to admission were more likely to have an ICU stay, although the effect was small. One might speculate that clinicians, accustomed to treating pain in opioid‐exposed patients, are using inappropriately large starting dosages of narcotics for inpatients without first assessing prior opioid exposure. Another possible explanation is that patients on COT are admitted to the hospital with less severe illness, potentially reflecting functional, social, or access limitations that compromise ability to manage illness in the outpatient setting. More detailed comparison of illness severity is beyond the scope of the present work.
Patient satisfaction with pain management is reflected in 2 of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) questions, and is publically reported.[27] HCAHPS results also figure in the formula for the Centers for Medicare and Medicaid Services value‐based purchasing.[28] Preadmission pain is predictive of postoperative pain[29, 30] and may shape patient expectations; how preadmission opioid use modulates nonsurgical pain and satisfaction with management in the medical inpatient remains to be studied. The high prevalence of prior COT underscores the importance of understanding characteristics of patients on COT, and potential differences and disparities in pain management, when designing interventions to augment patient satisfaction with pain management.
Although the age distribution and patterns of comorbidities differed between the opioid‐use groups, opioid therapy remained a small but significant predictor of hospital readmission; this association was independent of CNCP diagnosis. Functional outcomes are recognized as important measures of efficacy of outpatient pain management strategies,[31] with some evidence that opioids are associated with worse functioning.[32, 33] Functional limitations, as well as inadequately or inappropriately treated pain, may drive both admissions and readmissions. Alternately, COT may be a marker for unmeasured factors that increase a patient's risk of returning to the hospital. Further work is needed to elucidate the relationship between COT and healthcare utilization associated with the inpatient stay.
Our finding that patients on COT have an increased mortality risk is concerning, given the rapid expansion in use of these medications. Although pain is increasingly prevalent toward end of life,[34] we did not observe an association between either CNCP (data not shown) or occasional opioid use and mortality. COT may complicate chronic disease through adverse drug effects including respiratory depression, apnea, or endocrine or immune alteration. Complex chronically ill patients with conditions such as COPD, HF, or diabetes may be particularly susceptible to these effects. Incident use of morphine is associated with increased mortality in acute coronary syndrome and HF[35, 36]: we are not aware of any work describing the relationship between prior opioid use and incident use during hospitalization in medical patients.
Limitations
Our work focuses on hospitalized veterans, a population that remains predominately male, limiting generalizability of the findings. Rates of mental health diagnoses and PTSD, associated with CNCP and COT,[24, 37] are higher in this population than would be expected in a general hospitalized population. Because our outcomes included readmission, and our definition of opioid exposure was designed to reflect outpatient prescribing, we included only patients without recent hospitalization. Therefore, our results may not be generalizable to patients with frequent and recurring hospitalization.
Our definition of opioid exposure depended on pharmacy dispensing records; we are not able to confirm if veterans were taking the medications as prescribed. Further, we were not able to capture data on opioids prescribed by non‐VA providers, which may have led to underestimation of prevalence.
Our definitions of COT and CNCP are imperfect, and should be noted when comparing to other studies. Because we did not specify continuous 90‐day prescribing, we may have misclassified occasional opioid therapy as COT in comparison to other authors. That continuous prescribing is equivalent to continuous use assumes that patients take medications exactly as prescribed. We used occasional opioid therapy as a comparison group, and detailed the distribution of days prescribed among the COT group (see Supporting Information, Appendix C, in the online version of this article), to augment interpretability of these results. Our CNCP diagnosis was less inclusive than others,[2] as we omitted episodic pain (eg, migraine and sprains) and human immunodeficiency virus‐related pain. As COT for CNCP conditions lacks a robust evidence base,[38] defining pain diagnoses using administrative data to reflect conditions for which COT is used in a guideline‐concordant way remains difficult.
Last, differences observed between opioid‐use groups may be due to an unmeasured confounder not captured by the variables we included. Specifically, we did not include other long‐term outpatient medications in our models. It is possible that COT is part of a larger context of inappropriate prescribing, rather than a single‐medication effect on outcomes studied.
