Severe babesiosis

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Severe babesiosis

An 85‐year‐old male from rural southern New Jersey with history of innumerable tick bites over many years was admitted for fever of unknown origin. On hospital day 2, his hemoglobin dropped from 11.3 g/dL to 7.5 g/dL, with an associated elevated indirect bilirubin and lactate dehydrogenase. Blood smear showed numerous intracellular and extracellular trophozoites, with approximately 10% to 30% of red cells infected, consistent with severe babesiosis. Given his high parasitemia, new hypoxia, lethargy, and advanced age, treatment was initiated with intravenous antibiotics and red cell exchange transfusion.

Babesiosis should be considered on the differential diagnosis of hemolytic anemia in patients that live in or have traveled to endemic areas, especially with history of tick bites. The most common appearance on blood smear is round to oval rings with pale blue cytoplasm and a red‐staining nucleus (Fig. 1). Exoerythrocytic parasites or the pathognomonic Maltese Cross tetrad forms (not present in our patient's smear) help to differentiate from falciparum malaria.

The patient's parasite burden and clinical status markedly improved with treatment and he was discharged home. 0

Figure 1
Peripheral blood smear, with black arrow denoting an extraerythrocytic form; white arrow, an intraerythrocytic form.
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An 85‐year‐old male from rural southern New Jersey with history of innumerable tick bites over many years was admitted for fever of unknown origin. On hospital day 2, his hemoglobin dropped from 11.3 g/dL to 7.5 g/dL, with an associated elevated indirect bilirubin and lactate dehydrogenase. Blood smear showed numerous intracellular and extracellular trophozoites, with approximately 10% to 30% of red cells infected, consistent with severe babesiosis. Given his high parasitemia, new hypoxia, lethargy, and advanced age, treatment was initiated with intravenous antibiotics and red cell exchange transfusion.

Babesiosis should be considered on the differential diagnosis of hemolytic anemia in patients that live in or have traveled to endemic areas, especially with history of tick bites. The most common appearance on blood smear is round to oval rings with pale blue cytoplasm and a red‐staining nucleus (Fig. 1). Exoerythrocytic parasites or the pathognomonic Maltese Cross tetrad forms (not present in our patient's smear) help to differentiate from falciparum malaria.

The patient's parasite burden and clinical status markedly improved with treatment and he was discharged home. 0

Figure 1
Peripheral blood smear, with black arrow denoting an extraerythrocytic form; white arrow, an intraerythrocytic form.

An 85‐year‐old male from rural southern New Jersey with history of innumerable tick bites over many years was admitted for fever of unknown origin. On hospital day 2, his hemoglobin dropped from 11.3 g/dL to 7.5 g/dL, with an associated elevated indirect bilirubin and lactate dehydrogenase. Blood smear showed numerous intracellular and extracellular trophozoites, with approximately 10% to 30% of red cells infected, consistent with severe babesiosis. Given his high parasitemia, new hypoxia, lethargy, and advanced age, treatment was initiated with intravenous antibiotics and red cell exchange transfusion.

Babesiosis should be considered on the differential diagnosis of hemolytic anemia in patients that live in or have traveled to endemic areas, especially with history of tick bites. The most common appearance on blood smear is round to oval rings with pale blue cytoplasm and a red‐staining nucleus (Fig. 1). Exoerythrocytic parasites or the pathognomonic Maltese Cross tetrad forms (not present in our patient's smear) help to differentiate from falciparum malaria.

The patient's parasite burden and clinical status markedly improved with treatment and he was discharged home. 0

Figure 1
Peripheral blood smear, with black arrow denoting an extraerythrocytic form; white arrow, an intraerythrocytic form.
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Quality of Hospital Communication

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Health literacy and the quality of physician‐patient communication during hospitalization

It is well established that patients have difficulty understanding written health materials,1 medical terminology,2, 3 and other aspects of provider‐patient communication.4, 5 Such difficulties in communication can be magnified at transitions of care like hospital discharge.6 Patients often receive a large amount of information in a short period of time at discharge, and this information may be delivered in a way that is not straightforward or standardized.7, 8 When asked, patients commonly report a poor understanding of important self‐care instructions such as how to take medications upon returning home.9, 10 One study even showed that more than half of patients did not recall anyone providing instructions about how they should care for themselves after hospitalization.11 Poor medication management after hospital discharge contributes to adverse events,1215 inadequate disease control,16 and in the setting of cardiovascular disease, higher mortality.17, 18 Most adverse events after hospital discharge could be prevented or ameliorated through relatively simple means, including better communication among patients and providers.6, 1416, 1921 Greater attention to communication and care transitions could also reduce the number of unplanned rehospitalizations in the United States.22

Patients' health literacy is an important factor in effective health communication, yet little research has examined the role of health literacy in care transitions. Health literacy is defined as the extent to which an individual is able to obtain, process and understand basic health information and services needed to make appropriate health decisions.23, 24 Low health literacy is a prevalent problem in the United States, affecting approximately 40% of adults.25 Research has shown that low health literacy is associated with low self‐efficacy26 and less interaction in physician‐patient encounters,27 which in combination with physicians' use of complex medical language,28 may contribute to poor physician‐patient communication. Patients with low health literacy also have greater difficulty understanding prescription drug labels,29 limited knowledge of disease self‐management skills,30 a higher incidence of hospitalization,31 and higher mortality rates.3234

In order to elucidate the relationship between patient‐provider communication and health literacy in the hospital setting, we analyzed patients' ratings of their communication experience during their hospitalization. We report patients' perceptions of the clarity of communication and how this may vary by level of health literacy and other important patient characteristics.

Methods

Setting and Participants

Patients admitted to the general medical wards at Grady Memorial Hospital were recruited for participation. Grady Memorial Hospital is a public, urban teaching hospital located in Atlanta, GA. It serves a primarily low income, African American population, many of whom lack health insurance. Approximately 30% to 50% of patients at this hospital have inadequate health literacy skills.35

The present study was conducted as preliminary research for a randomized controlled trial to improve post‐discharge medication adherence among patients with acute coronary syndromes (ACS). The criteria for the present study mirrored those of the planned trial. Patients were eligible for the current study if they were admitted with suspected ACS and evidence of myocardial ischemia.36 Exclusion criteria included lack of cooperation/refusal to participate, unintelligible speech (eg, dysarthria), lack of English fluency (determined subjectively by interviewer), delirium (determined by lack of orientation to person, place, and time), severe hearing impairment (determined subjectively by interviewer), visual acuity worse than 20/60 (per pocket vision screening card), acute psychotic illness (per admission history), police custody, age younger than 18 years, no regular telephone number, administration of all medications by a caregiver, and not taking prescription medications in the 6 months before admission.

Data Collection and Measures

Enrollment occurred between August 2005 and April 2006, after approval was obtained from both the Emory University Institutional Review Board (IRB) and Grady Research Oversight Committee. Interested and willing participants provided written informed consent and subsequently completed an interviewer‐assisted questionnaire prior to hospital discharge to collect information regarding demographics and cardiovascular risk factors. To ensure that answers were not confounded by participants' inability to read the questionnaire text, all questions were read to participants by study interviewers, with the exception of the health literacy assessmentthe Rapid Estimate of Adult Literacy in Medicine (REALM).37 The REALM classifies a patient's literacy according to the number of medical terms from a list that the patient pronounces correctly. It correlates highly with other assessments of literacy and health literacy.38 Cognitive function was measured using the Mini‐Mental State Examination (MMSE).39

Research staff contacted patients by telephone approximately 2 weeks after hospital discharge to complete a survey which included the Interpersonal Processes of Care in Diverse Populations Questionnaire (IPC).40 The IPC is a validated, self‐report questionnaire with high internal consistency reliability. It was developed and normalized among ethnically diverse populations of low socioeconomic status. Items on the IPC originally referred to communication during the last 6 months in the outpatient clinic; they were reworded to refer to the recent hospitalization only. The research assistant administered 8 of 12 domains of the IPC that were most pertinent to rating the quality and clarity of patient communication with hospital physicians.41 Four other IPC domains that pertained to interpersonal style (eg, friendliness, emotional support) were not administered to minimize response burden. Each domain was comprised of 2 to 7 items, and responses were given on a 5‐point Likert scale. The 8 domains and sample items were as follows: (1) General clarity (eg, Did the doctors use medical words that you did not understand?); (2) Elicitation of and responsiveness to patient problems, concerns, and expectations (eg, Did the doctors listen carefully to what you had to say?); (3) Explanations of condition, progress, and prognosis (eg, Did the doctors make sure you understand your health problem?); (4) Explanations of processes of care (eg, Did the doctors explain why a test was being done?); (5) Explanations of self‐care (eg, Did the doctors tell you what you could do to take care of yourself at home?); (6) Empowerment (eg, Did the doctors make you feel that following your treatment plan would make a difference in your health?); (7) Decision‐making: responsiveness to patient preferences regarding decisions (eg, Did the doctors try to involve you or include you in decisions about your treatment?); and (8) Consideration of patient's desire and ability to comply with recommendations (eg, Did the doctors understand the kinds of problems you might have in doing the recommended treatment?).

Statistical Analysis

Patient characteristics were summarized using frequency, mean, and standard deviation measures. Nondichotomous measures were recategorized into dichotomous variables as follows: age (less than 55 years vs. 55 years or older), race (black vs. white or other), marital status (married or living with someone vs. living alone), education (less than high school vs. high school graduate), employment status (employed full/part time vs. unemployed/retired), MMSE score (cognitively impaired [MMSE score 24] vs. no significant cognitive impairment [MMSE score >24]),39 and health literacy score (inadequate [REALM score 0 to 44] vs. marginal or adequate [REALM score 45‐66]).38 Dichotomous variables were summarized using frequencies.

Scores for each individual IPC question ranged from 1 to 5 with lower scores indicating better communication, except for questions in the domain of general clarity where higher scores indicated better communication. Then, for each of the 8 domains, scores of the individual IPC questions within that domain were averaged.

Bivariate analyses were conducted for each of the 8 IPC domains, by level of health literacy and other relevant patient characteristics, using the independent samples t‐test. Multivariable linear regression models were then constructed to examine the independent association of health literacy with each of the 8 IPC domains, while controlling for other patient characteristics that were also found to be associated with IPC domain scores. Bivariate analyses were also conducted for each of the 27 individual IPC items, to gain an understanding of which items might be driving the overall effect. A 2‐sided P < 0.05 was considered statistically significant. All analyses were performed using SPSS 15 for Windows (SPSS, Chicago, IL).

Results

Patient Characteristics

A total of 109 eligible patients were approached, 100 agreed to participate and were enrolled in the hospital, and 84 of them completed the follow‐up interview by telephone to comprise the sample for this study (Table 1). Most of the 84 participants were under the age of 55 (54%), male (58%), African American (88%), unemployed (79%), lived alone (73%), and had completed high school (62%). Age ranged from 24 to 80 years, REALM score ranged from 0 to 66, and MMSE ranged from 12 to 30. A large proportion (44%) had inadequate health literacy skills, and 50% had cognitive impairment. Patients with inadequate health literacy were more likely to have not finished high school and to suffer cognitive impairment, P < 0.01 for each comparison.

Patient Characteristics (n = 84)
Characteristicn (%)
Age 
<55 years45 (54)
55 years39 (46)
Gender 
Male49 (58)
Female35 (42)
Race 
Black74 (88)
White or other10 (12)
Marital status 
Married or living with someone23 (27)
Living alone61 (73)
Education 
Did not complete high school32 (38)
High school graduate52 (62)
Employment status 
Employed (full/part time)18 (21)
Not employed66 (79)
Mini‐Mental State Exam 
Cognition impaired42 (50)
Cognition not impaired42 (50)
Health literacy 
Inadequate37 (44)
Marginal or adequate47 (56)

Hospital Communication Ratings by IPC Domains

Overall, patients' ratings of hospital communication were positive, with most IPC domain score means lying in the favorable half of the Likert scale (Table 2). The domains with the best communication ratings were responsiveness to patient concerns (mean = 1.68), explanations of condition and prognosis (mean = 1.75), and empowerment (mean 1.76). The domain of worst performance was consideration of patients' desire and ability to comply with recommendations (mean = 3.15).

Interpersonal Processes of Care (IPC) Domains Overall and by Level of Health Literacy
 IPC DomainTotal (n = 84), Mean (SD)Patients with Inadequate Literacy (n = 37), Mean (SD)Patients with Marginal or Adequate Literacy (n = 47), Mean (SD)P Value
  • Abbreviation: SD, standard deviation.

  • The range for all scores is 1 to 5. On the domain of General clarity, higher scores indicate more favorable responses. On other domains, lower scores indicate more favorable responses.

1General clarity*3.66 (1.00)3.36 (1.14)3.89 (0.74)0.02
2Responsiveness to patient concerns1.68 (0.68)1.86 (0.76)1.53 (0.58)0.03
3Explanations of condition and prognosis1.75 (0.87)1.93 (0.99)1.61 (0.74)0.09
4Explanations of processes of care2.01 (0.86)2.22 (0.96)1.84 (0.74)0.04
5Explanations of self‐care2.37 (1.04)2.42 (1.20)2.33 (0.90)0.71
6Empowerment1.76 (1.03)1.85 (1.27)1.69 (0.81)0.51
7Decision‐making2.34 (0.78)2.34 (0.80)2.34 (0.77)1.00
8Consideration of patients' desire and ability to comply with recommendations3.15 (1.19)3.24 (1.16)3.07 (1.23)0.54

In bivariate analyses that compared IPC domains by patients' level of health literacy, several differences emerged. Patients with inadequate health literacy skills gave significantly worse ratings to the quality of communication on the domains of general clarity (mean = 3.36 vs. 3.89 for patients with marginal or adequate health literacy, P = 0.02), Responsiveness to patient concerns (mean = 1.86 vs. 1.53, P = 0.03), and Explanations of processes of care (mean = 2.22 vs. 1.84, P = 0.04). On a fourth domain, Explanations of condition and prognosis, a nonsignificant trend was present (mean = 1.93 vs. 1.61, P = 0.09).

Fewer significant relationships were found between other patient characteristics and IPC domain scores. Patients who were age 55 or older provided worse ratings on explanations of self‐care (mean = 2.74 vs. 2.05 for patients under the age of 55, P = 0.003). Lower ratings on the domain of general clarity, which indicated unclear communication, were found among patients who had not graduated from high school (mean = 3.31 vs. 3.88 for high school graduates, P = 0.02) or who had cognitive impairment (mean = 3.39 vs. 3.93 for patients without impaired cognition, P = 0.01). No significant differences were present by gender or race.

Based on these bivariate relationships, terms for inadequate health literacy, age 55, Cognitive impairment, and high school graduation were entered into multivariable models that predicted scores on each of the 8 IPC domains. Inadequate health literacy was independently associated with Responsiveness to patient concerns ( = 0.512, P = 0.007) and Explanations of processes of care ( = 0.548, P = 0.023); a nonsignificant trend was present for consideration of patients' desire and ability to comply with recommendations ( = 0.582, P = 0.09). The association of age with explanations of self‐care remained after adjustment for the other variables ( = 0.705, P = 0.002). None of the patient characteristics was independently associated with ratings of general clarity.

IPC Item Responses

Examination of responses on the individual IPC items revealed the specific areas of difficulty in communication as rated by patients (Table 3). In the domain of general clarity, patients with inadequate literacy provided poorer ratings on the item pertaining to use of medical terminology (mean = 2.92 vs. 3.68 for patients with marginal or adequate literacy, P = 0.004). Regarding Responsiveness to patient concerns, differences by literacy were present in the item that pertained to patients being given enough time to say what they thought was important (mean = 2.27 vs. 1.51, P = 0.003). On the domain of explanations of processes of care, the item rated differently by patients with inadequate literacy referred to feeling confused about their care because doctors did not explain things well (mean = 2.51 vs. 1.83, P = 0.02).

Discussion

We used a validated instrument, the IPC,40 to examine patients' ratings of the quality and clarity of hospital‐based communication. Overall, patients provided favorable ratings in many domains, including those pertaining to Responsiveness to patient concerns and Explanations of condition and prognosis. Clinicians' consideration of patients' desire and ability to comply with recommendations was rated least favorably overall. This represents an important area for improvement, particularly when considering the prevalence of nonadherence to medical therapy after hospital discharge, which may be as high as 50%.9, 42 Nonadherence after hospital discharge contributes to avoidable emergency department visits,43 hospital readmissions,44 and higher mortality.18, 45 The results of this study suggest that hospital physicians should give greater consideration to patients' preferences and problems that they may have in following the treatment recommendations.16 Future research will determine the extent to which this may enhance post‐discharge adherence.

Another important finding is that patients with inadequate health literacy rated hospital‐based communication less favorably than did patients with marginal or adequate literacy. In bivariate analyses, this effect was seen on several domains, including general clarity, Responsiveness to patient concerns, and explanations of processes of care. The latter 2 relationships persisted after adjustment for age, cognitive impairment, and educational attainment. To our knowledge, this is the first study which examines the effect of health literacy on patients' ratings of hospital‐based communication.

The majority of the literature on health communication and health literacy focuses on the outpatient setting.34, 46 However, the quality and clarity of patient‐provider communication in the hospital is also critically important. Ineffective communication in the hospital contributes to poor care transitions and post‐discharge complications. Patients commonly leave the hospital with a poor understanding of what transpired (eg, diagnoses, treatment provided, major test results) and inadequate knowledge about the self‐care activities that they must perform upon returning home (eg, medication management, physical activity, follow‐up appointments).911 Poor communication is often cited as the main underlying and remediable factor behind medical errors, adverse events, and the readmissions that commonly occur after hospital discharge.6, 16, 20 The results of this study provide complementary evidence, showing that patients often feel they have experienced suboptimal communication in the hospital setting. These findings highlight an opportunity for improvement in care transitions and patient safety, particularly among patients with inadequate health literacy.

In outpatient research that utilized the IPC, Schillinger et al.41 found that patients with inadequate functional health literacy reported significantly worse communication on the domains of general clarity, explanations of processes of care, and Explanations of condition and prognosis. Subsequent analyses by Sudore et al.47 demonstrated that patients with inadequate or marginal health literacy more often reported that physicians did not give them enough time to say what they thought was important, did not explain processes of care well, and did not ask about problems in following the recommended treatment (Table 3, IPC items 3, 12, and 26, respectively). Our findings were very similar. These relatively consistent results across studies and populations strengthen the conclusion that patients with inadequate health literacy feel their physicians do not communicate as effectively in these areas.

Interpersonal Processes of Care (IPC) Items Overall and by Level of Health Literacy
IPC ItemsOverall (n = 84), Mean (SD)Inadequate Literacy (n = 37), Mean (SD)Marginal or Adequate Literacy (n = 47), Mean (SD)P Value
  • Abbreviation: SD, standard deviation.

  • On the domain of general clarity, higher scores indicate more favorable responses. On other domains, lower scores indicate more favorable responses.

General clarity*    
1. Did the doctors use medical words you did not understand?3.35 (1.14)2.92 (1.40)3.68 (0.73)0.004
2. Did you have trouble understanding your doctors because they spoke too fast?3.98 (1.06)3.81 (1.13)4.11 (1.01)0.21
Responsiveness to patient concerns    
3. Did the doctors give you enough time to say what you thought was important?1.85 (1.14)2.27 (1.28)1.51 (0.88)0.003
4. Did the doctors listen carefully to what you had to say?1.62 (0.88)1.76 (1.04)1.51 (0.72)0.22
5. Did the doctors ignore what you told them?1.70 (0.92)1.81 (1.09)1.62 (0.77)0.38
6. Did the doctors take your concerns seriously?1.55 (0.92)1.65 (0.98)1.47 (0.88)0.38
Explanations of condition and prognosis    
7. Did the doctors give you enough information about your health problems?1.88 (1.11)2.11 (1.27)1.70 (0.95)0.11
8. Did the doctors make sure you understand your health problems?1.62 (0.88)1.76 (0.98)1.51 (0.78)0.22
Explanations of processes of care    
9. Did the doctors explain why a test was being done?1.70 (1.10)1.89 (1.24)1.55 (0.95)0.16
10. Did the doctors explain how the test was done?2.20 (1.35)2.27 (1.39)2.15 (1.34)0.69
11. Did the doctors tell you what they were doing as they examined you?1.99 (1.20)2.22 (1.34)1.81 (1.06)0.13
12. Did you feel confused about what was going on with your medical care because doctors did not explain things well?2.13 (1.23)2.51 (1.47)1.83 (0.92)0.02
Explanations of self‐care    
13. Did the doctors tell you what you could do to take care of yourself at home?1.67 (1.09)1.81 (1.29)1.55 (0.90)0.31
14. Did the doctors tell you how to pay attention to your symptoms and when to call the doctor?2.01 (1.38)2.19 (1.60)1.87 (1.17)0.32
15. Did the doctors clearly explain how to take the medicine (that is when, how much and for how long)?1.88 (1.36)2.00 (1.53)1.79 (1.22)0.48
16. Did the doctors go over all the medicines you are taking?2.39 (1.55)2.51 (1.74)2.30 (1.40)0.54
17. Did the doctors give you written instruction about how to take the medicine (other than what was on the container)?3.29 (1.70)3.05 (1.75)3.48 (1.66)0.26
18. Did the doctors tell you the reason for taking each medicine?2.05 (1.43)2.24 (1.64)1.89 (1.24)0.29
19. Did the doctors tell you about side effects you might get from your medicine?3.32 (1.64)3.11 (1.73)3.49 (1.56)0.29
Empowerment    
20. Did doctors make you feel that following your treatment plan would make a difference in your health?1.75 (1.07)1.89 (1.27)1.64 (0.90)0.31
21. Did the doctors make you feel that your everyday activities such as your diet and lifestyle would make a difference in your health?1.77 (1.21)1.81 (1.41)1.74 (1.03)0.81
Decision‐making    
22. Did the doctors try to involve you or include you in decisions about your treatment?2.43 (1.55)2.30 (1.49)2.53 (1.60)0.49
23. Did the doctors ask how you felt about different treatments?3.08 (1.58)2.89 (1.66)3.23 (1.51)0.33
24. Did the doctors make decision without taking your preferences and opinions into account?2.23 (1.35)2.34 (1.55)2.15 (1.20)0.54
25. Did you feel pressured by doctors in the hospital to have a treatment you were not sure you wanted?1.60 (0.97)1.81 (1.18)1.43 (0.74)0.09
Consideration of patients' desire and ability to comply with recommendations    
26. Did the doctors ask if you might have any problems actually doing the recommended treatment (for example taking the medication correctly)?3.82 (1.47)4.08 (1.40)3.62 (1.51)0.15
27. Did the doctors understand the kinds of problems you might have in doing the recommended treatment?2.43 (1.44)2.26 (1.52)2.57 (1.38)0.34

Importantly, the differences in patient responses by literacy category were driven by a few IPC items. These items pertained to physicians' use of medical terminology, the amount of time they gave patients to express their concerns, and how well they explained the patients' medical care. Training physicians to improve their communication skills in these specific areas may improve their ability to communicate effectively with patients who have limited literacy skills. Indeed, published recommendations on how to improve the clarity of verbal communication emphasize just a few major areas, including limiting the amount of medical terminology used, effectively encouraging patients to ask questions and express their concerns, and asking patients to teach‐back key points to make sure the physician has provided adequate explanation.4851 The present study provides some evidence for those recommendations, which for the most part, have been based on clinical experience and expert opinion.

There remains a need for professional education about health literacy and techniques to improve communication with patients who may have limited literacy skills. Many experts advocate clear verbal communication with all patients, so‐called Universal Precautions.52 Although 10 years have passed since the American Medical Association (AMA) called for more work in this area,53 few curricula have been described in the literature.48, 5456 The extent to which health literacy curricula have been implemented in medical schools and other professional schools is unknown. The impact of such training on the communication skills of health care providers and patient outcomes is also unclear.

The strengths of this study include a relatively good response rate and use of a validated measure to grade the quality of physician‐patient communication. This measure, the IPC, has been used previously in the context of health literacy.41 Nevertheless, certain limitations should be acknowledged. First, the study was performed at a single teaching hospital, where patients had a high prevalence of inadequate health literacy. The findings may not generalize to other institutions that serve a different patient population or to nonacademic programs. Second, communication was assessed by patient report, rather than by recording patient‐provider discussions for rating by independent observers. While patient report is inherently more subjective, patients' own perceptions about the effectiveness of health communication are arguably more important than those of independent raters, and thus, the data source may not represent a true limitation. Third, patient responses were obtained approximately 2 weeks after hospital discharge, and accordingly, they are subject to recall bias, which may be greater among those with cognitive impairment. Finally, patients were directed to rate the communication of the overall group of physicians who took care of them in the hospital. Given the academic setting, patients typically received care from a team that included medical students, interns, a resident, and an attending physician. We were not able to determine whether patients' ratings were influenced by a specific member of the team, nor how ratings may have been influenced by certain characteristics of that team member (eg, year of training, prior communication skills training, race or gender concordance, etc).

In summary, by surveying patients soon after an acute care hospitalization, we determined that certain areas held room for improvement, such as consideration of patients' desire and ability to comply with treatment recommendations. Patients with inadequate health literacy reported lower quality physician‐patient communication on several domains. They expressed particular concern about physicians' use of medical terminology, not getting enough time to express their concerns, and not receiving clear enough explanations about the medical care. Efforts are needed to improve physicians' communication skills in these areas. Such training should be evaluated to determine if it has a beneficial effect on physician communication skills and patient outcomes.

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Article PDF
Issue
Journal of Hospital Medicine - 5(5)
Page Number
269-275
Legacy Keywords
communication, health literacy, transitions of care
Sections
Article PDF
Article PDF

It is well established that patients have difficulty understanding written health materials,1 medical terminology,2, 3 and other aspects of provider‐patient communication.4, 5 Such difficulties in communication can be magnified at transitions of care like hospital discharge.6 Patients often receive a large amount of information in a short period of time at discharge, and this information may be delivered in a way that is not straightforward or standardized.7, 8 When asked, patients commonly report a poor understanding of important self‐care instructions such as how to take medications upon returning home.9, 10 One study even showed that more than half of patients did not recall anyone providing instructions about how they should care for themselves after hospitalization.11 Poor medication management after hospital discharge contributes to adverse events,1215 inadequate disease control,16 and in the setting of cardiovascular disease, higher mortality.17, 18 Most adverse events after hospital discharge could be prevented or ameliorated through relatively simple means, including better communication among patients and providers.6, 1416, 1921 Greater attention to communication and care transitions could also reduce the number of unplanned rehospitalizations in the United States.22

Patients' health literacy is an important factor in effective health communication, yet little research has examined the role of health literacy in care transitions. Health literacy is defined as the extent to which an individual is able to obtain, process and understand basic health information and services needed to make appropriate health decisions.23, 24 Low health literacy is a prevalent problem in the United States, affecting approximately 40% of adults.25 Research has shown that low health literacy is associated with low self‐efficacy26 and less interaction in physician‐patient encounters,27 which in combination with physicians' use of complex medical language,28 may contribute to poor physician‐patient communication. Patients with low health literacy also have greater difficulty understanding prescription drug labels,29 limited knowledge of disease self‐management skills,30 a higher incidence of hospitalization,31 and higher mortality rates.3234

In order to elucidate the relationship between patient‐provider communication and health literacy in the hospital setting, we analyzed patients' ratings of their communication experience during their hospitalization. We report patients' perceptions of the clarity of communication and how this may vary by level of health literacy and other important patient characteristics.

Methods

Setting and Participants

Patients admitted to the general medical wards at Grady Memorial Hospital were recruited for participation. Grady Memorial Hospital is a public, urban teaching hospital located in Atlanta, GA. It serves a primarily low income, African American population, many of whom lack health insurance. Approximately 30% to 50% of patients at this hospital have inadequate health literacy skills.35

The present study was conducted as preliminary research for a randomized controlled trial to improve post‐discharge medication adherence among patients with acute coronary syndromes (ACS). The criteria for the present study mirrored those of the planned trial. Patients were eligible for the current study if they were admitted with suspected ACS and evidence of myocardial ischemia.36 Exclusion criteria included lack of cooperation/refusal to participate, unintelligible speech (eg, dysarthria), lack of English fluency (determined subjectively by interviewer), delirium (determined by lack of orientation to person, place, and time), severe hearing impairment (determined subjectively by interviewer), visual acuity worse than 20/60 (per pocket vision screening card), acute psychotic illness (per admission history), police custody, age younger than 18 years, no regular telephone number, administration of all medications by a caregiver, and not taking prescription medications in the 6 months before admission.

Data Collection and Measures

Enrollment occurred between August 2005 and April 2006, after approval was obtained from both the Emory University Institutional Review Board (IRB) and Grady Research Oversight Committee. Interested and willing participants provided written informed consent and subsequently completed an interviewer‐assisted questionnaire prior to hospital discharge to collect information regarding demographics and cardiovascular risk factors. To ensure that answers were not confounded by participants' inability to read the questionnaire text, all questions were read to participants by study interviewers, with the exception of the health literacy assessmentthe Rapid Estimate of Adult Literacy in Medicine (REALM).37 The REALM classifies a patient's literacy according to the number of medical terms from a list that the patient pronounces correctly. It correlates highly with other assessments of literacy and health literacy.38 Cognitive function was measured using the Mini‐Mental State Examination (MMSE).39

Research staff contacted patients by telephone approximately 2 weeks after hospital discharge to complete a survey which included the Interpersonal Processes of Care in Diverse Populations Questionnaire (IPC).40 The IPC is a validated, self‐report questionnaire with high internal consistency reliability. It was developed and normalized among ethnically diverse populations of low socioeconomic status. Items on the IPC originally referred to communication during the last 6 months in the outpatient clinic; they were reworded to refer to the recent hospitalization only. The research assistant administered 8 of 12 domains of the IPC that were most pertinent to rating the quality and clarity of patient communication with hospital physicians.41 Four other IPC domains that pertained to interpersonal style (eg, friendliness, emotional support) were not administered to minimize response burden. Each domain was comprised of 2 to 7 items, and responses were given on a 5‐point Likert scale. The 8 domains and sample items were as follows: (1) General clarity (eg, Did the doctors use medical words that you did not understand?); (2) Elicitation of and responsiveness to patient problems, concerns, and expectations (eg, Did the doctors listen carefully to what you had to say?); (3) Explanations of condition, progress, and prognosis (eg, Did the doctors make sure you understand your health problem?); (4) Explanations of processes of care (eg, Did the doctors explain why a test was being done?); (5) Explanations of self‐care (eg, Did the doctors tell you what you could do to take care of yourself at home?); (6) Empowerment (eg, Did the doctors make you feel that following your treatment plan would make a difference in your health?); (7) Decision‐making: responsiveness to patient preferences regarding decisions (eg, Did the doctors try to involve you or include you in decisions about your treatment?); and (8) Consideration of patient's desire and ability to comply with recommendations (eg, Did the doctors understand the kinds of problems you might have in doing the recommended treatment?).

Statistical Analysis

Patient characteristics were summarized using frequency, mean, and standard deviation measures. Nondichotomous measures were recategorized into dichotomous variables as follows: age (less than 55 years vs. 55 years or older), race (black vs. white or other), marital status (married or living with someone vs. living alone), education (less than high school vs. high school graduate), employment status (employed full/part time vs. unemployed/retired), MMSE score (cognitively impaired [MMSE score 24] vs. no significant cognitive impairment [MMSE score >24]),39 and health literacy score (inadequate [REALM score 0 to 44] vs. marginal or adequate [REALM score 45‐66]).38 Dichotomous variables were summarized using frequencies.

Scores for each individual IPC question ranged from 1 to 5 with lower scores indicating better communication, except for questions in the domain of general clarity where higher scores indicated better communication. Then, for each of the 8 domains, scores of the individual IPC questions within that domain were averaged.

Bivariate analyses were conducted for each of the 8 IPC domains, by level of health literacy and other relevant patient characteristics, using the independent samples t‐test. Multivariable linear regression models were then constructed to examine the independent association of health literacy with each of the 8 IPC domains, while controlling for other patient characteristics that were also found to be associated with IPC domain scores. Bivariate analyses were also conducted for each of the 27 individual IPC items, to gain an understanding of which items might be driving the overall effect. A 2‐sided P < 0.05 was considered statistically significant. All analyses were performed using SPSS 15 for Windows (SPSS, Chicago, IL).

Results

Patient Characteristics

A total of 109 eligible patients were approached, 100 agreed to participate and were enrolled in the hospital, and 84 of them completed the follow‐up interview by telephone to comprise the sample for this study (Table 1). Most of the 84 participants were under the age of 55 (54%), male (58%), African American (88%), unemployed (79%), lived alone (73%), and had completed high school (62%). Age ranged from 24 to 80 years, REALM score ranged from 0 to 66, and MMSE ranged from 12 to 30. A large proportion (44%) had inadequate health literacy skills, and 50% had cognitive impairment. Patients with inadequate health literacy were more likely to have not finished high school and to suffer cognitive impairment, P < 0.01 for each comparison.

Patient Characteristics (n = 84)
Characteristicn (%)
Age 
<55 years45 (54)
55 years39 (46)
Gender 
Male49 (58)
Female35 (42)
Race 
Black74 (88)
White or other10 (12)
Marital status 
Married or living with someone23 (27)
Living alone61 (73)
Education 
Did not complete high school32 (38)
High school graduate52 (62)
Employment status 
Employed (full/part time)18 (21)
Not employed66 (79)
Mini‐Mental State Exam 
Cognition impaired42 (50)
Cognition not impaired42 (50)
Health literacy 
Inadequate37 (44)
Marginal or adequate47 (56)

Hospital Communication Ratings by IPC Domains

Overall, patients' ratings of hospital communication were positive, with most IPC domain score means lying in the favorable half of the Likert scale (Table 2). The domains with the best communication ratings were responsiveness to patient concerns (mean = 1.68), explanations of condition and prognosis (mean = 1.75), and empowerment (mean 1.76). The domain of worst performance was consideration of patients' desire and ability to comply with recommendations (mean = 3.15).

Interpersonal Processes of Care (IPC) Domains Overall and by Level of Health Literacy
 IPC DomainTotal (n = 84), Mean (SD)Patients with Inadequate Literacy (n = 37), Mean (SD)Patients with Marginal or Adequate Literacy (n = 47), Mean (SD)P Value
  • Abbreviation: SD, standard deviation.

  • The range for all scores is 1 to 5. On the domain of General clarity, higher scores indicate more favorable responses. On other domains, lower scores indicate more favorable responses.

1General clarity*3.66 (1.00)3.36 (1.14)3.89 (0.74)0.02
2Responsiveness to patient concerns1.68 (0.68)1.86 (0.76)1.53 (0.58)0.03
3Explanations of condition and prognosis1.75 (0.87)1.93 (0.99)1.61 (0.74)0.09
4Explanations of processes of care2.01 (0.86)2.22 (0.96)1.84 (0.74)0.04
5Explanations of self‐care2.37 (1.04)2.42 (1.20)2.33 (0.90)0.71
6Empowerment1.76 (1.03)1.85 (1.27)1.69 (0.81)0.51
7Decision‐making2.34 (0.78)2.34 (0.80)2.34 (0.77)1.00
8Consideration of patients' desire and ability to comply with recommendations3.15 (1.19)3.24 (1.16)3.07 (1.23)0.54

In bivariate analyses that compared IPC domains by patients' level of health literacy, several differences emerged. Patients with inadequate health literacy skills gave significantly worse ratings to the quality of communication on the domains of general clarity (mean = 3.36 vs. 3.89 for patients with marginal or adequate health literacy, P = 0.02), Responsiveness to patient concerns (mean = 1.86 vs. 1.53, P = 0.03), and Explanations of processes of care (mean = 2.22 vs. 1.84, P = 0.04). On a fourth domain, Explanations of condition and prognosis, a nonsignificant trend was present (mean = 1.93 vs. 1.61, P = 0.09).

Fewer significant relationships were found between other patient characteristics and IPC domain scores. Patients who were age 55 or older provided worse ratings on explanations of self‐care (mean = 2.74 vs. 2.05 for patients under the age of 55, P = 0.003). Lower ratings on the domain of general clarity, which indicated unclear communication, were found among patients who had not graduated from high school (mean = 3.31 vs. 3.88 for high school graduates, P = 0.02) or who had cognitive impairment (mean = 3.39 vs. 3.93 for patients without impaired cognition, P = 0.01). No significant differences were present by gender or race.

Based on these bivariate relationships, terms for inadequate health literacy, age 55, Cognitive impairment, and high school graduation were entered into multivariable models that predicted scores on each of the 8 IPC domains. Inadequate health literacy was independently associated with Responsiveness to patient concerns ( = 0.512, P = 0.007) and Explanations of processes of care ( = 0.548, P = 0.023); a nonsignificant trend was present for consideration of patients' desire and ability to comply with recommendations ( = 0.582, P = 0.09). The association of age with explanations of self‐care remained after adjustment for the other variables ( = 0.705, P = 0.002). None of the patient characteristics was independently associated with ratings of general clarity.

IPC Item Responses

Examination of responses on the individual IPC items revealed the specific areas of difficulty in communication as rated by patients (Table 3). In the domain of general clarity, patients with inadequate literacy provided poorer ratings on the item pertaining to use of medical terminology (mean = 2.92 vs. 3.68 for patients with marginal or adequate literacy, P = 0.004). Regarding Responsiveness to patient concerns, differences by literacy were present in the item that pertained to patients being given enough time to say what they thought was important (mean = 2.27 vs. 1.51, P = 0.003). On the domain of explanations of processes of care, the item rated differently by patients with inadequate literacy referred to feeling confused about their care because doctors did not explain things well (mean = 2.51 vs. 1.83, P = 0.02).

Discussion

We used a validated instrument, the IPC,40 to examine patients' ratings of the quality and clarity of hospital‐based communication. Overall, patients provided favorable ratings in many domains, including those pertaining to Responsiveness to patient concerns and Explanations of condition and prognosis. Clinicians' consideration of patients' desire and ability to comply with recommendations was rated least favorably overall. This represents an important area for improvement, particularly when considering the prevalence of nonadherence to medical therapy after hospital discharge, which may be as high as 50%.9, 42 Nonadherence after hospital discharge contributes to avoidable emergency department visits,43 hospital readmissions,44 and higher mortality.18, 45 The results of this study suggest that hospital physicians should give greater consideration to patients' preferences and problems that they may have in following the treatment recommendations.16 Future research will determine the extent to which this may enhance post‐discharge adherence.

Another important finding is that patients with inadequate health literacy rated hospital‐based communication less favorably than did patients with marginal or adequate literacy. In bivariate analyses, this effect was seen on several domains, including general clarity, Responsiveness to patient concerns, and explanations of processes of care. The latter 2 relationships persisted after adjustment for age, cognitive impairment, and educational attainment. To our knowledge, this is the first study which examines the effect of health literacy on patients' ratings of hospital‐based communication.

The majority of the literature on health communication and health literacy focuses on the outpatient setting.34, 46 However, the quality and clarity of patient‐provider communication in the hospital is also critically important. Ineffective communication in the hospital contributes to poor care transitions and post‐discharge complications. Patients commonly leave the hospital with a poor understanding of what transpired (eg, diagnoses, treatment provided, major test results) and inadequate knowledge about the self‐care activities that they must perform upon returning home (eg, medication management, physical activity, follow‐up appointments).911 Poor communication is often cited as the main underlying and remediable factor behind medical errors, adverse events, and the readmissions that commonly occur after hospital discharge.6, 16, 20 The results of this study provide complementary evidence, showing that patients often feel they have experienced suboptimal communication in the hospital setting. These findings highlight an opportunity for improvement in care transitions and patient safety, particularly among patients with inadequate health literacy.

In outpatient research that utilized the IPC, Schillinger et al.41 found that patients with inadequate functional health literacy reported significantly worse communication on the domains of general clarity, explanations of processes of care, and Explanations of condition and prognosis. Subsequent analyses by Sudore et al.47 demonstrated that patients with inadequate or marginal health literacy more often reported that physicians did not give them enough time to say what they thought was important, did not explain processes of care well, and did not ask about problems in following the recommended treatment (Table 3, IPC items 3, 12, and 26, respectively). Our findings were very similar. These relatively consistent results across studies and populations strengthen the conclusion that patients with inadequate health literacy feel their physicians do not communicate as effectively in these areas.

Interpersonal Processes of Care (IPC) Items Overall and by Level of Health Literacy
IPC ItemsOverall (n = 84), Mean (SD)Inadequate Literacy (n = 37), Mean (SD)Marginal or Adequate Literacy (n = 47), Mean (SD)P Value
  • Abbreviation: SD, standard deviation.

  • On the domain of general clarity, higher scores indicate more favorable responses. On other domains, lower scores indicate more favorable responses.

General clarity*    
1. Did the doctors use medical words you did not understand?3.35 (1.14)2.92 (1.40)3.68 (0.73)0.004
2. Did you have trouble understanding your doctors because they spoke too fast?3.98 (1.06)3.81 (1.13)4.11 (1.01)0.21
Responsiveness to patient concerns    
3. Did the doctors give you enough time to say what you thought was important?1.85 (1.14)2.27 (1.28)1.51 (0.88)0.003
4. Did the doctors listen carefully to what you had to say?1.62 (0.88)1.76 (1.04)1.51 (0.72)0.22
5. Did the doctors ignore what you told them?1.70 (0.92)1.81 (1.09)1.62 (0.77)0.38
6. Did the doctors take your concerns seriously?1.55 (0.92)1.65 (0.98)1.47 (0.88)0.38
Explanations of condition and prognosis    
7. Did the doctors give you enough information about your health problems?1.88 (1.11)2.11 (1.27)1.70 (0.95)0.11
8. Did the doctors make sure you understand your health problems?1.62 (0.88)1.76 (0.98)1.51 (0.78)0.22
Explanations of processes of care    
9. Did the doctors explain why a test was being done?1.70 (1.10)1.89 (1.24)1.55 (0.95)0.16
10. Did the doctors explain how the test was done?2.20 (1.35)2.27 (1.39)2.15 (1.34)0.69
11. Did the doctors tell you what they were doing as they examined you?1.99 (1.20)2.22 (1.34)1.81 (1.06)0.13
12. Did you feel confused about what was going on with your medical care because doctors did not explain things well?2.13 (1.23)2.51 (1.47)1.83 (0.92)0.02
Explanations of self‐care    
13. Did the doctors tell you what you could do to take care of yourself at home?1.67 (1.09)1.81 (1.29)1.55 (0.90)0.31
14. Did the doctors tell you how to pay attention to your symptoms and when to call the doctor?2.01 (1.38)2.19 (1.60)1.87 (1.17)0.32
15. Did the doctors clearly explain how to take the medicine (that is when, how much and for how long)?1.88 (1.36)2.00 (1.53)1.79 (1.22)0.48
16. Did the doctors go over all the medicines you are taking?2.39 (1.55)2.51 (1.74)2.30 (1.40)0.54
17. Did the doctors give you written instruction about how to take the medicine (other than what was on the container)?3.29 (1.70)3.05 (1.75)3.48 (1.66)0.26
18. Did the doctors tell you the reason for taking each medicine?2.05 (1.43)2.24 (1.64)1.89 (1.24)0.29
19. Did the doctors tell you about side effects you might get from your medicine?3.32 (1.64)3.11 (1.73)3.49 (1.56)0.29
Empowerment    
20. Did doctors make you feel that following your treatment plan would make a difference in your health?1.75 (1.07)1.89 (1.27)1.64 (0.90)0.31
21. Did the doctors make you feel that your everyday activities such as your diet and lifestyle would make a difference in your health?1.77 (1.21)1.81 (1.41)1.74 (1.03)0.81
Decision‐making    
22. Did the doctors try to involve you or include you in decisions about your treatment?2.43 (1.55)2.30 (1.49)2.53 (1.60)0.49
23. Did the doctors ask how you felt about different treatments?3.08 (1.58)2.89 (1.66)3.23 (1.51)0.33
24. Did the doctors make decision without taking your preferences and opinions into account?2.23 (1.35)2.34 (1.55)2.15 (1.20)0.54
25. Did you feel pressured by doctors in the hospital to have a treatment you were not sure you wanted?1.60 (0.97)1.81 (1.18)1.43 (0.74)0.09
Consideration of patients' desire and ability to comply with recommendations    
26. Did the doctors ask if you might have any problems actually doing the recommended treatment (for example taking the medication correctly)?3.82 (1.47)4.08 (1.40)3.62 (1.51)0.15
27. Did the doctors understand the kinds of problems you might have in doing the recommended treatment?2.43 (1.44)2.26 (1.52)2.57 (1.38)0.34

Importantly, the differences in patient responses by literacy category were driven by a few IPC items. These items pertained to physicians' use of medical terminology, the amount of time they gave patients to express their concerns, and how well they explained the patients' medical care. Training physicians to improve their communication skills in these specific areas may improve their ability to communicate effectively with patients who have limited literacy skills. Indeed, published recommendations on how to improve the clarity of verbal communication emphasize just a few major areas, including limiting the amount of medical terminology used, effectively encouraging patients to ask questions and express their concerns, and asking patients to teach‐back key points to make sure the physician has provided adequate explanation.4851 The present study provides some evidence for those recommendations, which for the most part, have been based on clinical experience and expert opinion.

There remains a need for professional education about health literacy and techniques to improve communication with patients who may have limited literacy skills. Many experts advocate clear verbal communication with all patients, so‐called Universal Precautions.52 Although 10 years have passed since the American Medical Association (AMA) called for more work in this area,53 few curricula have been described in the literature.48, 5456 The extent to which health literacy curricula have been implemented in medical schools and other professional schools is unknown. The impact of such training on the communication skills of health care providers and patient outcomes is also unclear.

The strengths of this study include a relatively good response rate and use of a validated measure to grade the quality of physician‐patient communication. This measure, the IPC, has been used previously in the context of health literacy.41 Nevertheless, certain limitations should be acknowledged. First, the study was performed at a single teaching hospital, where patients had a high prevalence of inadequate health literacy. The findings may not generalize to other institutions that serve a different patient population or to nonacademic programs. Second, communication was assessed by patient report, rather than by recording patient‐provider discussions for rating by independent observers. While patient report is inherently more subjective, patients' own perceptions about the effectiveness of health communication are arguably more important than those of independent raters, and thus, the data source may not represent a true limitation. Third, patient responses were obtained approximately 2 weeks after hospital discharge, and accordingly, they are subject to recall bias, which may be greater among those with cognitive impairment. Finally, patients were directed to rate the communication of the overall group of physicians who took care of them in the hospital. Given the academic setting, patients typically received care from a team that included medical students, interns, a resident, and an attending physician. We were not able to determine whether patients' ratings were influenced by a specific member of the team, nor how ratings may have been influenced by certain characteristics of that team member (eg, year of training, prior communication skills training, race or gender concordance, etc).

In summary, by surveying patients soon after an acute care hospitalization, we determined that certain areas held room for improvement, such as consideration of patients' desire and ability to comply with treatment recommendations. Patients with inadequate health literacy reported lower quality physician‐patient communication on several domains. They expressed particular concern about physicians' use of medical terminology, not getting enough time to express their concerns, and not receiving clear enough explanations about the medical care. Efforts are needed to improve physicians' communication skills in these areas. Such training should be evaluated to determine if it has a beneficial effect on physician communication skills and patient outcomes.