CONCLUSION
Nearly 1 in 4 hospitalized veterans has current or recent COT at the time of hospital admission for nonsurgical conditions; nearly half have been prescribed any opioids. Practitioners designing interventions to improve pain management in the inpatient setting should account for prior opioid use. Patients who are on COT prior to hospitalization differ in age and comorbidities from their counterparts who are not on COT. Further elucidation of differences between opioid‐use groups may help providers address care needs during the transition to posthospitalization care. CNCP diagnoses and chronic opioid exposure are different entities and cannot serve as proxies in administrative data. Additional work on utilization and outcomes in specific patient populations may improve our understanding of the long‐term health effects of chronic opioid therapy.
Disclosures: Dr. Mosher is supported by the Veterans Administration (VA) Quality Scholars Fellowship, Office of Academic Affiliations, Department of Veterans Affairs. Dr. Cram is supported by a K24 award from NIAMS (AR062133) at the National Institutes of Health. The preliminary results of this article were presented at the Society of General Internal Medicine Annual Meeting in Denver, Colordao, April 2013. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. Data are available to researchers with VA accreditation, the statistical code and the protocol are available to interested readers by contacting Dr. Mosher. The authors report no conflict of interest in regard to this study.
- Age and gender trends in long‐term opioid analgesic use for noncancer pain. Am J Public Health. 2010;100:2541–2547. , , , et al.
- Trends in use of opioids for non‐cancer pain conditions 2000–2005 in commercial and Medicaid insurance plans: the TROUP study. Pain. 2008;138:440–449. , , , , , .
- Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education. Relieving pain in America: a blueprint for transforming prevention, care, education, and research. Washington, DC: National Academies Press; 2011.
- Long‐term opioid therapy reconsidered. Ann Intern Med. 2011;155:325–328. , , , .
- What are we treating with long‐term opioid therapy? Arch Intern Med. 2012;172:433–434. , .
- Opioids for chronic noncancer pain: a meta‐analysis of effectiveness and side effects. CMAJ. 2006;174:1589–1594. , , , .
- Opioids in chronic non‐cancer pain: systematic review of efficacy and safety. Pain. 2004;112:372–380. , , , .
- A systematic review of randomized trials of long‐term opioid management for chronic non‐cancer pain. Pain Physician. 2011;14:91–121. , , , et al.
- Rates of adverse events of long‐acting opioids in a state Medicaid program. Ann Pharmacother. 2007;41:921–928. , , , , , .
- Emergency department visits among recipients of chronic opioid therapy. Arch Intern Med. 2010;170:1425–1432. , , , et al.
- Emergency hospitalizations for adverse drug events in older Americans. N Engl J Med. 2011;365:2002–2012. , , , .
- Assessment and management of acute pain in adult medical inpatients: a systematic review. Pain Med. 2009;10:1183–1199. , .
- Acute pain management in opioid‐tolerant patients: a growing challenge. Anaesth Intensive Care. 2011;39:804–823. , , , .
- Acute pain management of the chronic pain patient on opiates: a survey of caregivers at University of Washington Medical Center. Clin J Pain. 1994;10:133–138. , , , .
- The Joint Commission and the FDA take steps to curb adverse events related to the use and misuse of opioid drugs. ED Manag. 2012;24:112–116.
- Tramadol. CMAJ. 2013;185:E352. , .
- Prescription opioid abuse in the United Kingdom. Br J Clin Pharmacol. 2013;76:823–824. , , , , .
- Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47:1245–1251. , , , .
- Bringing the war back home: mental health disorders among 103,788 US veterans returning from Iraq and Afghanistan seen at Department of Veterans Affairs facilities. Arch Intern Med. 2007;167:476–482. , , , , .
- Coding algorithms for defining comorbidities in ICD‐9‐CM and ICD‐10 administrative data. Med Care. 2005;43:1130–1139. , , , et al.
- Sex Differences in the medical care of VA patients with chronic non‐cancer pain [published online ahead of print June 26, 2013]. Pain Med. doi: 10.1111/pme.12177. , , , , , .
- Agency for Healthcare Research and Quality. Clinical Classifications Software (CCS) for ICD‐9‐CM. Available at: http://www.hcup‐us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed October 17, 2013.
- Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration. Med Care. 2013;51:368–373. , , , et al.
- Association of mental health disorders with prescription opioids and high‐risk opioid use in US veterans of Iraq and Afghanistan. JAMA. 2012;307:940–947. , , , et al.