It is well established that patients have difficulty understanding written health materials,1 medical terminology,2, 3 and other aspects of provider‐patient communication.4, 5 Such difficulties in communication can be magnified at transitions of care like hospital discharge.6 Patients often receive a large amount of information in a short period of time at discharge, and this information may be delivered in a way that is not straightforward or standardized.7, 8 When asked, patients commonly report a poor understanding of important self‐care instructions such as how to take medications upon returning home.9, 10 One study even showed that more than half of patients did not recall anyone providing instructions about how they should care for themselves after hospitalization.11 Poor medication management after hospital discharge contributes to adverse events,1215 inadequate disease control,16 and in the setting of cardiovascular disease, higher mortality.17, 18 Most adverse events after hospital discharge could be prevented or ameliorated through relatively simple means, including better communication among patients and providers.6, 1416, 1921 Greater attention to communication and care transitions could also reduce the number of unplanned rehospitalizations in the United States.22

Patients' health literacy is an important factor in effective health communication, yet little research has examined the role of health literacy in care transitions. Health literacy is defined as the extent to which an individual is able to obtain, process and understand basic health information and services needed to make appropriate health decisions.23, 24 Low health literacy is a prevalent problem in the United States, affecting approximately 40% of adults.25 Research has shown that low health literacy is associated with low self‐efficacy26 and less interaction in physician‐patient encounters,27 which in combination with physicians' use of complex medical language,28 may contribute to poor physician‐patient communication. Patients with low health literacy also have greater difficulty understanding prescription drug labels,29 limited knowledge of disease self‐management skills,30 a higher incidence of hospitalization,31 and higher mortality rates.3234

In order to elucidate the relationship between patient‐provider communication and health literacy in the hospital setting, we analyzed patients' ratings of their communication experience during their hospitalization. We report patients' perceptions of the clarity of communication and how this may vary by level of health literacy and other important patient characteristics.

Methods

Setting and Participants

Patients admitted to the general medical wards at Grady Memorial Hospital were recruited for participation. Grady Memorial Hospital is a public, urban teaching hospital located in Atlanta, GA. It serves a primarily low income, African American population, many of whom lack health insurance. Approximately 30% to 50% of patients at this hospital have inadequate health literacy skills.35

The present study was conducted as preliminary research for a randomized controlled trial to improve post‐discharge medication adherence among patients with acute coronary syndromes (ACS). The criteria for the present study mirrored those of the planned trial. Patients were eligible for the current study if they were admitted with suspected ACS and evidence of myocardial ischemia.36 Exclusion criteria included lack of cooperation/refusal to participate, unintelligible speech (eg, dysarthria), lack of English fluency (determined subjectively by interviewer), delirium (determined by lack of orientation to person, place, and time), severe hearing impairment (determined subjectively by interviewer), visual acuity worse than 20/60 (per pocket vision screening card), acute psychotic illness (per admission history), police custody, age younger than 18 years, no regular telephone number, administration of all medications by a caregiver, and not taking prescription medications in the 6 months before admission.

Data Collection and Measures

Enrollment occurred between August 2005 and April 2006, after approval was obtained from both the Emory University Institutional Review Board (IRB) and Grady Research Oversight Committee. Interested and willing participants provided written informed consent and subsequently completed an interviewer‐assisted questionnaire prior to hospital discharge to collect information regarding demographics and cardiovascular risk factors. To ensure that answers were not confounded by participants' inability to read the questionnaire text, all questions were read to participants by study interviewers, with the exception of the health literacy assessmentthe Rapid Estimate of Adult Literacy in Medicine (REALM).37 The REALM classifies a patient's literacy according to the number of medical terms from a list that the patient pronounces correctly. It correlates highly with other assessments of literacy and health literacy.38 Cognitive function was measured using the Mini‐Mental State Examination (MMSE).39

Research staff contacted patients by telephone approximately 2 weeks after hospital discharge to complete a survey which included the Interpersonal Processes of Care in Diverse Populations Questionnaire (IPC).40 The IPC is a validated, self‐report questionnaire with high internal consistency reliability. It was developed and normalized among ethnically diverse populations of low socioeconomic status. Items on the IPC originally referred to communication during the last 6 months in the outpatient clinic; they were reworded to refer to the recent hospitalization only. The research assistant administered 8 of 12 domains of the IPC that were most pertinent to rating the quality and clarity of patient communication with hospital physicians.41 Four other IPC domains that pertained to interpersonal style (eg, friendliness, emotional support) were not administered to minimize response burden. Each domain was comprised of 2 to 7 items, and responses were given on a 5‐point Likert scale. The 8 domains and sample items were as follows: (1) General clarity (eg, Did the doctors use medical words that you did not understand?); (2) Elicitation of and responsiveness to patient problems, concerns, and expectations (eg, Did the doctors listen carefully to what you had to say?); (3) Explanations of condition, progress, and prognosis (eg, Did the doctors make sure you understand your health problem?); (4) Explanations of processes of care (eg, Did the doctors explain why a test was being done?); (5) Explanations of self‐care (eg, Did the doctors tell you what you could do to take care of yourself at home?); (6) Empowerment (eg, Did the doctors make you feel that following your treatment plan would make a difference in your health?); (7) Decision‐making: responsiveness to patient preferences regarding decisions (eg, Did the doctors try to involve you or include you in decisions about your treatment?); and (8) Consideration of patient's desire and ability to comply with recommendations (eg, Did the doctors understand the kinds of problems you might have in doing the recommended treatment?).

Statistical Analysis

Patient characteristics were summarized using frequency, mean, and standard deviation measures. Nondichotomous measures were recategorized into dichotomous variables as follows: age (less than 55 years vs. 55 years or older), race (black vs. white or other), marital status (married or living with someone vs. living alone), education (less than high school vs. high school graduate), employment status (employed full/part time vs. unemployed/retired), MMSE score (cognitively impaired [MMSE score 24] vs. no significant cognitive impairment [MMSE score >24]),39 and health literacy score (inadequate [REALM score 0 to 44] vs. marginal or adequate [REALM score 45‐66]).38 Dichotomous variables were summarized using frequencies.

Scores for each individual IPC question ranged from 1 to 5 with lower scores indicating better communication, except for questions in the domain of general clarity where higher scores indicated better communication. Then, for each of the 8 domains, scores of the individual IPC questions within that domain were averaged.

Bivariate analyses were conducted for each of the 8 IPC domains, by level of health literacy and other relevant patient characteristics, using the independent samples t‐test. Multivariable linear regression models were then constructed to examine the independent association of health literacy with each of the 8 IPC domains, while controlling for other patient characteristics that were also found to be associated with IPC domain scores. Bivariate analyses were also conducted for each of the 27 individual IPC items, to gain an understanding of which items might be driving the overall effect. A 2‐sided P < 0.05 was considered statistically significant. All analyses were performed using SPSS 15 for Windows (SPSS, Chicago, IL).

Results

Patient Characteristics

A total of 109 eligible patients were approached, 100 agreed to participate and were enrolled in the hospital, and 84 of them completed the follow‐up interview by telephone to comprise the sample for this study (Table 1). Most of the 84 participants were under the age of 55 (54%), male (58%), African American (88%), unemployed (79%), lived alone (73%), and had completed high school (62%). Age ranged from 24 to 80 years, REALM score ranged from 0 to 66, and MMSE ranged from 12 to 30. A large proportion (44%) had inadequate health literacy skills, and 50% had cognitive impairment. Patients with inadequate health literacy were more likely to have not finished high school and to suffer cognitive impairment, P < 0.01 for each comparison.

Patient Characteristics (n = 84)
Characteristicn (%)
Age 
<55 years45 (54)
55 years39 (46)
Gender 
Male49 (58)
Female35 (42)
Race 
Black74 (88)
White or other10 (12)
Marital status 
Married or living with someone23 (27)
Living alone61 (73)
Education 
Did not complete high school32 (38)
High school graduate52 (62)
Employment status 
Employed (full/part time)18 (21)
Not employed66 (79)
Mini‐Mental State Exam 
Cognition impaired42 (50)
Cognition not impaired42 (50)
Health literacy 
Inadequate37 (44)
Marginal or adequate47 (56)

Hospital Communication Ratings by IPC Domains

Overall, patients' ratings of hospital communication were positive, with most IPC domain score means lying in the favorable half of the Likert scale (Table 2). The domains with the best communication ratings were responsiveness to patient concerns (mean = 1.68), explanations of condition and prognosis (mean = 1.75), and empowerment (mean 1.76). The domain of worst performance was consideration of patients' desire and ability to comply with recommendations (mean = 3.15).

Interpersonal Processes of Care (IPC) Domains Overall and by Level of Health Literacy
 IPC DomainTotal (n = 84), Mean (SD)Patients with Inadequate Literacy (n = 37), Mean (SD)Patients with Marginal or Adequate Literacy (n = 47), Mean (SD)P Value
  • Abbreviation: SD, standard deviation.

  • The range for all scores is 1 to 5. On the domain of General clarity, higher scores indicate more favorable responses. On other domains, lower scores indicate more favorable responses.

1General clarity*3.66 (1.00)3.36 (1.14)3.89 (0.74)0.02
2Responsiveness to patient concerns1.68 (0.68)1.86 (0.76)1.53 (0.58)0.03
3Explanations of condition and prognosis1.75 (0.87)1.93 (0.99)1.61 (0.74)0.09
4Explanations of processes of care2.01 (0.86)2.22 (0.96)1.84 (0.74)0.04
5Explanations of self‐care2.37 (1.04)2.42 (1.20)2.33 (0.90)0.71
6Empowerment1.76 (1.03)1.85 (1.27)1.69 (0.81)0.51
7Decision‐making2.34 (0.78)2.34 (0.80)2.34 (0.77)1.00
8Consideration of patients' desire and ability to comply with recommendations3.15 (1.19)3.24 (1.16)3.07 (1.23)0.54

In bivariate analyses that compared IPC domains by patients' level of health literacy, several differences emerged. Patients with inadequate health literacy skills gave significantly worse ratings to the quality of communication on the domains of general clarity (mean = 3.36 vs. 3.89 for patients with marginal or adequate health literacy, P = 0.02), Responsiveness to patient concerns (mean = 1.86 vs. 1.53, P = 0.03), and Explanations of processes of care (mean = 2.22 vs. 1.84, P = 0.04). On a fourth domain, Explanations of condition and prognosis, a nonsignificant trend was present (mean = 1.93 vs. 1.61, P = 0.09).

Fewer significant relationships were found between other patient characteristics and IPC domain scores. Patients who were age 55 or older provided worse ratings on explanations of self‐care (mean = 2.74 vs. 2.05 for patients under the age of 55, P = 0.003). Lower ratings on the domain of general clarity, which indicated unclear communication, were found among patients who had not graduated from high school (mean = 3.31 vs. 3.88 for high school graduates, P = 0.02) or who had cognitive impairment (mean = 3.39 vs. 3.93 for patients without impaired cognition, P = 0.01). No significant differences were present by gender or race.

Based on these bivariate relationships, terms for inadequate health literacy, age 55, Cognitive impairment, and high school graduation were entered into multivariable models that predicted scores on each of the 8 IPC domains. Inadequate health literacy was independently associated with Responsiveness to patient concerns ( = 0.512, P = 0.007) and Explanations of processes of care ( = 0.548, P = 0.023); a nonsignificant trend was present for consideration of patients' desire and ability to comply with recommendations ( = 0.582, P = 0.09). The association of age with explanations of self‐care remained after adjustment for the other variables ( = 0.705, P = 0.002). None of the patient characteristics was independently associated with ratings of general clarity.

IPC Item Responses

Examination of responses on the individual IPC items revealed the specific areas of difficulty in communication as rated by patients (Table 3). In the domain of general clarity, patients with inadequate literacy provided poorer ratings on the item pertaining to use of medical terminology (mean = 2.92 vs. 3.68 for patients with marginal or adequate literacy, P = 0.004). Regarding Responsiveness to patient concerns, differences by literacy were present in the item that pertained to patients being given enough time to say what they thought was important (mean = 2.27 vs. 1.51, P = 0.003). On the domain of explanations of processes of care, the item rated differently by patients with inadequate literacy referred to feeling confused about their care because doctors did not explain things well (mean = 2.51 vs. 1.83, P = 0.02).

Discussion

We used a validated instrument, the IPC,40 to examine patients' ratings of the quality and clarity of hospital‐based communication. Overall, patients provided favorable ratings in many domains, including those pertaining to Responsiveness to patient concerns and Explanations of condition and prognosis. Clinicians' consideration of patients' desire and ability to comply with recommendations was rated least favorably overall. This represents an important area for improvement, particularly when considering the prevalence of nonadherence to medical therapy after hospital discharge, which may be as high as 50%.9, 42 Nonadherence after hospital discharge contributes to avoidable emergency department visits,43 hospital readmissions,44 and higher mortality.18, 45 The results of this study suggest that hospital physicians should give greater consideration to patients' preferences and problems that they may have in following the treatment recommendations.16 Future research will determine the extent to which this may enhance post‐discharge adherence.

Another important finding is that patients with inadequate health literacy rated hospital‐based communication less favorably than did patients with marginal or adequate literacy. In bivariate analyses, this effect was seen on several domains, including general clarity, Responsiveness to patient concerns, and explanations of processes of care. The latter 2 relationships persisted after adjustment for age, cognitive impairment, and educational attainment. To our knowledge, this is the first study which examines the effect of health literacy on patients' ratings of hospital‐based communication.

The majority of the literature on health communication and health literacy focuses on the outpatient setting.34, 46 However, the quality and clarity of patient‐provider communication in the hospital is also critically important. Ineffective communication in the hospital contributes to poor care transitions and post‐discharge complications. Patients commonly leave the hospital with a poor understanding of what transpired (eg, diagnoses, treatment provided, major test results) and inadequate knowledge about the self‐care activities that they must perform upon returning home (eg, medication management, physical activity, follow‐up appointments).911 Poor communication is often cited as the main underlying and remediable factor behind medical errors, adverse events, and the readmissions that commonly occur after hospital discharge.6, 16, 20 The results of this study provide complementary evidence, showing that patients often feel they have experienced suboptimal communication in the hospital setting. These findings highlight an opportunity for improvement in care transitions and patient safety, particularly among patients with inadequate health literacy.

In outpatient research that utilized the IPC, Schillinger et al.41 found that patients with inadequate functional health literacy reported significantly worse communication on the domains of general clarity, explanations of processes of care, and Explanations of condition and prognosis. Subsequent analyses by Sudore et al.47 demonstrated that patients with inadequate or marginal health literacy more often reported that physicians did not give them enough time to say what they thought was important, did not explain processes of care well, and did not ask about problems in following the recommended treatment (Table 3, IPC items 3, 12, and 26, respectively). Our findings were very similar. These relatively consistent results across studies and populations strengthen the conclusion that patients with inadequate health literacy feel their physicians do not communicate as effectively in these areas.

Interpersonal Processes of Care (IPC) Items Overall and by Level of Health Literacy
IPC ItemsOverall (n = 84), Mean (SD)Inadequate Literacy (n = 37), Mean (SD)Marginal or Adequate Literacy (n = 47), Mean (SD)P Value
  • Abbreviation: SD, standard deviation.

  • On the domain of general clarity, higher scores indicate more favorable responses. On other domains, lower scores indicate more favorable responses.

General clarity*    
1. Did the doctors use medical words you did not understand?3.35 (1.14)2.92 (1.40)3.68 (0.73)0.004
2. Did you have trouble understanding your doctors because they spoke too fast?3.98 (1.06)3.81 (1.13)4.11 (1.01)0.21
Responsiveness to patient concerns    
3. Did the doctors give you enough time to say what you thought was important?1.85 (1.14)2.27 (1.28)1.51 (0.88)0.003
4. Did the doctors listen carefully to what you had to say?1.62 (0.88)1.76 (1.04)1.51 (0.72)0.22
5. Did the doctors ignore what you told them?1.70 (0.92)1.81 (1.09)1.62 (0.77)0.38
6. Did the doctors take your concerns seriously?1.55 (0.92)1.65 (0.98)1.47 (0.88)0.38
Explanations of condition and prognosis    
7. Did the doctors give you enough information about your health problems?1.88 (1.11)2.11 (1.27)1.70 (0.95)0.11
8. Did the doctors make sure you understand your health problems?1.62 (0.88)1.76 (0.98)1.51 (0.78)0.22
Explanations of processes of care    
9. Did the doctors explain why a test was being done?1.70 (1.10)1.89 (1.24)1.55 (0.95)0.16
10. Did the doctors explain how the test was done?2.20 (1.35)2.27 (1.39)2.15 (1.34)0.69
11. Did the doctors tell you what they were doing as they examined you?1.99 (1.20)2.22 (1.34)1.81 (1.06)0.13
12. Did you feel confused about what was going on with your medical care because doctors did not explain things well?2.13 (1.23)2.51 (1.47)1.83 (0.92)0.02
Explanations of self‐care    
13. Did the doctors tell you what you could do to take care of yourself at home?1.67 (1.09)1.81 (1.29)1.55 (0.90)0.31
14. Did the doctors tell you how to pay attention to your symptoms and when to call the doctor?2.01 (1.38)2.19 (1.60)1.87 (1.17)0.32
15. Did the doctors clearly explain how to take the medicine (that is when, how much and for how long)?1.88 (1.36)2.00 (1.53)1.79 (1.22)0.48
16. Did the doctors go over all the medicines you are taking?2.39 (1.55)2.51 (1.74)2.30 (1.40)0.54
17. Did the doctors give you written instruction about how to take the medicine (other than what was on the container)?3.29 (1.70)3.05 (1.75)3.48 (1.66)0.26
18. Did the doctors tell you the reason for taking each medicine?2.05 (1.43)2.24 (1.64)1.89 (1.24)0.29
19. Did the doctors tell you about side effects you might get from your medicine?3.32 (1.64)3.11 (1.73)3.49 (1.56)0.29
Empowerment    
20. Did doctors make you feel that following your treatment plan would make a difference in your health?1.75 (1.07)1.89 (1.27)1.64 (0.90)0.31
21. Did the doctors make you feel that your everyday activities such as your diet and lifestyle would make a difference in your health?1.77 (1.21)1.81 (1.41)1.74 (1.03)0.81
Decision‐making    
22. Did the doctors try to involve you or include you in decisions about your treatment?2.43 (1.55)2.30 (1.49)2.53 (1.60)0.49
23. Did the doctors ask how you felt about different treatments?3.08 (1.58)2.89 (1.66)3.23 (1.51)0.33
24. Did the doctors make decision without taking your preferences and opinions into account?2.23 (1.35)2.34 (1.55)2.15 (1.20)0.54
25. Did you feel pressured by doctors in the hospital to have a treatment you were not sure you wanted?1.60 (0.97)1.81 (1.18)1.43 (0.74)0.09
Consideration of patients' desire and ability to comply with recommendations    
26. Did the doctors ask if you might have any problems actually doing the recommended treatment (for example taking the medication correctly)?3.82 (1.47)4.08 (1.40)3.62 (1.51)0.15
27. Did the doctors understand the kinds of problems you might have in doing the recommended treatment?2.43 (1.44)2.26 (1.52)2.57 (1.38)0.34

Importantly, the differences in patient responses by literacy category were driven by a few IPC items. These items pertained to physicians' use of medical terminology, the amount of time they gave patients to express their concerns, and how well they explained the patients' medical care. Training physicians to improve their communication skills in these specific areas may improve their ability to communicate effectively with patients who have limited literacy skills. Indeed, published recommendations on how to improve the clarity of verbal communication emphasize just a few major areas, including limiting the amount of medical terminology used, effectively encouraging patients to ask questions and express their concerns, and asking patients to teach‐back key points to make sure the physician has provided adequate explanation.4851 The present study provides some evidence for those recommendations, which for the most part, have been based on clinical experience and expert opinion.

There remains a need for professional education about health literacy and techniques to improve communication with patients who may have limited literacy skills. Many experts advocate clear verbal communication with all patients, so‐called Universal Precautions.52 Although 10 years have passed since the American Medical Association (AMA) called for more work in this area,53 few curricula have been described in the literature.48, 5456 The extent to which health literacy curricula have been implemented in medical schools and other professional schools is unknown. The impact of such training on the communication skills of health care providers and patient outcomes is also unclear.

The strengths of this study include a relatively good response rate and use of a validated measure to grade the quality of physician‐patient communication. This measure, the IPC, has been used previously in the context of health literacy.41 Nevertheless, certain limitations should be acknowledged. First, the study was performed at a single teaching hospital, where patients had a high prevalence of inadequate health literacy. The findings may not generalize to other institutions that serve a different patient population or to nonacademic programs. Second, communication was assessed by patient report, rather than by recording patient‐provider discussions for rating by independent observers. While patient report is inherently more subjective, patients' own perceptions about the effectiveness of health communication are arguably more important than those of independent raters, and thus, the data source may not represent a true limitation. Third, patient responses were obtained approximately 2 weeks after hospital discharge, and accordingly, they are subject to recall bias, which may be greater among those with cognitive impairment. Finally, patients were directed to rate the communication of the overall group of physicians who took care of them in the hospital. Given the academic setting, patients typically received care from a team that included medical students, interns, a resident, and an attending physician. We were not able to determine whether patients' ratings were influenced by a specific member of the team, nor how ratings may have been influenced by certain characteristics of that team member (eg, year of training, prior communication skills training, race or gender concordance, etc).

In summary, by surveying patients soon after an acute care hospitalization, we determined that certain areas held room for improvement, such as consideration of patients' desire and ability to comply with treatment recommendations. Patients with inadequate health literacy reported lower quality physician‐patient communication on several domains. They expressed particular concern about physicians' use of medical terminology, not getting enough time to express their concerns, and not receiving clear enough explanations about the medical care. Efforts are needed to improve physicians' communication skills in these areas. Such training should be evaluated to determine if it has a beneficial effect on physician communication skills and patient outcomes.

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  33. Baker DW,Wolf MS,Feinglass J,Thompson JA,Gazmararian JA,Huang J.Health literacy and mortality among elderly persons.Arch Intern Med.2007;167(14):15031509.
  34. DeWalt DA,Berkman ND,Sheridan S,Lohr KN,Pignone MP.Literacy and health outcomes: a systematic review of the literature.J Gen Intern Med.2004;19(12):11291139.
  35. Williams MV,Parker RM,Baker DW, et al.Inadequate functional health literacy among patients at two public hospitals.JAMA.1995;274(21):16771682.
  36. Braunwald E,Antman EM,Beasley JW, et al.ACC/AHA 2002 guideline update for the management of patients with unstable angina and non‐ST‐segment elevation myocardial infarction–summary article: a report of the American College of Cardiology/American Heart Association task force on practice guidelines (Committee on the Management of Patients With Unstable Angina).J Am Coll Cardiol.2002;40(7):13661374.
  37. Davis TC,Crouch MA,Long SW, et al.Rapid assessment of literacy levels of adult primary care patients.Fam Med.1991;23(6):433435.
  38. Davis TC,Kennen EM,Gazmararian JA,Williams MV.Literacy testing in health care research. In: Schwartzberg JG, VanGeest JB, Wang CC, eds.Understanding Health Literacy.Chicago:American Medical Association;2005:157179.
  39. Folstein MF,Folstein SE,McHugh PR.“Mini‐Mental State”. A practical method for grading the cognitive state of patients for the clinician.J Psychiatr Res.1975;12:189198.
  40. Stewart AL,Napoles‐Springer A,Perez‐Stable EJ, et al.Interpersonal processes of care in diverse populations.Milbank Q.1999;77:305339.
  41. Schillinger D,Bindman AB,Wang F,Stewart AL,Piette J.Functional health literacy and the quality of physician‐patient communication among diabetes patients.Patient Educ Couns.2004;52(3):315323.
  42. Kripalani S,Price M,Vigil V,Epstein KR.Frequency and predictors of prescription‐related issues after hospital discharge.J Hosp Med.2008;3(1):1219.
  43. Hope CJ,Wu J,Tu W,Young J,Murray MD.Association of medication adherence, knowledge, and skills with emergency department visits by adults 50 years or older with congestive heart failure.Am J Health Syst Pharm.2004;61(19):20432049.
  44. Murray MD,Tu W,Wu J,Morrow D,Smith F,Brater DC.Factors associated with exacerbation of heart failure include treatment adherence and health literacy skills.Clin Pharmacol Ther.2009;85(6):651658.
  45. Spertus JA,Kettelkamp R,Vance C, et al.Prevalence, predictors, and outcomes of premature discontinuation of thienopyridine therapy after drug‐eluting stent placement: results from the PREMIER registry.Circulation.2006;113(24):28032809.
  46. Roter DL,Hall JA,Katz NR.Patient‐physician communication: a descriptive summary of the literature.Patient Educ Couns.1988;12:99119.
  47. Sudore RL,Landefeld CS,Pérez‐Stable EJ,Bibbins‐Domingo K,Williams BA,Schillinger D.Unraveling the relationship between literacy, language proficiency, and patient‐physician communication.Patient Educ Couns.2009;75(3):398402.
  48. Kripalani S,Weiss BD.Teaching about health literacy and clear communication.J Gen Intern Med.2006;21:888890.
  49. Weiss BD.Health Literacy: A Manual for Clinicians.Chicago, IL:American Medical Association;2003.
  50. Weiss BD,Coyne C.Communicating with patients who cannot read.N Engl J Med.1997;337:272274.
  51. Williams MV,Davis TC,Parker RM,Weiss BD.The role of health literacy in patient‐physician communication.Fam Med.2002;34(5):383389.
  52. Brown DR,Ludwig R,Buck GA,Durham D,Shumard T,Graham SS.Health literacy: universal precautions needed.J Allied Health.2004;33(2):150155.
  53. American Medical Association Council on Scientific Affairs.Health literacy.JAMA.1999;281:552557.
  54. Harper W,Cook S,Makoul G.Teaching medical students about health literacy: 2 Chicago initiatives.Am J Health Behav.2007;31Suppl 1:S111S114.
  55. Kripalani S,Jacobson KL,Brown S,Manning K,Rask KJ,Jacobson TA.Development and implementation of a health literacy training program for medical residents.Med Educ Online.2006;11(13):18.
  56. Manning KD,Kripalani S.The use of standardized patients to teach low‐literacy communication skills.Am J Health Behav.2007;31Suppl 1:S105S110.
References
  1. Davis TC,Crouch MA,Wills G,Miller S,Abdehou DM.The gap between patient reading comprehension and the readability of patient education materials.J Fam Pract.1990;31:533538.
  2. Boyle CM.Differences between patients' and doctors' interpretation of some common medical terms.BMJ.1970;1:286289.
  3. Gibbs R,Gibbs P,Henrich J.Patient understanding of commonly used medical vocabulary.J Fam Pract.1987;25:176178.
  4. Mayeaux EJ,Murphy PW,Arnold C,Davis TC,Jackson RH,Sentell T.Improving education for patients with low literacy skills.Am Fam Physician.1996;53:205211.
  5. Ong LM,de Haes JC,Hoos AM,Lammes FB.Doctor‐patient communication: a review of the literature.Soc Sci Med.1995;40:903918.
  6. Kripalani S,Jackson AT,Schnipper JL,Coleman EA.Promoting effective transitions of care at hospital discharge: a review of key issues for hospitalists.J Hosp Med.2007;2(5):314323.
  7. Coleman EA,Berenson RA.Lost in transition: challenges and opportunities for improving the quality of transitional care.Ann Intern Med.2004;141(7):533536.
  8. Greenwald JL,Denham CR,Jack BW.The hospital discharge: a review of a high risk care transition with highlights of a reengineered discharge process.J Patient Saf.2007;3(2):97106.
  9. Kripalani S,Henderson LE,Jacobson TA,Vaccarino V.Medication use among inner‐city patients after hospital discharge: patient reported barriers and solutions.Mayo Clin Proc.2008;83(5):529535.
  10. Makaryus AN,Friedman EA.Patients' understanding of their treatment plans and diagnosis at discharge.Mayo Clin Proc.2005;80(8):991994.
  11. Flacker J,Park W,Sims A.Hospital discharge information and older patients: do they get what they need?J Hosp Med.2007;2(5):291296.
  12. Stewart S,Pearson S.Uncovering a multitude of sins: medication management in the home post acute hospitalisation among the chronically ill.Aust NZ J Med.1999;29(2):220227.
  13. Moore C,Wisnivesky J,Williams S,McGinn T.Medical errors related to discontinuity of care from an inpatient to an outpatient setting.J Gen Intern Med.2003;18:646651.
  14. Coleman EA,Smith JD,Raha D,Min SJ.Posthospital medication discrepancies: prevalence and contributing factors.Arch Intern Med.2005;165(16):18421847.
  15. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138:161167.
  16. Cua YM,Kripalani S.Medication use in the transition from hospital to home.Ann Acad Med Singapore.2008;37(2):136141.
  17. Sud A,Kline‐Rogers EM,Eagle KA, et al.Adherence to medications by patients after acute coronary syndromes.Ann Pharmacother.2005;39(11):17921797.
  18. Ho PM,Spertus JA,Masoudi FA, et al.Impact of medication therapy discontinuation on mortality after myocardial infarction.Arch Intern Med.2006;166(17):18421847.
  19. Kripalani S,LeFevre F,Phillips CO,Williams MV,Basaviah P,Baker DW.Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care.JAMA.2007;297(8):831841.
  20. Bartlett G,Blais R,Tamblyn R,Clermont RJ,MacGibbon B.Impact of patient communication problems on the risk of preventable adverse events in acute care settings.CMAJ.2008;178(12):15551562.
  21. Witherington EM,Pirzada OM,Avery AJ.Communication gaps and readmissions to hospital for patients aged 75 years and older: observational study.Qual Saf Health Care.2008;17(1):7175.
  22. Jencks SF,Williams MV,Coleman EA.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360(14):14181428.
  23. Institute of Medicine.Health Literacy. A Prescription to End Confusion.Washington, DC:National Academies Press;2004.
  24. Selden CR,Zorn M,Ratzan S,Parker RM.Current Bibliographies in Medicine: Health Literacy.Bethesda, MD:National Library of Medicine;2000.
  25. Kutner M,Greenberg E,Baer J. National Assessment of Adult Literacy (NAAL). A first look at the literacy of America's adults in the 21st century. Available at: http://nces.ed.gov/naal. Accessed January2010.
  26. Baker DW,Parker RM,Williams MV, et al.The health care experience of patients with low literacy.Arch Fam Med.1996;5:329334.
  27. Katz MG,Jacobson TA,Veledar E,Kripalani S.Patient literacy and question‐asking behavior in the medical encounter: a mixed‐methods analysis.J Gen Intern Med.2007;22(6):782786.
  28. Castro CM,Wilson C,Wang F,Schillinger D.Babel babble: physicians' use of unclarified medical jargon with patients.Am J Health Behav.2007;31(Suppl 1):S85S95.
  29. Davis TC,Wolf MS,Bass PF, et al.Literacy and misunderstanding prescription drug labels.Ann Intern Med.2006;145(12):887894.
  30. Williams MV,Baker DW,Parker RM,Nurss JR.Relationship of functional health literacy to patients' knowledge of their chronic disease: a study of patients with hypertension and diabetes.Arch Intern Med.1998;158(2):166172.
  31. Baker DW,Parker RM,Williams MV,Clark WS.Health literacy and the risk of hospital admission.J Gen Intern Med.1998;13:791798.
  32. Sudore RL,Yaffe K,Satterfield S, et al.Limited literacy and mortality in the elderly: The Health, Aging, and Body Composition Study.J Gen Intern Med.2006;21(8):806812.
  33. Baker DW,Wolf MS,Feinglass J,Thompson JA,Gazmararian JA,Huang J.Health literacy and mortality among elderly persons.Arch Intern Med.2007;167(14):15031509.
  34. DeWalt DA,Berkman ND,Sheridan S,Lohr KN,Pignone MP.Literacy and health outcomes: a systematic review of the literature.J Gen Intern Med.2004;19(12):11291139.
  35. Williams MV,Parker RM,Baker DW, et al.Inadequate functional health literacy among patients at two public hospitals.JAMA.1995;274(21):16771682.
  36. Braunwald E,Antman EM,Beasley JW, et al.ACC/AHA 2002 guideline update for the management of patients with unstable angina and non‐ST‐segment elevation myocardial infarction–summary article: a report of the American College of Cardiology/American Heart Association task force on practice guidelines (Committee on the Management of Patients With Unstable Angina).J Am Coll Cardiol.2002;40(7):13661374.
  37. Davis TC,Crouch MA,Long SW, et al.Rapid assessment of literacy levels of adult primary care patients.Fam Med.1991;23(6):433435.
  38. Davis TC,Kennen EM,Gazmararian JA,Williams MV.Literacy testing in health care research. In: Schwartzberg JG, VanGeest JB, Wang CC, eds.Understanding Health Literacy.Chicago:American Medical Association;2005:157179.
  39. Folstein MF,Folstein SE,McHugh PR.“Mini‐Mental State”. A practical method for grading the cognitive state of patients for the clinician.J Psychiatr Res.1975;12:189198.
  40. Stewart AL,Napoles‐Springer A,Perez‐Stable EJ, et al.Interpersonal processes of care in diverse populations.Milbank Q.1999;77:305339.
  41. Schillinger D,Bindman AB,Wang F,Stewart AL,Piette J.Functional health literacy and the quality of physician‐patient communication among diabetes patients.Patient Educ Couns.2004;52(3):315323.
  42. Kripalani S,Price M,Vigil V,Epstein KR.Frequency and predictors of prescription‐related issues after hospital discharge.J Hosp Med.2008;3(1):1219.
  43. Hope CJ,Wu J,Tu W,Young J,Murray MD.Association of medication adherence, knowledge, and skills with emergency department visits by adults 50 years or older with congestive heart failure.Am J Health Syst Pharm.2004;61(19):20432049.
  44. Murray MD,Tu W,Wu J,Morrow D,Smith F,Brater DC.Factors associated with exacerbation of heart failure include treatment adherence and health literacy skills.Clin Pharmacol Ther.2009;85(6):651658.
  45. Spertus JA,Kettelkamp R,Vance C, et al.Prevalence, predictors, and outcomes of premature discontinuation of thienopyridine therapy after drug‐eluting stent placement: results from the PREMIER registry.Circulation.2006;113(24):28032809.
  46. Roter DL,Hall JA,Katz NR.Patient‐physician communication: a descriptive summary of the literature.Patient Educ Couns.1988;12:99119.
  47. Sudore RL,Landefeld CS,Pérez‐Stable EJ,Bibbins‐Domingo K,Williams BA,Schillinger D.Unraveling the relationship between literacy, language proficiency, and patient‐physician communication.Patient Educ Couns.2009;75(3):398402.
  48. Kripalani S,Weiss BD.Teaching about health literacy and clear communication.J Gen Intern Med.2006;21:888890.
  49. Weiss BD.Health Literacy: A Manual for Clinicians.Chicago, IL:American Medical Association;2003.
  50. Weiss BD,Coyne C.Communicating with patients who cannot read.N Engl J Med.1997;337:272274.
  51. Williams MV,Davis TC,Parker RM,Weiss BD.The role of health literacy in patient‐physician communication.Fam Med.2002;34(5):383389.
  52. Brown DR,Ludwig R,Buck GA,Durham D,Shumard T,Graham SS.Health literacy: universal precautions needed.J Allied Health.2004;33(2):150155.
  53. American Medical Association Council on Scientific Affairs.Health literacy.JAMA.1999;281:552557.
  54. Harper W,Cook S,Makoul G.Teaching medical students about health literacy: 2 Chicago initiatives.Am J Health Behav.2007;31Suppl 1:S111S114.
  55. Kripalani S,Jacobson KL,Brown S,Manning K,Rask KJ,Jacobson TA.Development and implementation of a health literacy training program for medical residents.Med Educ Online.2006;11(13):18.
  56. Manning KD,Kripalani S.The use of standardized patients to teach low‐literacy communication skills.Am J Health Behav.2007;31Suppl 1:S105S110.
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Hospitalists' Awareness of Patient Charges

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Hospitalists' awareness of patient charges associated with inpatient care

Hospitalists have been suggested to offer rational and efficient medical care through specialized knowledge about inpatient care services.1 The goal that hospitalists will use care resources more efficiently presumes hospitalists' accurate knowledge about charges and costs.

Data regarding physicians' awareness of care charges and its impact upon care is limited. An international meta‐analysis of clinicians' awareness of pharmaceutical prices demonstrated poor accuracy of physicians' estimates of charges, but effects of increasing their knowledge remained unexamined.2 Continuous exposure to education and alerts about charges have been demonstrated to diminish physicians' unnecessary use of specific laboratory assays in a single teaching hospital, in a pediatric emergency department, and an outpatient primary care system; test use declined when physicians were alerted to test charges at the point‐of‐care without negative impact upon clinical outcomes; when the notices ceased, utilization climbed back towards baseline levels. Specific to inpatient care, a single‐center study evaluated the impact of price‐alerts upon laboratory and imaging use, but showed no effects.36

Applicability of these existing data for contemporary hospitalists are limited, and most data were collected before hospitalists developed as an organized focus of practice. A review of existing literature revealed no published data demonstrating hospitalists' higher expert awareness of charges generated by inpatient care. Published comparisons of the care expense generated by hospitalists' care versus that of general internists or academic teams have shown minimal and inconsistent effects.79 Those data showing reduced costs from hospitalists were associated with small length‐of‐stay reductions, rather than more expert resource utilization.7 We measured the accuracy and precision of hospitalist's estimates of charges associated with services commonly used in inpatient care.

Setting

Two community‐based private, academic‐affiliated hospitals operated by a not‐for‐profit health system in Washington State, comprising together 895 inpatient beds. The questionnaire instrument was approved by the governing Institutional Review Board (IRB).

Methods

A list of true charges for 14 services, procedures, tests, and physician charges commonly ordered by adult medicine hospitalists was acquired directly from the responsible departments of a multi‐hospital system operated by a single non‐profit entity using a unified chargemaster. Specifically, we acquired the charge that a hypothetical self‐paying patient would receive for each service, excluding any adjustments exercised by other payer sources. The list of charges was reported to the organization's financial officers for affirmation. Physician charges were standardized to geographically‐adjusted Medicare charges obtained directly from the American Medical Assocation's online Common Procedural Terminology tool.10 A cross‐section of hospitalists (n = 25) was surveyed from a private hospitalist group and an academically‐affiliated hospitalist service. Hospitalists included US and international medical graduates, new‐graduates from residency training and clinicians with a range of prior experiences in academic centers, government hospital systems, and private primary care. Respondents were asked to estimate to the nearest dollar the billing charge that a hypothetical self‐pay patient would receive for each care item. Direct data collection was arranged through the groups' medical directors and occurred in the hospitalist groups' regular business meetings. The design gathered no data on individual characteristics of respondents such as domestic or international education, sex, age, or time in practice, nor from which practice‐group a given respondent originated, to affirm to participants that their responses could not be used to imply performance measures or quality profiles.

Findings

Hospitalists tended to rank the expense of items in essentially the correct order, as reflected by the rough trend of rising estimates compared to true‐charges (Figure 1). Of note, we did not ask hospitalists to discern the different charges of 2 appropriate competing clinical choices, but asked for estimated prices of diverse services. The range of respondents' estimates about each item was broad. The mean‐value of hospitalists' estimates for each care item was less revealing than the range and diversity of estimates about each care item. Accuracy of hospitalists' estimates of charges was poor. Only 10.8% of hospitalists' estimates were within 10% of the actual unadjusted charge, 17.8% within 20% of that charge, and 24.8% were within a 30% margin of accuracy. Summary results are presented (Table 1). Pearson's r correlation value between the unadjusted charges and the estimates made by hospitalists was 0.548, a coefficient of determination equal to 0.300. Thus, the true charges list we obtained had only a low‐grade association with hospitalists' estimates (Figure 1). Hospitalists' estimates about the charges of relatively‐expensive items (abdominal computed tomography [CT]) overlapped with their estimates about the least‐expensive items (such as a urine culture ). Inter‐hospitalist agreement about charges associated with each care item was also low; estimates for each care item charge varied over logarithmic orders of magnitude.

Accuracy of Hospitalists' Estimates of Charges Associated With Inpatient Care
Care Service Unadjusted Charge, USD$ Mean Estimate, USD$ Minimum Estimate, USD$ Maximum Estimate, USD$ % of Estimates Within 10% Accuracy % of Estimates Within 20% Accuracy % of Estimates Within 30% Accuracy
  • Abbreviations: CPT, Current Procedural Terminology; CT, computed tomography; ICU, intensive care unit; IV, intravenous; USD, US dollar.

Complete blood count 30 73 10 440 16 20 20
Complete metabolic panel 37 135 15 1200 4 16 16
Urinalysis with microscopy 37 53 15 105 12 20 24
Urine culture 26 77 20 200 4 16 20
Ward bed, charge per night 744 998 300 3000 20 20 20
ICU Bed, charge per night 1107 2018 750 6000 8 12 12
Chest x‐ray 271 169 60 700 12 16 24
CT scan, abdomen 2204 803 150 1800 0 4 4
Methylpredisolone 125 mg IV dose 26.63 63 3 200 4 20 24
Levofloxacin 500 mg IV dose 105.41 114 10 500 24 28 36
Levofloxacin 500 mg oral dose 29.78 25 4 70 12 12 20
Admission services (CPT code 99223) 169.56 225 100 700 8 36 52
Inpatient care services (CPT code 99232) 62.47 110 40 400 12 28 48
Central venous catheter placement (CPT 36569) 286.04 338 50 1200 8 16 28
Average % correct 10.8 17.8 24.8
Figure 1
Hospitalists' estimates of care charges vs. unadjusted chargemaster prices (note: scale is logarithmic).

Discussion

To date, hospitalist programs have shown little impact upon care costs when compared with other inpatient care staffing models. One limiting factor may be the opacity of medical care pricing. Patients have been demonstrated to have little access to knowledge of what care will cost them and complex barriers prevent them from gaining pricing information.11, 12 Hospitalists may be conjectured to serve as expert sources on the costs and values of medical care services on behalf of inpatients, but our observations suggest that hospitalists' actual knowledge of patient‐charges is lacking. The opacity of US medical prices to patients appears to extend to hospital‐care physicians as well. We observe that no widespread mechanism exists by which hospitalists would be well‐positioned to become informed about the actual charges their patients receive. Unadjusted chargemaster lists are generally restricted information, and would be difficult to access outside of participation in the charge‐notifications used in the existing studies cited above.

The inquiry was specifically limited to how closely hospitalists' estimates of the unadjusted charges for some commonly‐ordered items compare to the actual unadjusted chargemaster at their own institutions. We did not assess the hospitalists' perceptions about the accuracy of their estimates, nor the impact of specific hospitalist characteristics upon accuracy. Our sample's representation of the larger national population of hospital physicians is not established, but engenders no expectation that these clinicians' charge‐awareness is substantively different from that of hospitalists in most other institutions. It is not known what specific clinician or practice‐setting characteristics will direct charge‐awareness, or will influence the impact of charge‐awareness upon clinical practices.

The range of estimates different hospitalists made about the same care items in the same facilities was very broad, which argues that respondents did not estimate charges based upon a different knowledge base of which the investigators were unaware. This is important because our use of unadjusted charges to self‐pay patients as true prices is necessarily somewhat arbitrary. Chargemaster price may not reflect the institution's cost of performing the service, the different prices paid for a single service by different payer sources, nor reflect services' true value based upon outcomes. However, recognizing that these actual prices are somewhat artificial, the use of these prices suffices for the current inquiry, and does not negate our findings of hospitalists' low accuracy and low agreement. Also noteworthy, an unadjusted chargemaster can often represent the charges received by those uninsured US patients for whom payer‐source adjustment is inaccessible, and informs downstream accounting such as the value of unpaid care a hospital delivers annually.

The most immediate matter for examination among hospitalists is what effects increased charge‐awareness may exert upon clinical decisions and practice processes. It appears that the premise that hospitalists' exercise expert knowledge of costs is likely not valid; but it is unknown whether accurate charge‐awareness among hospitalists will improve cost‐reductions by hospitalists. In some payer‐arrangements, accurate charge‐awareness might engender reduced care quantity, rather than increased efficiency or quality. The impact of upgrading hospitalists' knowledge about the charges and costs they generate, and the most effective method to do so, is worthy of investigation; based upon this initial data we encourage and are undertaking a larger‐scale study and exploration of the effects of enhanced hospitalist charge‐awareness.

Conclusion

Hospitalists have low awareness of the charges associated with inpatient care. The opacity of hospital care pricing to patient populations extends also to hospitalist physicians. Hospitalists likely do not improve cost‐efficiency through expert knowledge of services' costs to patients. Education and reminder systems to apprise hospitalists of charges should be examined as possible tools to optimize the use of inpatient care resources.

References
  1. Kaluga ME,Charney P , et al.The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19(4):293201.
  2. Allan GM,Lexchin J,Wiebe N.Physician awareness of drug cost: a systematic review.PLoS Med.2007;4(9):e283.
  3. Hampers LC,Cha S,Gutglass DJ,Krug SE,Binns HJ.The effect of price information on test‐ordering behavior and patient outcomes in a pediatric emergency department.Pediatrics.1999;103(4):877882.
  4. Tierney WM,Miller EM,McDonald CJ.The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests.N Engl J Med1990;322:14991504.
  5. Miyakis S,Karamanog G,Liontos M,Mountokalakis TD.Factors contributing to inappropriate ordering of tests in an academic medical department and the effect of an educational feedback strategy.Postgrad Med J.2006;82:823829.
  6. David W. Bates,Gilad J. Kuperman,Jha Ashish, et al.Tanasijevic does the computerized display of charges affect inpatient ancillary test utilization?Arch Intern Med.1997;157(21):25012508.
  7. Coffman J,Rundall T.The impact of hospitalists on the cost and quality of inpatient care in the united states: a research synthesis.Med Care Res Rev.2005;62:379406.
  8. Everett G,Uddin N,Rudloff B.Comparison of hospital costs and length of stay for community internists, hospitalists, and academicians.J Gen Intern Med.2007;22:662667.
  9. Lindenauer P,Rothberg MB,Pekow PS,Kenwood C,Benjamin EM,Auerbach AD.Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357(25):25892600.
  10. American Medical Association, CPT and RVU Search utility. Available at: https://catalog.ama‐assn.org/Catalog/cpt/cpt_search.jsp. Accessed December 2009.
  11. Austin DA,Gravelle JG.Does price tranparency improve market efficiency? Implications of empirical evidence in other markets for the health sector. 2007, Congressional Research Service Report RL34101. Available at: http://ftp.fas.org/sgp/crs/secrecy/RL34101. Accessed December2009.
  12. Reinhardt UE.The pricing of US hospital services.Health Aff.2006;25(1):5769.
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Journal of Hospital Medicine - 5(5)
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Hospitalists have been suggested to offer rational and efficient medical care through specialized knowledge about inpatient care services.1 The goal that hospitalists will use care resources more efficiently presumes hospitalists' accurate knowledge about charges and costs.

Data regarding physicians' awareness of care charges and its impact upon care is limited. An international meta‐analysis of clinicians' awareness of pharmaceutical prices demonstrated poor accuracy of physicians' estimates of charges, but effects of increasing their knowledge remained unexamined.2 Continuous exposure to education and alerts about charges have been demonstrated to diminish physicians' unnecessary use of specific laboratory assays in a single teaching hospital, in a pediatric emergency department, and an outpatient primary care system; test use declined when physicians were alerted to test charges at the point‐of‐care without negative impact upon clinical outcomes; when the notices ceased, utilization climbed back towards baseline levels. Specific to inpatient care, a single‐center study evaluated the impact of price‐alerts upon laboratory and imaging use, but showed no effects.36

Applicability of these existing data for contemporary hospitalists are limited, and most data were collected before hospitalists developed as an organized focus of practice. A review of existing literature revealed no published data demonstrating hospitalists' higher expert awareness of charges generated by inpatient care. Published comparisons of the care expense generated by hospitalists' care versus that of general internists or academic teams have shown minimal and inconsistent effects.79 Those data showing reduced costs from hospitalists were associated with small length‐of‐stay reductions, rather than more expert resource utilization.7 We measured the accuracy and precision of hospitalist's estimates of charges associated with services commonly used in inpatient care.

Setting

Two community‐based private, academic‐affiliated hospitals operated by a not‐for‐profit health system in Washington State, comprising together 895 inpatient beds. The questionnaire instrument was approved by the governing Institutional Review Board (IRB).

Methods

A list of true charges for 14 services, procedures, tests, and physician charges commonly ordered by adult medicine hospitalists was acquired directly from the responsible departments of a multi‐hospital system operated by a single non‐profit entity using a unified chargemaster. Specifically, we acquired the charge that a hypothetical self‐paying patient would receive for each service, excluding any adjustments exercised by other payer sources. The list of charges was reported to the organization's financial officers for affirmation. Physician charges were standardized to geographically‐adjusted Medicare charges obtained directly from the American Medical Assocation's online Common Procedural Terminology tool.10 A cross‐section of hospitalists (n = 25) was surveyed from a private hospitalist group and an academically‐affiliated hospitalist service. Hospitalists included US and international medical graduates, new‐graduates from residency training and clinicians with a range of prior experiences in academic centers, government hospital systems, and private primary care. Respondents were asked to estimate to the nearest dollar the billing charge that a hypothetical self‐pay patient would receive for each care item. Direct data collection was arranged through the groups' medical directors and occurred in the hospitalist groups' regular business meetings. The design gathered no data on individual characteristics of respondents such as domestic or international education, sex, age, or time in practice, nor from which practice‐group a given respondent originated, to affirm to participants that their responses could not be used to imply performance measures or quality profiles.

Findings

Hospitalists tended to rank the expense of items in essentially the correct order, as reflected by the rough trend of rising estimates compared to true‐charges (Figure 1). Of note, we did not ask hospitalists to discern the different charges of 2 appropriate competing clinical choices, but asked for estimated prices of diverse services. The range of respondents' estimates about each item was broad. The mean‐value of hospitalists' estimates for each care item was less revealing than the range and diversity of estimates about each care item. Accuracy of hospitalists' estimates of charges was poor. Only 10.8% of hospitalists' estimates were within 10% of the actual unadjusted charge, 17.8% within 20% of that charge, and 24.8% were within a 30% margin of accuracy. Summary results are presented (Table 1). Pearson's r correlation value between the unadjusted charges and the estimates made by hospitalists was 0.548, a coefficient of determination equal to 0.300. Thus, the true charges list we obtained had only a low‐grade association with hospitalists' estimates (Figure 1). Hospitalists' estimates about the charges of relatively‐expensive items (abdominal computed tomography [CT]) overlapped with their estimates about the least‐expensive items (such as a urine culture ). Inter‐hospitalist agreement about charges associated with each care item was also low; estimates for each care item charge varied over logarithmic orders of magnitude.

Accuracy of Hospitalists' Estimates of Charges Associated With Inpatient Care
Care Service Unadjusted Charge, USD$ Mean Estimate, USD$ Minimum Estimate, USD$ Maximum Estimate, USD$ % of Estimates Within 10% Accuracy % of Estimates Within 20% Accuracy % of Estimates Within 30% Accuracy
  • Abbreviations: CPT, Current Procedural Terminology; CT, computed tomography; ICU, intensive care unit; IV, intravenous; USD, US dollar.

Complete blood count 30 73 10 440 16 20 20
Complete metabolic panel 37 135 15 1200 4 16 16
Urinalysis with microscopy 37 53 15 105 12 20 24
Urine culture 26 77 20 200 4 16 20
Ward bed, charge per night 744 998 300 3000 20 20 20
ICU Bed, charge per night 1107 2018 750 6000 8 12 12
Chest x‐ray 271 169 60 700 12 16 24
CT scan, abdomen 2204 803 150 1800 0 4 4
Methylpredisolone 125 mg IV dose 26.63 63 3 200 4 20 24
Levofloxacin 500 mg IV dose 105.41 114 10 500 24 28 36
Levofloxacin 500 mg oral dose 29.78 25 4 70 12 12 20
Admission services (CPT code 99223) 169.56 225 100 700 8 36 52
Inpatient care services (CPT code 99232) 62.47 110 40 400 12 28 48
Central venous catheter placement (CPT 36569) 286.04 338 50 1200 8 16 28
Average % correct 10.8 17.8 24.8
Figure 1
Hospitalists' estimates of care charges vs. unadjusted chargemaster prices (note: scale is logarithmic).

Discussion

To date, hospitalist programs have shown little impact upon care costs when compared with other inpatient care staffing models. One limiting factor may be the opacity of medical care pricing. Patients have been demonstrated to have little access to knowledge of what care will cost them and complex barriers prevent them from gaining pricing information.11, 12 Hospitalists may be conjectured to serve as expert sources on the costs and values of medical care services on behalf of inpatients, but our observations suggest that hospitalists' actual knowledge of patient‐charges is lacking. The opacity of US medical prices to patients appears to extend to hospital‐care physicians as well. We observe that no widespread mechanism exists by which hospitalists would be well‐positioned to become informed about the actual charges their patients receive. Unadjusted chargemaster lists are generally restricted information, and would be difficult to access outside of participation in the charge‐notifications used in the existing studies cited above.

The inquiry was specifically limited to how closely hospitalists' estimates of the unadjusted charges for some commonly‐ordered items compare to the actual unadjusted chargemaster at their own institutions. We did not assess the hospitalists' perceptions about the accuracy of their estimates, nor the impact of specific hospitalist characteristics upon accuracy. Our sample's representation of the larger national population of hospital physicians is not established, but engenders no expectation that these clinicians' charge‐awareness is substantively different from that of hospitalists in most other institutions. It is not known what specific clinician or practice‐setting characteristics will direct charge‐awareness, or will influence the impact of charge‐awareness upon clinical practices.

The range of estimates different hospitalists made about the same care items in the same facilities was very broad, which argues that respondents did not estimate charges based upon a different knowledge base of which the investigators were unaware. This is important because our use of unadjusted charges to self‐pay patients as true prices is necessarily somewhat arbitrary. Chargemaster price may not reflect the institution's cost of performing the service, the different prices paid for a single service by different payer sources, nor reflect services' true value based upon outcomes. However, recognizing that these actual prices are somewhat artificial, the use of these prices suffices for the current inquiry, and does not negate our findings of hospitalists' low accuracy and low agreement. Also noteworthy, an unadjusted chargemaster can often represent the charges received by those uninsured US patients for whom payer‐source adjustment is inaccessible, and informs downstream accounting such as the value of unpaid care a hospital delivers annually.

The most immediate matter for examination among hospitalists is what effects increased charge‐awareness may exert upon clinical decisions and practice processes. It appears that the premise that hospitalists' exercise expert knowledge of costs is likely not valid; but it is unknown whether accurate charge‐awareness among hospitalists will improve cost‐reductions by hospitalists. In some payer‐arrangements, accurate charge‐awareness might engender reduced care quantity, rather than increased efficiency or quality. The impact of upgrading hospitalists' knowledge about the charges and costs they generate, and the most effective method to do so, is worthy of investigation; based upon this initial data we encourage and are undertaking a larger‐scale study and exploration of the effects of enhanced hospitalist charge‐awareness.

Conclusion

Hospitalists have low awareness of the charges associated with inpatient care. The opacity of hospital care pricing to patient populations extends also to hospitalist physicians. Hospitalists likely do not improve cost‐efficiency through expert knowledge of services' costs to patients. Education and reminder systems to apprise hospitalists of charges should be examined as possible tools to optimize the use of inpatient care resources.

Hospitalists have been suggested to offer rational and efficient medical care through specialized knowledge about inpatient care services.1 The goal that hospitalists will use care resources more efficiently presumes hospitalists' accurate knowledge about charges and costs.

Data regarding physicians' awareness of care charges and its impact upon care is limited. An international meta‐analysis of clinicians' awareness of pharmaceutical prices demonstrated poor accuracy of physicians' estimates of charges, but effects of increasing their knowledge remained unexamined.2 Continuous exposure to education and alerts about charges have been demonstrated to diminish physicians' unnecessary use of specific laboratory assays in a single teaching hospital, in a pediatric emergency department, and an outpatient primary care system; test use declined when physicians were alerted to test charges at the point‐of‐care without negative impact upon clinical outcomes; when the notices ceased, utilization climbed back towards baseline levels. Specific to inpatient care, a single‐center study evaluated the impact of price‐alerts upon laboratory and imaging use, but showed no effects.36

Applicability of these existing data for contemporary hospitalists are limited, and most data were collected before hospitalists developed as an organized focus of practice. A review of existing literature revealed no published data demonstrating hospitalists' higher expert awareness of charges generated by inpatient care. Published comparisons of the care expense generated by hospitalists' care versus that of general internists or academic teams have shown minimal and inconsistent effects.79 Those data showing reduced costs from hospitalists were associated with small length‐of‐stay reductions, rather than more expert resource utilization.7 We measured the accuracy and precision of hospitalist's estimates of charges associated with services commonly used in inpatient care.

Setting

Two community‐based private, academic‐affiliated hospitals operated by a not‐for‐profit health system in Washington State, comprising together 895 inpatient beds. The questionnaire instrument was approved by the governing Institutional Review Board (IRB).

Methods

A list of true charges for 14 services, procedures, tests, and physician charges commonly ordered by adult medicine hospitalists was acquired directly from the responsible departments of a multi‐hospital system operated by a single non‐profit entity using a unified chargemaster. Specifically, we acquired the charge that a hypothetical self‐paying patient would receive for each service, excluding any adjustments exercised by other payer sources. The list of charges was reported to the organization's financial officers for affirmation. Physician charges were standardized to geographically‐adjusted Medicare charges obtained directly from the American Medical Assocation's online Common Procedural Terminology tool.10 A cross‐section of hospitalists (n = 25) was surveyed from a private hospitalist group and an academically‐affiliated hospitalist service. Hospitalists included US and international medical graduates, new‐graduates from residency training and clinicians with a range of prior experiences in academic centers, government hospital systems, and private primary care. Respondents were asked to estimate to the nearest dollar the billing charge that a hypothetical self‐pay patient would receive for each care item. Direct data collection was arranged through the groups' medical directors and occurred in the hospitalist groups' regular business meetings. The design gathered no data on individual characteristics of respondents such as domestic or international education, sex, age, or time in practice, nor from which practice‐group a given respondent originated, to affirm to participants that their responses could not be used to imply performance measures or quality profiles.

Findings

Hospitalists tended to rank the expense of items in essentially the correct order, as reflected by the rough trend of rising estimates compared to true‐charges (Figure 1). Of note, we did not ask hospitalists to discern the different charges of 2 appropriate competing clinical choices, but asked for estimated prices of diverse services. The range of respondents' estimates about each item was broad. The mean‐value of hospitalists' estimates for each care item was less revealing than the range and diversity of estimates about each care item. Accuracy of hospitalists' estimates of charges was poor. Only 10.8% of hospitalists' estimates were within 10% of the actual unadjusted charge, 17.8% within 20% of that charge, and 24.8% were within a 30% margin of accuracy. Summary results are presented (Table 1). Pearson's r correlation value between the unadjusted charges and the estimates made by hospitalists was 0.548, a coefficient of determination equal to 0.300. Thus, the true charges list we obtained had only a low‐grade association with hospitalists' estimates (Figure 1). Hospitalists' estimates about the charges of relatively‐expensive items (abdominal computed tomography [CT]) overlapped with their estimates about the least‐expensive items (such as a urine culture ). Inter‐hospitalist agreement about charges associated with each care item was also low; estimates for each care item charge varied over logarithmic orders of magnitude.

Accuracy of Hospitalists' Estimates of Charges Associated With Inpatient Care
Care Service Unadjusted Charge, USD$ Mean Estimate, USD$ Minimum Estimate, USD$ Maximum Estimate, USD$ % of Estimates Within 10% Accuracy % of Estimates Within 20% Accuracy % of Estimates Within 30% Accuracy
  • Abbreviations: CPT, Current Procedural Terminology; CT, computed tomography; ICU, intensive care unit; IV, intravenous; USD, US dollar.

Complete blood count 30 73 10 440 16 20 20
Complete metabolic panel 37 135 15 1200 4 16 16
Urinalysis with microscopy 37 53 15 105 12 20 24
Urine culture 26 77 20 200 4 16 20
Ward bed, charge per night 744 998 300 3000 20 20 20
ICU Bed, charge per night 1107 2018 750 6000 8 12 12
Chest x‐ray 271 169 60 700 12 16 24
CT scan, abdomen 2204 803 150 1800 0 4 4
Methylpredisolone 125 mg IV dose 26.63 63 3 200 4 20 24
Levofloxacin 500 mg IV dose 105.41 114 10 500 24 28 36
Levofloxacin 500 mg oral dose 29.78 25 4 70 12 12 20
Admission services (CPT code 99223) 169.56 225 100 700 8 36 52
Inpatient care services (CPT code 99232) 62.47 110 40 400 12 28 48
Central venous catheter placement (CPT 36569) 286.04 338 50 1200 8 16 28
Average % correct 10.8 17.8 24.8
Figure 1
Hospitalists' estimates of care charges vs. unadjusted chargemaster prices (note: scale is logarithmic).

Discussion

To date, hospitalist programs have shown little impact upon care costs when compared with other inpatient care staffing models. One limiting factor may be the opacity of medical care pricing. Patients have been demonstrated to have little access to knowledge of what care will cost them and complex barriers prevent them from gaining pricing information.11, 12 Hospitalists may be conjectured to serve as expert sources on the costs and values of medical care services on behalf of inpatients, but our observations suggest that hospitalists' actual knowledge of patient‐charges is lacking. The opacity of US medical prices to patients appears to extend to hospital‐care physicians as well. We observe that no widespread mechanism exists by which hospitalists would be well‐positioned to become informed about the actual charges their patients receive. Unadjusted chargemaster lists are generally restricted information, and would be difficult to access outside of participation in the charge‐notifications used in the existing studies cited above.

The inquiry was specifically limited to how closely hospitalists' estimates of the unadjusted charges for some commonly‐ordered items compare to the actual unadjusted chargemaster at their own institutions. We did not assess the hospitalists' perceptions about the accuracy of their estimates, nor the impact of specific hospitalist characteristics upon accuracy. Our sample's representation of the larger national population of hospital physicians is not established, but engenders no expectation that these clinicians' charge‐awareness is substantively different from that of hospitalists in most other institutions. It is not known what specific clinician or practice‐setting characteristics will direct charge‐awareness, or will influence the impact of charge‐awareness upon clinical practices.

The range of estimates different hospitalists made about the same care items in the same facilities was very broad, which argues that respondents did not estimate charges based upon a different knowledge base of which the investigators were unaware. This is important because our use of unadjusted charges to self‐pay patients as true prices is necessarily somewhat arbitrary. Chargemaster price may not reflect the institution's cost of performing the service, the different prices paid for a single service by different payer sources, nor reflect services' true value based upon outcomes. However, recognizing that these actual prices are somewhat artificial, the use of these prices suffices for the current inquiry, and does not negate our findings of hospitalists' low accuracy and low agreement. Also noteworthy, an unadjusted chargemaster can often represent the charges received by those uninsured US patients for whom payer‐source adjustment is inaccessible, and informs downstream accounting such as the value of unpaid care a hospital delivers annually.

The most immediate matter for examination among hospitalists is what effects increased charge‐awareness may exert upon clinical decisions and practice processes. It appears that the premise that hospitalists' exercise expert knowledge of costs is likely not valid; but it is unknown whether accurate charge‐awareness among hospitalists will improve cost‐reductions by hospitalists. In some payer‐arrangements, accurate charge‐awareness might engender reduced care quantity, rather than increased efficiency or quality. The impact of upgrading hospitalists' knowledge about the charges and costs they generate, and the most effective method to do so, is worthy of investigation; based upon this initial data we encourage and are undertaking a larger‐scale study and exploration of the effects of enhanced hospitalist charge‐awareness.

Conclusion

Hospitalists have low awareness of the charges associated with inpatient care. The opacity of hospital care pricing to patient populations extends also to hospitalist physicians. Hospitalists likely do not improve cost‐efficiency through expert knowledge of services' costs to patients. Education and reminder systems to apprise hospitalists of charges should be examined as possible tools to optimize the use of inpatient care resources.

References
  1. Kaluga ME,Charney P , et al.The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19(4):293201.
  2. Allan GM,Lexchin J,Wiebe N.Physician awareness of drug cost: a systematic review.PLoS Med.2007;4(9):e283.
  3. Hampers LC,Cha S,Gutglass DJ,Krug SE,Binns HJ.The effect of price information on test‐ordering behavior and patient outcomes in a pediatric emergency department.Pediatrics.1999;103(4):877882.
  4. Tierney WM,Miller EM,McDonald CJ.The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests.N Engl J Med1990;322:14991504.
  5. Miyakis S,Karamanog G,Liontos M,Mountokalakis TD.Factors contributing to inappropriate ordering of tests in an academic medical department and the effect of an educational feedback strategy.Postgrad Med J.2006;82:823829.
  6. David W. Bates,Gilad J. Kuperman,Jha Ashish, et al.Tanasijevic does the computerized display of charges affect inpatient ancillary test utilization?Arch Intern Med.1997;157(21):25012508.
  7. Coffman J,Rundall T.The impact of hospitalists on the cost and quality of inpatient care in the united states: a research synthesis.Med Care Res Rev.2005;62:379406.
  8. Everett G,Uddin N,Rudloff B.Comparison of hospital costs and length of stay for community internists, hospitalists, and academicians.J Gen Intern Med.2007;22:662667.
  9. Lindenauer P,Rothberg MB,Pekow PS,Kenwood C,Benjamin EM,Auerbach AD.Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357(25):25892600.
  10. American Medical Association, CPT and RVU Search utility. Available at: https://catalog.ama‐assn.org/Catalog/cpt/cpt_search.jsp. Accessed December 2009.
  11. Austin DA,Gravelle JG.Does price tranparency improve market efficiency? Implications of empirical evidence in other markets for the health sector. 2007, Congressional Research Service Report RL34101. Available at: http://ftp.fas.org/sgp/crs/secrecy/RL34101. Accessed December2009.
  12. Reinhardt UE.The pricing of US hospital services.Health Aff.2006;25(1):5769.
References
  1. Kaluga ME,Charney P , et al.The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19(4):293201.
  2. Allan GM,Lexchin J,Wiebe N.Physician awareness of drug cost: a systematic review.PLoS Med.2007;4(9):e283.
  3. Hampers LC,Cha S,Gutglass DJ,Krug SE,Binns HJ.The effect of price information on test‐ordering behavior and patient outcomes in a pediatric emergency department.Pediatrics.1999;103(4):877882.
  4. Tierney WM,Miller EM,McDonald CJ.The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests.N Engl J Med1990;322:14991504.
  5. Miyakis S,Karamanog G,Liontos M,Mountokalakis TD.Factors contributing to inappropriate ordering of tests in an academic medical department and the effect of an educational feedback strategy.Postgrad Med J.2006;82:823829.
  6. David W. Bates,Gilad J. Kuperman,Jha Ashish, et al.Tanasijevic does the computerized display of charges affect inpatient ancillary test utilization?Arch Intern Med.1997;157(21):25012508.
  7. Coffman J,Rundall T.The impact of hospitalists on the cost and quality of inpatient care in the united states: a research synthesis.Med Care Res Rev.2005;62:379406.
  8. Everett G,Uddin N,Rudloff B.Comparison of hospital costs and length of stay for community internists, hospitalists, and academicians.J Gen Intern Med.2007;22:662667.
  9. Lindenauer P,Rothberg MB,Pekow PS,Kenwood C,Benjamin EM,Auerbach AD.Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357(25):25892600.
  10. American Medical Association, CPT and RVU Search utility. Available at: https://catalog.ama‐assn.org/Catalog/cpt/cpt_search.jsp. Accessed December 2009.
  11. Austin DA,Gravelle JG.Does price tranparency improve market efficiency? Implications of empirical evidence in other markets for the health sector. 2007, Congressional Research Service Report RL34101. Available at: http://ftp.fas.org/sgp/crs/secrecy/RL34101. Accessed December2009.
  12. Reinhardt UE.The pricing of US hospital services.Health Aff.2006;25(1):5769.
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A Novel Approach to Physician Shortages

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Bleeding talent: A lesson from industry on embracing physician workforce challenges

The demand for physician talent is intensifying as the US healthcare system confronts an unprecedented confluence of demographic pressures. Not only will 78 million retiring baby‐boomers require significant healthcare resources, but tens of thousands of practicing physicians will themselves reach retirement age within the next decade.1 At the same time, factors like the large increase in the percentage of female physicians (who are more likely to work part time), the growth of nonpractice opportunities for MDs, and generational demands for greater worklife balance are creating a major supply‐demand mismatch within the physician workforce.2 In this demographic atmosphere, the ability to recruit and retain physician leaders confers tremendous value to healthcare enterprisesboth public and private. Recruiting and retaining strategies already weigh heavily in the most palpable shortage areas, like primary care, but the system faces widespread unmet demand for a variety of specialist and generalist practitioners.36

This article does not address the public policy implications of the upcoming physician shortage, recognition of which will lead to the largest increase in new medical school slots in decades.7 Rather, we set out to illustrate how successful nonmedical businesses are embracing a thoughtful, systematic approach to retaining talent, based on the philosophy that keeping and engaging valued employees is more efficient than recruiting and orienting replacements. We posit that the innovations used by progressive companies could apply to recruitment and retention challenges confronting medicine.

How Industry Approaches the Talent Vacuum: Talent Facilitation

Leaders outside of medicine have long acknowledged that changing demographics and a global economy are driving unprecedented employee turnover.8 In confronting a talent vacuum, forward‐thinking managers have prioritized retaining key talent (rather than hiring anew) in planning for the future.9, 10 Doing so begins with attempts to understand the relationship between workers and the workplace, with a particular emphasis on appreciating workers' priorities. Indeed, new executive positions with titles like Chief Learning Officer and Chief Experience Officer are appearing as companies realize a need for focused expertise beyond traditional human resource departments. These companies understand that offering higher salaries is not the only retention strategyand often not even the most effective one.

The Four Actions of Talent Facilitation

The talent facilitation process centers on four actions: attract, engage, develop, and retain. None of these actions can stand alone, and all should be present, to some degree, at all stages of a worker's tenure. To attract or engage an employee or practice partner is not a 1‐time hook, but a constant and dynamic process.

Importantly, the concepts addressed here are not specific to 1 type of corporate system, size, or management level. Although the early business focus had been on upper‐level and executive talent within large corporate settings, there is an increasing recognition that a dedicated talent strategy is useful wherever recruiting and retaining talented people is important (and where is it not?).

The ideas presented here may seem most applicable to leaders of large physician corporations, hospital‐owned physician groups, or large integrated healthcare systems (such as Kaiser) that employ physicians. However, we also believe that the ideas apply across‐the‐board in medicine, including entities such as small, private, physician‐owned groups. We argue that regardless of the exact practice structure, a limited pool of resources must be dedicated to the attraction and retention of talented partners or employees, or to the cost of replacing those people if they pursue other opportunities (or the cost of inefficient and disengaged physicians). While an integrated health system may have the resources and scale to hire a Chief Experience Officer, we do not anticipate that a 5‐partner private practice would. Rather, we point to examples to illustrate the talent facilitation paradigm as a tool to systematically frame the allocation of those resources. Undoubtedly, the specific shape of a thoughtful talent facilitation effort will vary when applied in a large urban academic medical center vs. an integrated healthcare system vs. a small physician private practice, but the basic principles remain the same.

Attract

Increasingly, companies approach talented prospects with dedicated marketing campaigns to convey the value of a work environment.11 Silicon Valley employee lounges with free massages and foosball tables are the iconic example of attraction, but the concept runs deeper. Today's workers seek access to state‐of‐the‐art ideas and technology and often want to be part of a larger vision. Many seek opportunities to integrate their own professional and personal aspirations into a particular job description.

Hospital executives have long recognized the importance of attracting physicians to their facility (after all, the physicians draw patients and thus generate revenue). The traditional approach has surrounded perks, from comfortable doctor's lounges to the latest in surgical technology. But, the stakes seem greater now than before, and successful talent facilitation strategies are going beyond the tried and true.

Clearly, physicians seek financially stable practice settings with historical success. But they may also seek evidence of a defined strategic plan focused on more than mere profitability. Physicians may gravitate to practice environments that endorse progressive movements like the No One Dies Alone campaign.12 Similarly, recognition of movements beyond healthcarea commitment to Leadership in Energy and Environmental Design (LEED) (Green Building Rating System; US Green Building Council [USGBC]; http://www.usgbc.org) designation,13 for examplemay help attract talented staff physicians or enhance a hospital system's ability to facilitate partnerships with desirable physician groups. Within small private practices, this may take the form of a collaborative dedication to physician wellness and burnout prevention, or in a shared commitment to some form of local or international service.

The current recruitment campaign of California's prison healthcare system offers an unlikely source of inspiration. The prison system was placed in receivership to address a shortage of competent physician staff and other inadequacies. A central feature of the campaign is an attractive starting salary and good benefits. But, the campaign does not rely on money alone. For example, the campaign's website (http://changingprisonhealthcare.org) promises that prison physicians will create the standard for correctional healthcare and join an historic effort to make a difference in people's lives (Figure 1). The campaign thus rebrands what might be seen as an unpalatable job into a legitimate and noble career option. Of course, the long‐term success of the campaign will rely on a genuine commitment to the campaign's ideals. But the point is this: if a prison system can deploy innovative attraction strategies, so can healthcare leaders at all levels.

Figure 1
Home page for the California state prison system's campaign to recruit new physicians.

Engage

The corporate tool being employed at this stage is a strategy called on‐boarding, which emphasizes a streamlined integration of newcomers with existing workers and culture, and prioritizes aligning organizational roles with a worker's specific skills and interests. On‐boarding also emphasizes the value of early and frequent provision of constructive feedback from same‐level peers or managers with advanced coaching skills.

Many companies use formal survey tools to measure employee engagement and regularly evaluate the proficiency of system leaders in the ability to engage their employees. An engineering firm executive recently told us (P.K., C.K.) that he performs detailed and frequent on‐the‐job interviews, even with company veterans. The primary goal of these interviews is to ensure that engineers spend at least 85% of their time on work that: (1) they find interesting and (2) allows for the application of their best skills. Wherever possible, traditional job descriptions are altered to achieve this. Inevitably, there is still work (15% in this particular corporate vision) that no one prefers but needs to get done, but this process of active recalibration minimizes this fraction to the degree possible.

Even within a small physician practice group, one can imagine how a strategic approach of inviting and acknowledging individual physician's professional goals and particular talents may challenge the long‐held belief that everyone within a group enjoys and must do the exact same job. Once these goals and talents are articulated, groups may find that allowing for more customized roles within the practice enhances professional satisfaction.

Social networking, collaboration, and sharing of best practices are staples of engaging companies. The Cisco and Qualcomm companies, for example, utilize elaborate e‐networks (rough corporate equivalents to Facebook) to foster collegial interaction within and across traditional hierarchical boundaries so that managers and executives directly engage the ideas of employees at every level.14 The premise is logical: engaged employees will be more likely to contribute innovative ideas which, when listened to, are more likely to engage employees.

Most physicians will recognize the traditional resident report as a model for engagement. Beyond its educational value, interaction with program leadership, social bonding, collaborative effort, and exploration of best practices add tremendous value. Many companies would jump at the chance to engrain a similar cultural staple. Enhancing this type of interaction in a postresidency setting may promote engagement in a given system, especially if it facilitates interactions between physicians and senior hospital leaders. Absent these types of interactions, ensuring regular provision of peer review and/or constructive feedback can help systematically enhance 2‐way communication and enhance engagement.

Develop

Talent development relies on mentorship reflecting a genuine interest in an individual's future. Development strategies include pairing formal annual talent reviews or (in the case of practice partners) formal peer review with strategic development plans. Effective development strategy may include transparent succession planning so that individuals are aware they are being groomed for future roles.

A well‐known adage suggests, People quit the manager or administrator, not the job. Development in this sense relies on presenting new opportunities and knowing that people flourish when allowed to explore multiple paths forward. In many companies, the role of Chief Experience or Chief Learning Officers is to enhance development planning. Consider how career coaching of young hospitalists could transform an infinitely portable and volatile commodity job, prone to burnout, into an engaged specialist of sorts with immense value to a hospital. Hospitalists have already demonstrated their potential as quality improvement leaders.15 Imagine if hospital leadership enlisted a young hospitalist in a relevant quality improvement task force, such as one working on preventing falls. With appropriate support, the physician could obtain skills for quality improvement evaluation that would not only enhance his or her engagement with the hospital system but also provide a valuable analysis for the hospital.

As an example of development strategy within a small practice setting, consider the following real‐life anecdote: a group of 4 physicians recently completed a long and expensive recruitment of a new partner. The new partner, intrigued by the local hospital's surgical robot technology, sought the support of her partners (who are not currently using the technology) to partake in an expensive robotics training program. The partners decided not to provide the financial support. The new partner subsequently left the group for a nearby practice opportunity that would provide for the training, and the group was faced with the loss of a partner (one‐fourth of the practice!) and the cost of repeating the recruiting process. A preemptive evaluation of the value of investing in the development of the new partner and enhancing that partner's professional development may have proven wise despite the significant up‐front costs. In this case the manager the new partner quit was the inflexibility of the practice trajectory.

Retain

The economic incentive to retain talented workers is not subtle. If it was, companies would not be funneling resources into Chief Experience Officers. Likewise, the estimated cost for a medical practice to replace an individual physician is as at least $250,000.16, 17

In retention, as in attraction, salary is only part of the equation.18 People want fair and competitive compensation, and may leave if they are not getting it, but they will not stay (and will not stay engaged) only for a salary bump. Retention is enhanced when workers can advance according to skills and talent, rather than mere tenure. An effective retention policy responds to people's desire to incorporate individual professional goals into their work and allows for people to customize their career rather than simply occupy a job class. Effective retention policy respects worklife balance and recognizes that this balance might look different for 2 people with the same job. It may take the form of positive reinforcement (rather than subtle disdain) for using vacation time or allowing for participation in international service projects. Many literally feel that they need to quit their job in order to take time off or explore other interests.

Worklife balance has been a longstanding issue in medicine, and innovative augmentation strategies may well help retain top talent. Today's successful medical school applicants not only show aptitude in the classroom, they often have many well‐developed nonmedical skills. No one can expect that medical training will somehow convince them to leave everything else behind. Moreover, today's residency graduates, already with Generation Y sensibilities, have completed their entire training under the auspices of the Accreditation Council for Graduate Medical Education (ACGME) duty hours regulations, which has made residents far more comfortable with shift work and defined hours.

At the other end of the generation spectrum, as large numbers of physicians ready for retirement, effective talent facilitation strategies may evaluate how to reoffer medicine as a valid option for senior physicians who still wish to work. Retaining these physicians will require an appreciation of their lifestyle goals, as they will likely find continuing a traditional practice role untenable. A recent survey of orthopedic surgeons 50 years of age or older found that having a part‐time option was a common reason they continued practicing, and that the option to work part‐time would have the most impact on keeping these surgeons working past age 65 years.19 Those working part‐time were doing so in a wide range of practice arrangements including private practice. However, one‐third of those surveyed said a part‐time option was not available to them. Clearly, in an environment of workforce shortages, physician‐leaders must begin to think about worklife balance not only for new doctors but for those considering retirement.

Critics will point out the financial drawbacks in the provision of worklife balance. But the cost may pale in comparison to the cost of replacing physician leaders. Moreover, engaged physicians are more likely to add value in the form of intangible capital such as patient satisfaction and practice innovation. As such, we argue that effective retention strategy in medicine is likely to be cost‐effective, even if it requires significant new up‐front resources.

Lessons From Industry

Doctors frequently assume that the challenges and obstacles confronted in healthcare are unique to medicine. But, for every phrase like When I started practice, I decided how long office visits were, not the insurance company, or Young doctors just don't want to work as hard, there is a parallel utterance in the greater business world. Luckily, there are now examples of the healthcare world learning lessons from business. For example, innovators in medical quality improvement found value in the experience of other industries.20 Airlines and automakers have long honed systems for error prevention and possess expertise that may curb errors in the hospital.2123

We suspect that the ideas and practice of talent facilitation have already made their way into some medical settings. A Google search reveals multiple opportunities for hospital‐based talent managers, and websites advertise the availability of talent consultants ready to lend their expertise to the medical world. In the arena of academic medicine, the University of California, San Francisco (UCSF) Division of Hospital Medicine put some of the ideas of talent facilitation into practice over the past year, in part in response to an increasingly competitive market for academic hospitalists.24 Leaders introduced a formal faculty development program that links junior faculty with mentors and facilitates early and frequent feedback across hierarchical boundaries.25 These more intentional mentoring efforts were accompanied by a seminar series aimed at the needs of new faculty members, a research incubator program, divisional grand rounds, and other web‐based and in‐person forums for sharing best practices and innovations. Less formal social events have also been promoted. Importantly, these sweeping strategies seek to encompass the needs of both teaching and nonteaching hospitalists within UCSF.26

Clearly, an academic hospitalist group with 45 faculty physicians has unique characteristics that inform the specifics of its talent facilitation strategy. The interventions discussed above are meant to represent examples of the types of strategies that may be utilized by physician groups once a decision is made to focus on talent management. Undoubtedly, the shape of such efforts will vary in diverse practice settings, but physician leaders have much to gain through further exploration of where these core principles already exist within medicine and where they may be more effectively deployed. By examining how multinational businesses are systematically applying the concepts of talent facilitation to address a global talent shortage, the doctoring profession might again take an outside hint to help inform its future.

References
  1. Long Term Care: Aging Baby Boom Generation Will Increase Demand and Burden on Federal and State Budgets. United States General Accounting Office Testimony before the Special Committee on Aging, US Senate. Hearing Before the Special Committee on Aging of the US Senate,2002. Available at:http://www.gao.gov/new.items/d02544t.pdf. Accessed July 2009.
  2. Salsberg E,Grover A.Physician workforce shortages: implications and issues for academic health centers and policymakers.Acad Med.2006;81(9):782787.
  3. Devi S.New York moves to tackle shortage of primary‐care doctors.Lancet.2008;371(9615):801802.
  4. Kimball AB,Resneck JS.The US dermatology workforce: a specialty remains in shortage.J Am Acad Dermatol.2008;59(5):741745.
  5. Brown AJ,Friedman AH.Challenges and opportunities for recruiting a new generation of neurosurgeons.Neurosurgery.2007;61(6):13141319.
  6. Cofer JB,Burns RP.The developing crisis in the national general surgery workforce.J Am Coll Surg.2008;206(5):790795.
  7. Medical School Enrollment Plans: Analysis of the 2007 AAMC Survey. Publication of the Association of American Medical Colleges, Center for Workforce Studies, April2008. Available at:http://www.aamc.org/workforce. Accessed July 2009.
  8. It's 2008: Do You Know Where Your Talent Is? Why acquisition and retention strategies don't work. Part 1 of a Deloitte Research Series on Talent Management.2008. Available at: http://www.deloitte.com/dtt/cda/content/UKConsulting_TalentMgtResearchReport.pdf. Accessed August 2009.
  9. Frank FD,Finnegan RP,Taylor CR.The race for talent: retaining and engaging workers in the 21st century.Hum Resour Plann.2004;27(3):1225.
  10. Expecting sales growth, CEOs cite worker retention as critical to success. March 1,2004. Available at:http://www.barometersurveys.com/production/barsurv.nsf/89343582e94adb6185256b84006c8ffe/9672ab2f54cf99f885256e5500768232?OpenDocument. Accessed July 2009.
  11. Jet Blue announces aviation university gateway program for pilot candidates: airline partners with Embry‐Riddle Aeronautical University, University of North Dakota, and Cape Air to fill pilot pipeline. January 30, 2008. Available at:http://investor.jetblue.com/phoenix.zhtml?c=131045287(4):487494.
  12. Mirsa‐Hebert AD,Kay R,Stoller JK.A review of physician turnover: rates, causes, and consequences.Am J Med Qual.2004;19(2):5666.
  13. Atkinson W,Mirsa‐Hebert A,Stoller JK.The impact on revenue of physician turnover: an assessment model and experience in a large healthcare center.J Med Pract Manage.2006;21(6):351355.
  14. Nohria N,Groysberg B,Lee LE.Employee motivation: a powerful new model.Harv Bus Rev.2008;86(7–8):78,84,160.
  15. Farley FA,Kramer J,Watkins‐Castillo S.Work satisfaction and retirement plans of orthopaedic surgeons 50 years of age or older.Clin Orthop Relat Res.2008;466(1):231238.
  16. Longo DR,Hewett JE,Ge B,Schubert S.The long road to patient safety: a status report on patient safety systems.JAMA.2005(22);294:28582865.
  17. Barker J.Error reduction through team leadership: what surgeons can learn from the airline industry.Clin Neurosurg.2007;54:195199.
  18. Furman C,Caplan R.Applying the Toyota production system: using a patient safety alert system to reduce error.Jt Comm J Qual Patient Saf.2007;33(7):376386.
  19. Raab SS,Andrew‐Jaja C,Condel JL,Dabbs DJ.Improving Papanikolaou test quality and reducing medical errors by using Toyota production system methods.Am J Obstet Gynecol.2006;194(1):5764.
  20. Society of Hospital Medicine Career Satisfaction Task Force. White Paper on Hospitalist Career Satisfaction.2006; 1–45. Available at: http://www.hospitalmedicine.org. Accessed July 2009.
  21. UCSF Department of Medicine, Division of Hospital Medicine, Faculty Development. Available at: http://hospsrvr.ucsf.edu/cme/fds.html. Accessed July 2009.
  22. Sehgal NL,Shah HM,Parekh V, et al. Non‐housestaff medicine services in academic centers: models and challenges.J Hosp Med.2008;3(3):247245.
Article PDF
Issue
Journal of Hospital Medicine - 5(5)
Page Number
306-310
Legacy Keywords
physician recruitment, physician shortage, talent facilitation, talent management
Sections
Article PDF
Article PDF

The demand for physician talent is intensifying as the US healthcare system confronts an unprecedented confluence of demographic pressures. Not only will 78 million retiring baby‐boomers require significant healthcare resources, but tens of thousands of practicing physicians will themselves reach retirement age within the next decade.1 At the same time, factors like the large increase in the percentage of female physicians (who are more likely to work part time), the growth of nonpractice opportunities for MDs, and generational demands for greater worklife balance are creating a major supply‐demand mismatch within the physician workforce.2 In this demographic atmosphere, the ability to recruit and retain physician leaders confers tremendous value to healthcare enterprisesboth public and private. Recruiting and retaining strategies already weigh heavily in the most palpable shortage areas, like primary care, but the system faces widespread unmet demand for a variety of specialist and generalist practitioners.36

This article does not address the public policy implications of the upcoming physician shortage, recognition of which will lead to the largest increase in new medical school slots in decades.7 Rather, we set out to illustrate how successful nonmedical businesses are embracing a thoughtful, systematic approach to retaining talent, based on the philosophy that keeping and engaging valued employees is more efficient than recruiting and orienting replacements. We posit that the innovations used by progressive companies could apply to recruitment and retention challenges confronting medicine.

How Industry Approaches the Talent Vacuum: Talent Facilitation

Leaders outside of medicine have long acknowledged that changing demographics and a global economy are driving unprecedented employee turnover.8 In confronting a talent vacuum, forward‐thinking managers have prioritized retaining key talent (rather than hiring anew) in planning for the future.9, 10 Doing so begins with attempts to understand the relationship between workers and the workplace, with a particular emphasis on appreciating workers' priorities. Indeed, new executive positions with titles like Chief Learning Officer and Chief Experience Officer are appearing as companies realize a need for focused expertise beyond traditional human resource departments. These companies understand that offering higher salaries is not the only retention strategyand often not even the most effective one.

The Four Actions of Talent Facilitation

The talent facilitation process centers on four actions: attract, engage, develop, and retain. None of these actions can stand alone, and all should be present, to some degree, at all stages of a worker's tenure. To attract or engage an employee or practice partner is not a 1‐time hook, but a constant and dynamic process.

Importantly, the concepts addressed here are not specific to 1 type of corporate system, size, or management level. Although the early business focus had been on upper‐level and executive talent within large corporate settings, there is an increasing recognition that a dedicated talent strategy is useful wherever recruiting and retaining talented people is important (and where is it not?).

The ideas presented here may seem most applicable to leaders of large physician corporations, hospital‐owned physician groups, or large integrated healthcare systems (such as Kaiser) that employ physicians. However, we also believe that the ideas apply across‐the‐board in medicine, including entities such as small, private, physician‐owned groups. We argue that regardless of the exact practice structure, a limited pool of resources must be dedicated to the attraction and retention of talented partners or employees, or to the cost of replacing those people if they pursue other opportunities (or the cost of inefficient and disengaged physicians). While an integrated health system may have the resources and scale to hire a Chief Experience Officer, we do not anticipate that a 5‐partner private practice would. Rather, we point to examples to illustrate the talent facilitation paradigm as a tool to systematically frame the allocation of those resources. Undoubtedly, the specific shape of a thoughtful talent facilitation effort will vary when applied in a large urban academic medical center vs. an integrated healthcare system vs. a small physician private practice, but the basic principles remain the same.

Attract

Increasingly, companies approach talented prospects with dedicated marketing campaigns to convey the value of a work environment.11 Silicon Valley employee lounges with free massages and foosball tables are the iconic example of attraction, but the concept runs deeper. Today's workers seek access to state‐of‐the‐art ideas and technology and often want to be part of a larger vision. Many seek opportunities to integrate their own professional and personal aspirations into a particular job description.

Hospital executives have long recognized the importance of attracting physicians to their facility (after all, the physicians draw patients and thus generate revenue). The traditional approach has surrounded perks, from comfortable doctor's lounges to the latest in surgical technology. But, the stakes seem greater now than before, and successful talent facilitation strategies are going beyond the tried and true.

Clearly, physicians seek financially stable practice settings with historical success. But they may also seek evidence of a defined strategic plan focused on more than mere profitability. Physicians may gravitate to practice environments that endorse progressive movements like the No One Dies Alone campaign.12 Similarly, recognition of movements beyond healthcarea commitment to Leadership in Energy and Environmental Design (LEED) (Green Building Rating System; US Green Building Council [USGBC]; http://www.usgbc.org) designation,13 for examplemay help attract talented staff physicians or enhance a hospital system's ability to facilitate partnerships with desirable physician groups. Within small private practices, this may take the form of a collaborative dedication to physician wellness and burnout prevention, or in a shared commitment to some form of local or international service.

The current recruitment campaign of California's prison healthcare system offers an unlikely source of inspiration. The prison system was placed in receivership to address a shortage of competent physician staff and other inadequacies. A central feature of the campaign is an attractive starting salary and good benefits. But, the campaign does not rely on money alone. For example, the campaign's website (http://changingprisonhealthcare.org) promises that prison physicians will create the standard for correctional healthcare and join an historic effort to make a difference in people's lives (Figure 1). The campaign thus rebrands what might be seen as an unpalatable job into a legitimate and noble career option. Of course, the long‐term success of the campaign will rely on a genuine commitment to the campaign's ideals. But the point is this: if a prison system can deploy innovative attraction strategies, so can healthcare leaders at all levels.

Figure 1
Home page for the California state prison system's campaign to recruit new physicians.

Engage

The corporate tool being employed at this stage is a strategy called on‐boarding, which emphasizes a streamlined integration of newcomers with existing workers and culture, and prioritizes aligning organizational roles with a worker's specific skills and interests. On‐boarding also emphasizes the value of early and frequent provision of constructive feedback from same‐level peers or managers with advanced coaching skills.

Many companies use formal survey tools to measure employee engagement and regularly evaluate the proficiency of system leaders in the ability to engage their employees. An engineering firm executive recently told us (P.K., C.K.) that he performs detailed and frequent on‐the‐job interviews, even with company veterans. The primary goal of these interviews is to ensure that engineers spend at least 85% of their time on work that: (1) they find interesting and (2) allows for the application of their best skills. Wherever possible, traditional job descriptions are altered to achieve this. Inevitably, there is still work (15% in this particular corporate vision) that no one prefers but needs to get done, but this process of active recalibration minimizes this fraction to the degree possible.

Even within a small physician practice group, one can imagine how a strategic approach of inviting and acknowledging individual physician's professional goals and particular talents may challenge the long‐held belief that everyone within a group enjoys and must do the exact same job. Once these goals and talents are articulated, groups may find that allowing for more customized roles within the practice enhances professional satisfaction.

Social networking, collaboration, and sharing of best practices are staples of engaging companies. The Cisco and Qualcomm companies, for example, utilize elaborate e‐networks (rough corporate equivalents to Facebook) to foster collegial interaction within and across traditional hierarchical boundaries so that managers and executives directly engage the ideas of employees at every level.14 The premise is logical: engaged employees will be more likely to contribute innovative ideas which, when listened to, are more likely to engage employees.

Most physicians will recognize the traditional resident report as a model for engagement. Beyond its educational value, interaction with program leadership, social bonding, collaborative effort, and exploration of best practices add tremendous value. Many companies would jump at the chance to engrain a similar cultural staple. Enhancing this type of interaction in a postresidency setting may promote engagement in a given system, especially if it facilitates interactions between physicians and senior hospital leaders. Absent these types of interactions, ensuring regular provision of peer review and/or constructive feedback can help systematically enhance 2‐way communication and enhance engagement.

Develop

Talent development relies on mentorship reflecting a genuine interest in an individual's future. Development strategies include pairing formal annual talent reviews or (in the case of practice partners) formal peer review with strategic development plans. Effective development strategy may include transparent succession planning so that individuals are aware they are being groomed for future roles.

A well‐known adage suggests, People quit the manager or administrator, not the job. Development in this sense relies on presenting new opportunities and knowing that people flourish when allowed to explore multiple paths forward. In many companies, the role of Chief Experience or Chief Learning Officers is to enhance development planning. Consider how career coaching of young hospitalists could transform an infinitely portable and volatile commodity job, prone to burnout, into an engaged specialist of sorts with immense value to a hospital. Hospitalists have already demonstrated their potential as quality improvement leaders.15 Imagine if hospital leadership enlisted a young hospitalist in a relevant quality improvement task force, such as one working on preventing falls. With appropriate support, the physician could obtain skills for quality improvement evaluation that would not only enhance his or her engagement with the hospital system but also provide a valuable analysis for the hospital.