- Correlates of prescription opioid initiation and long‐term opioid use in veterans with persistent pain. Clin J Pain. 2013;29:102–108. , , , , .
- Safe use of opioids in hospitals. Sentinel Event Alert. 2012;49:1–5.
- Centers for Medicare (2):2–9.
- The risk of severe postoperative pain: modification and validation of a clinical prediction rule. Anesth Analg. 2008;107:1330–1339. , , , , , .
- Preoperative predictors of moderate to intense acute postoperative pain in patients undergoing abdominal surgery. Acta Anaesthesiol Scand. 2002;46:1265–1271. , , , et al.
- Successful and unsuccessful outcomes with long‐term opioid therapy: a survey of physicians' opinions. J Palliat Med. 2006;9:50–56. , , , , , .
- Opioid use among low back pain patients in primary care: is opioid prescription associated with disability at 6‐month follow‐up? Pain. 2013;154:1038–1044. , , , .
- Disability Risk Identification Study Cohort. Early opioid prescription and subsequent disability among workers with back injuries: the Disability Risk Identification Study Cohort. Spine (Phila Pa 1976). 2008;33:199–204. , , , , ;
- The epidemiology of pain during the last 2 years of life. Ann Intern Med. 2010;153:563–569. , , , et al.
- Association of intravenous morphine use and outcomes in acute coronary syndromes: results from the CRUSADE Quality Improvement Initiative. Am Heart J. 2005;149:1043–1049. , , , et al.
- Use of intravenous morphine for acute decompensated heart failure in patients with and without acute coronary syndromes. Acute Card Care. 2011;13:76–80. , , , et al.
- VA mental health services utilization in Iraq and Afghanistan veterans in the first year of receiving new mental health diagnoses. J Trauma Stress. 2010;23:5–16. , , , et al.
- Long‐term opioid management for chronic noncancer pain. Cochrane Database Syst Rev. 2010;(1):CD006605. , , , et al.
- Age and gender trends in long‐term opioid analgesic use for noncancer pain. Am J Public Health. 2010;100:2541–2547. , , , et al.
- Trends in use of opioids for non‐cancer pain conditions 2000–2005 in commercial and Medicaid insurance plans: the TROUP study. Pain. 2008;138:440–449. , , , , , .
- Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education. Relieving pain in America: a blueprint for transforming prevention, care, education, and research. Washington, DC: National Academies Press; 2011.
- Long‐term opioid therapy reconsidered. Ann Intern Med. 2011;155:325–328. , , , .
- What are we treating with long‐term opioid therapy? Arch Intern Med. 2012;172:433–434. , .
- Opioids for chronic noncancer pain: a meta‐analysis of effectiveness and side effects. CMAJ. 2006;174:1589–1594. , , , .
- Opioids in chronic non‐cancer pain: systematic review of efficacy and safety. Pain. 2004;112:372–380. , , , .
- A systematic review of randomized trials of long‐term opioid management for chronic non‐cancer pain. Pain Physician. 2011;14:91–121. , , , et al.
- Rates of adverse events of long‐acting opioids in a state Medicaid program. Ann Pharmacother. 2007;41:921–928. , , , , , .
- Emergency department visits among recipients of chronic opioid therapy. Arch Intern Med. 2010;170:1425–1432. , , , et al.
- Emergency hospitalizations for adverse drug events in older Americans. N Engl J Med. 2011;365:2002–2012. , , , .
- Assessment and management of acute pain in adult medical inpatients: a systematic review. Pain Med. 2009;10:1183–1199. , .
- Acute pain management in opioid‐tolerant patients: a growing challenge. Anaesth Intensive Care. 2011;39:804–823. , , , .
- Acute pain management of the chronic pain patient on opiates: a survey of caregivers at University of Washington Medical Center. Clin J Pain. 1994;10:133–138. , , , .
- The Joint Commission and the FDA take steps to curb adverse events related to the use and misuse of opioid drugs. ED Manag. 2012;24:112–116.
- Tramadol. CMAJ. 2013;185:E352. , .
- Prescription opioid abuse in the United Kingdom. Br J Clin Pharmacol. 2013;76:823–824. , , , , .
- Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47:1245–1251. , , , .
- Bringing the war back home: mental health disorders among 103,788 US veterans returning from Iraq and Afghanistan seen at Department of Veterans Affairs facilities. Arch Intern Med. 2007;167:476–482. , , , , .