As an example of development strategy within a small practice setting, consider the following real‐life anecdote: a group of 4 physicians recently completed a long and expensive recruitment of a new partner. The new partner, intrigued by the local hospital's surgical robot technology, sought the support of her partners (who are not currently using the technology) to partake in an expensive robotics training program. The partners decided not to provide the financial support. The new partner subsequently left the group for a nearby practice opportunity that would provide for the training, and the group was faced with the loss of a partner (one‐fourth of the practice!) and the cost of repeating the recruiting process. A preemptive evaluation of the value of investing in the development of the new partner and enhancing that partner's professional development may have proven wise despite the significant up‐front costs. In this case the manager the new partner quit was the inflexibility of the practice trajectory.

Retain

The economic incentive to retain talented workers is not subtle. If it was, companies would not be funneling resources into Chief Experience Officers. Likewise, the estimated cost for a medical practice to replace an individual physician is as at least $250,000.16, 17

In retention, as in attraction, salary is only part of the equation.18 People want fair and competitive compensation, and may leave if they are not getting it, but they will not stay (and will not stay engaged) only for a salary bump. Retention is enhanced when workers can advance according to skills and talent, rather than mere tenure. An effective retention policy responds to people's desire to incorporate individual professional goals into their work and allows for people to customize their career rather than simply occupy a job class. Effective retention policy respects worklife balance and recognizes that this balance might look different for 2 people with the same job. It may take the form of positive reinforcement (rather than subtle disdain) for using vacation time or allowing for participation in international service projects. Many literally feel that they need to quit their job in order to take time off or explore other interests.

Worklife balance has been a longstanding issue in medicine, and innovative augmentation strategies may well help retain top talent. Today's successful medical school applicants not only show aptitude in the classroom, they often have many well‐developed nonmedical skills. No one can expect that medical training will somehow convince them to leave everything else behind. Moreover, today's residency graduates, already with Generation Y sensibilities, have completed their entire training under the auspices of the Accreditation Council for Graduate Medical Education (ACGME) duty hours regulations, which has made residents far more comfortable with shift work and defined hours.

At the other end of the generation spectrum, as large numbers of physicians ready for retirement, effective talent facilitation strategies may evaluate how to reoffer medicine as a valid option for senior physicians who still wish to work. Retaining these physicians will require an appreciation of their lifestyle goals, as they will likely find continuing a traditional practice role untenable. A recent survey of orthopedic surgeons 50 years of age or older found that having a part‐time option was a common reason they continued practicing, and that the option to work part‐time would have the most impact on keeping these surgeons working past age 65 years.19 Those working part‐time were doing so in a wide range of practice arrangements including private practice. However, one‐third of those surveyed said a part‐time option was not available to them. Clearly, in an environment of workforce shortages, physician‐leaders must begin to think about worklife balance not only for new doctors but for those considering retirement.

Critics will point out the financial drawbacks in the provision of worklife balance. But the cost may pale in comparison to the cost of replacing physician leaders. Moreover, engaged physicians are more likely to add value in the form of intangible capital such as patient satisfaction and practice innovation. As such, we argue that effective retention strategy in medicine is likely to be cost‐effective, even if it requires significant new up‐front resources.

Lessons From Industry

Doctors frequently assume that the challenges and obstacles confronted in healthcare are unique to medicine. But, for every phrase like When I started practice, I decided how long office visits were, not the insurance company, or Young doctors just don't want to work as hard, there is a parallel utterance in the greater business world. Luckily, there are now examples of the healthcare world learning lessons from business. For example, innovators in medical quality improvement found value in the experience of other industries.20 Airlines and automakers have long honed systems for error prevention and possess expertise that may curb errors in the hospital.2123

We suspect that the ideas and practice of talent facilitation have already made their way into some medical settings. A Google search reveals multiple opportunities for hospital‐based talent managers, and websites advertise the availability of talent consultants ready to lend their expertise to the medical world. In the arena of academic medicine, the University of California, San Francisco (UCSF) Division of Hospital Medicine put some of the ideas of talent facilitation into practice over the past year, in part in response to an increasingly competitive market for academic hospitalists.24 Leaders introduced a formal faculty development program that links junior faculty with mentors and facilitates early and frequent feedback across hierarchical boundaries.25 These more intentional mentoring efforts were accompanied by a seminar series aimed at the needs of new faculty members, a research incubator program, divisional grand rounds, and other web‐based and in‐person forums for sharing best practices and innovations. Less formal social events have also been promoted. Importantly, these sweeping strategies seek to encompass the needs of both teaching and nonteaching hospitalists within UCSF.26

Clearly, an academic hospitalist group with 45 faculty physicians has unique characteristics that inform the specifics of its talent facilitation strategy. The interventions discussed above are meant to represent examples of the types of strategies that may be utilized by physician groups once a decision is made to focus on talent management. Undoubtedly, the shape of such efforts will vary in diverse practice settings, but physician leaders have much to gain through further exploration of where these core principles already exist within medicine and where they may be more effectively deployed. By examining how multinational businesses are systematically applying the concepts of talent facilitation to address a global talent shortage, the doctoring profession might again take an outside hint to help inform its future.

The demand for physician talent is intensifying as the US healthcare system confronts an unprecedented confluence of demographic pressures. Not only will 78 million retiring baby‐boomers require significant healthcare resources, but tens of thousands of practicing physicians will themselves reach retirement age within the next decade.1 At the same time, factors like the large increase in the percentage of female physicians (who are more likely to work part time), the growth of nonpractice opportunities for MDs, and generational demands for greater worklife balance are creating a major supply‐demand mismatch within the physician workforce.2 In this demographic atmosphere, the ability to recruit and retain physician leaders confers tremendous value to healthcare enterprisesboth public and private. Recruiting and retaining strategies already weigh heavily in the most palpable shortage areas, like primary care, but the system faces widespread unmet demand for a variety of specialist and generalist practitioners.36

This article does not address the public policy implications of the upcoming physician shortage, recognition of which will lead to the largest increase in new medical school slots in decades.7 Rather, we set out to illustrate how successful nonmedical businesses are embracing a thoughtful, systematic approach to retaining talent, based on the philosophy that keeping and engaging valued employees is more efficient than recruiting and orienting replacements. We posit that the innovations used by progressive companies could apply to recruitment and retention challenges confronting medicine.

How Industry Approaches the Talent Vacuum: Talent Facilitation

Leaders outside of medicine have long acknowledged that changing demographics and a global economy are driving unprecedented employee turnover.8 In confronting a talent vacuum, forward‐thinking managers have prioritized retaining key talent (rather than hiring anew) in planning for the future.9, 10 Doing so begins with attempts to understand the relationship between workers and the workplace, with a particular emphasis on appreciating workers' priorities. Indeed, new executive positions with titles like Chief Learning Officer and Chief Experience Officer are appearing as companies realize a need for focused expertise beyond traditional human resource departments. These companies understand that offering higher salaries is not the only retention strategyand often not even the most effective one.

The Four Actions of Talent Facilitation

The talent facilitation process centers on four actions: attract, engage, develop, and retain. None of these actions can stand alone, and all should be present, to some degree, at all stages of a worker's tenure. To attract or engage an employee or practice partner is not a 1‐time hook, but a constant and dynamic process.

Importantly, the concepts addressed here are not specific to 1 type of corporate system, size, or management level. Although the early business focus had been on upper‐level and executive talent within large corporate settings, there is an increasing recognition that a dedicated talent strategy is useful wherever recruiting and retaining talented people is important (and where is it not?).

The ideas presented here may seem most applicable to leaders of large physician corporations, hospital‐owned physician groups, or large integrated healthcare systems (such as Kaiser) that employ physicians. However, we also believe that the ideas apply across‐the‐board in medicine, including entities such as small, private, physician‐owned groups. We argue that regardless of the exact practice structure, a limited pool of resources must be dedicated to the attraction and retention of talented partners or employees, or to the cost of replacing those people if they pursue other opportunities (or the cost of inefficient and disengaged physicians). While an integrated health system may have the resources and scale to hire a Chief Experience Officer, we do not anticipate that a 5‐partner private practice would. Rather, we point to examples to illustrate the talent facilitation paradigm as a tool to systematically frame the allocation of those resources. Undoubtedly, the specific shape of a thoughtful talent facilitation effort will vary when applied in a large urban academic medical center vs. an integrated healthcare system vs. a small physician private practice, but the basic principles remain the same.

Attract

Increasingly, companies approach talented prospects with dedicated marketing campaigns to convey the value of a work environment.11 Silicon Valley employee lounges with free massages and foosball tables are the iconic example of attraction, but the concept runs deeper. Today's workers seek access to state‐of‐the‐art ideas and technology and often want to be part of a larger vision. Many seek opportunities to integrate their own professional and personal aspirations into a particular job description.

Hospital executives have long recognized the importance of attracting physicians to their facility (after all, the physicians draw patients and thus generate revenue). The traditional approach has surrounded perks, from comfortable doctor's lounges to the latest in surgical technology. But, the stakes seem greater now than before, and successful talent facilitation strategies are going beyond the tried and true.

Clearly, physicians seek financially stable practice settings with historical success. But they may also seek evidence of a defined strategic plan focused on more than mere profitability. Physicians may gravitate to practice environments that endorse progressive movements like the No One Dies Alone campaign.12 Similarly, recognition of movements beyond healthcarea commitment to Leadership in Energy and Environmental Design (LEED) (Green Building Rating System; US Green Building Council [USGBC]; http://www.usgbc.org) designation,13 for examplemay help attract talented staff physicians or enhance a hospital system's ability to facilitate partnerships with desirable physician groups. Within small private practices, this may take the form of a collaborative dedication to physician wellness and burnout prevention, or in a shared commitment to some form of local or international service.

The current recruitment campaign of California's prison healthcare system offers an unlikely source of inspiration. The prison system was placed in receivership to address a shortage of competent physician staff and other inadequacies. A central feature of the campaign is an attractive starting salary and good benefits. But, the campaign does not rely on money alone. For example, the campaign's website (http://changingprisonhealthcare.org) promises that prison physicians will create the standard for correctional healthcare and join an historic effort to make a difference in people's lives (Figure 1). The campaign thus rebrands what might be seen as an unpalatable job into a legitimate and noble career option. Of course, the long‐term success of the campaign will rely on a genuine commitment to the campaign's ideals. But the point is this: if a prison system can deploy innovative attraction strategies, so can healthcare leaders at all levels.

Figure 1
Home page for the California state prison system's campaign to recruit new physicians.

Engage

The corporate tool being employed at this stage is a strategy called on‐boarding, which emphasizes a streamlined integration of newcomers with existing workers and culture, and prioritizes aligning organizational roles with a worker's specific skills and interests. On‐boarding also emphasizes the value of early and frequent provision of constructive feedback from same‐level peers or managers with advanced coaching skills.

Many companies use formal survey tools to measure employee engagement and regularly evaluate the proficiency of system leaders in the ability to engage their employees. An engineering firm executive recently told us (P.K., C.K.) that he performs detailed and frequent on‐the‐job interviews, even with company veterans. The primary goal of these interviews is to ensure that engineers spend at least 85% of their time on work that: (1) they find interesting and (2) allows for the application of their best skills. Wherever possible, traditional job descriptions are altered to achieve this. Inevitably, there is still work (15% in this particular corporate vision) that no one prefers but needs to get done, but this process of active recalibration minimizes this fraction to the degree possible.

Even within a small physician practice group, one can imagine how a strategic approach of inviting and acknowledging individual physician's professional goals and particular talents may challenge the long‐held belief that everyone within a group enjoys and must do the exact same job. Once these goals and talents are articulated, groups may find that allowing for more customized roles within the practice enhances professional satisfaction.

Social networking, collaboration, and sharing of best practices are staples of engaging companies. The Cisco and Qualcomm companies, for example, utilize elaborate e‐networks (rough corporate equivalents to Facebook) to foster collegial interaction within and across traditional hierarchical boundaries so that managers and executives directly engage the ideas of employees at every level.14 The premise is logical: engaged employees will be more likely to contribute innovative ideas which, when listened to, are more likely to engage employees.

Most physicians will recognize the traditional resident report as a model for engagement. Beyond its educational value, interaction with program leadership, social bonding, collaborative effort, and exploration of best practices add tremendous value. Many companies would jump at the chance to engrain a similar cultural staple. Enhancing this type of interaction in a postresidency setting may promote engagement in a given system, especially if it facilitates interactions between physicians and senior hospital leaders. Absent these types of interactions, ensuring regular provision of peer review and/or constructive feedback can help systematically enhance 2‐way communication and enhance engagement.

Develop

Talent development relies on mentorship reflecting a genuine interest in an individual's future. Development strategies include pairing formal annual talent reviews or (in the case of practice partners) formal peer review with strategic development plans. Effective development strategy may include transparent succession planning so that individuals are aware they are being groomed for future roles.

A well‐known adage suggests, People quit the manager or administrator, not the job. Development in this sense relies on presenting new opportunities and knowing that people flourish when allowed to explore multiple paths forward. In many companies, the role of Chief Experience or Chief Learning Officers is to enhance development planning. Consider how career coaching of young hospitalists could transform an infinitely portable and volatile commodity job, prone to burnout, into an engaged specialist of sorts with immense value to a hospital. Hospitalists have already demonstrated their potential as quality improvement leaders.15 Imagine if hospital leadership enlisted a young hospitalist in a relevant quality improvement task force, such as one working on preventing falls. With appropriate support, the physician could obtain skills for quality improvement evaluation that would not only enhance his or her engagement with the hospital system but also provide a valuable analysis for the hospital.

As an example of development strategy within a small practice setting, consider the following real‐life anecdote: a group of 4 physicians recently completed a long and expensive recruitment of a new partner. The new partner, intrigued by the local hospital's surgical robot technology, sought the support of her partners (who are not currently using the technology) to partake in an expensive robotics training program. The partners decided not to provide the financial support. The new partner subsequently left the group for a nearby practice opportunity that would provide for the training, and the group was faced with the loss of a partner (one‐fourth of the practice!) and the cost of repeating the recruiting process. A preemptive evaluation of the value of investing in the development of the new partner and enhancing that partner's professional development may have proven wise despite the significant up‐front costs. In this case the manager the new partner quit was the inflexibility of the practice trajectory.

Retain

The economic incentive to retain talented workers is not subtle. If it was, companies would not be funneling resources into Chief Experience Officers. Likewise, the estimated cost for a medical practice to replace an individual physician is as at least $250,000.16, 17

In retention, as in attraction, salary is only part of the equation.18 People want fair and competitive compensation, and may leave if they are not getting it, but they will not stay (and will not stay engaged) only for a salary bump. Retention is enhanced when workers can advance according to skills and talent, rather than mere tenure. An effective retention policy responds to people's desire to incorporate individual professional goals into their work and allows for people to customize their career rather than simply occupy a job class. Effective retention policy respects worklife balance and recognizes that this balance might look different for 2 people with the same job. It may take the form of positive reinforcement (rather than subtle disdain) for using vacation time or allowing for participation in international service projects. Many literally feel that they need to quit their job in order to take time off or explore other interests.

Worklife balance has been a longstanding issue in medicine, and innovative augmentation strategies may well help retain top talent. Today's successful medical school applicants not only show aptitude in the classroom, they often have many well‐developed nonmedical skills. No one can expect that medical training will somehow convince them to leave everything else behind. Moreover, today's residency graduates, already with Generation Y sensibilities, have completed their entire training under the auspices of the Accreditation Council for Graduate Medical Education (ACGME) duty hours regulations, which has made residents far more comfortable with shift work and defined hours.

At the other end of the generation spectrum, as large numbers of physicians ready for retirement, effective talent facilitation strategies may evaluate how to reoffer medicine as a valid option for senior physicians who still wish to work. Retaining these physicians will require an appreciation of their lifestyle goals, as they will likely find continuing a traditional practice role untenable. A recent survey of orthopedic surgeons 50 years of age or older found that having a part‐time option was a common reason they continued practicing, and that the option to work part‐time would have the most impact on keeping these surgeons working past age 65 years.19 Those working part‐time were doing so in a wide range of practice arrangements including private practice. However, one‐third of those surveyed said a part‐time option was not available to them. Clearly, in an environment of workforce shortages, physician‐leaders must begin to think about worklife balance not only for new doctors but for those considering retirement.

Critics will point out the financial drawbacks in the provision of worklife balance. But the cost may pale in comparison to the cost of replacing physician leaders. Moreover, engaged physicians are more likely to add value in the form of intangible capital such as patient satisfaction and practice innovation. As such, we argue that effective retention strategy in medicine is likely to be cost‐effective, even if it requires significant new up‐front resources.

Lessons From Industry

Doctors frequently assume that the challenges and obstacles confronted in healthcare are unique to medicine. But, for every phrase like When I started practice, I decided how long office visits were, not the insurance company, or Young doctors just don't want to work as hard, there is a parallel utterance in the greater business world. Luckily, there are now examples of the healthcare world learning lessons from business. For example, innovators in medical quality improvement found value in the experience of other industries.20 Airlines and automakers have long honed systems for error prevention and possess expertise that may curb errors in the hospital.2123

We suspect that the ideas and practice of talent facilitation have already made their way into some medical settings. A Google search reveals multiple opportunities for hospital‐based talent managers, and websites advertise the availability of talent consultants ready to lend their expertise to the medical world. In the arena of academic medicine, the University of California, San Francisco (UCSF) Division of Hospital Medicine put some of the ideas of talent facilitation into practice over the past year, in part in response to an increasingly competitive market for academic hospitalists.24 Leaders introduced a formal faculty development program that links junior faculty with mentors and facilitates early and frequent feedback across hierarchical boundaries.25 These more intentional mentoring efforts were accompanied by a seminar series aimed at the needs of new faculty members, a research incubator program, divisional grand rounds, and other web‐based and in‐person forums for sharing best practices and innovations. Less formal social events have also been promoted. Importantly, these sweeping strategies seek to encompass the needs of both teaching and nonteaching hospitalists within UCSF.26

Clearly, an academic hospitalist group with 45 faculty physicians has unique characteristics that inform the specifics of its talent facilitation strategy. The interventions discussed above are meant to represent examples of the types of strategies that may be utilized by physician groups once a decision is made to focus on talent management. Undoubtedly, the shape of such efforts will vary in diverse practice settings, but physician leaders have much to gain through further exploration of where these core principles already exist within medicine and where they may be more effectively deployed. By examining how multinational businesses are systematically applying the concepts of talent facilitation to address a global talent shortage, the doctoring profession might again take an outside hint to help inform its future.

References
  1. Long Term Care: Aging Baby Boom Generation Will Increase Demand and Burden on Federal and State Budgets. United States General Accounting Office Testimony before the Special Committee on Aging, US Senate. Hearing Before the Special Committee on Aging of the US Senate,2002. Available at:http://www.gao.gov/new.items/d02544t.pdf. Accessed July 2009.
  2. Salsberg E,Grover A.Physician workforce shortages: implications and issues for academic health centers and policymakers.Acad Med.2006;81(9):782787.
  3. Devi S.New York moves to tackle shortage of primary‐care doctors.Lancet.2008;371(9615):801802.
  4. Kimball AB,Resneck JS.The US dermatology workforce: a specialty remains in shortage.J Am Acad Dermatol.2008;59(5):741745.
  5. Brown AJ,Friedman AH.Challenges and opportunities for recruiting a new generation of neurosurgeons.Neurosurgery.2007;61(6):13141319.
  6. Cofer JB,Burns RP.The developing crisis in the national general surgery workforce.J Am Coll Surg.2008;206(5):790795.
  7. Medical School Enrollment Plans: Analysis of the 2007 AAMC Survey. Publication of the Association of American Medical Colleges, Center for Workforce Studies, April2008. Available at:http://www.aamc.org/workforce. Accessed July 2009.
  8. It's 2008: Do You Know Where Your Talent Is? Why acquisition and retention strategies don't work. Part 1 of a Deloitte Research Series on Talent Management.2008. Available at: http://www.deloitte.com/dtt/cda/content/UKConsulting_TalentMgtResearchReport.pdf. Accessed August 2009.
  9. Frank FD,Finnegan RP,Taylor CR.The race for talent: retaining and engaging workers in the 21st century.Hum Resour Plann.2004;27(3):1225.
  10. Expecting sales growth, CEOs cite worker retention as critical to success. March 1,2004. Available at:http://www.barometersurveys.com/production/barsurv.nsf/89343582e94adb6185256b84006c8ffe/9672ab2f54cf99f885256e5500768232?OpenDocument. Accessed July 2009.
  11. Jet Blue announces aviation university gateway program for pilot candidates: airline partners with Embry‐Riddle Aeronautical University, University of North Dakota, and Cape Air to fill pilot pipeline. January 30, 2008. Available at:http://investor.jetblue.com/phoenix.zhtml?c=131045287(4):487494.
  12. Mirsa‐Hebert AD,Kay R,Stoller JK.A review of physician turnover: rates, causes, and consequences.Am J Med Qual.2004;19(2):5666.
  13. Atkinson W,Mirsa‐Hebert A,Stoller JK.The impact on revenue of physician turnover: an assessment model and experience in a large healthcare center.J Med Pract Manage.2006;21(6):351355.
  14. Nohria N,Groysberg B,Lee LE.Employee motivation: a powerful new model.Harv Bus Rev.2008;86(7–8):78,84,160.
  15. Farley FA,Kramer J,Watkins‐Castillo S.Work satisfaction and retirement plans of orthopaedic surgeons 50 years of age or older.Clin Orthop Relat Res.2008;466(1):231238.
  16. Longo DR,Hewett JE,Ge B,Schubert S.The long road to patient safety: a status report on patient safety systems.JAMA.2005(22);294:28582865.
  17. Barker J.Error reduction through team leadership: what surgeons can learn from the airline industry.Clin Neurosurg.2007;54:195199.
  18. Furman C,Caplan R.Applying the Toyota production system: using a patient safety alert system to reduce error.Jt Comm J Qual Patient Saf.2007;33(7):376386.
  19. Raab SS,Andrew‐Jaja C,Condel JL,Dabbs DJ.Improving Papanikolaou test quality and reducing medical errors by using Toyota production system methods.Am J Obstet Gynecol.2006;194(1):5764.
  20. Society of Hospital Medicine Career Satisfaction Task Force. White Paper on Hospitalist Career Satisfaction.2006; 1–45. Available at: http://www.hospitalmedicine.org. Accessed July 2009.
  21. UCSF Department of Medicine, Division of Hospital Medicine, Faculty Development. Available at: http://hospsrvr.ucsf.edu/cme/fds.html. Accessed July 2009.
  22. Sehgal NL,Shah HM,Parekh V, et al. Non‐housestaff medicine services in academic centers: models and challenges.J Hosp Med.2008;3(3):247245.
References
  1. Long Term Care: Aging Baby Boom Generation Will Increase Demand and Burden on Federal and State Budgets. United States General Accounting Office Testimony before the Special Committee on Aging, US Senate. Hearing Before the Special Committee on Aging of the US Senate,2002. Available at:http://www.gao.gov/new.items/d02544t.pdf. Accessed July 2009.
  2. Salsberg E,Grover A.Physician workforce shortages: implications and issues for academic health centers and policymakers.Acad Med.2006;81(9):782787.
  3. Devi S.New York moves to tackle shortage of primary‐care doctors.Lancet.2008;371(9615):801802.
  4. Kimball AB,Resneck JS.The US dermatology workforce: a specialty remains in shortage.J Am Acad Dermatol.2008;59(5):741745.
  5. Brown AJ,Friedman AH.Challenges and opportunities for recruiting a new generation of neurosurgeons.Neurosurgery.2007;61(6):13141319.
  6. Cofer JB,Burns RP.The developing crisis in the national general surgery workforce.J Am Coll Surg.2008;206(5):790795.
  7. Medical School Enrollment Plans: Analysis of the 2007 AAMC Survey. Publication of the Association of American Medical Colleges, Center for Workforce Studies, April2008. Available at:http://www.aamc.org/workforce. Accessed July 2009.
  8. It's 2008: Do You Know Where Your Talent Is? Why acquisition and retention strategies don't work. Part 1 of a Deloitte Research Series on Talent Management.2008. Available at: http://www.deloitte.com/dtt/cda/content/UKConsulting_TalentMgtResearchReport.pdf. Accessed August 2009.
  9. Frank FD,Finnegan RP,Taylor CR.The race for talent: retaining and engaging workers in the 21st century.Hum Resour Plann.2004;27(3):1225.
  10. Expecting sales growth, CEOs cite worker retention as critical to success. March 1,2004. Available at:http://www.barometersurveys.com/production/barsurv.nsf/89343582e94adb6185256b84006c8ffe/9672ab2f54cf99f885256e5500768232?OpenDocument. Accessed July 2009.
  11. Jet Blue announces aviation university gateway program for pilot candidates: airline partners with Embry‐Riddle Aeronautical University, University of North Dakota, and Cape Air to fill pilot pipeline. January 30, 2008. Available at:http://investor.jetblue.com/phoenix.zhtml?c=131045287(4):487494.
  12. Mirsa‐Hebert AD,Kay R,Stoller JK.A review of physician turnover: rates, causes, and consequences.Am J Med Qual.2004;19(2):5666.
  13. Atkinson W,Mirsa‐Hebert A,Stoller JK.The impact on revenue of physician turnover: an assessment model and experience in a large healthcare center.J Med Pract Manage.2006;21(6):351355.
  14. Nohria N,Groysberg B,Lee LE.Employee motivation: a powerful new model.Harv Bus Rev.2008;86(7–8):78,84,160.
  15. Farley FA,Kramer J,Watkins‐Castillo S.Work satisfaction and retirement plans of orthopaedic surgeons 50 years of age or older.Clin Orthop Relat Res.2008;466(1):231238.
  16. Longo DR,Hewett JE,Ge B,Schubert S.The long road to patient safety: a status report on patient safety systems.JAMA.2005(22);294:28582865.
  17. Barker J.Error reduction through team leadership: what surgeons can learn from the airline industry.Clin Neurosurg.2007;54:195199.
  18. Furman C,Caplan R.Applying the Toyota production system: using a patient safety alert system to reduce error.Jt Comm J Qual Patient Saf.2007;33(7):376386.
  19. Raab SS,Andrew‐Jaja C,Condel JL,Dabbs DJ.Improving Papanikolaou test quality and reducing medical errors by using Toyota production system methods.Am J Obstet Gynecol.2006;194(1):5764.
  20. Society of Hospital Medicine Career Satisfaction Task Force. White Paper on Hospitalist Career Satisfaction.2006; 1–45. Available at: http://www.hospitalmedicine.org. Accessed July 2009.
  21. UCSF Department of Medicine, Division of Hospital Medicine, Faculty Development. Available at: http://hospsrvr.ucsf.edu/cme/fds.html. Accessed July 2009.
  22. Sehgal NL,Shah HM,Parekh V, et al. Non‐housestaff medicine services in academic centers: models and challenges.J Hosp Med.2008;3(3):247245.
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Bleeding talent: A lesson from industry on embracing physician workforce challenges
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Macrolides and Quinolones for AECOPD

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Comparative effectiveness of macrolides and quinolones for patients hospitalized with acute exacerbations of chronic obstructive pulmonary disease (AECOPD)

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are responsible for more than 600,000 hospitalizations annually, resulting in direct costs of over $20 billion.1 Bacterial infections appear responsible for 50% of such exacerbations,25 and current COPD guidelines recommend treatment with antibiotics for patients with severe exacerbations or a change in sputum.1,69 These recommendations are based on a number of small randomized trials, most of which were conducted more than 20 years ago using narrow spectrum antibiotics that are no longer commonly used.10 Only 4 studies, totaling 321 subjects, included hospitalized patients, and most studies excluded patients who required steroids. Because no clinical trials have compared different antibiotic regimens for AECOPD, existing guidelines offer a range of treatment options, including amoxicillin‐clavulonate, macrolides, quinolones, cephalosporins, aminopenicillins, and tetracyclines.

Among hospitalized patients, macrolides and quinolones appear to be the most frequently prescribed antibiotics.11 Both are available in oral formulations, have excellent bioavailability, and are administered once daily. In addition to their antimicrobial activity, macrolides are believed to have antiinflammatory effects, which could be especially advantageous in AECOPD.1214 In trials of chronic bronchitis, however, fluoroquinolones have been shown to reduce the risk of recurrent exacerbation when compared to macrolides.15 The wide variation that has been observed in antibiotic selection for patients hospitalized for AECOPD suggests a high degree of uncertainty among clinicians about the benefits of different treatment options.11 Given the limited evidence from randomized trials, we sought to evaluate the comparative effectiveness of macrolides and quinolones among a large, representative sample of patients hospitalized with AECOPD.

Subjects and Methods

Setting and Subjects

We conducted a retrospective cohort study of all patients hospitalized between January 1 and December 31, 2001 for AECOPD at any 1 of 375 acute care facilities in the United States that participated in Premier's Perspective, a voluntary, fee‐supported database developed for measuring quality and healthcare utilization. Participating hospitals represent all geographical regions, and are primarily small‐sized to medium‐sized nonteaching hospitals located mostly in urban areas. In addition to the information contained in the standard hospital discharge file (Uniform Billing 92) such as patient age, International Classification of Disease, 9th Edition, Clinical Modification (ICD‐9‐CM) codes, the Perspective database includes a date‐stamped log of all billed items, including diagnostic tests, medications, and other treatments, as well as costs, for individual patients. The study was approved by the Institutional Review Board of Baystate Medical Center.

Patients were included if they had a primary diagnosis consistent with AECOPD (ICD‐9 codes 491.21 and 493.22) or a primary diagnosis of respiratory failure (ICD‐9 codes 518.81 and 518.84) paired with secondary diagnosis of AECOPD; they also had to receive at least 2 consecutive days of either a macrolide or a quinolone, started within 48 hours of hospitalization. Patients receiving both antibiotics were excluded, but patients who received additional antibiotics were included. To enhance the specificity of our diagnosis codes, we limited our study to patients age 40 years.16 Because mechanical ventilation initiated after hospital day 2 was an outcome measure, we excluded patients admitted directly to the intensive care unit. We also excluded: those with other bacterial infections, such as pneumonia or cellulitis, who might have another indication for antibiotics; those with a length of stay <2 days, because we could not ascertain whether they received a full course of antibiotics; patients with a secondary diagnosis of pulmonary embolism or pneumothorax; and those whose attending physicians were not internists, family practitioners, hospitalists, pulmonologists, or intensivists. For patients with more than 1 admission during the study period, we included only the first admission.

Data Elements

For each patient, we assessed age, gender, race, marital and insurance status, principal diagnosis, comorbidities, and specialty of the attending physician. Comorbidities were identified from ICD‐9 secondary diagnosis codes and Diagnosis Related Groups using Healthcare Cost and Utilization Project Comorbidity Software (version 3.1), based on the work of Elixhauser et al.17 In addition, to assess disease severity we recorded the presence of chronic pulmonary heart disease, the number of admissions for COPD during the 12 months prior to the index admission, and arterial blood gas testing.18,19 We also identified pharmacy or diagnostic charges for interventions that were recommended in current guidelines (beta‐adrenergic and anticholinergic bronchodilators, steroids, and noninvasive positive‐pressure ventilation); those that were not recommended or were of uncertain benefit (methylxanthine bronchodilators, spirometry/pulmonary function testing, mucolytic medications, chest physiotherapy, and sputum testing); and drugs that might be associated with severe exacerbations or end‐stage COPD (loop diuretics, morphine, and nutritional supplements).1,69 Hospitals were categorized by region (Northeast, South, Midwest, or West), bed size, setting (urban vs. rural), ownership, and teaching status.

Antibiotic Class and Outcome Variables

Our primary predictor variable was the antibiotic initiated during the first 2 hospital days and continued for at least 2 days, regardless of other antibiotics the patient may have received during the course of hospitalization. Because we anticipated low in‐hospital mortality, our primary outcome was a composite measure of treatment failure, defined as initiation of mechanical ventilation after hospital day 2, in‐hospital mortality, or readmission for COPD within 30 days of discharge.20 Secondary outcomes included hospital costs and length of stay, as well as allergic reactions identified by ICD‐9 code, and antibiotic‐associated diarrhea, defined as treatment with either metronidazole or oral vancomycin begun after hospital day 3.

Statistical Analysis

Summary statistics were computed using frequencies and percents for categorical variables; and means, medians, standard deviations, and interquartile ranges for continuous variables. Associations between antibiotic selection and patient and hospital characteristics were assessed using chi‐square tests for categorical variables and z‐tests for continuous variables.

We developed a series of multivariable models to evaluate the impact of initial antibiotic selection on the risk of treatment failure, length of stay, and total cost. In order to account for the effects of within‐hospital correlation, generalized estimating equation (GEE) models with a logit link were used to assess the effect of antibiotic selection on the risk of treatment failure, and identity link models were used for analyses of length of stay and cost. Unadjusted and covariate‐adjusted models for treatment failure were evaluated with and without adjustments for propensity score. A propensity score is the probability that a given patient would receive treatment with a macrolide, derived from a nonparsimonious model in which treatment with a macrolide was considered the outcome. The propensity model included all patient characteristics, other early treatments and tests, comorbidities, hospital and physician characteristics, and selected interaction terms.21 Length of stay and cost were trimmed at 3 standard deviations above the mean, and natural log‐transformed values were modeled due to extreme positive skew. In addition, we carried out matched analyses in which we compared the outcomes of patients who were treated with a macrolide to those with similar propensity scores (ie, with similar likelihood of receiving a macrolide) who received a quinolone.22

Finally, to reduce the threat of residual confounding by indication, which can occur if sicker patients are more likely to receive a particular antibiotic, we developed a grouped treatment model, in which all patients treated at the same hospital were assigned a probability of treatment with a macrolide equal to the overall treatment rate at that hospital.23 This is an adaptation of instrumental variable analysis, a well‐accepted technique in econometrics with growing use in health care.24,25 It attempts to assess whether patients treated at a hospital at which quinolones are used more frequently have better outcomes than patients treated at hospitals at which macrolides are used more frequently, while adjusting for other patient, physician, and hospital variables. It ignores the actual treatment the patient received, and instead substitutes the hospital's rate of macrolide use. By grouping treatment at the hospital level, this method greatly reduces the possibility of residual selection bias, unless hospitals that use a lot of macrolides have patients who differ in a consistent way from hospitals which use mostly quinolones.

All analyses were performed using SAS version 9.1 (SAS Institute, Inc., Cary, NC).

Results

Of 26,248 AECOPD patients treated with antibiotics, 19,608 patients met the inclusion criteria; of these, 6139 (31%) were treated initially with a macrolide; the median age was 70 years; 60% were female; and 78% were white. A total of 86% of patients had a primary diagnosis of obstructive chronic bronchitis with acute exacerbation, and 6% had respiratory failure. The most common comorbidities were hypertension, diabetes, and congestive heart failure. Twenty‐two percent had been admitted at least once in the preceding 12 months. Treatment failure occurred in 7.7% of patients, and 1.3% died in the hospital. Mean length of stay was 4.8 days. Hospital prescribing rates for macrolides varied from 0% to 100%, with a mean of 33% and an interquartile range of 14% to 46% (Supporting Appendix Figure 1).

Compared to patients receiving macrolides, those receiving quinolones were older, more likely to have respiratory failure, to be cared for by a pulmonologist, and to have an admission in the previous year (Table 1). They were also more likely to be treated with bronchodilators, methylxanthines, steroids, diuretics, and noninvasive positive pressure ventilation, and to have an arterial blood gas, but less likely to receive concomitant treatment with a cephalosporin (11% vs. 57%). With the exception of cephalosporin treatment, these differences were small, but due to the large sample were statistically significant. Comorbidities were similar in both groups. Patients in the quinolone group were also more likely to experience treatment failure (8.1% vs. 6.8%), death (1.5% vs. 1.0%), and antibiotic‐associated diarrhea (1.1% vs. 0.5%).

Selected Characteristics of Patients with AECOPD Who Were Treated Initially With a Quinolone or a Macrolide
 Complete CohortPropensity‐matched Subsample
CharacteristicQuinolone (n = 13469)Macrolide (n = 6139)P ValueQuinolone (n = 5610)Macrolide (n = 5610)P Value
  • NOTE: A complete list of patient characteristics and outcomes can be found in Supporting Appendix Tables 1 and 2.

  • Abbreviations: AECOPD, acute exacerbations of chronic obstructive pulmonary disease; SD, standard deviation.

  • Refers to all antibiotics received during the hospitalization, not limited to the first 2 days. Patients may receive more than 1 antibiotic, so percentages do not sum to 100.

Antibiotics received during hospitalization* [n (%)]      
Macrolide264 (2)6139 (100) 119 (2)5610 (100) 
Quinolone13469 (100)459 (8) 5610 (100)424 (8) 
Cephalosporin1696 (13)3579 (59)<0.001726 (13)3305 (59)<0.001
Tetracycline231 (2)75 (2)0.01101 (2)73 (2)0.06
Other antibiotics397 (3)220 (4)0.02166 (3)193 (3)0.03
Age (years) (mean [SD])69.1 (11.4)68.2 (11.8)<0.00168.6 (11.7)68.5 (11.7)0.58
Male sex (n [%])5447 (40)2440 (40)0.362207 (39)2196 (39)0.85
Race/ethnic group (n [%])  <0.001  0.44
White10454 (78)4758 (78) 4359 (78)4368 (78) 
Black1060 (8)540 (9) 470 (8)455 (8) 
Hispanic463 (3)144 (2) 157 (3)134 (2) 
Other1492 (11)697 (11) 624 (11)653 (12) 
Primary diagnosis (n [%])  <0.001  0.78
Obstructive chronic bronchitis with acute exacerbation11650 (87)5298 (86) 4884 (87)4860 (87) 
Chronic obstructive asthma/asthma with COPD908 (7)569 (9) 466 (8)486 (9) 
Respiratory failure911 (7)272 (4) 260 (5)264 (5) 
Admissions in the prior year (n [%])  <0.001  0.84
09846 (73)4654 (76) 4249 (76)4231 (75) 
11918 (14)816 (13) 747 (13)750 (13) 
2+1085 (8)445 (7) 397 (7)420 (8) 
Missing620 (5)224 (4) 217 (4)209 (4) 
Physician specialty (n [%])  <0.001  0.84
Internal medicine/hospitalist7069 (53)3321 (54) 3032 (54)3072 (55) 
Family/general medicine3569 (27)2074 (34) 1824 (33)1812 (32) 
Pulmonologist2776 (21)727 (12) 738 (13)711 (13) 
Critical care/emntensivist55 (0)17 (0) 16 (0)15 (0) 
Tests on hospital day 1 or 2 (n [%])      
Arterial blood gas8084 (60)3377 (55)<0.0013195 (57)3129 (56)0.22
Sputum test1741 (13)766 (13)0.3920 (0)16 (0)0.62
Medications/therapies on hospital day 1 or 2 (n [%])      
Short‐acting bronchodilators7555 (56)3242 (53)<0.0012969 (53)2820 (50)0.005
Long‐acting beta‐2 agonists2068 (15)748 (12)<0.001704 (13)719 (13)0.69
Methylxanthine bronchodilators3051 (23)1149 (19)<0.0011102 (20)1093 (20)0.85
Steroids  0.04  0.68
Intravenous11148 (83)4989 (81) 4547 (81)4581 (82) 
Oral772 (6)376 (6) 334 (6)330 (6) 
Severity indicators (n [%])      
Chronic pulmonary heart disease890 (7)401 (7)0.85337 (6)368 (7)0.24
Sleep apnea586 (4)234 (4)0.08211 (4)218 (4)0.77
Noninvasive positive pressure ventilation391 (3)128 (2)<0.001128 (2)114 (2)0.40
Loop diuretics4838 (36)1971 (32)<0.0011884 (34)1862 (33)0.67
Hospital characteristics (n [%])      
Staffed beds  <0.001  0.71
62003483 (26)1688 (28) 1610 (29)1586 (28) 
2013003132 (23)1198 (20) 1174 (21)1154 (21) 
3015004265 (32)2047 (33) 1809 (32)1867 (33) 
500+2589 (19)1206 (20) 1017 (18)1003 (18) 
Hospital region (n [%])  <0.001  0.65
South8562 (64)3270 (53) 3212 (57)3160 (56) 
Midwest2602 (19)1444 (24) 1170 (21)1216 (22) 
Northeast1163 (9)871 (14) 687 (12)704 (13) 
West1142 (9)554 (9) 541 (10)530 (9) 
Teaching hospital  <0.001  0.63
No12090 (90)5037 (82) 4896 (87)4878 (87) 
Yes1379 (10)1102 (18) 714 (13)732 (13) 
Comorbidities (n [%])      
Congestive heart failure2673 (20)1147 (19)0.061081 (19)1060 (19)0.63
Metastatic cancer134 (1)27 (0)<0.00134 (1)38 (1)0.72
Depression1419 (11)669 (11)0.45598 (11)603 (11)0.90
Deficiency anemias1155 (9)476 (8)0.05426 (8)432 (8)0.86
Solid tumor without metastasis1487 (11)586 (10)0.002550 (10)552 (10)0.97
Hypothyroidism1267 (9)527 (9)0.07481 (9)482 (9)1.00
Peripheral vascular disease821 (6)312 (5)0.005287 (5)288 (5)1.00
Paralysis165 (1)46 (1)0.00349 (1)51 (1)0.92
Obesity957 (7)435 (7)0.98386 (7)398 (7)0.68
Hypertension5793 (43)2688 (44)0.312474 (44)2468 (44)0.92
Diabetes  0.04  0.45
Without chronic complications2630 (20)1127 (18) 1057 (19)1066 (19) 
With chronic complications298 (2)116 (2) 115 (2)97 (2) 

In the unadjusted analysis, compared to patients receiving quinolones, those treated with macrolides were less likely to experience treatment failure (OR, 0.83; 95% CI, 0.740.94) (Table 2). Adjusting for all patient, hospital, and physician covariates, including the propensity for treatment with macrolides, increased the OR to 0.89 and the results were no longer significant (95% CI, 0.781.01). Propensity matching successfully balanced all measured covariates except for the use of short‐acting bronchodilators and additional antibiotics (Table 1). In the propensity‐matched sample (Figure 1), quinolone‐treated patients were more likely to experience antibiotic‐associated diarrhea (1.2% vs. 0.6%; P = 0.0003) and late mechanical ventilation (1.3% vs. 0.8%; P = 0.02). There were no differences in adjusted cost or length of stay between the 2 groups. The results of the grouped treatment analysis, substituting the hospital's specific rate of macrolide use in place of the actual treatment that each patient received suggested that the 2 antibiotics were associated with similar rates of treatment failure. The OR for a 100% hospital rate of macrolide treatment vs. a 0% rate was 1.01 (95% CI, 0.751.35).

Figure 1
Outcomes of quinolone‐treated and macrolide‐treated patients in the sample matched by propensity score.
Outcomes of Patients Treated With Macrolides Compared to Those Treated With Quinolones for Acute Exacerbation of COPD
 Treatment FailureCostLOS
ModelsOR95% CIRatio95% CIRatio95% CI
  • Abbreviations: AIDS, acquired immune deficiency syndrome; CI, confidence interval; COPD, chronic obstructive pulmonary disease; LOS, length of stay; OR, odds ratio.

  • Logistic regression model incorporating nonparsimonious propensity score only.

  • Covariates in all models included: age; gender; primary diagnosis; region; teaching status; sleep apnea; hypertension; depression, paralysis; other neurological disorders; weight loss; heart failure; pulmonary circulation disease; valve disease; metastatic cancer; and initiation of oral or intravenous steroids, short‐acting bronchodilators, arterial blood gas, loop diuretics, methylxanthine bronchodilators, and morphine in the first 2 hospitals days. In addition, LOS and cost models included: insurance; attending physician specialty; hospital bed size; admission source; prior year admissions for COPD; chronic pulmonary heart disease; diabetes; hypothyroid; deficiency anemia; obesity; peripheral vascular disease; blood loss; alcohol abuse; drug abuse; AIDS; and initiation of long acting beta‐2 agonists, noninvasive ventilation, mucolytic medications, chest physiotherapy, and sputum testing in the first 2 hospital days. The LOS model also included pulmonary function tests. The cost model also included rural population, renal failure, psychoses, and solid tumor without metastasis. Interactions in the treatment failure model were the following: age with arterial blood gas, and loop diuretics with heart failure and with paralysis. Interactions in the LOS model were the following: loop diuretics with heart failure; and long‐acting beta‐2 agonists with gender and metastatic cancer. Interactions in the cost model were loop diuretics with heart failure, sleep apnea and chronic pulmonary heart disease, long‐acting beta‐2 agonists with metastatic cancer, and obesity with hypothyroid.

  • Each macrolide‐treated patient was matched on propensity with 1 quinolone‐treated patient. Of 6139 macrolide‐treated patients, 5610 (91.4%) were matched.

  • In place of actual treatment received, the subjects are assigned a probability of treatment corresponding to the hospital's overall macrolide rate.

  • In addition to covariates in the treatment failure model, the group treatment model also included race/ethnicity and attending physician specialty.

Unadjusted0.830.730.930.980.971.000.960.950.98
Adjusted for propensity score only*0.890.791.011.000.981.010.980.971.00
Adjusted for covariates0.870.770.991.000.991.020.990.971.00
Adjusted for covariates and propensity score0.890.781.011.000.991.020.980.971.00
Matched sample, unadjusted0.870.751.000.990.981.010.990.971.01
Matched sample, adjusted for unbalanced variables0.870.751.011.000.981.020.990.971.01
Grouped treatment model, unadjusted0.900.681.190.970.891.060.920.870.96
Group treatment model, adjusted for covariates1.010.751.350.960.881.050.960.911.00

Discussion

In this large observational study conducted at 375 hospitals, we took advantage of a natural experiment in which antibiotic prescribing patterns varied widely across hospitals to compare the effectiveness of 2 common antibiotic regimens for AECOPD. Treatment with macrolides and quinolones were associated with a similar risk of treatment failure, costs, and length of stay; however, patients treated with macrolides were less likely to experience late mechanical ventilation or treatment for antibiotic‐associated diarrhea.

Despite broad consensus in COPD guidelines that patients with severe acute exacerbations should receive antibiotics, there is little agreement about the preferred empiric agent. Controversy exists regarding antibiotics' comparative effectiveness, and even over which pathogens cause COPD exacerbations. Given the frequency of hospitalization for AECOPD, understanding the comparative effectiveness of treatments in this setting could have important implications for health outcomes and costs. Unfortunately, most antibiotic studies in AECOPD were conducted >20 years ago, using antibiotics that rarely appeared in our sample.26 Consequently, clinical practice guidelines offer conflicting recommendations. For example, the National Institute for Clinical Excellence recommends empirical treatment with an aminopenicillin, a macrolide, or a tetracycline,8 while the American Thoracic Society recommends amoxicillin‐clavulonate or a fluoroquinolone.9

As might be expected in light of so much uncertainty, we found wide variation in prescribing patterns across hospitals. Overall, approximately one‐third of patients received a macrolide and two‐thirds a quinolone. Both regimens provide adequate coverage of H. influenza, S. pneumoniae, and M. catarrhalis, and conform to at least 1 COPD guideline. Nevertheless, patients receiving macrolides often received a cephalosporin as well; this pattern of treatment suggests that antibiotic selection is likely to have been influenced more by guidelines for the treatment of community‐acquired pneumonia than COPD.

Previous studies comparing antibiotic effectiveness suffer from shortcomings that limit their application to patients hospitalized with AECOPD. First, they enrolled patients with chronic bronchitis, and included patients without obstructive lung disease, and most studies included patients as young as 18 years old. Second, many either did not include treatment with steroids or excluded patients receiving more than 10 mg of prednisone daily. Third, almost all enrolled only ambulatory patients.

While there are no studies comparing quinolones and macrolides in patients hospitalized for AECOPD, a meta‐analysis comparing quinolones, macrolides, and amoxicillin‐clavulonate identified 19 trials of ambulatory patients with chronic bronchitis. That study found that all 3 drugs had similar efficacy initially, but that quinolones resulted in the fewest relapses over a 26‐week period.27 Macrolides and quinolones had similar rates of adverse effects. In contrast, we did not find a difference in treatment failure, cost, or length of stay, but did find a higher rate of diarrhea associated with quinolones. Others have also documented an association between fluoroquinolones and C. difficile diarrhea.2830 This trend, first noted in 2001, is of particular concern because the fluoroquinolone‐resistant strains appear to be hypervirulent and have been associated with nosocomial epidemics.3134

Our study has several limitations. First, its observational design leaves open the possibility of selection bias. For this reason we analyzed our data in several ways, including using a grouped treatment approach, an adaptation of the instrumental variable technique, and accepted only those differences which were consistent across all models. Second, our study used claims data, and therefore we could not directly adjust for physiological measures of severity. However, the highly detailed nature of the data allowed us to adjust for numerous tests and treatments that reflected the clinician's assessment of the patient's severity, as well as the number of prior COPD admissions. Third, we cannot exclude the possibility that some patients may have had concurrent pneumonia without an ICD‐9 code. We think that the number would be small because reimbursement for pneumonia is generally higher than for COPD, so hospitals have an incentive to code pneumonia as the principal diagnosis when present. Finally, we compared initial antibiotics only. More than one‐quarter of our patients received an additional antibiotic before discharge. In particular, patients receiving macrolides were often prescribed a concomitant cephalosporin. We do not know to what extent these additional antibiotics may have affected the outcomes.

Despite the large number of patients hospitalized annually for AECOPD, there are no randomized trials comparing different antibiotics in this population. Studies comparing antibiotics in chronic bronchitis can offer little guidance, since they have primarily focused on proving equivalence between existing antibiotics and newer, more expensive formulations.35 Because many of the patients enrolled in such trials do not benefit from antibiotics at all, either because they do not have COPD or because their exacerbation is not caused by bacteria, it is relatively easy to prove equivalence. Given that AECOPD is one of the leading causes of hospitalization in the United States, large, randomized trials comparing the effectiveness of different antibiotics should be a high priority. In the meantime, macrolides (often given together with cephalosporins) and quinolones appear to be equally effective initial antibiotic choices; considering antibiotic‐associated diarrhea, macrolides appear to be the safer of the 2.

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References
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Issue
Journal of Hospital Medicine - 5(5)
Page Number
261-267
Legacy Keywords
antibiotics, chronic obstructive, pulmonary disease, effectiveness, treatment
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Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are responsible for more than 600,000 hospitalizations annually, resulting in direct costs of over $20 billion.1 Bacterial infections appear responsible for 50% of such exacerbations,25 and current COPD guidelines recommend treatment with antibiotics for patients with severe exacerbations or a change in sputum.1,69 These recommendations are based on a number of small randomized trials, most of which were conducted more than 20 years ago using narrow spectrum antibiotics that are no longer commonly used.10 Only 4 studies, totaling 321 subjects, included hospitalized patients, and most studies excluded patients who required steroids. Because no clinical trials have compared different antibiotic regimens for AECOPD, existing guidelines offer a range of treatment options, including amoxicillin‐clavulonate, macrolides, quinolones, cephalosporins, aminopenicillins, and tetracyclines.

Among hospitalized patients, macrolides and quinolones appear to be the most frequently prescribed antibiotics.11 Both are available in oral formulations, have excellent bioavailability, and are administered once daily. In addition to their antimicrobial activity, macrolides are believed to have antiinflammatory effects, which could be especially advantageous in AECOPD.1214 In trials of chronic bronchitis, however, fluoroquinolones have been shown to reduce the risk of recurrent exacerbation when compared to macrolides.15 The wide variation that has been observed in antibiotic selection for patients hospitalized for AECOPD suggests a high degree of uncertainty among clinicians about the benefits of different treatment options.11 Given the limited evidence from randomized trials, we sought to evaluate the comparative effectiveness of macrolides and quinolones among a large, representative sample of patients hospitalized with AECOPD.

Subjects and Methods

Setting and Subjects

We conducted a retrospective cohort study of all patients hospitalized between January 1 and December 31, 2001 for AECOPD at any 1 of 375 acute care facilities in the United States that participated in Premier's Perspective, a voluntary, fee‐supported database developed for measuring quality and healthcare utilization. Participating hospitals represent all geographical regions, and are primarily small‐sized to medium‐sized nonteaching hospitals located mostly in urban areas. In addition to the information contained in the standard hospital discharge file (Uniform Billing 92) such as patient age, International Classification of Disease, 9th Edition, Clinical Modification (ICD‐9‐CM) codes, the Perspective database includes a date‐stamped log of all billed items, including diagnostic tests, medications, and other treatments, as well as costs, for individual patients. The study was approved by the Institutional Review Board of Baystate Medical Center.

Patients were included if they had a primary diagnosis consistent with AECOPD (ICD‐9 codes 491.21 and 493.22) or a primary diagnosis of respiratory failure (ICD‐9 codes 518.81 and 518.84) paired with secondary diagnosis of AECOPD; they also had to receive at least 2 consecutive days of either a macrolide or a quinolone, started within 48 hours of hospitalization. Patients receiving both antibiotics were excluded, but patients who received additional antibiotics were included. To enhance the specificity of our diagnosis codes, we limited our study to patients age 40 years.16 Because mechanical ventilation initiated after hospital day 2 was an outcome measure, we excluded patients admitted directly to the intensive care unit. We also excluded: those with other bacterial infections, such as pneumonia or cellulitis, who might have another indication for antibiotics; those with a length of stay <2 days, because we could not ascertain whether they received a full course of antibiotics; patients with a secondary diagnosis of pulmonary embolism or pneumothorax; and those whose attending physicians were not internists, family practitioners, hospitalists, pulmonologists, or intensivists. For patients with more than 1 admission during the study period, we included only the first admission.

Data Elements

For each patient, we assessed age, gender, race, marital and insurance status, principal diagnosis, comorbidities, and specialty of the attending physician. Comorbidities were identified from ICD‐9 secondary diagnosis codes and Diagnosis Related Groups using Healthcare Cost and Utilization Project Comorbidity Software (version 3.1), based on the work of Elixhauser et al.17 In addition, to assess disease severity we recorded the presence of chronic pulmonary heart disease, the number of admissions for COPD during the 12 months prior to the index admission, and arterial blood gas testing.18,19 We also identified pharmacy or diagnostic charges for interventions that were recommended in current guidelines (beta‐adrenergic and anticholinergic bronchodilators, steroids, and noninvasive positive‐pressure ventilation); those that were not recommended or were of uncertain benefit (methylxanthine bronchodilators, spirometry/pulmonary function testing, mucolytic medications, chest physiotherapy, and sputum testing); and drugs that might be associated with severe exacerbations or end‐stage COPD (loop diuretics, morphine, and nutritional supplements).1,69 Hospitals were categorized by region (Northeast, South, Midwest, or West), bed size, setting (urban vs. rural), ownership, and teaching status.

Antibiotic Class and Outcome Variables

Our primary predictor variable was the antibiotic initiated during the first 2 hospital days and continued for at least 2 days, regardless of other antibiotics the patient may have received during the course of hospitalization. Because we anticipated low in‐hospital mortality, our primary outcome was a composite measure of treatment failure, defined as initiation of mechanical ventilation after hospital day 2, in‐hospital mortality, or readmission for COPD within 30 days of discharge.20 Secondary outcomes included hospital costs and length of stay, as well as allergic reactions identified by ICD‐9 code, and antibiotic‐associated diarrhea, defined as treatment with either metronidazole or oral vancomycin begun after hospital day 3.

Statistical Analysis

Summary statistics were computed using frequencies and percents for categorical variables; and means, medians, standard deviations, and interquartile ranges for continuous variables. Associations between antibiotic selection and patient and hospital characteristics were assessed using chi‐square tests for categorical variables and z‐tests for continuous variables.

We developed a series of multivariable models to evaluate the impact of initial antibiotic selection on the risk of treatment failure, length of stay, and total cost. In order to account for the effects of within‐hospital correlation, generalized estimating equation (GEE) models with a logit link were used to assess the effect of antibiotic selection on the risk of treatment failure, and identity link models were used for analyses of length of stay and cost. Unadjusted and covariate‐adjusted models for treatment failure were evaluated with and without adjustments for propensity score. A propensity score is the probability that a given patient would receive treatment with a macrolide, derived from a nonparsimonious model in which treatment with a macrolide was considered the outcome. The propensity model included all patient characteristics, other early treatments and tests, comorbidities, hospital and physician characteristics, and selected interaction terms.21 Length of stay and cost were trimmed at 3 standard deviations above the mean, and natural log‐transformed values were modeled due to extreme positive skew. In addition, we carried out matched analyses in which we compared the outcomes of patients who were treated with a macrolide to those with similar propensity scores (ie, with similar likelihood of receiving a macrolide) who received a quinolone.22

Finally, to reduce the threat of residual confounding by indication, which can occur if sicker patients are more likely to receive a particular antibiotic, we developed a grouped treatment model, in which all patients treated at the same hospital were assigned a probability of treatment with a macrolide equal to the overall treatment rate at that hospital.23 This is an adaptation of instrumental variable analysis, a well‐accepted technique in econometrics with growing use in health care.24,25 It attempts to assess whether patients treated at a hospital at which quinolones are used more frequently have better outcomes than patients treated at hospitals at which macrolides are used more frequently, while adjusting for other patient, physician, and hospital variables. It ignores the actual treatment the patient received, and instead substitutes the hospital's rate of macrolide use. By grouping treatment at the hospital level, this method greatly reduces the possibility of residual selection bias, unless hospitals that use a lot of macrolides have patients who differ in a consistent way from hospitals which use mostly quinolones.

All analyses were performed using SAS version 9.1 (SAS Institute, Inc., Cary, NC).

Results

Of 26,248 AECOPD patients treated with antibiotics, 19,608 patients met the inclusion criteria; of these, 6139 (31%) were treated initially with a macrolide; the median age was 70 years; 60% were female; and 78% were white. A total of 86% of patients had a primary diagnosis of obstructive chronic bronchitis with acute exacerbation, and 6% had respiratory failure. The most common comorbidities were hypertension, diabetes, and congestive heart failure. Twenty‐two percent had been admitted at least once in the preceding 12 months. Treatment failure occurred in 7.7% of patients, and 1.3% died in the hospital. Mean length of stay was 4.8 days. Hospital prescribing rates for macrolides varied from 0% to 100%, with a mean of 33% and an interquartile range of 14% to 46% (Supporting Appendix Figure 1).

Compared to patients receiving macrolides, those receiving quinolones were older, more likely to have respiratory failure, to be cared for by a pulmonologist, and to have an admission in the previous year (Table 1). They were also more likely to be treated with bronchodilators, methylxanthines, steroids, diuretics, and noninvasive positive pressure ventilation, and to have an arterial blood gas, but less likely to receive concomitant treatment with a cephalosporin (11% vs. 57%). With the exception of cephalosporin treatment, these differences were small, but due to the large sample were statistically significant. Comorbidities were similar in both groups. Patients in the quinolone group were also more likely to experience treatment failure (8.1% vs. 6.8%), death (1.5% vs. 1.0%), and antibiotic‐associated diarrhea (1.1% vs. 0.5%).

Selected Characteristics of Patients with AECOPD Who Were Treated Initially With a Quinolone or a Macrolide
 Complete CohortPropensity‐matched Subsample
CharacteristicQuinolone (n = 13469)Macrolide (n = 6139)P ValueQuinolone (n = 5610)Macrolide (n = 5610)P Value
  • NOTE: A complete list of patient characteristics and outcomes can be found in Supporting Appendix Tables 1 and 2.

  • Abbreviations: AECOPD, acute exacerbations of chronic obstructive pulmonary disease; SD, standard deviation.

  • Refers to all antibiotics received during the hospitalization, not limited to the first 2 days. Patients may receive more than 1 antibiotic, so percentages do not sum to 100.

Antibiotics received during hospitalization* [n (%)]      
Macrolide264 (2)6139 (100) 119 (2)5610 (100) 
Quinolone13469 (100)459 (8) 5610 (100)424 (8) 
Cephalosporin1696 (13)3579 (59)<0.001726 (13)3305 (59)<0.001
Tetracycline231 (2)75 (2)0.01101 (2)73 (2)0.06
Other antibiotics397 (3)220 (4)0.02166 (3)193 (3)0.03
Age (years) (mean [SD])69.1 (11.4)68.2 (11.8)<0.00168.6 (11.7)68.5 (11.7)0.58
Male sex (n [%])5447 (40)2440 (40)0.362207 (39)2196 (39)0.85
Race/ethnic group (n [%])  <0.001  0.44
White10454 (78)4758 (78) 4359 (78)4368 (78) 
Black1060 (8)540 (9) 470 (8)455 (8) 
Hispanic463 (3)144 (2) 157 (3)134 (2) 
Other1492 (11)697 (11) 624 (11)653 (12) 
Primary diagnosis (n [%])  <0.001  0.78
Obstructive chronic bronchitis with acute exacerbation11650 (87)5298 (86) 4884 (87)4860 (87) 
Chronic obstructive asthma/asthma with COPD908 (7)569 (9) 466 (8)486 (9) 
Respiratory failure911 (7)272 (4) 260 (5)264 (5) 
Admissions in the prior year (n [%])  <0.001  0.84
09846 (73)4654 (76) 4249 (76)4231 (75) 
11918 (14)816 (13) 747 (13)750 (13) 
2+1085 (8)445 (7) 397 (7)420 (8) 
Missing620 (5)224 (4) 217 (4)209 (4) 
Physician specialty (n [%])  <0.001  0.84
Internal medicine/hospitalist7069 (53)3321 (54) 3032 (54)3072 (55) 
Family/general medicine3569 (27)2074 (34) 1824 (33)1812 (32) 
Pulmonologist2776 (21)727 (12) 738 (13)711 (13) 
Critical care/emntensivist55 (0)17 (0) 16 (0)15 (0) 
Tests on hospital day 1 or 2 (n [%])      
Arterial blood gas8084 (60)3377 (55)<0.0013195 (57)3129 (56)0.22
Sputum test1741 (13)766 (13)0.3920 (0)16 (0)0.62
Medications/therapies on hospital day 1 or 2 (n [%])      
Short‐acting bronchodilators7555 (56)3242 (53)<0.0012969 (53)2820 (50)0.005
Long‐acting beta‐2 agonists2068 (15)748 (12)<0.001704 (13)719 (13)0.69
Methylxanthine bronchodilators3051 (23)1149 (19)<0.0011102 (20)1093 (20)0.85
Steroids  0.04  0.68
Intravenous11148 (83)4989 (81) 4547 (81)4581 (82) 
Oral772 (6)376 (6) 334 (6)330 (6) 
Severity indicators (n [%])      
Chronic pulmonary heart disease890 (7)401 (7)0.85337 (6)368 (7)0.24
Sleep apnea586 (4)234 (4)0.08211 (4)218 (4)0.77
Noninvasive positive pressure ventilation391 (3)128 (2)<0.001128 (2)114 (2)0.40
Loop diuretics4838 (36)1971 (32)<0.0011884 (34)1862 (33)0.67
Hospital characteristics (n [%])      
Staffed beds  <0.001  0.71
62003483 (26)1688 (28) 1610 (29)1586 (28) 
2013003132 (23)1198 (20) 1174 (21)1154 (21) 
3015004265 (32)2047 (33) 1809 (32)1867 (33) 
500+2589 (19)1206 (20) 1017 (18)1003 (18) 
Hospital region (n [%])  <0.001  0.65
South8562 (64)3270 (53) 3212 (57)3160 (56) 
Midwest2602 (19)1444 (24) 1170 (21)1216 (22) 
Northeast1163 (9)871 (14) 687 (12)704 (13) 
West1142 (9)554 (9) 541 (10)530 (9) 
Teaching hospital  <0.001  0.63
No12090 (90)5037 (82) 4896 (87)4878 (87) 
Yes1379 (10)1102 (18) 714 (13)732 (13) 
Comorbidities (n [%])      
Congestive heart failure2673 (20)1147 (19)0.061081 (19)1060 (19)0.63
Metastatic cancer134 (1)27 (0)<0.00134 (1)38 (1)0.72
Depression1419 (11)669 (11)0.45598 (11)603 (11)0.90
Deficiency anemias1155 (9)476 (8)0.05426 (8)432 (8)0.86
Solid tumor without metastasis1487 (11)586 (10)0.002550 (10)552 (10)0.97
Hypothyroidism1267 (9)527 (9)0.07481 (9)482 (9)1.00
Peripheral vascular disease821 (6)312 (5)0.005287 (5)288 (5)1.00
Paralysis165 (1)46 (1)0.00349 (1)51 (1)0.92
Obesity957 (7)435 (7)0.98386 (7)398 (7)0.68
Hypertension5793 (43)2688 (44)0.312474 (44)2468 (44)0.92
Diabetes  0.04  0.45
Without chronic complications2630 (20)1127 (18) 1057 (19)1066 (19) 
With chronic complications298 (2)116 (2) 115 (2)97 (2) 

In the unadjusted analysis, compared to patients receiving quinolones, those treated with macrolides were less likely to experience treatment failure (OR, 0.83; 95% CI, 0.740.94) (Table 2). Adjusting for all patient, hospital, and physician covariates, including the propensity for treatment with macrolides, increased the OR to 0.89 and the results were no longer significant (95% CI, 0.781.01). Propensity matching successfully balanced all measured covariates except for the use of short‐acting bronchodilators and additional antibiotics (Table 1). In the propensity‐matched sample (Figure 1), quinolone‐treated patients were more likely to experience antibiotic‐associated diarrhea (1.2% vs. 0.6%; P = 0.0003) and late mechanical ventilation (1.3% vs. 0.8%; P = 0.02). There were no differences in adjusted cost or length of stay between the 2 groups. The results of the grouped treatment analysis, substituting the hospital's specific rate of macrolide use in place of the actual treatment that each patient received suggested that the 2 antibiotics were associated with similar rates of treatment failure. The OR for a 100% hospital rate of macrolide treatment vs. a 0% rate was 1.01 (95% CI, 0.751.35).

Figure 1
Outcomes of quinolone‐treated and macrolide‐treated patients in the sample matched by propensity score.
Outcomes of Patients Treated With Macrolides Compared to Those Treated With Quinolones for Acute Exacerbation of COPD
 Treatment FailureCostLOS
ModelsOR95% CIRatio95% CIRatio95% CI
  • Abbreviations: AIDS, acquired immune deficiency syndrome; CI, confidence interval; COPD, chronic obstructive pulmonary disease; LOS, length of stay; OR, odds ratio.

  • Logistic regression model incorporating nonparsimonious propensity score only.

  • Covariates in all models included: age; gender; primary diagnosis; region; teaching status; sleep apnea; hypertension; depression, paralysis; other neurological disorders; weight loss; heart failure; pulmonary circulation disease; valve disease; metastatic cancer; and initiation of oral or intravenous steroids, short‐acting bronchodilators, arterial blood gas, loop diuretics, methylxanthine bronchodilators, and morphine in the first 2 hospitals days. In addition, LOS and cost models included: insurance; attending physician specialty; hospital bed size; admission source; prior year admissions for COPD; chronic pulmonary heart disease; diabetes; hypothyroid; deficiency anemia; obesity; peripheral vascular disease; blood loss; alcohol abuse; drug abuse; AIDS; and initiation of long acting beta‐2 agonists, noninvasive ventilation, mucolytic medications, chest physiotherapy, and sputum testing in the first 2 hospital days. The LOS model also included pulmonary function tests. The cost model also included rural population, renal failure, psychoses, and solid tumor without metastasis. Interactions in the treatment failure model were the following: age with arterial blood gas, and loop diuretics with heart failure and with paralysis. Interactions in the LOS model were the following: loop diuretics with heart failure; and long‐acting beta‐2 agonists with gender and metastatic cancer. Interactions in the cost model were loop diuretics with heart failure, sleep apnea and chronic pulmonary heart disease, long‐acting beta‐2 agonists with metastatic cancer, and obesity with hypothyroid.

  • Each macrolide‐treated patient was matched on propensity with 1 quinolone‐treated patient. Of 6139 macrolide‐treated patients, 5610 (91.4%) were matched.

  • In place of actual treatment received, the subjects are assigned a probability of treatment corresponding to the hospital's overall macrolide rate.

  • In addition to covariates in the treatment failure model, the group treatment model also included race/ethnicity and attending physician specialty.

Unadjusted0.830.730.930.980.971.000.960.950.98
Adjusted for propensity score only*0.890.791.011.000.981.010.980.971.00
Adjusted for covariates0.870.770.991.000.991.020.990.971.00
Adjusted for covariates and propensity score0.890.781.011.000.991.020.980.971.00
Matched sample, unadjusted0.870.751.000.990.981.010.990.971.01
Matched sample, adjusted for unbalanced variables0.870.751.011.000.981.020.990.971.01
Grouped treatment model, unadjusted0.900.681.190.970.891.060.920.870.96
Group treatment model, adjusted for covariates1.010.751.350.960.881.050.960.911.00

Discussion

In this large observational study conducted at 375 hospitals, we took advantage of a natural experiment in which antibiotic prescribing patterns varied widely across hospitals to compare the effectiveness of 2 common antibiotic regimens for AECOPD. Treatment with macrolides and quinolones were associated with a similar risk of treatment failure, costs, and length of stay; however, patients treated with macrolides were less likely to experience late mechanical ventilation or treatment for antibiotic‐associated diarrhea.

Despite broad consensus in COPD guidelines that patients with severe acute exacerbations should receive antibiotics, there is little agreement about the preferred empiric agent. Controversy exists regarding antibiotics' comparative effectiveness, and even over which pathogens cause COPD exacerbations. Given the frequency of hospitalization for AECOPD, understanding the comparative effectiveness of treatments in this setting could have important implications for health outcomes and costs. Unfortunately, most antibiotic studies in AECOPD were conducted >20 years ago, using antibiotics that rarely appeared in our sample.26 Consequently, clinical practice guidelines offer conflicting recommendations. For example, the National Institute for Clinical Excellence recommends empirical treatment with an aminopenicillin, a macrolide, or a tetracycline,8 while the American Thoracic Society recommends amoxicillin‐clavulonate or a fluoroquinolone.9

As might be expected in light of so much uncertainty, we found wide variation in prescribing patterns across hospitals. Overall, approximately one‐third of patients received a macrolide and two‐thirds a quinolone. Both regimens provide adequate coverage of H. influenza, S. pneumoniae, and M. catarrhalis, and conform to at least 1 COPD guideline. Nevertheless, patients receiving macrolides often received a cephalosporin as well; this pattern of treatment suggests that antibiotic selection is likely to have been influenced more by guidelines for the treatment of community‐acquired pneumonia than COPD.

Previous studies comparing antibiotic effectiveness suffer from shortcomings that limit their application to patients hospitalized with AECOPD. First, they enrolled patients with chronic bronchitis, and included patients without obstructive lung disease, and most studies included patients as young as 18 years old. Second, many either did not include treatment with steroids or excluded patients receiving more than 10 mg of prednisone daily. Third, almost all enrolled only ambulatory patients.

While there are no studies comparing quinolones and macrolides in patients hospitalized for AECOPD, a meta‐analysis comparing quinolones, macrolides, and amoxicillin‐clavulonate identified 19 trials of ambulatory patients with chronic bronchitis. That study found that all 3 drugs had similar efficacy initially, but that quinolones resulted in the fewest relapses over a 26‐week period.27 Macrolides and quinolones had similar rates of adverse effects. In contrast, we did not find a difference in treatment failure, cost, or length of stay, but did find a higher rate of diarrhea associated with quinolones. Others have also documented an association between fluoroquinolones and C. difficile diarrhea.2830 This trend, first noted in 2001, is of particular concern because the fluoroquinolone‐resistant strains appear to be hypervirulent and have been associated with nosocomial epidemics.3134

Our study has several limitations. First, its observational design leaves open the possibility of selection bias. For this reason we analyzed our data in several ways, including using a grouped treatment approach, an adaptation of the instrumental variable technique, and accepted only those differences which were consistent across all models. Second, our study used claims data, and therefore we could not directly adjust for physiological measures of severity. However, the highly detailed nature of the data allowed us to adjust for numerous tests and treatments that reflected the clinician's assessment of the patient's severity, as well as the number of prior COPD admissions. Third, we cannot exclude the possibility that some patients may have had concurrent pneumonia without an ICD‐9 code. We think that the number would be small because reimbursement for pneumonia is generally higher than for COPD, so hospitals have an incentive to code pneumonia as the principal diagnosis when present. Finally, we compared initial antibiotics only. More than one‐quarter of our patients received an additional antibiotic before discharge. In particular, patients receiving macrolides were often prescribed a concomitant cephalosporin. We do not know to what extent these additional antibiotics may have affected the outcomes.

Despite the large number of patients hospitalized annually for AECOPD, there are no randomized trials comparing different antibiotics in this population. Studies comparing antibiotics in chronic bronchitis can offer little guidance, since they have primarily focused on proving equivalence between existing antibiotics and newer, more expensive formulations.35 Because many of the patients enrolled in such trials do not benefit from antibiotics at all, either because they do not have COPD or because their exacerbation is not caused by bacteria, it is relatively easy to prove equivalence. Given that AECOPD is one of the leading causes of hospitalization in the United States, large, randomized trials comparing the effectiveness of different antibiotics should be a high priority. In the meantime, macrolides (often given together with cephalosporins) and quinolones appear to be equally effective initial antibiotic choices; considering antibiotic‐associated diarrhea, macrolides appear to be the safer of the 2.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are responsible for more than 600,000 hospitalizations annually, resulting in direct costs of over $20 billion.1 Bacterial infections appear responsible for 50% of such exacerbations,25 and current COPD guidelines recommend treatment with antibiotics for patients with severe exacerbations or a change in sputum.1,69 These recommendations are based on a number of small randomized trials, most of which were conducted more than 20 years ago using narrow spectrum antibiotics that are no longer commonly used.10 Only 4 studies, totaling 321 subjects, included hospitalized patients, and most studies excluded patients who required steroids. Because no clinical trials have compared different antibiotic regimens for AECOPD, existing guidelines offer a range of treatment options, including amoxicillin‐clavulonate, macrolides, quinolones, cephalosporins, aminopenicillins, and tetracyclines.

Among hospitalized patients, macrolides and quinolones appear to be the most frequently prescribed antibiotics.11 Both are available in oral formulations, have excellent bioavailability, and are administered once daily. In addition to their antimicrobial activity, macrolides are believed to have antiinflammatory effects, which could be especially advantageous in AECOPD.1214 In trials of chronic bronchitis, however, fluoroquinolones have been shown to reduce the risk of recurrent exacerbation when compared to macrolides.15 The wide variation that has been observed in antibiotic selection for patients hospitalized for AECOPD suggests a high degree of uncertainty among clinicians about the benefits of different treatment options.11 Given the limited evidence from randomized trials, we sought to evaluate the comparative effectiveness of macrolides and quinolones among a large, representative sample of patients hospitalized with AECOPD.

Subjects and Methods

Setting and Subjects

We conducted a retrospective cohort study of all patients hospitalized between January 1 and December 31, 2001 for AECOPD at any 1 of 375 acute care facilities in the United States that participated in Premier's Perspective, a voluntary, fee‐supported database developed for measuring quality and healthcare utilization. Participating hospitals represent all geographical regions, and are primarily small‐sized to medium‐sized nonteaching hospitals located mostly in urban areas. In addition to the information contained in the standard hospital discharge file (Uniform Billing 92) such as patient age, International Classification of Disease, 9th Edition, Clinical Modification (ICD‐9‐CM) codes, the Perspective database includes a date‐stamped log of all billed items, including diagnostic tests, medications, and other treatments, as well as costs, for individual patients. The study was approved by the Institutional Review Board of Baystate Medical Center.

Patients were included if they had a primary diagnosis consistent with AECOPD (ICD‐9 codes 491.21 and 493.22) or a primary diagnosis of respiratory failure (ICD‐9 codes 518.81 and 518.84) paired with secondary diagnosis of AECOPD; they also had to receive at least 2 consecutive days of either a macrolide or a quinolone, started within 48 hours of hospitalization. Patients receiving both antibiotics were excluded, but patients who received additional antibiotics were included. To enhance the specificity of our diagnosis codes, we limited our study to patients age 40 years.16 Because mechanical ventilation initiated after hospital day 2 was an outcome measure, we excluded patients admitted directly to the intensive care unit. We also excluded: those with other bacterial infections, such as pneumonia or cellulitis, who might have another indication for antibiotics; those with a length of stay <2 days, because we could not ascertain whether they received a full course of antibiotics; patients with a secondary diagnosis of pulmonary embolism or pneumothorax; and those whose attending physicians were not internists, family practitioners, hospitalists, pulmonologists, or intensivists. For patients with more than 1 admission during the study period, we included only the first admission.

Data Elements

For each patient, we assessed age, gender, race, marital and insurance status, principal diagnosis, comorbidities, and specialty of the attending physician. Comorbidities were identified from ICD‐9 secondary diagnosis codes and Diagnosis Related Groups using Healthcare Cost and Utilization Project Comorbidity Software (version 3.1), based on the work of Elixhauser et al.17 In addition, to assess disease severity we recorded the presence of chronic pulmonary heart disease, the number of admissions for COPD during the 12 months prior to the index admission, and arterial blood gas testing.18,19 We also identified pharmacy or diagnostic charges for interventions that were recommended in current guidelines (beta‐adrenergic and anticholinergic bronchodilators, steroids, and noninvasive positive‐pressure ventilation); those that were not recommended or were of uncertain benefit (methylxanthine bronchodilators, spirometry/pulmonary function testing, mucolytic medications, chest physiotherapy, and sputum testing); and drugs that might be associated with severe exacerbations or end‐stage COPD (loop diuretics, morphine, and nutritional supplements).1,69 Hospitals were categorized by region (Northeast, South, Midwest, or West), bed size, setting (urban vs. rural), ownership, and teaching status.

Antibiotic Class and Outcome Variables

Our primary predictor variable was the antibiotic initiated during the first 2 hospital days and continued for at least 2 days, regardless of other antibiotics the patient may have received during the course of hospitalization. Because we anticipated low in‐hospital mortality, our primary outcome was a composite measure of treatment failure, defined as initiation of mechanical ventilation after hospital day 2, in‐hospital mortality, or readmission for COPD within 30 days of discharge.20 Secondary outcomes included hospital costs and length of stay, as well as allergic reactions identified by ICD‐9 code, and antibiotic‐associated diarrhea, defined as treatment with either metronidazole or oral vancomycin begun after hospital day 3.

Statistical Analysis

Summary statistics were computed using frequencies and percents for categorical variables; and means, medians, standard deviations, and interquartile ranges for continuous variables. Associations between antibiotic selection and patient and hospital characteristics were assessed using chi‐square tests for categorical variables and z‐tests for continuous variables.

We developed a series of multivariable models to evaluate the impact of initial antibiotic selection on the risk of treatment failure, length of stay, and total cost. In order to account for the effects of within‐hospital correlation, generalized estimating equation (GEE) models with a logit link were used to assess the effect of antibiotic selection on the risk of treatment failure, and identity link models were used for analyses of length of stay and cost. Unadjusted and covariate‐adjusted models for treatment failure were evaluated with and without adjustments for propensity score. A propensity score is the probability that a given patient would receive treatment with a macrolide, derived from a nonparsimonious model in which treatment with a macrolide was considered the outcome. The propensity model included all patient characteristics, other early treatments and tests, comorbidities, hospital and physician characteristics, and selected interaction terms.21 Length of stay and cost were trimmed at 3 standard deviations above the mean, and natural log‐transformed values were modeled due to extreme positive skew. In addition, we carried out matched analyses in which we compared the outcomes of patients who were treated with a macrolide to those with similar propensity scores (ie, with similar likelihood of receiving a macrolide) who received a quinolone.22

Finally, to reduce the threat of residual confounding by indication, which can occur if sicker patients are more likely to receive a particular antibiotic, we developed a grouped treatment model, in which all patients treated at the same hospital were assigned a probability of treatment with a macrolide equal to the overall treatment rate at that hospital.23 This is an adaptation of instrumental variable analysis, a well‐accepted technique in econometrics with growing use in health care.24,25 It attempts to assess whether patients treated at a hospital at which quinolones are used more frequently have better outcomes than patients treated at hospitals at which macrolides are used more frequently, while adjusting for other patient, physician, and hospital variables. It ignores the actual treatment the patient received, and instead substitutes the hospital's rate of macrolide use. By grouping treatment at the hospital level, this method greatly reduces the possibility of residual selection bias, unless hospitals that use a lot of macrolides have patients who differ in a consistent way from hospitals which use mostly quinolones.

All analyses were performed using SAS version 9.1 (SAS Institute, Inc., Cary, NC).

Results

Of 26,248 AECOPD patients treated with antibiotics, 19,608 patients met the inclusion criteria; of these, 6139 (31%) were treated initially with a macrolide; the median age was 70 years; 60% were female; and 78% were white. A total of 86% of patients had a primary diagnosis of obstructive chronic bronchitis with acute exacerbation, and 6% had respiratory failure. The most common comorbidities were hypertension, diabetes, and congestive heart failure. Twenty‐two percent had been admitted at least once in the preceding 12 months. Treatment failure occurred in 7.7% of patients, and 1.3% died in the hospital. Mean length of stay was 4.8 days. Hospital prescribing rates for macrolides varied from 0% to 100%, with a mean of 33% and an interquartile range of 14% to 46% (Supporting Appendix Figure 1).

Compared to patients receiving macrolides, those receiving quinolones were older, more likely to have respiratory failure, to be cared for by a pulmonologist, and to have an admission in the previous year (Table 1). They were also more likely to be treated with bronchodilators, methylxanthines, steroids, diuretics, and noninvasive positive pressure ventilation, and to have an arterial blood gas, but less likely to receive concomitant treatment with a cephalosporin (11% vs. 57%). With the exception of cephalosporin treatment, these differences were small, but due to the large sample were statistically significant. Comorbidities were similar in both groups. Patients in the quinolone group were also more likely to experience treatment failure (8.1% vs. 6.8%), death (1.5% vs. 1.0%), and antibiotic‐associated diarrhea (1.1% vs. 0.5%).

Selected Characteristics of Patients with AECOPD Who Were Treated Initially With a Quinolone or a Macrolide
 Complete CohortPropensity‐matched Subsample
CharacteristicQuinolone (n = 13469)Macrolide (n = 6139)P ValueQuinolone (n = 5610)Macrolide (n = 5610)P Value
  • NOTE: A complete list of patient characteristics and outcomes can be found in Supporting Appendix Tables 1 and 2.

  • Abbreviations: AECOPD, acute exacerbations of chronic obstructive pulmonary disease; SD, standard deviation.

  • Refers to all antibiotics received during the hospitalization, not limited to the first 2 days. Patients may receive more than 1 antibiotic, so percentages do not sum to 100.

Antibiotics received during hospitalization* [n (%)]      
Macrolide264 (2)6139 (100) 119 (2)5610 (100) 
Quinolone13469 (100)459 (8) 5610 (100)424 (8) 
Cephalosporin1696 (13)3579 (59)<0.001726 (13)3305 (59)<0.001
Tetracycline231 (2)75 (2)0.01101 (2)73 (2)0.06
Other antibiotics397 (3)220 (4)0.02166 (3)193 (3)0.03
Age (years) (mean [SD])69.1 (11.4)68.2 (11.8)<0.00168.6 (11.7)68.5 (11.7)0.58
Male sex (n [%])5447 (40)2440 (40)0.362207 (39)2196 (39)0.85
Race/ethnic group (n [%])  <0.001  0.44
White10454 (78)4758 (78) 4359 (78)4368 (78) 
Black1060 (8)540 (9) 470 (8)455 (8) 
Hispanic463 (3)144 (2) 157 (3)134 (2) 
Other1492 (11)697 (11) 624 (11)653 (12) 
Primary diagnosis (n [%])  <0.001  0.78
Obstructive chronic bronchitis with acute exacerbation11650 (87)5298 (86) 4884 (87)4860 (87) 
Chronic obstructive asthma/asthma with COPD908 (7)569 (9) 466 (8)486 (9) 
Respiratory failure911 (7)272 (4) 260 (5)264 (5) 
Admissions in the prior year (n [%])  <0.001  0.84
09846 (73)4654 (76) 4249 (76)4231 (75) 
11918 (14)816 (13) 747 (13)750 (13) 
2+1085 (8)445 (7) 397 (7)420 (8) 
Missing620 (5)224 (4) 217 (4)209 (4) 
Physician specialty (n [%])  <0.001  0.84
Internal medicine/hospitalist7069 (53)3321 (54) 3032 (54)3072 (55) 
Family/general medicine3569 (27)2074 (34) 1824 (33)1812 (32) 
Pulmonologist2776 (21)727 (12) 738 (13)711 (13) 
Critical care/emntensivist55 (0)17 (0) 16 (0)15 (0) 
Tests on hospital day 1 or 2 (n [%])      
Arterial blood gas8084 (60)3377 (55)<0.0013195 (57)3129 (56)0.22
Sputum test1741 (13)766 (13)0.3920 (0)16 (0)0.62
Medications/therapies on hospital day 1 or 2 (n [%])      
Short‐acting bronchodilators7555 (56)3242 (53)<0.0012969 (53)2820 (50)0.005
Long‐acting beta‐2 agonists2068 (15)748 (12)<0.001704 (13)719 (13)0.69
Methylxanthine bronchodilators3051 (23)1149 (19)<0.0011102 (20)1093 (20)0.85
Steroids  0.04  0.68
Intravenous11148 (83)4989 (81) 4547 (81)4581 (82) 
Oral772 (6)376 (6) 334 (6)330 (6) 
Severity indicators (n [%])      
Chronic pulmonary heart disease890 (7)401 (7)0.85337 (6)368 (7)0.24
Sleep apnea586 (4)234 (4)0.08211 (4)218 (4)0.77
Noninvasive positive pressure ventilation391 (3)128 (2)<0.001128 (2)114 (2)0.40
Loop diuretics4838 (36)1971 (32)<0.0011884 (34)1862 (33)0.67
Hospital characteristics (n [%])      
Staffed beds  <0.001  0.71
62003483 (26)1688 (28) 1610 (29)1586 (28) 
2013003132 (23)1198 (20) 1174 (21)1154 (21) 
3015004265 (32)2047 (33) 1809 (32)1867 (33) 
500+2589 (19)1206 (20) 1017 (18)1003 (18) 
Hospital region (n [%])  <0.001  0.65
South8562 (64)3270 (53) 3212 (57)3160 (56) 
Midwest2602 (19)1444 (24) 1170 (21)1216 (22) 
Northeast1163 (9)871 (14) 687 (12)704 (13) 
West1142 (9)554 (9) 541 (10)530 (9) 
Teaching hospital  <0.001  0.63
No12090 (90)5037 (82) 4896 (87)4878 (87) 
Yes1379 (10)1102 (18) 714 (13)732 (13) 
Comorbidities (n [%])      
Congestive heart failure2673 (20)1147 (19)0.061081 (19)1060 (19)0.63
Metastatic cancer134 (1)27 (0)<0.00134 (1)38 (1)0.72
Depression1419 (11)669 (11)0.45598 (11)603 (11)0.90
Deficiency anemias1155 (9)476 (8)0.05426 (8)432 (8)0.86
Solid tumor without metastasis1487 (11)586 (10)0.002550 (10)552 (10)0.97
Hypothyroidism1267 (9)527 (9)0.07481 (9)482 (9)1.00
Peripheral vascular disease821 (6)312 (5)0.005287 (5)288 (5)1.00
Paralysis165 (1)46 (1)0.00349 (1)51 (1)0.92
Obesity957 (7)435 (7)0.98386 (7)398 (7)0.68
Hypertension5793 (43)2688 (44)0.312474 (44)2468 (44)0.92
Diabetes  0.04  0.45
Without chronic complications2630 (20)1127 (18) 1057 (19)1066 (19) 
With chronic complications298 (2)116 (2) 115 (2)97 (2) 

In the unadjusted analysis, compared to patients receiving quinolones, those treated with macrolides were less likely to experience treatment failure (OR, 0.83; 95% CI, 0.740.94) (Table 2). Adjusting for all patient, hospital, and physician covariates, including the propensity for treatment with macrolides, increased the OR to 0.89 and the results were no longer significant (95% CI, 0.781.01). Propensity matching successfully balanced all measured covariates except for the use of short‐acting bronchodilators and additional antibiotics (Table 1). In the propensity‐matched sample (Figure 1), quinolone‐treated patients were more likely to experience antibiotic‐associated diarrhea (1.2% vs. 0.6%; P = 0.0003) and late mechanical ventilation (1.3% vs. 0.8%; P = 0.02). There were no differences in adjusted cost or length of stay between the 2 groups. The results of the grouped treatment analysis, substituting the hospital's specific rate of macrolide use in place of the actual treatment that each patient received suggested that the 2 antibiotics were associated with similar rates of treatment failure. The OR for a 100% hospital rate of macrolide treatment vs. a 0% rate was 1.01 (95% CI, 0.751.35).

Figure 1
Outcomes of quinolone‐treated and macrolide‐treated patients in the sample matched by propensity score.
Outcomes of Patients Treated With Macrolides Compared to Those Treated With Quinolones for Acute Exacerbation of COPD
 Treatment FailureCostLOS
ModelsOR95% CIRatio95% CIRatio95% CI
  • Abbreviations: AIDS, acquired immune deficiency syndrome; CI, confidence interval; COPD, chronic obstructive pulmonary disease; LOS, length of stay; OR, odds ratio.

  • Logistic regression model incorporating nonparsimonious propensity score only.

  • Covariates in all models included: age; gender; primary diagnosis; region; teaching status; sleep apnea; hypertension; depression, paralysis; other neurological disorders; weight loss; heart failure; pulmonary circulation disease; valve disease; metastatic cancer; and initiation of oral or intravenous steroids, short‐acting bronchodilators, arterial blood gas, loop diuretics, methylxanthine bronchodilators, and morphine in the first 2 hospitals days. In addition, LOS and cost models included: insurance; attending physician specialty; hospital bed size; admission source; prior year admissions for COPD; chronic pulmonary heart disease; diabetes; hypothyroid; deficiency anemia; obesity; peripheral vascular disease; blood loss; alcohol abuse; drug abuse; AIDS; and initiation of long acting beta‐2 agonists, noninvasive ventilation, mucolytic medications, chest physiotherapy, and sputum testing in the first 2 hospital days. The LOS model also included pulmonary function tests. The cost model also included rural population, renal failure, psychoses, and solid tumor without metastasis. Interactions in the treatment failure model were the following: age with arterial blood gas, and loop diuretics with heart failure and with paralysis. Interactions in the LOS model were the following: loop diuretics with heart failure; and long‐acting beta‐2 agonists with gender and metastatic cancer. Interactions in the cost model were loop diuretics with heart failure, sleep apnea and chronic pulmonary heart disease, long‐acting beta‐2 agonists with metastatic cancer, and obesity with hypothyroid.

  • Each macrolide‐treated patient was matched on propensity with 1 quinolone‐treated patient. Of 6139 macrolide‐treated patients, 5610 (91.4%) were matched.

  • In place of actual treatment received, the subjects are assigned a probability of treatment corresponding to the hospital's overall macrolide rate.

  • In addition to covariates in the treatment failure model, the group treatment model also included race/ethnicity and attending physician specialty.

Unadjusted0.830.730.930.980.971.000.960.950.98
Adjusted for propensity score only*0.890.791.011.000.981.010.980.971.00
Adjusted for covariates0.870.770.991.000.991.020.990.971.00
Adjusted for covariates and propensity score0.890.781.011.000.991.020.980.971.00
Matched sample, unadjusted0.870.751.000.990.981.010.990.971.01
Matched sample, adjusted for unbalanced variables0.870.751.011.000.981.020.990.971.01
Grouped treatment model, unadjusted0.900.681.190.970.891.060.920.870.96
Group treatment model, adjusted for covariates1.010.751.350.960.881.050.960.911.00

Discussion

In this large observational study conducted at 375 hospitals, we took advantage of a natural experiment in which antibiotic prescribing patterns varied widely across hospitals to compare the effectiveness of 2 common antibiotic regimens for AECOPD. Treatment with macrolides and quinolones were associated with a similar risk of treatment failure, costs, and length of stay; however, patients treated with macrolides were less likely to experience late mechanical ventilation or treatment for antibiotic‐associated diarrhea.

Despite broad consensus in COPD guidelines that patients with severe acute exacerbations should receive antibiotics, there is little agreement about the preferred empiric agent. Controversy exists regarding antibiotics' comparative effectiveness, and even over which pathogens cause COPD exacerbations. Given the frequency of hospitalization for AECOPD, understanding the comparative effectiveness of treatments in this setting could have important implications for health outcomes and costs. Unfortunately, most antibiotic studies in AECOPD were conducted >20 years ago, using antibiotics that rarely appeared in our sample.26 Consequently, clinical practice guidelines offer conflicting recommendations. For example, the National Institute for Clinical Excellence recommends empirical treatment with an aminopenicillin, a macrolide, or a tetracycline,8 while the American Thoracic Society recommends amoxicillin‐clavulonate or a fluoroquinolone.9

As might be expected in light of so much uncertainty, we found wide variation in prescribing patterns across hospitals. Overall, approximately one‐third of patients received a macrolide and two‐thirds a quinolone. Both regimens provide adequate coverage of H. influenza, S. pneumoniae, and M. catarrhalis, and conform to at least 1 COPD guideline. Nevertheless, patients receiving macrolides often received a cephalosporin as well; this pattern of treatment suggests that antibiotic selection is likely to have been influenced more by guidelines for the treatment of community‐acquired pneumonia than COPD.

Previous studies comparing antibiotic effectiveness suffer from shortcomings that limit their application to patients hospitalized with AECOPD. First, they enrolled patients with chronic bronchitis, and included patients without obstructive lung disease, and most studies included patients as young as 18 years old. Second, many either did not include treatment with steroids or excluded patients receiving more than 10 mg of prednisone daily. Third, almost all enrolled only ambulatory patients.

While there are no studies comparing quinolones and macrolides in patients hospitalized for AECOPD, a meta‐analysis comparing quinolones, macrolides, and amoxicillin‐clavulonate identified 19 trials of ambulatory patients with chronic bronchitis. That study found that all 3 drugs had similar efficacy initially, but that quinolones resulted in the fewest relapses over a 26‐week period.27 Macrolides and quinolones had similar rates of adverse effects. In contrast, we did not find a difference in treatment failure, cost, or length of stay, but did find a higher rate of diarrhea associated with quinolones. Others have also documented an association between fluoroquinolones and C. difficile diarrhea.2830 This trend, first noted in 2001, is of particular concern because the fluoroquinolone‐resistant strains appear to be hypervirulent and have been associated with nosocomial epidemics.3134

Our study has several limitations. First, its observational design leaves open the possibility of selection bias. For this reason we analyzed our data in several ways, including using a grouped treatment approach, an adaptation of the instrumental variable technique, and accepted only those differences which were consistent across all models. Second, our study used claims data, and therefore we could not directly adjust for physiological measures of severity. However, the highly detailed nature of the data allowed us to adjust for numerous tests and treatments that reflected the clinician's assessment of the patient's severity, as well as the number of prior COPD admissions. Third, we cannot exclude the possibility that some patients may have had concurrent pneumonia without an ICD‐9 code. We think that the number would be small because reimbursement for pneumonia is generally higher than for COPD, so hospitals have an incentive to code pneumonia as the principal diagnosis when present. Finally, we compared initial antibiotics only. More than one‐quarter of our patients received an additional antibiotic before discharge. In particular, patients receiving macrolides were often prescribed a concomitant cephalosporin. We do not know to what extent these additional antibiotics may have affected the outcomes.