- Coding algorithms for defining comorbidities in ICD‐9‐CM and ICD‐10 administrative data. Med Care. 2005;43:1130–1139. , , , et al.
- Sex Differences in the medical care of VA patients with chronic non‐cancer pain [published online ahead of print June 26, 2013]. Pain Med. doi: 10.1111/pme.12177. , , , , , .
- Agency for Healthcare Research and Quality. Clinical Classifications Software (CCS) for ICD‐9‐CM. Available at: http://www.hcup‐us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed October 17, 2013.
- Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration. Med Care. 2013;51:368–373. , , , et al.
- Association of mental health disorders with prescription opioids and high‐risk opioid use in US veterans of Iraq and Afghanistan. JAMA. 2012;307:940–947. , , , et al.
- Correlates of prescription opioid initiation and long‐term opioid use in veterans with persistent pain. Clin J Pain. 2013;29:102–108. , , , , .
- Safe use of opioids in hospitals. Sentinel Event Alert. 2012;49:1–5.
- Centers for Medicare (2):2–9.
- The risk of severe postoperative pain: modification and validation of a clinical prediction rule. Anesth Analg. 2008;107:1330–1339. , , , , , .
- Preoperative predictors of moderate to intense acute postoperative pain in patients undergoing abdominal surgery. Acta Anaesthesiol Scand. 2002;46:1265–1271. , , , et al.
- Successful and unsuccessful outcomes with long‐term opioid therapy: a survey of physicians' opinions. J Palliat Med. 2006;9:50–56. , , , , , .
- Opioid use among low back pain patients in primary care: is opioid prescription associated with disability at 6‐month follow‐up? Pain. 2013;154:1038–1044. , , , .
- Disability Risk Identification Study Cohort. Early opioid prescription and subsequent disability among workers with back injuries: the Disability Risk Identification Study Cohort. Spine (Phila Pa 1976). 2008;33:199–204. , , , , ;
- The epidemiology of pain during the last 2 years of life. Ann Intern Med. 2010;153:563–569. , , , et al.
- Association of intravenous morphine use and outcomes in acute coronary syndromes: results from the CRUSADE Quality Improvement Initiative. Am Heart J. 2005;149:1043–1049. , , , et al.
- Use of intravenous morphine for acute decompensated heart failure in patients with and without acute coronary syndromes. Acute Card Care. 2011;13:76–80. , , , et al.
- VA mental health services utilization in Iraq and Afghanistan veterans in the first year of receiving new mental health diagnoses. J Trauma Stress. 2010;23:5–16. , , , et al.
- Long‐term opioid management for chronic noncancer pain. Cochrane Database Syst Rev. 2010;(1):CD006605. , , , et al.
© 2013 Society of Hospital Medicine
USPSTF changes ABI screening recommendation
The U.S. Preventive Services Task Force (USPSTF) updated its earlier recommendations regarding the validity of using the ankle-brachial index (ABI) in the September Annals of Internal Medicine. In 2006, the USPSTF recommended against screening for PAD (D recommendation; Am Fam Physician 2006; 73:497).
The USPSTF now concludes that evidence is insufficient to make a recommendation. (I recommendation) and published both its systemic evidence review and recommendations.
The U.S. Preventive Services Task Force (USPSTF) updated its earlier recommendations regarding the validity of using the ankle-brachial index (ABI) in the September Annals of Internal Medicine. In 2006, the USPSTF recommended against screening for PAD (D recommendation; Am Fam Physician 2006; 73:497).
The USPSTF now concludes that evidence is insufficient to make a recommendation. (I recommendation) and published both its systemic evidence review and recommendations.
The U.S. Preventive Services Task Force (USPSTF) updated its earlier recommendations regarding the validity of using the ankle-brachial index (ABI) in the September Annals of Internal Medicine. In 2006, the USPSTF recommended against screening for PAD (D recommendation; Am Fam Physician 2006; 73:497).
The USPSTF now concludes that evidence is insufficient to make a recommendation. (I recommendation) and published both its systemic evidence review and recommendations.
Is your patient’s poor recall more than just a ‘senior moment’?
Memory and other cognitive complaints are common among the general population and become more prevalent with age.1 People who have significant emotional investment in their cognitive competence, mood disturbance, somatic symptoms, and anxiety or related disorders are likely to worry more about their cognitive functioning as they age.
Common complaints
Age-related complaints, typically beginning by age 50, often include problems retaining or retrieving names, difficulty recalling details of conversations and written materials, and hazy recollection of remote events and the time frame of recent life events. Common complaints involve difficulties with mental calculations, multi-tasking (including vulnerability to distraction), and problems keeping track of and organizing information. The most common complaint is difficulty with remembering the reason for entering a room.
More concerning are complaints involving recurrent lapses in judgment or forgetfulness with significant implications for everyday living (eg, physical safety, job performance, travel, and finances), especially when validated by friends or family members and coupled with decline in at least 1 activity of daily living, and poor insight.
Helping your forgetful patient
Office evaluation with brief cognitive screening instruments—namely, the Montreal Cognitive Assessment and the recent revision of the Mini-Mental State Examination—might help clarify the clinical presentation. Proceed with caution: Screening tests tap a limited number of neurocognitive functions and can generate a false-negative result among brighter and better educated patients and a false-positive result among the less intelligent and less educated.2 Applying age- and education-corrected norms can reduce misclassification but does not eliminate it.
Screening measures can facilitate decision-making regarding the need for more comprehensive psychometric assessment. Such evaluations sample a broader range of neurobehavioral domains, in greater depth, and provide a more nuanced picture of a patient’s neurocognition.
Findings on a battery of psychological and neuropsychological tests that might evoke concern include problems with incidental, anterograde, and recent memory that are not satisfactorily explained by: age and education or vocational training; estimated premorbid intelligence; residual neurodevelopmental disorders (attention, learning, and autistic-spectrum disorders); situational, sociocultural, and psychiatric factors; and motivational influences—notably, malingering.
Some difficulties with memory are highly associated with mild cognitive impairment or early dementia:
• anterograde memory (involving a reduced rate of verbal and nonverbal learning over repeated trials)
• poor retention
• accelerated forgetting of newly learned information
• failure to benefit from recognition and other mnemonic cues
• so-called source error confusion—a misattribution that involves difficulty differentiating target information from competing information, as reflected in confabulation errors and an elevated rate of intrusion errors.
Disclosure
Dr. Pollak reports no financial relationship with any company whose products are mentioned in this article or with manufacturers of competing products.
1. Weiner MF, Garrett R, Bret ME. Neuropsychiatric assessment and diagnosis. In: Weiner MF, Lipton AM, eds. Clinical manual of Alzheimer disease and other dementias. Arlington, VA: American Psychiatric Publishing, Inc.; 2012: 3-46.
2. Strauss E, Sherman EMS, Spreen O. A compendium of neuropsychological tests: administration, norms and commentary: third edition. New York, NY: Oxford University Press; 2006.
Memory and other cognitive complaints are common among the general population and become more prevalent with age.1 People who have significant emotional investment in their cognitive competence, mood disturbance, somatic symptoms, and anxiety or related disorders are likely to worry more about their cognitive functioning as they age.
Common complaints
Age-related complaints, typically beginning by age 50, often include problems retaining or retrieving names, difficulty recalling details of conversations and written materials, and hazy recollection of remote events and the time frame of recent life events. Common complaints involve difficulties with mental calculations, multi-tasking (including vulnerability to distraction), and problems keeping track of and organizing information. The most common complaint is difficulty with remembering the reason for entering a room.
More concerning are complaints involving recurrent lapses in judgment or forgetfulness with significant implications for everyday living (eg, physical safety, job performance, travel, and finances), especially when validated by friends or family members and coupled with decline in at least 1 activity of daily living, and poor insight.
Helping your forgetful patient
Office evaluation with brief cognitive screening instruments—namely, the Montreal Cognitive Assessment and the recent revision of the Mini-Mental State Examination—might help clarify the clinical presentation. Proceed with caution: Screening tests tap a limited number of neurocognitive functions and can generate a false-negative result among brighter and better educated patients and a false-positive result among the less intelligent and less educated.2 Applying age- and education-corrected norms can reduce misclassification but does not eliminate it.
Screening measures can facilitate decision-making regarding the need for more comprehensive psychometric assessment. Such evaluations sample a broader range of neurobehavioral domains, in greater depth, and provide a more nuanced picture of a patient’s neurocognition.