Despite the large number of patients hospitalized annually for AECOPD, there are no randomized trials comparing different antibiotics in this population. Studies comparing antibiotics in chronic bronchitis can offer little guidance, since they have primarily focused on proving equivalence between existing antibiotics and newer, more expensive formulations.35 Because many of the patients enrolled in such trials do not benefit from antibiotics at all, either because they do not have COPD or because their exacerbation is not caused by bacteria, it is relatively easy to prove equivalence. Given that AECOPD is one of the leading causes of hospitalization in the United States, large, randomized trials comparing the effectiveness of different antibiotics should be a high priority. In the meantime, macrolides (often given together with cephalosporins) and quinolones appear to be equally effective initial antibiotic choices; considering antibiotic‐associated diarrhea, macrolides appear to be the safer of the 2.

References
  1. Snow V,Lascher S,Mottur‐Pilson C.Evidence base for management of acute exacerbations of chronic obstructive pulmonary disease.Ann Intern Med.2001;134:595599.
  2. Lieberman D,Ben‐Yaakov M,Lazarovich Z, et al.Infectious etiologies in acute exacerbation of COPD.Diagn Microbiol Infect Dis.2001;40:95102.
  3. Groenewegen KH,Wouters EF.Bacterial infections in patients requiring admission for an acute exacerbation of COPD; a 1‐year prospective study.Respir Med.2003;97:770777.
  4. Rosell A,Monso E,Soler N, et al.Microbiologic determinants of exacerbation in chronic obstructive pulmonary disease.Arch Intern Med.2005;165:891897.
  5. Sethi S,Murphy TF.Infection in the pathogenesis and course of chronic obstructive pulmonary disease.N Engl J Med.2008;359:23552365.
  6. Rabe KF,Hurd S,Anzueto A, et al.Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary.Am J Respir Crit Care Med.2007;176:532555.
  7. O'Donnell DE,Hernandez P,Kaplan A, et al.Canadian Thoracic Society recommendations for management of chronic obstructive pulmonary disease—2008 update—highlights for primary care.Can Respir J.2008;15(suppl A):1A8A.
  8. Chronic obstructive pulmonary disease.National clinical guideline on management of chronic obstructive pulmonary disease in adults in primary and secondary care.Thorax.2004;59(suppl 1):1232.
  9. Celli BR,MacNee W,Agusti A, et al.Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper.Eur Respir J.2004;23:932946.
  10. Ram FS,Rodriguez‐Roisin R,Granados‐Navarrete A,Garcia‐Aymerich J,Barnes NC.Antibiotics for exacerbations of chronic obstructive pulmonary disease.Cochrane Database Syst Rev.2006:CD004403.
  11. Lindenauer PK,Pekow P,Gao S,Crawford AS,Gutierrez B,Benjamin EM.Quality of care for patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease.Ann Intern Med.2006;144:894903.
  12. Parnham MJ,Culic O,Erakovic V, et al.Modulation of neutrophil and inflammation markers in chronic obstructive pulmonary disease by short‐term azithromycin treatment.Eur J Pharmacol.2005;517:132143.
  13. Culic O,Erakovic V,Cepelak I, et al.Azithromycin modulates neutrophil function and circulating inflammatory mediators in healthy human subjects.Eur J Pharmacol.2002;450:277289.
  14. Basyigit I,Yildiz F,Ozkara SK,Yildirim E,Boyaci H,Ilgazli A.The effect of clarithromycin on inflammatory markers in chronic obstructive pulmonary disease: preliminary data.Ann Pharmacother.2004;38:14001405.
  15. Wilson R,Schentag JJ,Ball P,Mandell L.A comparison of gemifloxacin and clarithromycin in acute exacerbations of chronic bronchitis and long‐term clinical outcomes.Clin Ther.2002;24:639652.
  16. Patil SP,Krishnan JA,Lechtzin N,Diette GB.In‐hospital mortality following acute exacerbations of chronic obstructive pulmonary disease.Arch Intern Med.2003;163:11801186.
  17. Elixhauser A,Steiner C,Harris DR,Coffey RM.Comorbidity measures for use with administrative data.Med Care.1998;36:827.
  18. Connors AF,Dawson NV,Thomas C, et al.Outcomes following acute exacerbation of severe chronic obstructive lung disease. The SUPPORT investigators (Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments).Am J Respir Crit Care Med.1996;154:959967.
  19. Groenewegen KH,Schols AMWJ,Wouters EFM.Mortality and mortality‐related factors after hospitalization for acute exacerbation of COPD.Chest.2003;124:459467.
  20. Niewoehner DE,Erbland ML,Deupree RH, et al.Effect of systemic glucocorticoids on exacerbations of chronic obstructive pulmonary disease.N Engl J Med.1999;340:19411947.
  21. D'Agostino RB.Propensity score methods for bias reduction in the comparison of a treatment to a non‐randomized control group.Stat Med.1998;17:22652281.
  22. Parsons L.Reducing bias in a propensity score matched‐pair sample using greedy matching techniques. Proceedings of the Twenty‐Sixth Annual SAS Users Group International Conference. Cary, NC: SAS Institute;2001,214216.
  23. Johnston SC,Henneman T,McCulloch CE,van der Laan M.Modeling treatment effects on binary outcomes with grouped‐treatment variables and individual covariates.Am J Epidemiol.2002;156:753760.
  24. McClellan M,McNeil BJ,Newhouse JP.Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. [see Comment].JAMA.1994;272:859866.
  25. Stukel TA,Fisher ES,Wennberg DE,Alter DA,Gottlieb DJ,Vermeulen MJ.Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods.JAMA.2007;297:278285.
  26. Saint S,Bent S,Vittinghoff E,Grady D.Antibiotics in chronic obstructive pulmonary disease exacerbations. A meta‐analysis.JAMA.1995;273:957960.
  27. Siempos II,Dimopoulos G,Korbila IP,Manta K,Falagas ME.Macrolides, quinolones and amoxicillin/clavulanate for chronic bronchitis: a meta‐analysis.Eur Respir J.2007;29:11271137.
  28. Owens RC,Donskey CJ,Gaynes RP,Loo VG,Muto CA.Antimicrobial‐associated risk factors for Clostridium difficile infection.Clin Infect Dis.2008;46(suppl 1):S19S31.
  29. Wilson R,Allegra L,Huchon G, et al.Short‐term and long‐term outcomes of moxifloxacin compared to standard antibiotic treatment in acute exacerbations of chronic bronchitis.Chest.2004;125:953964.
  30. Wilson R,Langan C,Ball P,Bateman K,Pypstra R.Oral gemifloxacin once daily for 5 days compared with sequential therapy with i.v. ceftriaxone/oral cefuroxime (maximum of 10 days) in the treatment of hospitalized patients with acute exacerbations of chronic bronchitis.Respir Med.2003;97:242249.
  31. Muto CA,Pokrywka M,Shutt K, et al.A large outbreak of Clostridium difficile‐associated disease with an unexpected proportion of deaths and colectomies at a teaching hospital following increased fluoroquinolone use.Infect Control Hosp Epidemiol.2005;26:273280.
  32. McDonald LC,Killgore GE,Thompson A, et al.An epidemic, toxin gene‐variant strain of Clostridium difficile.N Engl J Med.2005;353:24332441.
  33. Gaynes R,Rimland D,Killum E, et al.Outbreak of Clostridium difficile infection in a long‐term care facility: association with gatifloxacin use.Clin Infect Dis.2004;38:640645.
  34. Loo VG,Poirier L,Miller MA, et al.A predominantly clonal multi‐institutional outbreak of Clostridium difficile‐associated diarrhea with high morbidity and mortality.N Engl J Med.2005;353:24422449.
  35. Miravitlles M,Torres A.No more equivalence trials for antibiotics in exacerbations of COPD, please.Chest.2004;125:811813.
References
  1. Snow V,Lascher S,Mottur‐Pilson C.Evidence base for management of acute exacerbations of chronic obstructive pulmonary disease.Ann Intern Med.2001;134:595599.
  2. Lieberman D,Ben‐Yaakov M,Lazarovich Z, et al.Infectious etiologies in acute exacerbation of COPD.Diagn Microbiol Infect Dis.2001;40:95102.
  3. Groenewegen KH,Wouters EF.Bacterial infections in patients requiring admission for an acute exacerbation of COPD; a 1‐year prospective study.Respir Med.2003;97:770777.
  4. Rosell A,Monso E,Soler N, et al.Microbiologic determinants of exacerbation in chronic obstructive pulmonary disease.Arch Intern Med.2005;165:891897.
  5. Sethi S,Murphy TF.Infection in the pathogenesis and course of chronic obstructive pulmonary disease.N Engl J Med.2008;359:23552365.
  6. Rabe KF,Hurd S,Anzueto A, et al.Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary.Am J Respir Crit Care Med.2007;176:532555.
  7. O'Donnell DE,Hernandez P,Kaplan A, et al.Canadian Thoracic Society recommendations for management of chronic obstructive pulmonary disease—2008 update—highlights for primary care.Can Respir J.2008;15(suppl A):1A8A.
  8. Chronic obstructive pulmonary disease.National clinical guideline on management of chronic obstructive pulmonary disease in adults in primary and secondary care.Thorax.2004;59(suppl 1):1232.
  9. Celli BR,MacNee W,Agusti A, et al.Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper.Eur Respir J.2004;23:932946.
  10. Ram FS,Rodriguez‐Roisin R,Granados‐Navarrete A,Garcia‐Aymerich J,Barnes NC.Antibiotics for exacerbations of chronic obstructive pulmonary disease.Cochrane Database Syst Rev.2006:CD004403.
  11. Lindenauer PK,Pekow P,Gao S,Crawford AS,Gutierrez B,Benjamin EM.Quality of care for patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease.Ann Intern Med.2006;144:894903.
  12. Parnham MJ,Culic O,Erakovic V, et al.Modulation of neutrophil and inflammation markers in chronic obstructive pulmonary disease by short‐term azithromycin treatment.Eur J Pharmacol.2005;517:132143.
  13. Culic O,Erakovic V,Cepelak I, et al.Azithromycin modulates neutrophil function and circulating inflammatory mediators in healthy human subjects.Eur J Pharmacol.2002;450:277289.
  14. Basyigit I,Yildiz F,Ozkara SK,Yildirim E,Boyaci H,Ilgazli A.The effect of clarithromycin on inflammatory markers in chronic obstructive pulmonary disease: preliminary data.Ann Pharmacother.2004;38:14001405.
  15. Wilson R,Schentag JJ,Ball P,Mandell L.A comparison of gemifloxacin and clarithromycin in acute exacerbations of chronic bronchitis and long‐term clinical outcomes.Clin Ther.2002;24:639652.
  16. Patil SP,Krishnan JA,Lechtzin N,Diette GB.In‐hospital mortality following acute exacerbations of chronic obstructive pulmonary disease.Arch Intern Med.2003;163:11801186.
  17. Elixhauser A,Steiner C,Harris DR,Coffey RM.Comorbidity measures for use with administrative data.Med Care.1998;36:827.
  18. Connors AF,Dawson NV,Thomas C, et al.Outcomes following acute exacerbation of severe chronic obstructive lung disease. The SUPPORT investigators (Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments).Am J Respir Crit Care Med.1996;154:959967.
  19. Groenewegen KH,Schols AMWJ,Wouters EFM.Mortality and mortality‐related factors after hospitalization for acute exacerbation of COPD.Chest.2003;124:459467.
  20. Niewoehner DE,Erbland ML,Deupree RH, et al.Effect of systemic glucocorticoids on exacerbations of chronic obstructive pulmonary disease.N Engl J Med.1999;340:19411947.
  21. D'Agostino RB.Propensity score methods for bias reduction in the comparison of a treatment to a non‐randomized control group.Stat Med.1998;17:22652281.
  22. Parsons L.Reducing bias in a propensity score matched‐pair sample using greedy matching techniques. Proceedings of the Twenty‐Sixth Annual SAS Users Group International Conference. Cary, NC: SAS Institute;2001,214216.
  23. Johnston SC,Henneman T,McCulloch CE,van der Laan M.Modeling treatment effects on binary outcomes with grouped‐treatment variables and individual covariates.Am J Epidemiol.2002;156:753760.
  24. McClellan M,McNeil BJ,Newhouse JP.Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. [see Comment].JAMA.1994;272:859866.
  25. Stukel TA,Fisher ES,Wennberg DE,Alter DA,Gottlieb DJ,Vermeulen MJ.Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods.JAMA.2007;297:278285.
  26. Saint S,Bent S,Vittinghoff E,Grady D.Antibiotics in chronic obstructive pulmonary disease exacerbations. A meta‐analysis.JAMA.1995;273:957960.
  27. Siempos II,Dimopoulos G,Korbila IP,Manta K,Falagas ME.Macrolides, quinolones and amoxicillin/clavulanate for chronic bronchitis: a meta‐analysis.Eur Respir J.2007;29:11271137.
  28. Owens RC,Donskey CJ,Gaynes RP,Loo VG,Muto CA.Antimicrobial‐associated risk factors for Clostridium difficile infection.Clin Infect Dis.2008;46(suppl 1):S19S31.
  29. Wilson R,Allegra L,Huchon G, et al.Short‐term and long‐term outcomes of moxifloxacin compared to standard antibiotic treatment in acute exacerbations of chronic bronchitis.Chest.2004;125:953964.
  30. Wilson R,Langan C,Ball P,Bateman K,Pypstra R.Oral gemifloxacin once daily for 5 days compared with sequential therapy with i.v. ceftriaxone/oral cefuroxime (maximum of 10 days) in the treatment of hospitalized patients with acute exacerbations of chronic bronchitis.Respir Med.2003;97:242249.
  31. Muto CA,Pokrywka M,Shutt K, et al.A large outbreak of Clostridium difficile‐associated disease with an unexpected proportion of deaths and colectomies at a teaching hospital following increased fluoroquinolone use.Infect Control Hosp Epidemiol.2005;26:273280.
  32. McDonald LC,Killgore GE,Thompson A, et al.An epidemic, toxin gene‐variant strain of Clostridium difficile.N Engl J Med.2005;353:24332441.
  33. Gaynes R,Rimland D,Killum E, et al.Outbreak of Clostridium difficile infection in a long‐term care facility: association with gatifloxacin use.Clin Infect Dis.2004;38:640645.
  34. Loo VG,Poirier L,Miller MA, et al.A predominantly clonal multi‐institutional outbreak of Clostridium difficile‐associated diarrhea with high morbidity and mortality.N Engl J Med.2005;353:24422449.
  35. Miravitlles M,Torres A.No more equivalence trials for antibiotics in exacerbations of COPD, please.Chest.2004;125:811813.
Issue
Journal of Hospital Medicine - 5(5)
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Journal of Hospital Medicine - 5(5)
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Comparative effectiveness of macrolides and quinolones for patients hospitalized with acute exacerbations of chronic obstructive pulmonary disease (AECOPD)
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Comparative effectiveness of macrolides and quinolones for patients hospitalized with acute exacerbations of chronic obstructive pulmonary disease (AECOPD)
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antibiotics, chronic obstructive, pulmonary disease, effectiveness, treatment
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Evaluation of Hemostasis

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An important factor in preoperative screening

A previously healthy 25‐year‐old Guatemalan man presented to the emergency department with 1 day of fever, nausea, vomiting, and right lower quadrant abdominal pain. A computed tomography (CT) scan revealed acute appendicitis. The patient underwent an uncomplicated laparoscopic appendectomy and was discharged in stable condition after 48 hours.

Five days after the operation he returned to the emergency department with abdominal pain, nausea, vomiting, and lightheadedness. He was tachycardic, and his hemoglobin was 9.5 g/dL (normal, 13.3‐17.7 g/dL), decreased from 14.4 g/dL prior to his appendectomy. A CT scan showed intraperitoneal blood with active extravasation of contrast at the site of the appendectomy.

Additional laboratory testing revealed an activated partial thromboplastin time (aPTT) of 52 seconds (normal, <37 seconds) and protime (also prothrombin time [PT]) of 14 seconds (normal, <14.1 seconds). The platelet count was 449,000/L (normal, 150‐400,000/L) and the fibrinogen level was 337 mg/dL (normal, 170‐440 mg/dL). Crystalloid and packed red blood cells were administered. Since further laboratory evaluation of the prolonged aPTT was not immediately available, the patient was empirically treated with fresh frozen plasma (FFP), cryoprecipitate, and Factor VIII/von Willebrand factor concentrate. At laparotomy, bleeding was observed at the previous operative site, and 2 L of intraperitoneal blood was evacuated.

The next morning, Factor VIII and Factor IX (FIX) activities and the ristocetin cofactor study performed on specimens obtained immediately prior to the second operation were normal, but the FIX activity was 5% of normal. The diagnosis of FXI deficiency was made and 2 to 3 units of FFP (the amount necessary to maintain the patient's measured FXI activity near 20% of normal) were transfused daily. Nine days of FFP infusions were required to achieve complete wound hemostasis. The patient had no further bleeding episodes after discharge.

Upon further interviewing, the patient revealed that 2 months prior he sustained a small laceration on his arm that bled for a long time and that his brother had experienced prolonged bleeding after a dental extraction.

Commentary

Routine performance of preprocedural laboratory testing, and complete reliance on the results as a means of excluding a propensity to bleeding, may not only lead to excessive testing and delayed procedures, but also provides false reassurance because normal routine laboratory studies cannot be used to exclude some bleeding disorders (Table 1).

Disorders of Hemostasis Not Detected Routinely by the Activated Partial Thromboplastin Time, Protime, or Platelet Count
Von Willebrand disease
Mild hemophilia A (Factor VIII deficiency)
Mild hemophilia B (Factor IX deficiency)
Mild hemophilia C (Factor XI deficiency)
Qualitative platelet disorders (congenital or acquired)
Factor XIII deficiency
Disorders of fibrinolysis (eg, antiplasmin deficiency, plasminogen activator inhibitor type 1 deficiency)
Disorders of the vasculature or integument (hereditary hemorrhagic telangiectasia, Ehlers‐Danlos syndrome)

Most studies evaluating routine laboratory testing of hemostatic variables prior to invasive procedures come from patients undergoing elective general surgery. A 1988 study concluded that there is no benefit in the routine preoperative use of the PT, aPTT, platelet count, and bleeding time in the absence of clinical evidence of a hemostatic defect, as assessed by a patient questionnaire and a thorough physical examination.1 A subsequent European, prospective, multicenter study confirmed that abnormalities of preoperative laboratory screening in the absence of a history of bleeding or clinical abnormality were not associated with worse surgical morbidity or mortality, compared to patients with normal screening laboratory studies.2 A recent systematic review has also confirmed the poor positive predictive value of screening tests when used in isolation, and recommended a history‐based and physical exam‐based approach.3 Questionnaires have been shown to be particularly important tools for eliciting clinically significant bleeding disorders that may require revision of the surgical plan.1,4

FXI is a serine protease whose activity is crucial for robust fibrin clot formation and inhibition of fibrinolysis at sites of vascular injury.5 FXI deficiency is an autosomal recessive disorder with an incidence of 1 per 1,000,000 in the general population, with a significantly higher incidence in the Ashkenazi Jewish population. While the risk of spontaneous hemorrhage is typically low, life‐threatening bleeding may occur after surgery or trauma. The severity of the measured FXI level deficiency does not always correlate with risk of bleeding. Periprocedural prophylaxis and treatment of bleeding aim to replace FXI to the low‐normal range by administering FXI concentrate, (not available in the United States) or FFP. Antifibrinolytic agents such as tranexamic acid or ϵ‐aminocaproic acid may be used adjunctively in cases of mucosal bleeding.5

In this case, preoperative screening, either using a questionnaire or careful history‐taking, would have identified the patient's personal and family history of bleeding and prompted appropriate preoperative coagulation testing, which could have exposed the hemostatic defect, allowing for modification of the perioperative medical plan.

In summary, preoperative bleeding evaluations should be performed routinely and should begin with a careful history (use of a questionnaire may be considered) and physical examination. Excessive bleeding after prior surgery, trauma, dental extractions, parturition, or circumcision; bleeding tendency in family members; current use of medications that may increase bleeding risk (such as anticoagulants or aspirin); and physical signs associated with bleeding should be assessed. If clinical details fail to expose a potential bleeding disorder, it is safe and cost‐effective1 to proceed with surgery without performing additional laboratory testing. In contrast, any abnormality on the clinical assessment should trigger preoperative laboratory analysis of basic hemostatic parameters, which may prompt further testing or hematology consultation.

References
  1. Rohrer MJ,Michelotti MC,Nahrwold DL.A prospective evaluation of the efficacy of preoperative coagulation testing.Ann Surg.1988;208(5):554557.
  2. Houry S,Georgeac C,Hay JM,Fingerhut A,Boudet MJ.A prospective multicenter evaluation of preoperative hemostatic screening tests. The French Associations for Surgical Research.Am J Surg.1995;170(1):1923.
  3. Chee YL,Crawford JC,Watson HG,Greaves M.Guidelines on the assessment of bleeding risk prior to surgery or invasive procedures.Br J Haematol.2008;140:496504.
  4. Koscielny J,Ziemer S,Radtke H, et al.A practical concept for preoperative identification of patients with impaired primary hemostasis.Clin Appl Thrombosis Haemost.2004;10(3):195204.
  5. Gomez K,Bolton‐Maggs P.Factor XI deficiency.Haemophilia.2008;14(6):11831189.
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A previously healthy 25‐year‐old Guatemalan man presented to the emergency department with 1 day of fever, nausea, vomiting, and right lower quadrant abdominal pain. A computed tomography (CT) scan revealed acute appendicitis. The patient underwent an uncomplicated laparoscopic appendectomy and was discharged in stable condition after 48 hours.

Five days after the operation he returned to the emergency department with abdominal pain, nausea, vomiting, and lightheadedness. He was tachycardic, and his hemoglobin was 9.5 g/dL (normal, 13.3‐17.7 g/dL), decreased from 14.4 g/dL prior to his appendectomy. A CT scan showed intraperitoneal blood with active extravasation of contrast at the site of the appendectomy.

Additional laboratory testing revealed an activated partial thromboplastin time (aPTT) of 52 seconds (normal, <37 seconds) and protime (also prothrombin time [PT]) of 14 seconds (normal, <14.1 seconds). The platelet count was 449,000/L (normal, 150‐400,000/L) and the fibrinogen level was 337 mg/dL (normal, 170‐440 mg/dL). Crystalloid and packed red blood cells were administered. Since further laboratory evaluation of the prolonged aPTT was not immediately available, the patient was empirically treated with fresh frozen plasma (FFP), cryoprecipitate, and Factor VIII/von Willebrand factor concentrate. At laparotomy, bleeding was observed at the previous operative site, and 2 L of intraperitoneal blood was evacuated.

The next morning, Factor VIII and Factor IX (FIX) activities and the ristocetin cofactor study performed on specimens obtained immediately prior to the second operation were normal, but the FIX activity was 5% of normal. The diagnosis of FXI deficiency was made and 2 to 3 units of FFP (the amount necessary to maintain the patient's measured FXI activity near 20% of normal) were transfused daily. Nine days of FFP infusions were required to achieve complete wound hemostasis. The patient had no further bleeding episodes after discharge.

Upon further interviewing, the patient revealed that 2 months prior he sustained a small laceration on his arm that bled for a long time and that his brother had experienced prolonged bleeding after a dental extraction.

Commentary

Routine performance of preprocedural laboratory testing, and complete reliance on the results as a means of excluding a propensity to bleeding, may not only lead to excessive testing and delayed procedures, but also provides false reassurance because normal routine laboratory studies cannot be used to exclude some bleeding disorders (Table 1).

Disorders of Hemostasis Not Detected Routinely by the Activated Partial Thromboplastin Time, Protime, or Platelet Count
Von Willebrand disease
Mild hemophilia A (Factor VIII deficiency)
Mild hemophilia B (Factor IX deficiency)
Mild hemophilia C (Factor XI deficiency)
Qualitative platelet disorders (congenital or acquired)
Factor XIII deficiency
Disorders of fibrinolysis (eg, antiplasmin deficiency, plasminogen activator inhibitor type 1 deficiency)
Disorders of the vasculature or integument (hereditary hemorrhagic telangiectasia, Ehlers‐Danlos syndrome)

Most studies evaluating routine laboratory testing of hemostatic variables prior to invasive procedures come from patients undergoing elective general surgery. A 1988 study concluded that there is no benefit in the routine preoperative use of the PT, aPTT, platelet count, and bleeding time in the absence of clinical evidence of a hemostatic defect, as assessed by a patient questionnaire and a thorough physical examination.1 A subsequent European, prospective, multicenter study confirmed that abnormalities of preoperative laboratory screening in the absence of a history of bleeding or clinical abnormality were not associated with worse surgical morbidity or mortality, compared to patients with normal screening laboratory studies.2 A recent systematic review has also confirmed the poor positive predictive value of screening tests when used in isolation, and recommended a history‐based and physical exam‐based approach.3 Questionnaires have been shown to be particularly important tools for eliciting clinically significant bleeding disorders that may require revision of the surgical plan.1,4

FXI is a serine protease whose activity is crucial for robust fibrin clot formation and inhibition of fibrinolysis at sites of vascular injury.5 FXI deficiency is an autosomal recessive disorder with an incidence of 1 per 1,000,000 in the general population, with a significantly higher incidence in the Ashkenazi Jewish population. While the risk of spontaneous hemorrhage is typically low, life‐threatening bleeding may occur after surgery or trauma. The severity of the measured FXI level deficiency does not always correlate with risk of bleeding. Periprocedural prophylaxis and treatment of bleeding aim to replace FXI to the low‐normal range by administering FXI concentrate, (not available in the United States) or FFP. Antifibrinolytic agents such as tranexamic acid or ϵ‐aminocaproic acid may be used adjunctively in cases of mucosal bleeding.5

In this case, preoperative screening, either using a questionnaire or careful history‐taking, would have identified the patient's personal and family history of bleeding and prompted appropriate preoperative coagulation testing, which could have exposed the hemostatic defect, allowing for modification of the perioperative medical plan.

In summary, preoperative bleeding evaluations should be performed routinely and should begin with a careful history (use of a questionnaire may be considered) and physical examination. Excessive bleeding after prior surgery, trauma, dental extractions, parturition, or circumcision; bleeding tendency in family members; current use of medications that may increase bleeding risk (such as anticoagulants or aspirin); and physical signs associated with bleeding should be assessed. If clinical details fail to expose a potential bleeding disorder, it is safe and cost‐effective1 to proceed with surgery without performing additional laboratory testing. In contrast, any abnormality on the clinical assessment should trigger preoperative laboratory analysis of basic hemostatic parameters, which may prompt further testing or hematology consultation.

A previously healthy 25‐year‐old Guatemalan man presented to the emergency department with 1 day of fever, nausea, vomiting, and right lower quadrant abdominal pain. A computed tomography (CT) scan revealed acute appendicitis. The patient underwent an uncomplicated laparoscopic appendectomy and was discharged in stable condition after 48 hours.

Five days after the operation he returned to the emergency department with abdominal pain, nausea, vomiting, and lightheadedness. He was tachycardic, and his hemoglobin was 9.5 g/dL (normal, 13.3‐17.7 g/dL), decreased from 14.4 g/dL prior to his appendectomy. A CT scan showed intraperitoneal blood with active extravasation of contrast at the site of the appendectomy.

Additional laboratory testing revealed an activated partial thromboplastin time (aPTT) of 52 seconds (normal, <37 seconds) and protime (also prothrombin time [PT]) of 14 seconds (normal, <14.1 seconds). The platelet count was 449,000/L (normal, 150‐400,000/L) and the fibrinogen level was 337 mg/dL (normal, 170‐440 mg/dL). Crystalloid and packed red blood cells were administered. Since further laboratory evaluation of the prolonged aPTT was not immediately available, the patient was empirically treated with fresh frozen plasma (FFP), cryoprecipitate, and Factor VIII/von Willebrand factor concentrate. At laparotomy, bleeding was observed at the previous operative site, and 2 L of intraperitoneal blood was evacuated.

The next morning, Factor VIII and Factor IX (FIX) activities and the ristocetin cofactor study performed on specimens obtained immediately prior to the second operation were normal, but the FIX activity was 5% of normal. The diagnosis of FXI deficiency was made and 2 to 3 units of FFP (the amount necessary to maintain the patient's measured FXI activity near 20% of normal) were transfused daily. Nine days of FFP infusions were required to achieve complete wound hemostasis. The patient had no further bleeding episodes after discharge.

Upon further interviewing, the patient revealed that 2 months prior he sustained a small laceration on his arm that bled for a long time and that his brother had experienced prolonged bleeding after a dental extraction.

Commentary

Routine performance of preprocedural laboratory testing, and complete reliance on the results as a means of excluding a propensity to bleeding, may not only lead to excessive testing and delayed procedures, but also provides false reassurance because normal routine laboratory studies cannot be used to exclude some bleeding disorders (Table 1).

Disorders of Hemostasis Not Detected Routinely by the Activated Partial Thromboplastin Time, Protime, or Platelet Count
Von Willebrand disease
Mild hemophilia A (Factor VIII deficiency)
Mild hemophilia B (Factor IX deficiency)
Mild hemophilia C (Factor XI deficiency)
Qualitative platelet disorders (congenital or acquired)
Factor XIII deficiency
Disorders of fibrinolysis (eg, antiplasmin deficiency, plasminogen activator inhibitor type 1 deficiency)
Disorders of the vasculature or integument (hereditary hemorrhagic telangiectasia, Ehlers‐Danlos syndrome)

Most studies evaluating routine laboratory testing of hemostatic variables prior to invasive procedures come from patients undergoing elective general surgery. A 1988 study concluded that there is no benefit in the routine preoperative use of the PT, aPTT, platelet count, and bleeding time in the absence of clinical evidence of a hemostatic defect, as assessed by a patient questionnaire and a thorough physical examination.1 A subsequent European, prospective, multicenter study confirmed that abnormalities of preoperative laboratory screening in the absence of a history of bleeding or clinical abnormality were not associated with worse surgical morbidity or mortality, compared to patients with normal screening laboratory studies.2 A recent systematic review has also confirmed the poor positive predictive value of screening tests when used in isolation, and recommended a history‐based and physical exam‐based approach.3 Questionnaires have been shown to be particularly important tools for eliciting clinically significant bleeding disorders that may require revision of the surgical plan.1,4

FXI is a serine protease whose activity is crucial for robust fibrin clot formation and inhibition of fibrinolysis at sites of vascular injury.5 FXI deficiency is an autosomal recessive disorder with an incidence of 1 per 1,000,000 in the general population, with a significantly higher incidence in the Ashkenazi Jewish population. While the risk of spontaneous hemorrhage is typically low, life‐threatening bleeding may occur after surgery or trauma. The severity of the measured FXI level deficiency does not always correlate with risk of bleeding. Periprocedural prophylaxis and treatment of bleeding aim to replace FXI to the low‐normal range by administering FXI concentrate, (not available in the United States) or FFP. Antifibrinolytic agents such as tranexamic acid or ϵ‐aminocaproic acid may be used adjunctively in cases of mucosal bleeding.5

In this case, preoperative screening, either using a questionnaire or careful history‐taking, would have identified the patient's personal and family history of bleeding and prompted appropriate preoperative coagulation testing, which could have exposed the hemostatic defect, allowing for modification of the perioperative medical plan.

In summary, preoperative bleeding evaluations should be performed routinely and should begin with a careful history (use of a questionnaire may be considered) and physical examination. Excessive bleeding after prior surgery, trauma, dental extractions, parturition, or circumcision; bleeding tendency in family members; current use of medications that may increase bleeding risk (such as anticoagulants or aspirin); and physical signs associated with bleeding should be assessed. If clinical details fail to expose a potential bleeding disorder, it is safe and cost‐effective1 to proceed with surgery without performing additional laboratory testing. In contrast, any abnormality on the clinical assessment should trigger preoperative laboratory analysis of basic hemostatic parameters, which may prompt further testing or hematology consultation.

References
  1. Rohrer MJ,Michelotti MC,Nahrwold DL.A prospective evaluation of the efficacy of preoperative coagulation testing.Ann Surg.1988;208(5):554557.
  2. Houry S,Georgeac C,Hay JM,Fingerhut A,Boudet MJ.A prospective multicenter evaluation of preoperative hemostatic screening tests. The French Associations for Surgical Research.Am J Surg.1995;170(1):1923.
  3. Chee YL,Crawford JC,Watson HG,Greaves M.Guidelines on the assessment of bleeding risk prior to surgery or invasive procedures.Br J Haematol.2008;140:496504.
  4. Koscielny J,Ziemer S,Radtke H, et al.A practical concept for preoperative identification of patients with impaired primary hemostasis.Clin Appl Thrombosis Haemost.2004;10(3):195204.
  5. Gomez K,Bolton‐Maggs P.Factor XI deficiency.Haemophilia.2008;14(6):11831189.
References
  1. Rohrer MJ,Michelotti MC,Nahrwold DL.A prospective evaluation of the efficacy of preoperative coagulation testing.Ann Surg.1988;208(5):554557.
  2. Houry S,Georgeac C,Hay JM,Fingerhut A,Boudet MJ.A prospective multicenter evaluation of preoperative hemostatic screening tests. The French Associations for Surgical Research.Am J Surg.1995;170(1):1923.
  3. Chee YL,Crawford JC,Watson HG,Greaves M.Guidelines on the assessment of bleeding risk prior to surgery or invasive procedures.Br J Haematol.2008;140:496504.
  4. Koscielny J,Ziemer S,Radtke H, et al.A practical concept for preoperative identification of patients with impaired primary hemostasis.Clin Appl Thrombosis Haemost.2004;10(3):195204.
  5. Gomez K,Bolton‐Maggs P.Factor XI deficiency.Haemophilia.2008;14(6):11831189.
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Spontaneous Retroperitoneal Hematoma

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Spontaneous retroperitoneal hematoma originating at lumbar arteries in context of cirrhosis

A 56‐year‐old male presented to the emergency department with a 2‐week history of increasing abdominal girth, nausea, vomiting, and lower extremity edema. His girlfriend had also noted a yellow tinge to his skin and eyes. His past medical history was significant for bipolar disorder, alcohol‐related seizures, and pneumonia. He had no allergies and denied medications prior to admission. Family history was negative for liver disease and social history was notable for ongoing tobacco use and alcohol dependence. He was afebrile with stable vital signs. Physical examination demonstrated an alert gentleman whose answers to questions required occasional factual correction by his partner. His abdomen was distended and nontender with prominent vasculature and shifting dullness. Lower extremity edema was symmetric and bilateral, rated as 2+. Scattered spider angiomata and a fine bilateral hand tremor without asterixis were also noted. Initial laboratory data demonstrated a white blood cell count of 13,900/L, hematocrit 37%, and platelet count 176,000/L. His sodium was 130 mg/dL, blood urea nitrogen (BUN) 1 mg/dL, and creatinine 0.7 mg/dL. International normalized ratio (INR) was 1.8, aspartate aminotransferase (AST) was 117 U/L, alanine aminotransferase (ALT) 33 U/L, alkaline phosphatase 191 U/L, total bilirubin 9.2 mg/dL, total protein 7.0 g/dL, and albumin 1.9 g/dL. Abdominal ultrasound revealed a diffusely hyperechoic liver with a large amount of ascites.

The patient was admitted with the diagnoses of presumed alcoholic hepatitis and end‐stage liver disease. Model for End‐Stage Liver Disease (MELD) score was 21 and discriminant function 16.8. Paracentesis demonstrated a serum‐ascites albumin gradient of >1.1 and no evidence of spontaneous bacterial peritonitis. Diuresis was initiated. He was placed on unfractionated heparin at a dose of 5000 units every 8 hours for deep venous thrombosis (DVT) prophylaxis. By hospital day 3, the patient's laboratory values had improved, yet his stay was prolonged by alcohol withdrawal requiring benzodiazepines, altered mental status presumed secondary to hepatic encephalopathy, acute renal failure, aspiration pneumonia, and persistent unexplained leukocytosis. He required medical restraints during this time given confusion and propensity to ambulate without assistance, yet sustained no falls or other known trauma in care delivered during this time.

Between days 14 and 17, the patient's hematocrit fell from 36% to 30%; vital signs remained stable. He underwent an uncomplicated, ultrasound‐guided therapeutic paracentesis, which yielded 1.4 L of straw‐colored fluid on the afternoon of day 17; the procedure was attempted only on the right side. On the morning of day 18, the patient's blood pressure dropped to 78/55 mmHg with a pulse of 123 beats per minute; he became pale and unresponsive. Physical examination was notable for somnolence and a tender, warm left flank mass, contralateral to his paracentesis site. No flank or periumbilical ecchymoses were identified. Complete blood count demonstrated a white blood count (WBC) of 22,970/L, hematocrit 16%, and platelet count 104,000/L. INR was 2.0, unchanged from the last check on day 10. Partial thromboplastin time was 41 seconds and fibrinogen was 293 mg/dL (normal 150‐400 mg/dL). Peripheral blood smear was negative for red cell fragments. Blood chemistries revealed a sodium of 134 mg/dL, bicarbonate 20 mEq/L, anion gap 7, BUN 24 mg/dL, and creatinine 1.6 mg/dL (up from 1.0 mg/dL the previous day). His venous lactate level was 4.6 mmol/L and arterial blood gas sampling on room air demonstrated a pH of 7.35, partial pressure of carbon dioxide (pCO2) 29 mmHg, partial pressure of oxygen (pCO2) 54 mmHg, and bicarbonate 15 mEq/L. A femoral introducer was placed for volume resuscitation and the patient was urgently transfused with packed red blood cells (PRBCs) and fresh‐frozen plasma (FFP) to correct his coagulopathy. Computed tomography of the abdomen revealed a large left retroperitoneal hematoma measuring 15 15 22 cm3 (Figure 1). Despite transfusion, his hematocrit continued to fall. Urgent angiography was performed, upon which he was found to have active bleeding from the left L3‐L5 lumbar arteries. These were successfully embolized. He required PRBCs and FFP transfusion only once following this procedure. Given a transient decrease in his urine output, his bladder pressures were followed closely for evidence of abdominal compartment syndrome, which did not develop. He was transferred from the intensive care unit (ICU) to the floor on day 20, where his physical exam and hematocrit remained stable and his delirium slowly cleared. He was ultimately discharged to a skilled nursing facility on day 33.

Figure 1
Noncontrast computed tomography (CT) scan of the abdomen and pelvis demonstrating a 15 × 15 × 22 cm3 left retroperitoneal mass anterior to the left psoas with dependent hyperdensity consistent with a hematocrit‐fluid level.

Discussion

Spontaneous retroperitoneal hematoma is a well‐recognized entity that may present with the classic triad of abdominal or groin pain, palpable abdominal or flank mass, and shock or lower extremity motor or sensory changes due to femoral nerve compression.1 Although classically described as skin findings associated with retroperitoneal hemorrhage, Cullen and Grey‐Turner signs are relatively late findings that may not develop in all patients.

Retroperitoneal hemorrhage is well‐recognized as a result of iatrogenic anticoagulation,1 but has been reported less commonly as a result of coagulopathy related to cirrhosis. Di Bisceglie and Richart2 describe 2 patients with MELD scores of 29 and 25, respectively, who developed spontaneous retroperitoneal and rectus muscle hemorrhage. Both had evidence of associated disseminated intravascular coagulation (DIC) and died. Even less common is spontaneous lumbar artery rupture, occurring rarely in the absence of trauma or instrumentation. One reported bleed developed in the context of systemic anticoagulation for a mechanical valve.3 Halak et al.4 relate a case in which the only known risk factor was chronic renal disease. Hama et al.5 describe a patient with a history of alcoholic liver cirrhosis and INR of 2.3 whose lumbar artery rupture was successfully managed with transcatheter arterial embolization.

It is difficult to ascertain the effect of prophylactic anticoagulation in development of this particular hemorrhage. Retroperitoneal bleeding is a very rare complication of pharmacologic prophylaxis for DVT reported with both low‐molecular weight and unfractionated heparins.6 There may be additive risk of prophylaxis in a cirrhotic patient with baseline elevated INR and thrombocytopenia, particularly in the context of renal failure.

Options in the management of spontaneous retroperitoneal hematoma include transarterial embolization, percutaneous decompression, and open surgery. Nonoperative management of these bleeds when possible may be preferable in cirrhotic patients, as their baseline liver disease renders them higher‐risk candidates for surgery. There are no randomized trials comparing these approaches.1

In summary, we report the case of a 56‐year‐old man with end‐stage liver disease and associated coagulopathy without evidence of DIC who survived to discharge with intravascular management of a spontaneous retroperitoneal hemorrhage from the lumbar arteries. To our knowledge, this is the second reported case of spontaneous retroperitoneal hemorrhage in a cirrhotic in which the lumbar arteries were implicated and the first in which multiple arteries were found to be bleeding simultaneously. Any hospitalized patient who develops abdominal pain, flank pain, or hemodynamic instability in the context of coagulopathy, regardless of cause, warrants evaluation for retroperitoneal bleed. Appropriate early management includes immediate resuscitation, intensive monitoring, urgent imaging, and surgical and interventional radiology consultation in order to prevent a fatal outcome.

References
  1. Chan YC,Morales JP,Reidy JF,Taylor PR.Management of spontaneous and iatrogenic retroperitoneal haemorrhage: conservative management, endovascular intervention or open surgery?Int J Clin Pract.2007;62:16041613.
  2. Di Bisceglie AM,Richart JM.Spontaneous retroperitoneal and rectus muscle hemorrhage as a potentially lethal complication of cirrhosis.Liver Int.2006;26:12911293.
  3. Schuster F,Stosslein F,Steinbach F.Spontaneous rupture of a lumbar artery. A rare etiology of retroperitoneal hematoma.Urologe A.2003;42:840844.
  4. Halak M,Kligman M,Loberman Z,Eyal E,Karmeli R.Spontaneous ruptured lumbar artery in a chronic renal failure patient.Eur J Vasc Endovasc Surg.2001;21:569571.
  5. Hama Y,Iwasaki Y,Kawaguchi A.Spontaneous rupture of the lumbar artery.Intern Med.2004;43:759.
  6. Leonardi MJ,McGory ML,Ko CY.The rate of bleeding complications after pharmacologic deep venous thrombosis prophylaxis.Arch Surg.2006;141:790799.
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A 56‐year‐old male presented to the emergency department with a 2‐week history of increasing abdominal girth, nausea, vomiting, and lower extremity edema. His girlfriend had also noted a yellow tinge to his skin and eyes. His past medical history was significant for bipolar disorder, alcohol‐related seizures, and pneumonia. He had no allergies and denied medications prior to admission. Family history was negative for liver disease and social history was notable for ongoing tobacco use and alcohol dependence. He was afebrile with stable vital signs. Physical examination demonstrated an alert gentleman whose answers to questions required occasional factual correction by his partner. His abdomen was distended and nontender with prominent vasculature and shifting dullness. Lower extremity edema was symmetric and bilateral, rated as 2+. Scattered spider angiomata and a fine bilateral hand tremor without asterixis were also noted. Initial laboratory data demonstrated a white blood cell count of 13,900/L, hematocrit 37%, and platelet count 176,000/L. His sodium was 130 mg/dL, blood urea nitrogen (BUN) 1 mg/dL, and creatinine 0.7 mg/dL. International normalized ratio (INR) was 1.8, aspartate aminotransferase (AST) was 117 U/L, alanine aminotransferase (ALT) 33 U/L, alkaline phosphatase 191 U/L, total bilirubin 9.2 mg/dL, total protein 7.0 g/dL, and albumin 1.9 g/dL. Abdominal ultrasound revealed a diffusely hyperechoic liver with a large amount of ascites.

The patient was admitted with the diagnoses of presumed alcoholic hepatitis and end‐stage liver disease. Model for End‐Stage Liver Disease (MELD) score was 21 and discriminant function 16.8. Paracentesis demonstrated a serum‐ascites albumin gradient of >1.1 and no evidence of spontaneous bacterial peritonitis. Diuresis was initiated. He was placed on unfractionated heparin at a dose of 5000 units every 8 hours for deep venous thrombosis (DVT) prophylaxis. By hospital day 3, the patient's laboratory values had improved, yet his stay was prolonged by alcohol withdrawal requiring benzodiazepines, altered mental status presumed secondary to hepatic encephalopathy, acute renal failure, aspiration pneumonia, and persistent unexplained leukocytosis. He required medical restraints during this time given confusion and propensity to ambulate without assistance, yet sustained no falls or other known trauma in care delivered during this time.

Between days 14 and 17, the patient's hematocrit fell from 36% to 30%; vital signs remained stable. He underwent an uncomplicated, ultrasound‐guided therapeutic paracentesis, which yielded 1.4 L of straw‐colored fluid on the afternoon of day 17; the procedure was attempted only on the right side. On the morning of day 18, the patient's blood pressure dropped to 78/55 mmHg with a pulse of 123 beats per minute; he became pale and unresponsive. Physical examination was notable for somnolence and a tender, warm left flank mass, contralateral to his paracentesis site. No flank or periumbilical ecchymoses were identified. Complete blood count demonstrated a white blood count (WBC) of 22,970/L, hematocrit 16%, and platelet count 104,000/L. INR was 2.0, unchanged from the last check on day 10. Partial thromboplastin time was 41 seconds and fibrinogen was 293 mg/dL (normal 150‐400 mg/dL). Peripheral blood smear was negative for red cell fragments. Blood chemistries revealed a sodium of 134 mg/dL, bicarbonate 20 mEq/L, anion gap 7, BUN 24 mg/dL, and creatinine 1.6 mg/dL (up from 1.0 mg/dL the previous day). His venous lactate level was 4.6 mmol/L and arterial blood gas sampling on room air demonstrated a pH of 7.35, partial pressure of carbon dioxide (pCO2) 29 mmHg, partial pressure of oxygen (pCO2) 54 mmHg, and bicarbonate 15 mEq/L. A femoral introducer was placed for volume resuscitation and the patient was urgently transfused with packed red blood cells (PRBCs) and fresh‐frozen plasma (FFP) to correct his coagulopathy. Computed tomography of the abdomen revealed a large left retroperitoneal hematoma measuring 15 15 22 cm3 (Figure 1). Despite transfusion, his hematocrit continued to fall. Urgent angiography was performed, upon which he was found to have active bleeding from the left L3‐L5 lumbar arteries. These were successfully embolized. He required PRBCs and FFP transfusion only once following this procedure. Given a transient decrease in his urine output, his bladder pressures were followed closely for evidence of abdominal compartment syndrome, which did not develop. He was transferred from the intensive care unit (ICU) to the floor on day 20, where his physical exam and hematocrit remained stable and his delirium slowly cleared. He was ultimately discharged to a skilled nursing facility on day 33.

Figure 1
Noncontrast computed tomography (CT) scan of the abdomen and pelvis demonstrating a 15 × 15 × 22 cm3 left retroperitoneal mass anterior to the left psoas with dependent hyperdensity consistent with a hematocrit‐fluid level.

Discussion

Spontaneous retroperitoneal hematoma is a well‐recognized entity that may present with the classic triad of abdominal or groin pain, palpable abdominal or flank mass, and shock or lower extremity motor or sensory changes due to femoral nerve compression.1 Although classically described as skin findings associated with retroperitoneal hemorrhage, Cullen and Grey‐Turner signs are relatively late findings that may not develop in all patients.

Retroperitoneal hemorrhage is well‐recognized as a result of iatrogenic anticoagulation,1 but has been reported less commonly as a result of coagulopathy related to cirrhosis. Di Bisceglie and Richart2 describe 2 patients with MELD scores of 29 and 25, respectively, who developed spontaneous retroperitoneal and rectus muscle hemorrhage. Both had evidence of associated disseminated intravascular coagulation (DIC) and died. Even less common is spontaneous lumbar artery rupture, occurring rarely in the absence of trauma or instrumentation. One reported bleed developed in the context of systemic anticoagulation for a mechanical valve.3 Halak et al.4 relate a case in which the only known risk factor was chronic renal disease. Hama et al.5 describe a patient with a history of alcoholic liver cirrhosis and INR of 2.3 whose lumbar artery rupture was successfully managed with transcatheter arterial embolization.

It is difficult to ascertain the effect of prophylactic anticoagulation in development of this particular hemorrhage. Retroperitoneal bleeding is a very rare complication of pharmacologic prophylaxis for DVT reported with both low‐molecular weight and unfractionated heparins.6 There may be additive risk of prophylaxis in a cirrhotic patient with baseline elevated INR and thrombocytopenia, particularly in the context of renal failure.

Options in the management of spontaneous retroperitoneal hematoma include transarterial embolization, percutaneous decompression, and open surgery. Nonoperative management of these bleeds when possible may be preferable in cirrhotic patients, as their baseline liver disease renders them higher‐risk candidates for surgery. There are no randomized trials comparing these approaches.1

In summary, we report the case of a 56‐year‐old man with end‐stage liver disease and associated coagulopathy without evidence of DIC who survived to discharge with intravascular management of a spontaneous retroperitoneal hemorrhage from the lumbar arteries. To our knowledge, this is the second reported case of spontaneous retroperitoneal hemorrhage in a cirrhotic in which the lumbar arteries were implicated and the first in which multiple arteries were found to be bleeding simultaneously. Any hospitalized patient who develops abdominal pain, flank pain, or hemodynamic instability in the context of coagulopathy, regardless of cause, warrants evaluation for retroperitoneal bleed. Appropriate early management includes immediate resuscitation, intensive monitoring, urgent imaging, and surgical and interventional radiology consultation in order to prevent a fatal outcome.

A 56‐year‐old male presented to the emergency department with a 2‐week history of increasing abdominal girth, nausea, vomiting, and lower extremity edema. His girlfriend had also noted a yellow tinge to his skin and eyes. His past medical history was significant for bipolar disorder, alcohol‐related seizures, and pneumonia. He had no allergies and denied medications prior to admission. Family history was negative for liver disease and social history was notable for ongoing tobacco use and alcohol dependence. He was afebrile with stable vital signs. Physical examination demonstrated an alert gentleman whose answers to questions required occasional factual correction by his partner. His abdomen was distended and nontender with prominent vasculature and shifting dullness. Lower extremity edema was symmetric and bilateral, rated as 2+. Scattered spider angiomata and a fine bilateral hand tremor without asterixis were also noted. Initial laboratory data demonstrated a white blood cell count of 13,900/L, hematocrit 37%, and platelet count 176,000/L. His sodium was 130 mg/dL, blood urea nitrogen (BUN) 1 mg/dL, and creatinine 0.7 mg/dL. International normalized ratio (INR) was 1.8, aspartate aminotransferase (AST) was 117 U/L, alanine aminotransferase (ALT) 33 U/L, alkaline phosphatase 191 U/L, total bilirubin 9.2 mg/dL, total protein 7.0 g/dL, and albumin 1.9 g/dL. Abdominal ultrasound revealed a diffusely hyperechoic liver with a large amount of ascites.

The patient was admitted with the diagnoses of presumed alcoholic hepatitis and end‐stage liver disease. Model for End‐Stage Liver Disease (MELD) score was 21 and discriminant function 16.8. Paracentesis demonstrated a serum‐ascites albumin gradient of >1.1 and no evidence of spontaneous bacterial peritonitis. Diuresis was initiated. He was placed on unfractionated heparin at a dose of 5000 units every 8 hours for deep venous thrombosis (DVT) prophylaxis. By hospital day 3, the patient's laboratory values had improved, yet his stay was prolonged by alcohol withdrawal requiring benzodiazepines, altered mental status presumed secondary to hepatic encephalopathy, acute renal failure, aspiration pneumonia, and persistent unexplained leukocytosis. He required medical restraints during this time given confusion and propensity to ambulate without assistance, yet sustained no falls or other known trauma in care delivered during this time.

Between days 14 and 17, the patient's hematocrit fell from 36% to 30%; vital signs remained stable. He underwent an uncomplicated, ultrasound‐guided therapeutic paracentesis, which yielded 1.4 L of straw‐colored fluid on the afternoon of day 17; the procedure was attempted only on the right side. On the morning of day 18, the patient's blood pressure dropped to 78/55 mmHg with a pulse of 123 beats per minute; he became pale and unresponsive. Physical examination was notable for somnolence and a tender, warm left flank mass, contralateral to his paracentesis site. No flank or periumbilical ecchymoses were identified. Complete blood count demonstrated a white blood count (WBC) of 22,970/L, hematocrit 16%, and platelet count 104,000/L. INR was 2.0, unchanged from the last check on day 10. Partial thromboplastin time was 41 seconds and fibrinogen was 293 mg/dL (normal 150‐400 mg/dL). Peripheral blood smear was negative for red cell fragments. Blood chemistries revealed a sodium of 134 mg/dL, bicarbonate 20 mEq/L, anion gap 7, BUN 24 mg/dL, and creatinine 1.6 mg/dL (up from 1.0 mg/dL the previous day). His venous lactate level was 4.6 mmol/L and arterial blood gas sampling on room air demonstrated a pH of 7.35, partial pressure of carbon dioxide (pCO2) 29 mmHg, partial pressure of oxygen (pCO2) 54 mmHg, and bicarbonate 15 mEq/L. A femoral introducer was placed for volume resuscitation and the patient was urgently transfused with packed red blood cells (PRBCs) and fresh‐frozen plasma (FFP) to correct his coagulopathy. Computed tomography of the abdomen revealed a large left retroperitoneal hematoma measuring 15 15 22 cm3 (Figure 1). Despite transfusion, his hematocrit continued to fall. Urgent angiography was performed, upon which he was found to have active bleeding from the left L3‐L5 lumbar arteries. These were successfully embolized. He required PRBCs and FFP transfusion only once following this procedure. Given a transient decrease in his urine output, his bladder pressures were followed closely for evidence of abdominal compartment syndrome, which did not develop. He was transferred from the intensive care unit (ICU) to the floor on day 20, where his physical exam and hematocrit remained stable and his delirium slowly cleared. He was ultimately discharged to a skilled nursing facility on day 33.

Figure 1
Noncontrast computed tomography (CT) scan of the abdomen and pelvis demonstrating a 15 × 15 × 22 cm3 left retroperitoneal mass anterior to the left psoas with dependent hyperdensity consistent with a hematocrit‐fluid level.

Discussion

Spontaneous retroperitoneal hematoma is a well‐recognized entity that may present with the classic triad of abdominal or groin pain, palpable abdominal or flank mass, and shock or lower extremity motor or sensory changes due to femoral nerve compression.1 Although classically described as skin findings associated with retroperitoneal hemorrhage, Cullen and Grey‐Turner signs are relatively late findings that may not develop in all patients.

Retroperitoneal hemorrhage is well‐recognized as a result of iatrogenic anticoagulation,1 but has been reported less commonly as a result of coagulopathy related to cirrhosis. Di Bisceglie and Richart2 describe 2 patients with MELD scores of 29 and 25, respectively, who developed spontaneous retroperitoneal and rectus muscle hemorrhage. Both had evidence of associated disseminated intravascular coagulation (DIC) and died. Even less common is spontaneous lumbar artery rupture, occurring rarely in the absence of trauma or instrumentation. One reported bleed developed in the context of systemic anticoagulation for a mechanical valve.3 Halak et al.4 relate a case in which the only known risk factor was chronic renal disease. Hama et al.5 describe a patient with a history of alcoholic liver cirrhosis and INR of 2.3 whose lumbar artery rupture was successfully managed with transcatheter arterial embolization.

It is difficult to ascertain the effect of prophylactic anticoagulation in development of this particular hemorrhage. Retroperitoneal bleeding is a very rare complication of pharmacologic prophylaxis for DVT reported with both low‐molecular weight and unfractionated heparins.6 There may be additive risk of prophylaxis in a cirrhotic patient with baseline elevated INR and thrombocytopenia, particularly in the context of renal failure.

Options in the management of spontaneous retroperitoneal hematoma include transarterial embolization, percutaneous decompression, and open surgery. Nonoperative management of these bleeds when possible may be preferable in cirrhotic patients, as their baseline liver disease renders them higher‐risk candidates for surgery. There are no randomized trials comparing these approaches.1

In summary, we report the case of a 56‐year‐old man with end‐stage liver disease and associated coagulopathy without evidence of DIC who survived to discharge with intravascular management of a spontaneous retroperitoneal hemorrhage from the lumbar arteries. To our knowledge, this is the second reported case of spontaneous retroperitoneal hemorrhage in a cirrhotic in which the lumbar arteries were implicated and the first in which multiple arteries were found to be bleeding simultaneously. Any hospitalized patient who develops abdominal pain, flank pain, or hemodynamic instability in the context of coagulopathy, regardless of cause, warrants evaluation for retroperitoneal bleed. Appropriate early management includes immediate resuscitation, intensive monitoring, urgent imaging, and surgical and interventional radiology consultation in order to prevent a fatal outcome.

References
  1. Chan YC,Morales JP,Reidy JF,Taylor PR.Management of spontaneous and iatrogenic retroperitoneal haemorrhage: conservative management, endovascular intervention or open surgery?Int J Clin Pract.2007;62:16041613.
  2. Di Bisceglie AM,Richart JM.Spontaneous retroperitoneal and rectus muscle hemorrhage as a potentially lethal complication of cirrhosis.Liver Int.2006;26:12911293.
  3. Schuster F,Stosslein F,Steinbach F.Spontaneous rupture of a lumbar artery. A rare etiology of retroperitoneal hematoma.Urologe A.2003;42:840844.
  4. Halak M,Kligman M,Loberman Z,Eyal E,Karmeli R.Spontaneous ruptured lumbar artery in a chronic renal failure patient.Eur J Vasc Endovasc Surg.2001;21:569571.
  5. Hama Y,Iwasaki Y,Kawaguchi A.Spontaneous rupture of the lumbar artery.Intern Med.2004;43:759.
  6. Leonardi MJ,McGory ML,Ko CY.The rate of bleeding complications after pharmacologic deep venous thrombosis prophylaxis.Arch Surg.2006;141:790799.
References
  1. Chan YC,Morales JP,Reidy JF,Taylor PR.Management of spontaneous and iatrogenic retroperitoneal haemorrhage: conservative management, endovascular intervention or open surgery?Int J Clin Pract.2007;62:16041613.
  2. Di Bisceglie AM,Richart JM.Spontaneous retroperitoneal and rectus muscle hemorrhage as a potentially lethal complication of cirrhosis.Liver Int.2006;26:12911293.
  3. Schuster F,Stosslein F,Steinbach F.Spontaneous rupture of a lumbar artery. A rare etiology of retroperitoneal hematoma.Urologe A.2003;42:840844.
  4. Halak M,Kligman M,Loberman Z,Eyal E,Karmeli R.Spontaneous ruptured lumbar artery in a chronic renal failure patient.Eur J Vasc Endovasc Surg.2001;21:569571.
  5. Hama Y,Iwasaki Y,Kawaguchi A.Spontaneous rupture of the lumbar artery.Intern Med.2004;43:759.
  6. Leonardi MJ,McGory ML,Ko CY.The rate of bleeding complications after pharmacologic deep venous thrombosis prophylaxis.Arch Surg.2006;141:790799.
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Spontaneous retroperitoneal hematoma originating at lumbar arteries in context of cirrhosis
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“Patchy” pneumonia

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A case of “patchy” pneumonia

A 49‐year‐old retired air‐force officer presented with productive cough lasting 1 week. He was in good health except for back pain after a motor vehicle accident, 2 years ago, for which he took unspecified pain medications. His condition rapidly deteriorated necessitating mechanical ventilation. Chest x‐ray showed multilobar pneumonia (Figure 1). Blood and sputum cultures grew Serratia marcescens. Chest computed tomography (CT)‐scan showed multilobar involvement with debris in his airways that was thought to be respiratory secretions (Figure 2, arrows). He remained intubated for 2 weeks before his clinical status improved enough to permit extubation. Immediately following extubation, he coughed up a ball of crumpled, plastic material. His condition subsequently improved dramatically with complete radiological resolution of his pneumonia. On analyzing the foreign body, it turned out to be a fenestrated fentanyl patch (Figure 3). The patient then reported that he often cut‐up used fentanyl patches and sucked on them for an extra high. In retrospect, the endobronchial debris was actually the patch straddling the carina and had prevented rapid recovery. Aspiration of unusual foreign bodies have been reported in the literature. This foreign body masqueraded as respiratory secretions on imaging preventing early detection. This clinical encounter brings to light yet another innovative, but potentially deadly, form of drug abuse. It also emphasizes the importance of early bronchoscopy in nonresolving pneumonias.

Figure 1
Mutilobar pneumonia.
Figure 2
Endobronchial debris.
Figure 3
The fenestrated fentanyl patch.
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A 49‐year‐old retired air‐force officer presented with productive cough lasting 1 week. He was in good health except for back pain after a motor vehicle accident, 2 years ago, for which he took unspecified pain medications. His condition rapidly deteriorated necessitating mechanical ventilation. Chest x‐ray showed multilobar pneumonia (Figure 1). Blood and sputum cultures grew Serratia marcescens. Chest computed tomography (CT)‐scan showed multilobar involvement with debris in his airways that was thought to be respiratory secretions (Figure 2, arrows). He remained intubated for 2 weeks before his clinical status improved enough to permit extubation. Immediately following extubation, he coughed up a ball of crumpled, plastic material. His condition subsequently improved dramatically with complete radiological resolution of his pneumonia. On analyzing the foreign body, it turned out to be a fenestrated fentanyl patch (Figure 3). The patient then reported that he often cut‐up used fentanyl patches and sucked on them for an extra high. In retrospect, the endobronchial debris was actually the patch straddling the carina and had prevented rapid recovery. Aspiration of unusual foreign bodies have been reported in the literature. This foreign body masqueraded as respiratory secretions on imaging preventing early detection. This clinical encounter brings to light yet another innovative, but potentially deadly, form of drug abuse. It also emphasizes the importance of early bronchoscopy in nonresolving pneumonias.

Figure 1
Mutilobar pneumonia.
Figure 2
Endobronchial debris.
Figure 3
The fenestrated fentanyl patch.

A 49‐year‐old retired air‐force officer presented with productive cough lasting 1 week. He was in good health except for back pain after a motor vehicle accident, 2 years ago, for which he took unspecified pain medications. His condition rapidly deteriorated necessitating mechanical ventilation. Chest x‐ray showed multilobar pneumonia (Figure 1). Blood and sputum cultures grew Serratia marcescens. Chest computed tomography (CT)‐scan showed multilobar involvement with debris in his airways that was thought to be respiratory secretions (Figure 2, arrows). He remained intubated for 2 weeks before his clinical status improved enough to permit extubation. Immediately following extubation, he coughed up a ball of crumpled, plastic material. His condition subsequently improved dramatically with complete radiological resolution of his pneumonia. On analyzing the foreign body, it turned out to be a fenestrated fentanyl patch (Figure 3). The patient then reported that he often cut‐up used fentanyl patches and sucked on them for an extra high. In retrospect, the endobronchial debris was actually the patch straddling the carina and had prevented rapid recovery. Aspiration of unusual foreign bodies have been reported in the literature. This foreign body masqueraded as respiratory secretions on imaging preventing early detection. This clinical encounter brings to light yet another innovative, but potentially deadly, form of drug abuse. It also emphasizes the importance of early bronchoscopy in nonresolving pneumonias.

Figure 1
Mutilobar pneumonia.
Figure 2
Endobronchial debris.
Figure 3
The fenestrated fentanyl patch.
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Journal of Hospital Medicine - 5(5)
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Language Barriers and Hospital Care

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Influence of language barriers on outcomes of hospital care for general medicine inpatients

Forty‐five‐million Americans speak a language other than English and more than 19 million of these speak English less than very wellor are limited English proficient (LEP).1 The number of non‐English‐speaking and LEP people in the US has risen in recent decades, presenting a challenge to healthcare systems to provide high‐quality, patient‐centered care for these patients.2

For outpatients, language barriers are a fundamental contributor to gaps in health care. In the clinic setting, patients who do not speak English well have less access to a usual source of care and lower rates of physician visits and preventive services.36 Even when patients with language barriers do have access to care, they have poorer adherence, decreased comprehension of their diagnoses, decreased satisfaction with care, and increased medication complications.710

Few studies, however, have examined how language influences outcomes of hospital care. Compared to English‐speakers, patients who do not speak English well may experience longer lengths of stay,11 and have more adverse events while in the hospital.12 However, these previous studies have not investigated outcomes immediately post‐hospitalization, such as readmission rates and mortality, nor have they directly addressed the interaction between ethnicity and language.

To understand these questions, we analyzed data collected from a university‐based teaching hospital which cares for patients of diverse cultural and language backgrounds. Using these data, we examined how patients' primary language influenced hospital costs, length of stay (LOS), 30‐day readmission, and 30‐day mortality risk.

Patients and Methods

Patient Population and Setting

Our study examined patients admitted to the General Medicine Service at the University of California, San Francisco Medical Center (UCSF) between July 1, 2001 and June 30th, 2003, the time period during which UCSF participated in the Multicenter Hospitalist Trial (MHT) a prospective quasi‐randomized trial of hospitalist care for general medicine patients.13, 14

UCSF Moffitt‐Long Hospital is a 400‐bed urban academic medical center which provides services to the City and County of San Francisco, an ethnically and linguistically diverse area. UCSF employs staff language interpreters in Spanish, Chinese and Russian who travel to its many outpatient clinics, Comprehensive Cancer Center, Children's Hospital, as well as to Moffitt‐Long Hospital upon request; phone interpretation is also available when in‐person interpreters are not available, for off hours needs and for less common languages. During the period of this study there were no specific inpatient guidelines in place for use of interpretation services at UCSF, nor were there any specific interventions targeting LEP or non‐English speaking inpatients.

Patients were eligible for the MHT if they were 18 years of age or older and admitted at random to a hospitalist or non‐hospitalist physician (eg, outpatient general internist attending on average 1‐month/year); a minority of patients were cared for directly by their primary care physician while in the hospital, and were excluded. For purposes of our study, which merged MHT data with hospital administrative data on primary language, we further excluded all admissions for patients for whom primary language was missing (n = 5), whose listing was unknown or other language (n = 78), sign language (n = 3) or whose language was listed but was not one of the included languages (n = 258). Included languages were English, Chinese, Russian and Spanish. Because LOS and cost data were skewed, we excluded those admissions with the top 1% longest stays and the top 1% highest cost (n = 176); these exclusions did not alter the proportion of admissions across language and ethnicity. In addition, we excluded 102 admissions that were missing data on cost and 11 with costs <$500 and which were likely to be erroneous. Our research was approved by the UCSF Institutional Review Board.

Data Sources

We collected administrative data from Transition Systems Inc (TSI, Boston, MA) billing databases at UCSF as part of the MHT. These data include patient demographics, insurance, costs, ICD‐9CM diagnostic codes, admission and discharge dates in Uniform Bill 92 format. Patient mortality information was collected as part of the MHT using the National Death Index.14

Language data were collected from a separate patient‐registration database (STOR) at UCSF. Information on a patient's primary language is entered at the time each patient first registers at UCSF, whether for the index hospitalization or for prior clinic visits, and is based generally on patient self report. As part of our validation step, we cross‐checked 829 STOR language entries against patient reports and found 91% agreement with the majority of the errors classifying non‐English speakers as English‐speakers.

Measures

Predictor

Our primary language variable was derived using language designations collected from patient registration databases described above. Using these data we specified our key language groups as English, Chinese (Cantonese or Mandarin), Russian, or Spanish.

Outcomes

LOS and total cost of hospital stay for each hospitalization derived from administrative data sources. Readmissions were identified at the time patients were readmitted to UCSF (eg, flagged in administrative data). Mortality was determined by whether an individual patient with an admission in the database was recorded in the National Death Index as dead within 30‐days of admission.

Covariates

Additional covariates included age at admission, gender, ethnicity as recorded in registration databases (White, African American, Asian, Latino, Other), insurance, principal billing diagnosis, whether or not a patient received intensive care unit (ICU) care, type of admitting attending physician (Hospitalist/non‐Hospitalist), and an administrative Charlson comorbidity score.15 To collapse the principal diagnoses into categories, we used the Healthcare Cost and Utilization Project (HCUP)'s Clinical Classification System, which allowed us to classify each diagnosis in 1 of 14 generally accepted categories.16

Analysis

Statistical analyses were performed using STATA statistical software (STATACorp, Version 9, College Station, TX). We examined descriptive means and proportions for all variables, including sociodemographic, hospitalization, comorbidity and outcome variables. We compared English and non‐English speakers on all covariate and outcome variables using t‐tests for comparison of means and chi‐square for comparison of categorical variables.

It was not possible to fully test the language‐by‐ethnicity interactionwhether or not the impact of language varied by ethnic groupbecause many cells of the joint distribution were very sparse (eg, the sample contained very few non‐English‐speaking African Americans). Therefore, to better understand the influence of English vs. non‐English language usage across different ethnic groups, we created a combined language‐ethnicity predictor variable which categorized each subject first by language and then for the English‐speakers by ethnicity. For example, a Chinese, Spanish or Russian speaker would be categorized as such, and an English‐speaker could fall into the English‐White, English‐African American, English‐Asian or English‐Latino group. This allowed us to test whether there were any differences in language effects across the White, Asian, and Latino ethnicities, and any difference in ethnicity effects among English‐speakers.

Because cost and LOS were skewed, we used negative binomial models for LOS and log transformed costs. We performed a sensitivity analysis testing whether our results were robust to the exclusion of the admissions with the top 1% LOS and top 1% cost. We used logistic regression for the 30‐day readmission and mortality outcomes.

Our primary predictor was the language‐ethnicity variable described above. To determine the independent association between this predictor and our key outcomes, we then built models which included additional potential confounders selected either for face validity or because of observed confounding with other covariates. Our inclusion of potential confounders was limited by the variables available in the administrative database; thus, we were not able to pursue detailed analyses of communication and literacy factors and their interaction with our predictor or their independent impact on outcomes. Models also included a linear spline with a single knot at age 65 years as a further adjustment for age in Medicare recipients.1719 For the 30‐day readmission outcome model, we excluded those admissions for which the patient either died in the hospital or was discharged to hospice care. Within each model we tested the impact of a language barrier using custom contrasts. This allowed us to examine the language‐ethnicity effect aggregating all non‐English speakers compared to all English‐speakers, comparing each non‐English speaking group to all English‐speakers, comparing Chinese speakers to English‐speaking Asians and Spanish speakers to English‐speaking‐Latinos, as well as to test whether the effect of English language is the same across ethnicities.

Results

Admission Characteristics of the Sample

A total of 7023 patients were admitted to the General Medicine service, 5877 (84%) of whom were English‐speakers and 1146 (16%) non‐English‐speakers (Table 1). Overall, half of the admitted patients were women (50%), and the vast majority was insured (93%). The most common principal diagnoses were respiratory and gastrointestinal disorders. Only a small number of non‐English speakers 164 (14%) were recorded in the UCSF Interpreter Services database as having had any interaction with a professional staff interpreter during their hospitalization.

Admission Characteristics and Bivariate Association of Having an English vs. a Non‐English Primary Language With Predictor Variables of Interest for Patients Admitted to the Medical Service of the UCSF Hospital From 7/2001 to 6/2003 (n = 7023)
 English (n = 5877) n (%)Non‐English (n = 1146) n (%)
  • NOTE: Percents may not add to 100 due to rounding error.

  • Abbreviations: CI, confidence interval; NOS, not otherwise specified; SD, standard deviation.

Socio‐economic variables  
Language‐ethnicity  
English  
White3066 (52.2) 
African American1351 (23.0) 
Asian544 (9.3) 
Latino298 (5.1) 
Other618 (10.5) 
Chinese speakers 584 (51.0)
Spanish speakers 272 (25.3)
Russian speakers 290 (23.7)
Age mean (SD) (range 18‐105)58.8 (20.3)72.3 (15.5)
Gender  
Male2967 (50.5)514 (44.8)
Female2910 (49.5)632 (55.2)
Insurance  
Medicare2878 (49.0)800 (69.8)
Medicaid1201 (20.4)193 (16.8)
Commercial1358 (23.1)106 (9.3)
Charity/other440 (7.5)47 (4.1)
Hospitalization variables  
Admitted to ICU  
Yes721 (12.3)149 (13.0)
Attending physician  
Hospitalist3950 (67.2)781 (68.2)
Comorbidity variables  
Principal Diagnosis  
Respiratory disorder1061 (18.1)225 (19.6)
Gastrointestinal disorder963 (16.4)205 (17.9)
Circulatory disorder613 (10.4)140 (12.2)
Endocrine/metabolism671 (11.4)80 (7.0)
Injury/poisoning475 (8.1)64 (5.6)
Malignancy395 (6.7)107 (9.3)
Renal/urinary disorder383 (6.5)108 (9.4)
Skin disorder278 (4.7)28 (2.9)
Infection/fatigue NOS206 (3.5)45 (3.4)
Blood disorder (non‐malignant)189 (3.2)38 (3.3)
Musculoskeletal/connective tissue disorder164 (2.8)33 (2.9)
Mental disorder/substance abuse171 (2.9)7 (0.6)
Nervous system/brain infection137 (2.3)26 (2.3)
Unclassified171 (2.9)40 (3.5)
Charlson Index score mean (SD)0.97 1.331.10 1.42

Among English speakers, Whites and African Americans were the most common ethnicities; however, more than 500 admissions were categorized as Asian ethnicity, and more than 600 as patients of other ethnicity. Close to 300 admissions were for Latinos. Among non‐English speakers, Chinese speakers had the largest number of admissions (n = 584), while Spanish and Russian speakers had similar numbers (n = 272 and 290 respectively).

Non‐English speakers were older, more likely to be female, more likely to be insured by Medicare, and more likely to have a higher comorbidity index score. While comorbidity scores were similar among non‐English speakers (Chinese 1.13 1.50; Russian 1.09 1.37; Spanish 1.06 1.30), they differed considerably among English speakers (White 0.94 1.29; African American 1.05 1.40; Asian 1.04 1.45; Latino 0.89 1.23; Other 0.91 1.29).

Hospital Outcome by Language‐Ethnicity Group (Table 2)

When aggregated together, non‐English speakers were somewhat more likely to be dead at 30‐days and have lower cost admissions; however, they did not differ from English speakers on LOS or readmission rates. While differences among disaggregated language‐ethnicity groups were not all statistically significant, English‐speaking Whites had the longest LOS (mean = 4.9 days) and highest costs (mean = $10,530). English‐speaking African Americans, Chinese and Spanish speakers had the highest 30‐day readmission rates; whereas, English‐speaking Latinos and Russian speakers had markedly lower 30‐day readmission rates (2.5% and 6.4%, respectively). Chinese speakers had the highest 30‐day mortality, followed by English speaking Whites and Asians.

Bivariate results of Hospital Outcome Measures Across Language‐Ethnicity Groups (n = 7023)
Language‐Ethnicity GroupsLOS* Mean #Days (SD)Cost Mean Cost $ (SD)30‐Day Readmission, n (%)30‐Day Mortality, n (%)
  • Abbreviation: SD, standard deviation.

  • P < 0.05 for overall comparison across language‐ethnicity groups.

  • P < 0.001 for overall comparison across language‐ethnicity groups.

  • Excludes those admissions for patients who died in the hospital or were discharged to hospice.

  • Includes death during hospitalization.

English speakers (all)4.7 (4.5)10,035 (15,041)648 (11.9)613 (10.4)
White4.9 (5.1)10,530 (15,894)322 (11.4)377 (12.3)
African American4.5 (4.8)9107 (13,314)227 (17.5)91 (6.7)
Asian4.3 (4.5)9933 (15,607)43 (8.8)67 (12.3)
Latino4.6 (4.8)9823 (14,113)7 (2.5)18 (6.0)
Other4.5 (4.8)9662 (14,016)49 (8.5)60 (9.7)
Non‐English speakers (all)4.5 (4.5)9515 (13,213)117 (11.0)147 (12.8)
Chinese speakers4.5 (4.6)9505 (12,841)69 (12.8)85 (14.6)
Spanish speakers4.5 (4.5)9115 (13,846)31 (12.0)28 (10.3)
Russian speakers4.7 (4.2)9846 (13,360)17 (6.4)34 (11.7)

We further investigated differences among English speakers to better understand the very high rate of readmission for African Americans and the very low rate for English‐speaking Latinos. African Americans were on average younger than other English speakers (55 19 years vs. 60 21 years; P < 0.001); but, they had higher comorbidity scores than other English speakers (1.05 1.40 vs. 0.94 1.31; P = 0.008), and were more likely to be admitted for non‐malignant blood disorders (eg, sickle cell disease), endocrine disorders (eg, diabetes mellitus), and circulatory disorders (eg, stroke). In contrast, English‐speaking Latinos were also younger than other English speakers (53 21 years vs. 59 20 years; P < 0.001), but they trended toward lower comorbidity scores (0.87 1.23 vs. 0.97 1.33; P = 0.2), and were more likely to be admitted for gastrointestinal and musculoskeletal disorders, and less likely to be admitted for malignancy and endocrine disorders.

Multivariate Analyses: Association of Aggregated and Disaggregated Language‐Ethnicity Groups With Hospital Outcomes (Table 3)

In multivariate models examining aggregated language‐ethnicity groups, non‐English speakers had a trend toward higher odds of readmission at 30‐days post‐discharge than the English‐speaking group (odds ratio [OR], 1.3; 95% confidence interval [CI], 1.0‐1.7). There were no significant differences for LOS, cost, or 30‐day mortality. Compared to English speakers, Chinese and Spanish speakers had 70% and 50% higher adjusted odds of readmission at 30‐days post‐discharge respectively, while Russian speakers' odds of readmission was not increased. Additionally, Chinese speakers had 7% shorter LOS than English‐speakers. There were no significant differences among any of the language‐ethnicity groups for 30‐day mortality. The increased odds of readmission for Chinese and Spanish speakers compared to English speakers was robust to reinclusion of the admissions with the top 1% LOS and top 1% cost.

Multivariate Models Examining Association of Aggregated and Disaggregated Language‐Ethnicity Groups with Hospital Outcomes for All Admissions to the Medicine Service at UCSF Hospital From 7/2001 to 6/2003
Language CategorizationLOS, % Difference (95% CI)Total Cost, % Difference (95% CI)30‐Day Readmission,* OR (95% CI)Mortality, OR (95% CI)
  • NOTE: All regression models adjusted for age, gender, admission to the ICU, principle diagnosis, Charlson Co‐morbidity Index, Insurance, age‐spline, attending physician service. Significant results are in bold.

  • Abbreviations: CI, confidence interval; ICU, intensive care unit; LOS, length of stay; OR, odds ratio; UCSF, University of California, San Francisco Medical Center.

  • Excludes those admissions for patients who died in the hospital or were discharged to hospice.

All English speakersReferenceReferenceReferenceReference
Non‐English speakers3.1 (8.7 to 3.1)2.5 (8.3 to 2.1)1.3 (1.0 to 1.7)0.9 (0.7 to 1.2)
All English speakersReferenceReferenceReferenceReference
Chinese speakers7.2 (13.9 to 0)5.3 (12.2 to 2.1)1.7 (1.2 to 2.3)1.0 (0.8 to 1.4)
Spanish speakers3.0 (12.6 to 7.6)3.0 (12.7 to 7.7)1.5 (1.0 to 2.3)0.9 (0.6 to 1.5)
Russian speakers1.5 (8.3 to 12.2)0.9 (8.9 to 11.8)0.8 (0.5 to 1.4)0.8 (0.5 to 1.2)

Multivariate Analyses: Association of Language for Asians and Latinos, and of Ethnicity for English speakers, With Hospital Outcomes (Table 4)

Both Chinese and Spanish speakers had significantly higher odds of 30‐day readmission than their English speaking Asian and Latino counterparts. There were no significant differences in LOS, cost, or 30‐day mortality in this within‐ethnicity analysis. Among English speakers, admissions for patients with Asian ethnicity were 15% shorter and resulted in 9% lower costs than for Whites. While LOS and cost were similar for English‐speaking Latino and White admissions, English‐speaking Latinos had markedly lower odds of 30‐day readmission than their White counterparts. Whereas African‐Americans had 6% shorter LOS, 40% higher odds of readmission and 30% lower odds of mortality at 30‐days than English speaking Whites.

Multivariate Models Examining Association of Language (for Asians and Latinos) and of Ethnicity (for English Speakers) With Hospital Outcomes for All Admissions to the Medicine Service at UCSF Hospital From 7/2001 to 6/2003
Language‐Ethnicity ComparisonsLOS, % Difference (95% CI)Total Cost, % Difference (95% CI)30‐Day Readmission,* OR (95% CI)Mortality, OR (95% CI)
  • NOTE: All regression models adjusted for age, gender, admission to the ICU, principle diagnosis, Charlson Co‐morbidity Index, insurance, age‐spline, attending physician service. Significant results are in bold.

  • Abbreviations: CI, confidence interval; ICU, intensive care unit; LOS, length of stay; OR, odds ratio; UCSF, University of California, San Francisco Medical Center.

  • Excludes those admissions for patients who died in the hospital or were discharged to hospice.

English speaking AsiansReferenceReferenceReferenceReference
Chinese speakers2.2 (7.4 to 12.7)0.3 (9.2 to 10.7)1.5 (1.0 to 2.3)0.8 (0.6 to 1.2)
English speaking LatinosReferenceReferenceReferenceReference
Spanish speakers4.5 (16.8 to 9.5)1.2 (14.0 to 13.5)5.7 (2.4 to 13.2)1.2 (0.6 to 2.4)
English‐WhiteReferenceReferenceReferenceReference
English‐African American6.2 (11.3 to 0.9)4.4 (9.6 to 1.1)1.4 (1.1 to 1.7)0.6 (0.5 to 0.8)
English‐Asian14.6 (20.9 to 7.9)8.6 (15.4 to 1.4)0.8 (0.5 to 1.0)1.0 (0.7 to 1.4)
English‐Latino4.5 (13.5 to 5.4)5.0 (14.0 to 5.0)0.2 (0.1 to 0.4)0.6 (0.4 to 1.0)

Conclusion/Discussion

Our results indicate that language barriers may contribute to higher readmission rates for non‐English speakers, but that they have less impact on care efficiency or mortality. This finding of an association between language and readmission, without a similar association with efficiency, suggests a potentially communication‐critical step in care.20, 21 Patients with language barriers are more likely to experience adverse events, and those events are often caused by errors in communication.12 It is conceivable that higher readmission risk for Chinese and Spanish speakers in our study was, at least in part, due to gaps in communication that are present in all patient groups, and exacerbated by the presence of a language barrier. This barrier is likely present during hospitalization but magnified at discharge, limiting caregivers' ability to understand patients' needs for home care, while simultaneously limiting patients' understanding of the discharge plan. After discharge, it is also possible that non‐English speakers are less able to communicate their needs as they arise, ormore subtlyfeel less supported by a primarily English speaking healthcare system. As in other clinical arenas,22 it is quite possible that increased access to professional interpreters in the hospital setting, and particularly at the time of discharge, would enhance communication and outcomes for LEP patients. Our interpreter services data showed that the patients in our study had quite limited access to staff professional interpreters.

Our findings differ somewhat from those of John‐Baptiste et al.,11 who found that language barriers contributed to increased LOS for patients with cardiac and major surgical diagnoses. Our study's findings are akin to recent research suggesting that being a monolingual Spanish speaker or receiving interpreter services may not significantly impact LOS or cost of hospitalization,23 and that LOS and in‐hospital mortality do not differ for non‐English speakers and English speakers after acute myocardial infarction.24 These studies, along with our results, suggest that that care efficiency in the hospital may be driven much more by clinical acuity (eg, the need to respond rapidly to urgent clinical signs such as hypotension, fever and respiratory distress) than by adequacy of communication. For example, elderly LEP patients may be even more likely than English speakers to have vigilant family members at the bedside throughout their hospitalization due to their need for communication assistance; these family members can quickly alert hospital staff to concerning changes in the patient's condition.

Our results also suggest the possibility that language and ethnicity are not monolithic concepts, and that even within language and ethnic groups there are potential differences in care pattern. For example, not speaking English may be a surrogate marker for unmeasured factors such as social supports and access to care. Language is intimately associated with culture; it remains plausible that cultural differences between highly acculturated and less acculturated members of a given ethno‐cultural group may have contributed to our observed differences in readmission rates. Differences in culture and associated factors, such as social support or use of multiple hospital systems, may account for lack of higher readmission risk in Russian speakers, while Chinese and Spanish speakers had higher readmission risk.

In addition, our finding that English‐speaking Latinos had lower readmission risk than any other group may be more consistent with their clinical characteristicseg, younger age, fewer comorbiditiesthan with cultural factors. Our finding that African American patients had the highest readmission risk in our hospital was both surprising and concerning. Some of this increased risk may be explained by clinical characteristics, such as higher comorbidities and higher rates of diagnoses leading to frequent admissions (eg, sickle cell disease); however, the reasons for this disparity deserves further investigation.

Our study has limitations. First, our data are administrative, and lack information about patients' educational attainment, social support, acculturation, utilization of other hospital systems, and usual source of care. Despite this, we were able to account for many significant covariates that might contribute to readmission rates, including age, insurance status, gender, comorbidities, and admission to the intensive care unit.2528 Second, our information about patients' English language proficiency is limited. While direct assessments of English proficiency are more accurate ways to determine a patient's ability to communicate with health care providers in English,29 our language validation work conducted in preparation for this study suggests that most of our patients recorded as having a non‐English primary language (87%) also have a low score on a language acculturation scale.

Third, only 14% of our non‐English speaking subjects utilized professional staff interpreters, and we had no information on the use of professional telephonic interpreters, or ad hoc interpretersfamily members, non‐interpreter staff membersand their impact on our results. It is well‐documented that ad hoc interpreters are used frequently in healthcare, particularly in the hospital setting, and thus we can assume this to be true in our study.30, 31 As noted above, it is likely that the advocacy of family members and friends at the bedside helped to minimize potential differences in care efficiency for patients with language barriers. Finally, our study was performed at a single university based hospital and may not produce results which are applicable to other care settings.

Our findings point to several avenues for future research on language barriers and hospitalized patients. First, the field would benefit from an examination of the impact of easy access to professional interpreters during hospitalization on outcomes of hospital care, in particular on readmission rates. Second, there is need for development and assessment of best practices for creating a culture of professional interpreter utilization in the hospital among physicians and nursing staff. Third, investigation of the role of caregiver presence in the hospital room and how this might differ by patient culture, age and language ability may further elucidate some of the differences across language groups observed in our study. Lastly, a more granular investigation of clinician‐patient communication and the importance of interpersonal processes of care on both patient satisfaction and understanding of and adherence to discharge instructions could lead to the development of detailed interventions to enhance this communication and these outcomes as it has done for communication‐sensitive outcomes in the outpatient arena.3234

In summary, our study suggests that higher risk for readmission can be added to the unfortunate list of outcomes which are worsened due to language barriers, pointing to transition from the hospital as a potentially communication‐critical step in care which may be amenable to intervention. Our findings also suggest that this risk can vary even between groups of patients who do not speak English primarily. Whether and to what degree language and communication barriers aloneincluding access to professional interpreters and patient‐centered communicationduring hospitalization, or differences in caregiver social support both during and after hospitalization as well as access to care post‐hospitalization contribute to these findings is a worthy subject of future research.

Acknowledgements

The authors acknowledge Dr. Eliseo J. Prez‐Stable for his mentorship on this project.

References
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  3. Fiscella K,Franks P,Doescher MP,Saver BG.Disparities in health care by race, ethnicity, and language among the insured: findings from a national sample.Med Care.2002;40(1):5259.
  4. Fox SA,Stein JA.The effect of physician‐patient communication on mammography utilization by different ethnic groups.Med Care.1991;29(11):10651082.
  5. Kirkman‐Liff B,Mondragon D.Language of interview:relevance for research of Southwest Hispanics.Am J Pub Health.1991;81(11):13991404.
  6. Woloshin S,Schwartz LM,Katz SJ,Welch HG.Is language a barrier to the use of preventive services?J Gen Intern Med.1997;12(8):472477.
  7. Carasquillo O,Orav EJ,Brennan TA,Burstin HR.Impact of language barriers on patient satisfaction in an emergency department.JGIM.1999;14:8287.
  8. Crane JA.Patient comprehension of doctor‐patient communication on discharge from the emergency department.J Emerg Med.1997;15(1):17.
  9. Gandhi TK,Burstin HR,Cook EF, et al.Drug complications in outpatients.J Gen Intern Med.2000;15:149154.
  10. Manson A.Language concordance as a determinant of patient compliance and emergency room use in patients with asthma.Med Care.1988;26(12):11191128.
  11. John‐Baptiste A,Naglie G,Tomlinson G,et al.The effect of English language proficiency on length of stay and in‐hospital mortality.J Gen Intern Med.2004;19:221228.
  12. Divi C,Koss RG,Schmaltz SP,Loeb JM.Language proficiency and adverse events in US hospitals: a pilot study.Int J Qual Health Care.2007;19(2):6067.
  13. Auerbach AD,Katz R,Pantilat SZ, et al.Factors associated with discussion of care plans and code status at the time of hospital admission: results from the Multicenter Hospitalist Study.J Hosp Med.2008;3(6):437445.
  14. Vasilevskis EE,Meltzer D,Schnipper J, et al.Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non‐hospitalists.J Gen Intern Med.2008;23(9):13991406.
  15. Charlson ME,Pompei P,Ales KL,MacKenzie CR.A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40(5):373383.
  16. AHRQ. Healthcare Cost and Utilizlation Project: Tools 132(3):191200.
  17. Bessaoud F,Daures JP,Molinari N.Free knot splines for logistic models and threshold selection.Comput Methods Programs Biomed.2005;77(1):19.
  18. Boucher KM,Slattery ML,Berry TD,Quesenberry C,Anderson K.Statistical methods in epidemiology: a comparison of statistical methods to analyze dose‐response and trend analysis in epidemiologic studies.J Clin Epidemiol.1998;51(12):12231233.
  19. The Care Transitions Project. Health Care Policy and Research, Practitioner Tools. Available at: http://www.caretransitions.org/practitioner_tools.asp.
  20. AHRQ. Improving safety at the point of care. Available at: http://www.ahrq.gov/qual/pips. Accessed January2010.
  21. Karliner L,Jacobs E,Chen A,Mutha S.Do professional interpreters improve clinical care for patients with limited english proficiency? A systematic review of the literature.Health Serv Res.2007;42(2):727754.
  22. Jacobs EA,Sadowski LS,Rathouz PJ.The impact of an enhanced interpreter service intervention on hospital costs and patient satisfaction.J Gen Intern Med.2007;22 Suppl 2:306311.
  23. Grubbs V,Bibbins‐Domingo K,Fernandez A,Chattopadhyay A,Bindman AB.Acute myocardial infarction length of stay and hospital mortality are not associated with language preference.J Gen Intern Med.2008;23(2):190194.
  24. Campbell SE,Seymour DG,Primrose WR.A systematic literature review of factors affecting outcome in older medical patients admitted to hospital.Age Ageing.2004;33(2):110–115.
  25. Fan JS,Kao WF,Yen DH,Wang LM,Huang CI,Lee CH.Risk factors and prognostic predictors of unexpected intensive care unit admission within 3 days after ED discharge.Am J Emerg Med.2007;25(9):10091014.
  26. Howie‐Esquivel J,Dracup K.Effect of gender, ethnicity, pulmonary disease, and symptom stability on rehospitalization in patients with heart failure.Am J Cardiol.2007;100(7):11391144.
  27. Kind AJ,Smith MA,Frytak JR,Finch MD.Bouncing back: patterns and predictors of complicated transitions 30 days after hospitalization for acute ischemic stroke.J Am Geriatr Soc.2007;55(3):365373.
  28. Karliner L,Napoles‐Springer A,Schillinger D,Bibbins‐Domingo K,Perez‐Stable E.Identification of limited English proficient patients in clinical care.J Gen Intern Med.2008;23(10):15551560.
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Article PDF
Issue
Journal of Hospital Medicine - 5(5)
Page Number
276-282
Legacy Keywords
communication, continuity of care transition and discharge planning, quality improvement
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Article PDF
Article PDF

Forty‐five‐million Americans speak a language other than English and more than 19 million of these speak English less than very wellor are limited English proficient (LEP).1 The number of non‐English‐speaking and LEP people in the US has risen in recent decades, presenting a challenge to healthcare systems to provide high‐quality, patient‐centered care for these patients.2

For outpatients, language barriers are a fundamental contributor to gaps in health care. In the clinic setting, patients who do not speak English well have less access to a usual source of care and lower rates of physician visits and preventive services.36 Even when patients with language barriers do have access to care, they have poorer adherence, decreased comprehension of their diagnoses, decreased satisfaction with care, and increased medication complications.710

Few studies, however, have examined how language influences outcomes of hospital care. Compared to English‐speakers, patients who do not speak English well may experience longer lengths of stay,11 and have more adverse events while in the hospital.12 However, these previous studies have not investigated outcomes immediately post‐hospitalization, such as readmission rates and mortality, nor have they directly addressed the interaction between ethnicity and language.

To understand these questions, we analyzed data collected from a university‐based teaching hospital which cares for patients of diverse cultural and language backgrounds. Using these data, we examined how patients' primary language influenced hospital costs, length of stay (LOS), 30‐day readmission, and 30‐day mortality risk.

Patients and Methods

Patient Population and Setting

Our study examined patients admitted to the General Medicine Service at the University of California, San Francisco Medical Center (UCSF) between July 1, 2001 and June 30th, 2003, the time period during which UCSF participated in the Multicenter Hospitalist Trial (MHT) a prospective quasi‐randomized trial of hospitalist care for general medicine patients.13, 14

UCSF Moffitt‐Long Hospital is a 400‐bed urban academic medical center which provides services to the City and County of San Francisco, an ethnically and linguistically diverse area. UCSF employs staff language interpreters in Spanish, Chinese and Russian who travel to its many outpatient clinics, Comprehensive Cancer Center, Children's Hospital, as well as to Moffitt‐Long Hospital upon request; phone interpretation is also available when in‐person interpreters are not available, for off hours needs and for less common languages. During the period of this study there were no specific inpatient guidelines in place for use of interpretation services at UCSF, nor were there any specific interventions targeting LEP or non‐English speaking inpatients.