Findings on a battery of psychological and neuropsychological tests that might evoke concern include problems with incidental, anterograde, and recent memory that are not satisfactorily explained by: age and education or vocational training; estimated premorbid intelligence; residual neurodevelopmental disorders (attention, learning, and autistic-spectrum disorders); situational, sociocultural, and psychiatric factors; and motivational influences—notably, malingering.
Some difficulties with memory are highly associated with mild cognitive impairment or early dementia:
• anterograde memory (involving a reduced rate of verbal and nonverbal learning over repeated trials)
• poor retention
• accelerated forgetting of newly learned information
• failure to benefit from recognition and other mnemonic cues
• so-called source error confusion—a misattribution that involves difficulty differentiating target information from competing information, as reflected in confabulation errors and an elevated rate of intrusion errors.
Disclosure
Dr. Pollak reports no financial relationship with any company whose products are mentioned in this article or with manufacturers of competing products.
Memory and other cognitive complaints are common among the general population and become more prevalent with age.1 People who have significant emotional investment in their cognitive competence, mood disturbance, somatic symptoms, and anxiety or related disorders are likely to worry more about their cognitive functioning as they age.
Common complaints
Age-related complaints, typically beginning by age 50, often include problems retaining or retrieving names, difficulty recalling details of conversations and written materials, and hazy recollection of remote events and the time frame of recent life events. Common complaints involve difficulties with mental calculations, multi-tasking (including vulnerability to distraction), and problems keeping track of and organizing information. The most common complaint is difficulty with remembering the reason for entering a room.
More concerning are complaints involving recurrent lapses in judgment or forgetfulness with significant implications for everyday living (eg, physical safety, job performance, travel, and finances), especially when validated by friends or family members and coupled with decline in at least 1 activity of daily living, and poor insight.
Helping your forgetful patient
Office evaluation with brief cognitive screening instruments—namely, the Montreal Cognitive Assessment and the recent revision of the Mini-Mental State Examination—might help clarify the clinical presentation. Proceed with caution: Screening tests tap a limited number of neurocognitive functions and can generate a false-negative result among brighter and better educated patients and a false-positive result among the less intelligent and less educated.2 Applying age- and education-corrected norms can reduce misclassification but does not eliminate it.
Screening measures can facilitate decision-making regarding the need for more comprehensive psychometric assessment. Such evaluations sample a broader range of neurobehavioral domains, in greater depth, and provide a more nuanced picture of a patient’s neurocognition.
Findings on a battery of psychological and neuropsychological tests that might evoke concern include problems with incidental, anterograde, and recent memory that are not satisfactorily explained by: age and education or vocational training; estimated premorbid intelligence; residual neurodevelopmental disorders (attention, learning, and autistic-spectrum disorders); situational, sociocultural, and psychiatric factors; and motivational influences—notably, malingering.
Some difficulties with memory are highly associated with mild cognitive impairment or early dementia:
• anterograde memory (involving a reduced rate of verbal and nonverbal learning over repeated trials)
• poor retention
• accelerated forgetting of newly learned information
• failure to benefit from recognition and other mnemonic cues
• so-called source error confusion—a misattribution that involves difficulty differentiating target information from competing information, as reflected in confabulation errors and an elevated rate of intrusion errors.
Disclosure
Dr. Pollak reports no financial relationship with any company whose products are mentioned in this article or with manufacturers of competing products.
1. Weiner MF, Garrett R, Bret ME. Neuropsychiatric assessment and diagnosis. In: Weiner MF, Lipton AM, eds. Clinical manual of Alzheimer disease and other dementias. Arlington, VA: American Psychiatric Publishing, Inc.; 2012: 3-46.
2. Strauss E, Sherman EMS, Spreen O. A compendium of neuropsychological tests: administration, norms and commentary: third edition. New York, NY: Oxford University Press; 2006.
1. Weiner MF, Garrett R, Bret ME. Neuropsychiatric assessment and diagnosis. In: Weiner MF, Lipton AM, eds. Clinical manual of Alzheimer disease and other dementias. Arlington, VA: American Psychiatric Publishing, Inc.; 2012: 3-46.
2. Strauss E, Sherman EMS, Spreen O. A compendium of neuropsychological tests: administration, norms and commentary: third edition. New York, NY: Oxford University Press; 2006.