Patients were eligible for the MHT if they were 18 years of age or older and admitted at random to a hospitalist or non‐hospitalist physician (eg, outpatient general internist attending on average 1‐month/year); a minority of patients were cared for directly by their primary care physician while in the hospital, and were excluded. For purposes of our study, which merged MHT data with hospital administrative data on primary language, we further excluded all admissions for patients for whom primary language was missing (n = 5), whose listing was unknown or other language (n = 78), sign language (n = 3) or whose language was listed but was not one of the included languages (n = 258). Included languages were English, Chinese, Russian and Spanish. Because LOS and cost data were skewed, we excluded those admissions with the top 1% longest stays and the top 1% highest cost (n = 176); these exclusions did not alter the proportion of admissions across language and ethnicity. In addition, we excluded 102 admissions that were missing data on cost and 11 with costs <$500 and which were likely to be erroneous. Our research was approved by the UCSF Institutional Review Board.

Data Sources

We collected administrative data from Transition Systems Inc (TSI, Boston, MA) billing databases at UCSF as part of the MHT. These data include patient demographics, insurance, costs, ICD‐9CM diagnostic codes, admission and discharge dates in Uniform Bill 92 format. Patient mortality information was collected as part of the MHT using the National Death Index.14

Language data were collected from a separate patient‐registration database (STOR) at UCSF. Information on a patient's primary language is entered at the time each patient first registers at UCSF, whether for the index hospitalization or for prior clinic visits, and is based generally on patient self report. As part of our validation step, we cross‐checked 829 STOR language entries against patient reports and found 91% agreement with the majority of the errors classifying non‐English speakers as English‐speakers.

Measures

Predictor

Our primary language variable was derived using language designations collected from patient registration databases described above. Using these data we specified our key language groups as English, Chinese (Cantonese or Mandarin), Russian, or Spanish.

Outcomes

LOS and total cost of hospital stay for each hospitalization derived from administrative data sources. Readmissions were identified at the time patients were readmitted to UCSF (eg, flagged in administrative data). Mortality was determined by whether an individual patient with an admission in the database was recorded in the National Death Index as dead within 30‐days of admission.

Covariates

Additional covariates included age at admission, gender, ethnicity as recorded in registration databases (White, African American, Asian, Latino, Other), insurance, principal billing diagnosis, whether or not a patient received intensive care unit (ICU) care, type of admitting attending physician (Hospitalist/non‐Hospitalist), and an administrative Charlson comorbidity score.15 To collapse the principal diagnoses into categories, we used the Healthcare Cost and Utilization Project (HCUP)'s Clinical Classification System, which allowed us to classify each diagnosis in 1 of 14 generally accepted categories.16

Analysis

Statistical analyses were performed using STATA statistical software (STATACorp, Version 9, College Station, TX). We examined descriptive means and proportions for all variables, including sociodemographic, hospitalization, comorbidity and outcome variables. We compared English and non‐English speakers on all covariate and outcome variables using t‐tests for comparison of means and chi‐square for comparison of categorical variables.

It was not possible to fully test the language‐by‐ethnicity interactionwhether or not the impact of language varied by ethnic groupbecause many cells of the joint distribution were very sparse (eg, the sample contained very few non‐English‐speaking African Americans). Therefore, to better understand the influence of English vs. non‐English language usage across different ethnic groups, we created a combined language‐ethnicity predictor variable which categorized each subject first by language and then for the English‐speakers by ethnicity. For example, a Chinese, Spanish or Russian speaker would be categorized as such, and an English‐speaker could fall into the English‐White, English‐African American, English‐Asian or English‐Latino group. This allowed us to test whether there were any differences in language effects across the White, Asian, and Latino ethnicities, and any difference in ethnicity effects among English‐speakers.

Because cost and LOS were skewed, we used negative binomial models for LOS and log transformed costs. We performed a sensitivity analysis testing whether our results were robust to the exclusion of the admissions with the top 1% LOS and top 1% cost. We used logistic regression for the 30‐day readmission and mortality outcomes.

Our primary predictor was the language‐ethnicity variable described above. To determine the independent association between this predictor and our key outcomes, we then built models which included additional potential confounders selected either for face validity or because of observed confounding with other covariates. Our inclusion of potential confounders was limited by the variables available in the administrative database; thus, we were not able to pursue detailed analyses of communication and literacy factors and their interaction with our predictor or their independent impact on outcomes. Models also included a linear spline with a single knot at age 65 years as a further adjustment for age in Medicare recipients.1719 For the 30‐day readmission outcome model, we excluded those admissions for which the patient either died in the hospital or was discharged to hospice care. Within each model we tested the impact of a language barrier using custom contrasts. This allowed us to examine the language‐ethnicity effect aggregating all non‐English speakers compared to all English‐speakers, comparing each non‐English speaking group to all English‐speakers, comparing Chinese speakers to English‐speaking Asians and Spanish speakers to English‐speaking‐Latinos, as well as to test whether the effect of English language is the same across ethnicities.

Results

Admission Characteristics of the Sample

A total of 7023 patients were admitted to the General Medicine service, 5877 (84%) of whom were English‐speakers and 1146 (16%) non‐English‐speakers (Table 1). Overall, half of the admitted patients were women (50%), and the vast majority was insured (93%). The most common principal diagnoses were respiratory and gastrointestinal disorders. Only a small number of non‐English speakers 164 (14%) were recorded in the UCSF Interpreter Services database as having had any interaction with a professional staff interpreter during their hospitalization.

Admission Characteristics and Bivariate Association of Having an English vs. a Non‐English Primary Language With Predictor Variables of Interest for Patients Admitted to the Medical Service of the UCSF Hospital From 7/2001 to 6/2003 (n = 7023)
 English (n = 5877) n (%)Non‐English (n = 1146) n (%)
  • NOTE: Percents may not add to 100 due to rounding error.

  • Abbreviations: CI, confidence interval; NOS, not otherwise specified; SD, standard deviation.

Socio‐economic variables  
Language‐ethnicity  
English  
White3066 (52.2) 
African American1351 (23.0) 
Asian544 (9.3) 
Latino298 (5.1) 
Other618 (10.5) 
Chinese speakers 584 (51.0)
Spanish speakers 272 (25.3)
Russian speakers 290 (23.7)
Age mean (SD) (range 18‐105)58.8 (20.3)72.3 (15.5)
Gender  
Male2967 (50.5)514 (44.8)
Female2910 (49.5)632 (55.2)
Insurance  
Medicare2878 (49.0)800 (69.8)
Medicaid1201 (20.4)193 (16.8)
Commercial1358 (23.1)106 (9.3)
Charity/other440 (7.5)47 (4.1)
Hospitalization variables  
Admitted to ICU  
Yes721 (12.3)149 (13.0)
Attending physician  
Hospitalist3950 (67.2)781 (68.2)
Comorbidity variables  
Principal Diagnosis  
Respiratory disorder1061 (18.1)225 (19.6)
Gastrointestinal disorder963 (16.4)205 (17.9)
Circulatory disorder613 (10.4)140 (12.2)
Endocrine/metabolism671 (11.4)80 (7.0)
Injury/poisoning475 (8.1)64 (5.6)
Malignancy395 (6.7)107 (9.3)
Renal/urinary disorder383 (6.5)108 (9.4)
Skin disorder278 (4.7)28 (2.9)
Infection/fatigue NOS206 (3.5)45 (3.4)
Blood disorder (non‐malignant)189 (3.2)38 (3.3)
Musculoskeletal/connective tissue disorder164 (2.8)33 (2.9)
Mental disorder/substance abuse171 (2.9)7 (0.6)
Nervous system/brain infection137 (2.3)26 (2.3)
Unclassified171 (2.9)40 (3.5)
Charlson Index score mean (SD)0.97 1.331.10 1.42

Among English speakers, Whites and African Americans were the most common ethnicities; however, more than 500 admissions were categorized as Asian ethnicity, and more than 600 as patients of other ethnicity. Close to 300 admissions were for Latinos. Among non‐English speakers, Chinese speakers had the largest number of admissions (n = 584), while Spanish and Russian speakers had similar numbers (n = 272 and 290 respectively).

Non‐English speakers were older, more likely to be female, more likely to be insured by Medicare, and more likely to have a higher comorbidity index score. While comorbidity scores were similar among non‐English speakers (Chinese 1.13 1.50; Russian 1.09 1.37; Spanish 1.06 1.30), they differed considerably among English speakers (White 0.94 1.29; African American 1.05 1.40; Asian 1.04 1.45; Latino 0.89 1.23; Other 0.91 1.29).

Hospital Outcome by Language‐Ethnicity Group (Table 2)

When aggregated together, non‐English speakers were somewhat more likely to be dead at 30‐days and have lower cost admissions; however, they did not differ from English speakers on LOS or readmission rates. While differences among disaggregated language‐ethnicity groups were not all statistically significant, English‐speaking Whites had the longest LOS (mean = 4.9 days) and highest costs (mean = $10,530). English‐speaking African Americans, Chinese and Spanish speakers had the highest 30‐day readmission rates; whereas, English‐speaking Latinos and Russian speakers had markedly lower 30‐day readmission rates (2.5% and 6.4%, respectively). Chinese speakers had the highest 30‐day mortality, followed by English speaking Whites and Asians.

Bivariate results of Hospital Outcome Measures Across Language‐Ethnicity Groups (n = 7023)
Language‐Ethnicity GroupsLOS* Mean #Days (SD)Cost Mean Cost $ (SD)30‐Day Readmission, n (%)30‐Day Mortality, n (%)
  • Abbreviation: SD, standard deviation.

  • P < 0.05 for overall comparison across language‐ethnicity groups.

  • P < 0.001 for overall comparison across language‐ethnicity groups.

  • Excludes those admissions for patients who died in the hospital or were discharged to hospice.

  • Includes death during hospitalization.

English speakers (all)4.7 (4.5)10,035 (15,041)648 (11.9)613 (10.4)
White4.9 (5.1)10,530 (15,894)322 (11.4)377 (12.3)
African American4.5 (4.8)9107 (13,314)227 (17.5)91 (6.7)
Asian4.3 (4.5)9933 (15,607)43 (8.8)67 (12.3)
Latino4.6 (4.8)9823 (14,113)7 (2.5)18 (6.0)
Other4.5 (4.8)9662 (14,016)49 (8.5)60 (9.7)
Non‐English speakers (all)4.5 (4.5)9515 (13,213)117 (11.0)147 (12.8)
Chinese speakers4.5 (4.6)9505 (12,841)69 (12.8)85 (14.6)
Spanish speakers4.5 (4.5)9115 (13,846)31 (12.0)28 (10.3)
Russian speakers4.7 (4.2)9846 (13,360)17 (6.4)34 (11.7)

We further investigated differences among English speakers to better understand the very high rate of readmission for African Americans and the very low rate for English‐speaking Latinos. African Americans were on average younger than other English speakers (55 19 years vs. 60 21 years; P < 0.001); but, they had higher comorbidity scores than other English speakers (1.05 1.40 vs. 0.94 1.31; P = 0.008), and were more likely to be admitted for non‐malignant blood disorders (eg, sickle cell disease), endocrine disorders (eg, diabetes mellitus), and circulatory disorders (eg, stroke). In contrast, English‐speaking Latinos were also younger than other English speakers (53 21 years vs. 59 20 years; P < 0.001), but they trended toward lower comorbidity scores (0.87 1.23 vs. 0.97 1.33; P = 0.2), and were more likely to be admitted for gastrointestinal and musculoskeletal disorders, and less likely to be admitted for malignancy and endocrine disorders.

Multivariate Analyses: Association of Aggregated and Disaggregated Language‐Ethnicity Groups With Hospital Outcomes (Table 3)

In multivariate models examining aggregated language‐ethnicity groups, non‐English speakers had a trend toward higher odds of readmission at 30‐days post‐discharge than the English‐speaking group (odds ratio [OR], 1.3; 95% confidence interval [CI], 1.0‐1.7). There were no significant differences for LOS, cost, or 30‐day mortality. Compared to English speakers, Chinese and Spanish speakers had 70% and 50% higher adjusted odds of readmission at 30‐days post‐discharge respectively, while Russian speakers' odds of readmission was not increased. Additionally, Chinese speakers had 7% shorter LOS than English‐speakers. There were no significant differences among any of the language‐ethnicity groups for 30‐day mortality. The increased odds of readmission for Chinese and Spanish speakers compared to English speakers was robust to reinclusion of the admissions with the top 1% LOS and top 1% cost.

Multivariate Models Examining Association of Aggregated and Disaggregated Language‐Ethnicity Groups with Hospital Outcomes for All Admissions to the Medicine Service at UCSF Hospital From 7/2001 to 6/2003
Language CategorizationLOS, % Difference (95% CI)Total Cost, % Difference (95% CI)30‐Day Readmission,* OR (95% CI)Mortality, OR (95% CI)
  • NOTE: All regression models adjusted for age, gender, admission to the ICU, principle diagnosis, Charlson Co‐morbidity Index, Insurance, age‐spline, attending physician service. Significant results are in bold.

  • Abbreviations: CI, confidence interval; ICU, intensive care unit; LOS, length of stay; OR, odds ratio; UCSF, University of California, San Francisco Medical Center.

  • Excludes those admissions for patients who died in the hospital or were discharged to hospice.

All English speakersReferenceReferenceReferenceReference
Non‐English speakers3.1 (8.7 to 3.1)2.5 (8.3 to 2.1)1.3 (1.0 to 1.7)0.9 (0.7 to 1.2)
All English speakersReferenceReferenceReferenceReference
Chinese speakers7.2 (13.9 to 0)5.3 (12.2 to 2.1)1.7 (1.2 to 2.3)1.0 (0.8 to 1.4)
Spanish speakers3.0 (12.6 to 7.6)3.0 (12.7 to 7.7)1.5 (1.0 to 2.3)0.9 (0.6 to 1.5)
Russian speakers1.5 (8.3 to 12.2)0.9 (8.9 to 11.8)0.8 (0.5 to 1.4)0.8 (0.5 to 1.2)

Multivariate Analyses: Association of Language for Asians and Latinos, and of Ethnicity for English speakers, With Hospital Outcomes (Table 4)

Both Chinese and Spanish speakers had significantly higher odds of 30‐day readmission than their English speaking Asian and Latino counterparts. There were no significant differences in LOS, cost, or 30‐day mortality in this within‐ethnicity analysis. Among English speakers, admissions for patients with Asian ethnicity were 15% shorter and resulted in 9% lower costs than for Whites. While LOS and cost were similar for English‐speaking Latino and White admissions, English‐speaking Latinos had markedly lower odds of 30‐day readmission than their White counterparts. Whereas African‐Americans had 6% shorter LOS, 40% higher odds of readmission and 30% lower odds of mortality at 30‐days than English speaking Whites.

Multivariate Models Examining Association of Language (for Asians and Latinos) and of Ethnicity (for English Speakers) With Hospital Outcomes for All Admissions to the Medicine Service at UCSF Hospital From 7/2001 to 6/2003
Language‐Ethnicity ComparisonsLOS, % Difference (95% CI)Total Cost, % Difference (95% CI)30‐Day Readmission,* OR (95% CI)Mortality, OR (95% CI)
  • NOTE: All regression models adjusted for age, gender, admission to the ICU, principle diagnosis, Charlson Co‐morbidity Index, insurance, age‐spline, attending physician service. Significant results are in bold.

  • Abbreviations: CI, confidence interval; ICU, intensive care unit; LOS, length of stay; OR, odds ratio; UCSF, University of California, San Francisco Medical Center.

  • Excludes those admissions for patients who died in the hospital or were discharged to hospice.

English speaking AsiansReferenceReferenceReferenceReference
Chinese speakers2.2 (7.4 to 12.7)0.3 (9.2 to 10.7)1.5 (1.0 to 2.3)0.8 (0.6 to 1.2)
English speaking LatinosReferenceReferenceReferenceReference
Spanish speakers4.5 (16.8 to 9.5)1.2 (14.0 to 13.5)5.7 (2.4 to 13.2)1.2 (0.6 to 2.4)
English‐WhiteReferenceReferenceReferenceReference
English‐African American6.2 (11.3 to 0.9)4.4 (9.6 to 1.1)1.4 (1.1 to 1.7)0.6 (0.5 to 0.8)
English‐Asian14.6 (20.9 to 7.9)8.6 (15.4 to 1.4)0.8 (0.5 to 1.0)1.0 (0.7 to 1.4)
English‐Latino4.5 (13.5 to 5.4)5.0 (14.0 to 5.0)0.2 (0.1 to 0.4)0.6 (0.4 to 1.0)

Conclusion/Discussion

Our results indicate that language barriers may contribute to higher readmission rates for non‐English speakers, but that they have less impact on care efficiency or mortality. This finding of an association between language and readmission, without a similar association with efficiency, suggests a potentially communication‐critical step in care.20, 21 Patients with language barriers are more likely to experience adverse events, and those events are often caused by errors in communication.12 It is conceivable that higher readmission risk for Chinese and Spanish speakers in our study was, at least in part, due to gaps in communication that are present in all patient groups, and exacerbated by the presence of a language barrier. This barrier is likely present during hospitalization but magnified at discharge, limiting caregivers' ability to understand patients' needs for home care, while simultaneously limiting patients' understanding of the discharge plan. After discharge, it is also possible that non‐English speakers are less able to communicate their needs as they arise, ormore subtlyfeel less supported by a primarily English speaking healthcare system. As in other clinical arenas,22 it is quite possible that increased access to professional interpreters in the hospital setting, and particularly at the time of discharge, would enhance communication and outcomes for LEP patients. Our interpreter services data showed that the patients in our study had quite limited access to staff professional interpreters.

Our findings differ somewhat from those of John‐Baptiste et al.,11 who found that language barriers contributed to increased LOS for patients with cardiac and major surgical diagnoses. Our study's findings are akin to recent research suggesting that being a monolingual Spanish speaker or receiving interpreter services may not significantly impact LOS or cost of hospitalization,23 and that LOS and in‐hospital mortality do not differ for non‐English speakers and English speakers after acute myocardial infarction.24 These studies, along with our results, suggest that that care efficiency in the hospital may be driven much more by clinical acuity (eg, the need to respond rapidly to urgent clinical signs such as hypotension, fever and respiratory distress) than by adequacy of communication. For example, elderly LEP patients may be even more likely than English speakers to have vigilant family members at the bedside throughout their hospitalization due to their need for communication assistance; these family members can quickly alert hospital staff to concerning changes in the patient's condition.

Our results also suggest the possibility that language and ethnicity are not monolithic concepts, and that even within language and ethnic groups there are potential differences in care pattern. For example, not speaking English may be a surrogate marker for unmeasured factors such as social supports and access to care. Language is intimately associated with culture; it remains plausible that cultural differences between highly acculturated and less acculturated members of a given ethno‐cultural group may have contributed to our observed differences in readmission rates. Differences in culture and associated factors, such as social support or use of multiple hospital systems, may account for lack of higher readmission risk in Russian speakers, while Chinese and Spanish speakers had higher readmission risk.

In addition, our finding that English‐speaking Latinos had lower readmission risk than any other group may be more consistent with their clinical characteristicseg, younger age, fewer comorbiditiesthan with cultural factors. Our finding that African American patients had the highest readmission risk in our hospital was both surprising and concerning. Some of this increased risk may be explained by clinical characteristics, such as higher comorbidities and higher rates of diagnoses leading to frequent admissions (eg, sickle cell disease); however, the reasons for this disparity deserves further investigation.

Our study has limitations. First, our data are administrative, and lack information about patients' educational attainment, social support, acculturation, utilization of other hospital systems, and usual source of care. Despite this, we were able to account for many significant covariates that might contribute to readmission rates, including age, insurance status, gender, comorbidities, and admission to the intensive care unit.2528 Second, our information about patients' English language proficiency is limited. While direct assessments of English proficiency are more accurate ways to determine a patient's ability to communicate with health care providers in English,29 our language validation work conducted in preparation for this study suggests that most of our patients recorded as having a non‐English primary language (87%) also have a low score on a language acculturation scale.

Third, only 14% of our non‐English speaking subjects utilized professional staff interpreters, and we had no information on the use of professional telephonic interpreters, or ad hoc interpretersfamily members, non‐interpreter staff membersand their impact on our results. It is well‐documented that ad hoc interpreters are used frequently in healthcare, particularly in the hospital setting, and thus we can assume this to be true in our study.30, 31 As noted above, it is likely that the advocacy of family members and friends at the bedside helped to minimize potential differences in care efficiency for patients with language barriers. Finally, our study was performed at a single university based hospital and may not produce results which are applicable to other care settings.

Our findings point to several avenues for future research on language barriers and hospitalized patients. First, the field would benefit from an examination of the impact of easy access to professional interpreters during hospitalization on outcomes of hospital care, in particular on readmission rates. Second, there is need for development and assessment of best practices for creating a culture of professional interpreter utilization in the hospital among physicians and nursing staff. Third, investigation of the role of caregiver presence in the hospital room and how this might differ by patient culture, age and language ability may further elucidate some of the differences across language groups observed in our study. Lastly, a more granular investigation of clinician‐patient communication and the importance of interpersonal processes of care on both patient satisfaction and understanding of and adherence to discharge instructions could lead to the development of detailed interventions to enhance this communication and these outcomes as it has done for communication‐sensitive outcomes in the outpatient arena.3234

In summary, our study suggests that higher risk for readmission can be added to the unfortunate list of outcomes which are worsened due to language barriers, pointing to transition from the hospital as a potentially communication‐critical step in care which may be amenable to intervention. Our findings also suggest that this risk can vary even between groups of patients who do not speak English primarily. Whether and to what degree language and communication barriers aloneincluding access to professional interpreters and patient‐centered communicationduring hospitalization, or differences in caregiver social support both during and after hospitalization as well as access to care post‐hospitalization contribute to these findings is a worthy subject of future research.

Acknowledgements

The authors acknowledge Dr. Eliseo J. Prez‐Stable for his mentorship on this project.

Forty‐five‐million Americans speak a language other than English and more than 19 million of these speak English less than very wellor are limited English proficient (LEP).1 The number of non‐English‐speaking and LEP people in the US has risen in recent decades, presenting a challenge to healthcare systems to provide high‐quality, patient‐centered care for these patients.2

For outpatients, language barriers are a fundamental contributor to gaps in health care. In the clinic setting, patients who do not speak English well have less access to a usual source of care and lower rates of physician visits and preventive services.36 Even when patients with language barriers do have access to care, they have poorer adherence, decreased comprehension of their diagnoses, decreased satisfaction with care, and increased medication complications.710

Few studies, however, have examined how language influences outcomes of hospital care. Compared to English‐speakers, patients who do not speak English well may experience longer lengths of stay,11 and have more adverse events while in the hospital.12 However, these previous studies have not investigated outcomes immediately post‐hospitalization, such as readmission rates and mortality, nor have they directly addressed the interaction between ethnicity and language.

To understand these questions, we analyzed data collected from a university‐based teaching hospital which cares for patients of diverse cultural and language backgrounds. Using these data, we examined how patients' primary language influenced hospital costs, length of stay (LOS), 30‐day readmission, and 30‐day mortality risk.

Patients and Methods

Patient Population and Setting

Our study examined patients admitted to the General Medicine Service at the University of California, San Francisco Medical Center (UCSF) between July 1, 2001 and June 30th, 2003, the time period during which UCSF participated in the Multicenter Hospitalist Trial (MHT) a prospective quasi‐randomized trial of hospitalist care for general medicine patients.13, 14

UCSF Moffitt‐Long Hospital is a 400‐bed urban academic medical center which provides services to the City and County of San Francisco, an ethnically and linguistically diverse area. UCSF employs staff language interpreters in Spanish, Chinese and Russian who travel to its many outpatient clinics, Comprehensive Cancer Center, Children's Hospital, as well as to Moffitt‐Long Hospital upon request; phone interpretation is also available when in‐person interpreters are not available, for off hours needs and for less common languages. During the period of this study there were no specific inpatient guidelines in place for use of interpretation services at UCSF, nor were there any specific interventions targeting LEP or non‐English speaking inpatients.

Patients were eligible for the MHT if they were 18 years of age or older and admitted at random to a hospitalist or non‐hospitalist physician (eg, outpatient general internist attending on average 1‐month/year); a minority of patients were cared for directly by their primary care physician while in the hospital, and were excluded. For purposes of our study, which merged MHT data with hospital administrative data on primary language, we further excluded all admissions for patients for whom primary language was missing (n = 5), whose listing was unknown or other language (n = 78), sign language (n = 3) or whose language was listed but was not one of the included languages (n = 258). Included languages were English, Chinese, Russian and Spanish. Because LOS and cost data were skewed, we excluded those admissions with the top 1% longest stays and the top 1% highest cost (n = 176); these exclusions did not alter the proportion of admissions across language and ethnicity. In addition, we excluded 102 admissions that were missing data on cost and 11 with costs <$500 and which were likely to be erroneous. Our research was approved by the UCSF Institutional Review Board.

Data Sources

We collected administrative data from Transition Systems Inc (TSI, Boston, MA) billing databases at UCSF as part of the MHT. These data include patient demographics, insurance, costs, ICD‐9CM diagnostic codes, admission and discharge dates in Uniform Bill 92 format. Patient mortality information was collected as part of the MHT using the National Death Index.14

Language data were collected from a separate patient‐registration database (STOR) at UCSF. Information on a patient's primary language is entered at the time each patient first registers at UCSF, whether for the index hospitalization or for prior clinic visits, and is based generally on patient self report. As part of our validation step, we cross‐checked 829 STOR language entries against patient reports and found 91% agreement with the majority of the errors classifying non‐English speakers as English‐speakers.

Measures

Predictor

Our primary language variable was derived using language designations collected from patient registration databases described above. Using these data we specified our key language groups as English, Chinese (Cantonese or Mandarin), Russian, or Spanish.

Outcomes

LOS and total cost of hospital stay for each hospitalization derived from administrative data sources. Readmissions were identified at the time patients were readmitted to UCSF (eg, flagged in administrative data). Mortality was determined by whether an individual patient with an admission in the database was recorded in the National Death Index as dead within 30‐days of admission.

Covariates

Additional covariates included age at admission, gender, ethnicity as recorded in registration databases (White, African American, Asian, Latino, Other), insurance, principal billing diagnosis, whether or not a patient received intensive care unit (ICU) care, type of admitting attending physician (Hospitalist/non‐Hospitalist), and an administrative Charlson comorbidity score.15 To collapse the principal diagnoses into categories, we used the Healthcare Cost and Utilization Project (HCUP)'s Clinical Classification System, which allowed us to classify each diagnosis in 1 of 14 generally accepted categories.16

Analysis

Statistical analyses were performed using STATA statistical software (STATACorp, Version 9, College Station, TX). We examined descriptive means and proportions for all variables, including sociodemographic, hospitalization, comorbidity and outcome variables. We compared English and non‐English speakers on all covariate and outcome variables using t‐tests for comparison of means and chi‐square for comparison of categorical variables.

It was not possible to fully test the language‐by‐ethnicity interactionwhether or not the impact of language varied by ethnic groupbecause many cells of the joint distribution were very sparse (eg, the sample contained very few non‐English‐speaking African Americans). Therefore, to better understand the influence of English vs. non‐English language usage across different ethnic groups, we created a combined language‐ethnicity predictor variable which categorized each subject first by language and then for the English‐speakers by ethnicity. For example, a Chinese, Spanish or Russian speaker would be categorized as such, and an English‐speaker could fall into the English‐White, English‐African American, English‐Asian or English‐Latino group. This allowed us to test whether there were any differences in language effects across the White, Asian, and Latino ethnicities, and any difference in ethnicity effects among English‐speakers.

Because cost and LOS were skewed, we used negative binomial models for LOS and log transformed costs. We performed a sensitivity analysis testing whether our results were robust to the exclusion of the admissions with the top 1% LOS and top 1% cost. We used logistic regression for the 30‐day readmission and mortality outcomes.

Our primary predictor was the language‐ethnicity variable described above. To determine the independent association between this predictor and our key outcomes, we then built models which included additional potential confounders selected either for face validity or because of observed confounding with other covariates. Our inclusion of potential confounders was limited by the variables available in the administrative database; thus, we were not able to pursue detailed analyses of communication and literacy factors and their interaction with our predictor or their independent impact on outcomes. Models also included a linear spline with a single knot at age 65 years as a further adjustment for age in Medicare recipients.1719 For the 30‐day readmission outcome model, we excluded those admissions for which the patient either died in the hospital or was discharged to hospice care. Within each model we tested the impact of a language barrier using custom contrasts. This allowed us to examine the language‐ethnicity effect aggregating all non‐English speakers compared to all English‐speakers, comparing each non‐English speaking group to all English‐speakers, comparing Chinese speakers to English‐speaking Asians and Spanish speakers to English‐speaking‐Latinos, as well as to test whether the effect of English language is the same across ethnicities.

Results

Admission Characteristics of the Sample

A total of 7023 patients were admitted to the General Medicine service, 5877 (84%) of whom were English‐speakers and 1146 (16%) non‐English‐speakers (Table 1). Overall, half of the admitted patients were women (50%), and the vast majority was insured (93%). The most common principal diagnoses were respiratory and gastrointestinal disorders. Only a small number of non‐English speakers 164 (14%) were recorded in the UCSF Interpreter Services database as having had any interaction with a professional staff interpreter during their hospitalization.

Admission Characteristics and Bivariate Association of Having an English vs. a Non‐English Primary Language With Predictor Variables of Interest for Patients Admitted to the Medical Service of the UCSF Hospital From 7/2001 to 6/2003 (n = 7023)
 English (n = 5877) n (%)Non‐English (n = 1146) n (%)
  • NOTE: Percents may not add to 100 due to rounding error.

  • Abbreviations: CI, confidence interval; NOS, not otherwise specified; SD, standard deviation.

Socio‐economic variables  
Language‐ethnicity  
English  
White3066 (52.2) 
African American1351 (23.0) 
Asian544 (9.3) 
Latino298 (5.1) 
Other618 (10.5) 
Chinese speakers 584 (51.0)
Spanish speakers 272 (25.3)
Russian speakers 290 (23.7)
Age mean (SD) (range 18‐105)58.8 (20.3)72.3 (15.5)
Gender  
Male2967 (50.5)514 (44.8)
Female2910 (49.5)632 (55.2)
Insurance  
Medicare2878 (49.0)800 (69.8)
Medicaid1201 (20.4)193 (16.8)
Commercial1358 (23.1)106 (9.3)
Charity/other440 (7.5)47 (4.1)
Hospitalization variables  
Admitted to ICU  
Yes721 (12.3)149 (13.0)
Attending physician  
Hospitalist3950 (67.2)781 (68.2)
Comorbidity variables  
Principal Diagnosis  
Respiratory disorder1061 (18.1)225 (19.6)
Gastrointestinal disorder963 (16.4)205 (17.9)
Circulatory disorder613 (10.4)140 (12.2)
Endocrine/metabolism671 (11.4)80 (7.0)
Injury/poisoning475 (8.1)64 (5.6)
Malignancy395 (6.7)107 (9.3)
Renal/urinary disorder383 (6.5)108 (9.4)
Skin disorder278 (4.7)28 (2.9)
Infection/fatigue NOS206 (3.5)45 (3.4)
Blood disorder (non‐malignant)189 (3.2)38 (3.3)
Musculoskeletal/connective tissue disorder164 (2.8)33 (2.9)
Mental disorder/substance abuse171 (2.9)7 (0.6)
Nervous system/brain infection137 (2.3)26 (2.3)
Unclassified171 (2.9)40 (3.5)
Charlson Index score mean (SD)0.97 1.331.10 1.42

Among English speakers, Whites and African Americans were the most common ethnicities; however, more than 500 admissions were categorized as Asian ethnicity, and more than 600 as patients of other ethnicity. Close to 300 admissions were for Latinos. Among non‐English speakers, Chinese speakers had the largest number of admissions (n = 584), while Spanish and Russian speakers had similar numbers (n = 272 and 290 respectively).

Non‐English speakers were older, more likely to be female, more likely to be insured by Medicare, and more likely to have a higher comorbidity index score. While comorbidity scores were similar among non‐English speakers (Chinese 1.13 1.50; Russian 1.09 1.37; Spanish 1.06 1.30), they differed considerably among English speakers (White 0.94 1.29; African American 1.05 1.40; Asian 1.04 1.45; Latino 0.89 1.23; Other 0.91 1.29).

Hospital Outcome by Language‐Ethnicity Group (Table 2)

When aggregated together, non‐English speakers were somewhat more likely to be dead at 30‐days and have lower cost admissions; however, they did not differ from English speakers on LOS or readmission rates. While differences among disaggregated language‐ethnicity groups were not all statistically significant, English‐speaking Whites had the longest LOS (mean = 4.9 days) and highest costs (mean = $10,530). English‐speaking African Americans, Chinese and Spanish speakers had the highest 30‐day readmission rates; whereas, English‐speaking Latinos and Russian speakers had markedly lower 30‐day readmission rates (2.5% and 6.4%, respectively). Chinese speakers had the highest 30‐day mortality, followed by English speaking Whites and Asians.

Bivariate results of Hospital Outcome Measures Across Language‐Ethnicity Groups (n = 7023)
Language‐Ethnicity GroupsLOS* Mean #Days (SD)Cost Mean Cost $ (SD)30‐Day Readmission, n (%)30‐Day Mortality, n (%)
  • Abbreviation: SD, standard deviation.

  • P < 0.05 for overall comparison across language‐ethnicity groups.

  • P < 0.001 for overall comparison across language‐ethnicity groups.

  • Excludes those admissions for patients who died in the hospital or were discharged to hospice.

  • Includes death during hospitalization.

English speakers (all)4.7 (4.5)10,035 (15,041)648 (11.9)613 (10.4)
White4.9 (5.1)10,530 (15,894)322 (11.4)377 (12.3)
African American4.5 (4.8)9107 (13,314)227 (17.5)91 (6.7)
Asian4.3 (4.5)9933 (15,607)43 (8.8)67 (12.3)
Latino4.6 (4.8)9823 (14,113)7 (2.5)18 (6.0)
Other4.5 (4.8)9662 (14,016)49 (8.5)60 (9.7)
Non‐English speakers (all)4.5 (4.5)9515 (13,213)117 (11.0)147 (12.8)
Chinese speakers4.5 (4.6)9505 (12,841)69 (12.8)85 (14.6)
Spanish speakers4.5 (4.5)9115 (13,846)31 (12.0)28 (10.3)
Russian speakers4.7 (4.2)9846 (13,360)17 (6.4)34 (11.7)

We further investigated differences among English speakers to better understand the very high rate of readmission for African Americans and the very low rate for English‐speaking Latinos. African Americans were on average younger than other English speakers (55 19 years vs. 60 21 years; P < 0.001); but, they had higher comorbidity scores than other English speakers (1.05 1.40 vs. 0.94 1.31; P = 0.008), and were more likely to be admitted for non‐malignant blood disorders (eg, sickle cell disease), endocrine disorders (eg, diabetes mellitus), and circulatory disorders (eg, stroke). In contrast, English‐speaking Latinos were also younger than other English speakers (53 21 years vs. 59 20 years; P < 0.001), but they trended toward lower comorbidity scores (0.87 1.23 vs. 0.97 1.33; P = 0.2), and were more likely to be admitted for gastrointestinal and musculoskeletal disorders, and less likely to be admitted for malignancy and endocrine disorders.

Multivariate Analyses: Association of Aggregated and Disaggregated Language‐Ethnicity Groups With Hospital Outcomes (Table 3)

In multivariate models examining aggregated language‐ethnicity groups, non‐English speakers had a trend toward higher odds of readmission at 30‐days post‐discharge than the English‐speaking group (odds ratio [OR], 1.3; 95% confidence interval [CI], 1.0‐1.7). There were no significant differences for LOS, cost, or 30‐day mortality. Compared to English speakers, Chinese and Spanish speakers had 70% and 50% higher adjusted odds of readmission at 30‐days post‐discharge respectively, while Russian speakers' odds of readmission was not increased. Additionally, Chinese speakers had 7% shorter LOS than English‐speakers. There were no significant differences among any of the language‐ethnicity groups for 30‐day mortality. The increased odds of readmission for Chinese and Spanish speakers compared to English speakers was robust to reinclusion of the admissions with the top 1% LOS and top 1% cost.

Multivariate Models Examining Association of Aggregated and Disaggregated Language‐Ethnicity Groups with Hospital Outcomes for All Admissions to the Medicine Service at UCSF Hospital From 7/2001 to 6/2003
Language CategorizationLOS, % Difference (95% CI)Total Cost, % Difference (95% CI)30‐Day Readmission,* OR (95% CI)Mortality, OR (95% CI)
  • NOTE: All regression models adjusted for age, gender, admission to the ICU, principle diagnosis, Charlson Co‐morbidity Index, Insurance, age‐spline, attending physician service. Significant results are in bold.

  • Abbreviations: CI, confidence interval; ICU, intensive care unit; LOS, length of stay; OR, odds ratio; UCSF, University of California, San Francisco Medical Center.

  • Excludes those admissions for patients who died in the hospital or were discharged to hospice.

All English speakersReferenceReferenceReferenceReference
Non‐English speakers3.1 (8.7 to 3.1)2.5 (8.3 to 2.1)1.3 (1.0 to 1.7)0.9 (0.7 to 1.2)
All English speakersReferenceReferenceReferenceReference
Chinese speakers7.2 (13.9 to 0)5.3 (12.2 to 2.1)1.7 (1.2 to 2.3)1.0 (0.8 to 1.4)
Spanish speakers3.0 (12.6 to 7.6)3.0 (12.7 to 7.7)1.5 (1.0 to 2.3)0.9 (0.6 to 1.5)
Russian speakers1.5 (8.3 to 12.2)0.9 (8.9 to 11.8)0.8 (0.5 to 1.4)0.8 (0.5 to 1.2)

Multivariate Analyses: Association of Language for Asians and Latinos, and of Ethnicity for English speakers, With Hospital Outcomes (Table 4)

Both Chinese and Spanish speakers had significantly higher odds of 30‐day readmission than their English speaking Asian and Latino counterparts. There were no significant differences in LOS, cost, or 30‐day mortality in this within‐ethnicity analysis. Among English speakers, admissions for patients with Asian ethnicity were 15% shorter and resulted in 9% lower costs than for Whites. While LOS and cost were similar for English‐speaking Latino and White admissions, English‐speaking Latinos had markedly lower odds of 30‐day readmission than their White counterparts. Whereas African‐Americans had 6% shorter LOS, 40% higher odds of readmission and 30% lower odds of mortality at 30‐days than English speaking Whites.

Multivariate Models Examining Association of Language (for Asians and Latinos) and of Ethnicity (for English Speakers) With Hospital Outcomes for All Admissions to the Medicine Service at UCSF Hospital From 7/2001 to 6/2003
Language‐Ethnicity ComparisonsLOS, % Difference (95% CI)Total Cost, % Difference (95% CI)30‐Day Readmission,* OR (95% CI)Mortality, OR (95% CI)
  • NOTE: All regression models adjusted for age, gender, admission to the ICU, principle diagnosis, Charlson Co‐morbidity Index, insurance, age‐spline, attending physician service. Significant results are in bold.

  • Abbreviations: CI, confidence interval; ICU, intensive care unit; LOS, length of stay; OR, odds ratio; UCSF, University of California, San Francisco Medical Center.

  • Excludes those admissions for patients who died in the hospital or were discharged to hospice.

English speaking AsiansReferenceReferenceReferenceReference
Chinese speakers2.2 (7.4 to 12.7)0.3 (9.2 to 10.7)1.5 (1.0 to 2.3)0.8 (0.6 to 1.2)
English speaking LatinosReferenceReferenceReferenceReference
Spanish speakers4.5 (16.8 to 9.5)1.2 (14.0 to 13.5)5.7 (2.4 to 13.2)1.2 (0.6 to 2.4)
English‐WhiteReferenceReferenceReferenceReference
English‐African American6.2 (11.3 to 0.9)4.4 (9.6 to 1.1)1.4 (1.1 to 1.7)0.6 (0.5 to 0.8)
English‐Asian14.6 (20.9 to 7.9)8.6 (15.4 to 1.4)0.8 (0.5 to 1.0)1.0 (0.7 to 1.4)
English‐Latino4.5 (13.5 to 5.4)5.0 (14.0 to 5.0)0.2 (0.1 to 0.4)0.6 (0.4 to 1.0)

Conclusion/Discussion

Our results indicate that language barriers may contribute to higher readmission rates for non‐English speakers, but that they have less impact on care efficiency or mortality. This finding of an association between language and readmission, without a similar association with efficiency, suggests a potentially communication‐critical step in care.20, 21 Patients with language barriers are more likely to experience adverse events, and those events are often caused by errors in communication.12 It is conceivable that higher readmission risk for Chinese and Spanish speakers in our study was, at least in part, due to gaps in communication that are present in all patient groups, and exacerbated by the presence of a language barrier. This barrier is likely present during hospitalization but magnified at discharge, limiting caregivers' ability to understand patients' needs for home care, while simultaneously limiting patients' understanding of the discharge plan. After discharge, it is also possible that non‐English speakers are less able to communicate their needs as they arise, ormore subtlyfeel less supported by a primarily English speaking healthcare system. As in other clinical arenas,22 it is quite possible that increased access to professional interpreters in the hospital setting, and particularly at the time of discharge, would enhance communication and outcomes for LEP patients. Our interpreter services data showed that the patients in our study had quite limited access to staff professional interpreters.

Our findings differ somewhat from those of John‐Baptiste et al.,11 who found that language barriers contributed to increased LOS for patients with cardiac and major surgical diagnoses. Our study's findings are akin to recent research suggesting that being a monolingual Spanish speaker or receiving interpreter services may not significantly impact LOS or cost of hospitalization,23 and that LOS and in‐hospital mortality do not differ for non‐English speakers and English speakers after acute myocardial infarction.24 These studies, along with our results, suggest that that care efficiency in the hospital may be driven much more by clinical acuity (eg, the need to respond rapidly to urgent clinical signs such as hypotension, fever and respiratory distress) than by adequacy of communication. For example, elderly LEP patients may be even more likely than English speakers to have vigilant family members at the bedside throughout their hospitalization due to their need for communication assistance; these family members can quickly alert hospital staff to concerning changes in the patient's condition.

Our results also suggest the possibility that language and ethnicity are not monolithic concepts, and that even within language and ethnic groups there are potential differences in care pattern. For example, not speaking English may be a surrogate marker for unmeasured factors such as social supports and access to care. Language is intimately associated with culture; it remains plausible that cultural differences between highly acculturated and less acculturated members of a given ethno‐cultural group may have contributed to our observed differences in readmission rates. Differences in culture and associated factors, such as social support or use of multiple hospital systems, may account for lack of higher readmission risk in Russian speakers, while Chinese and Spanish speakers had higher readmission risk.

In addition, our finding that English‐speaking Latinos had lower readmission risk than any other group may be more consistent with their clinical characteristicseg, younger age, fewer comorbiditiesthan with cultural factors. Our finding that African American patients had the highest readmission risk in our hospital was both surprising and concerning. Some of this increased risk may be explained by clinical characteristics, such as higher comorbidities and higher rates of diagnoses leading to frequent admissions (eg, sickle cell disease); however, the reasons for this disparity deserves further investigation.

Our study has limitations. First, our data are administrative, and lack information about patients' educational attainment, social support, acculturation, utilization of other hospital systems, and usual source of care. Despite this, we were able to account for many significant covariates that might contribute to readmission rates, including age, insurance status, gender, comorbidities, and admission to the intensive care unit.2528 Second, our information about patients' English language proficiency is limited. While direct assessments of English proficiency are more accurate ways to determine a patient's ability to communicate with health care providers in English,29 our language validation work conducted in preparation for this study suggests that most of our patients recorded as having a non‐English primary language (87%) also have a low score on a language acculturation scale.

Third, only 14% of our non‐English speaking subjects utilized professional staff interpreters, and we had no information on the use of professional telephonic interpreters, or ad hoc interpretersfamily members, non‐interpreter staff membersand their impact on our results. It is well‐documented that ad hoc interpreters are used frequently in healthcare, particularly in the hospital setting, and thus we can assume this to be true in our study.30, 31 As noted above, it is likely that the advocacy of family members and friends at the bedside helped to minimize potential differences in care efficiency for patients with language barriers. Finally, our study was performed at a single university based hospital and may not produce results which are applicable to other care settings.

Our findings point to several avenues for future research on language barriers and hospitalized patients. First, the field would benefit from an examination of the impact of easy access to professional interpreters during hospitalization on outcomes of hospital care, in particular on readmission rates. Second, there is need for development and assessment of best practices for creating a culture of professional interpreter utilization in the hospital among physicians and nursing staff. Third, investigation of the role of caregiver presence in the hospital room and how this might differ by patient culture, age and language ability may further elucidate some of the differences across language groups observed in our study. Lastly, a more granular investigation of clinician‐patient communication and the importance of interpersonal processes of care on both patient satisfaction and understanding of and adherence to discharge instructions could lead to the development of detailed interventions to enhance this communication and these outcomes as it has done for communication‐sensitive outcomes in the outpatient arena.3234

In summary, our study suggests that higher risk for readmission can be added to the unfortunate list of outcomes which are worsened due to language barriers, pointing to transition from the hospital as a potentially communication‐critical step in care which may be amenable to intervention. Our findings also suggest that this risk can vary even between groups of patients who do not speak English primarily. Whether and to what degree language and communication barriers aloneincluding access to professional interpreters and patient‐centered communicationduring hospitalization, or differences in caregiver social support both during and after hospitalization as well as access to care post‐hospitalization contribute to these findings is a worthy subject of future research.

Acknowledgements

The authors acknowledge Dr. Eliseo J. Prez‐Stable for his mentorship on this project.

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  33. Street RL,O'Malley KJ,Cooper LA,Haidet P.Understanding concordance in patient‐physician relationships: personal and ethnic dimensions of shared identity.Ann Fam Med.2008;6(3):198–205.
References
  1. Shin H,Bruno R. Language Use and English‐Speaking Ability:2000. Available at: http://www.census.gov/prod/2003pubs/c2kbr‐29.pdf. Accessed January 2010.
  2. U.S. Department of Health and Human Services.2006National Healthcare Disparities Report. AHRQ Publication No. 070012; 2006.
  3. Fiscella K,Franks P,Doescher MP,Saver BG.Disparities in health care by race, ethnicity, and language among the insured: findings from a national sample.Med Care.2002;40(1):5259.
  4. Fox SA,Stein JA.The effect of physician‐patient communication on mammography utilization by different ethnic groups.Med Care.1991;29(11):10651082.
  5. Kirkman‐Liff B,Mondragon D.Language of interview:relevance for research of Southwest Hispanics.Am J Pub Health.1991;81(11):13991404.
  6. Woloshin S,Schwartz LM,Katz SJ,Welch HG.Is language a barrier to the use of preventive services?J Gen Intern Med.1997;12(8):472477.
  7. Carasquillo O,Orav EJ,Brennan TA,Burstin HR.Impact of language barriers on patient satisfaction in an emergency department.JGIM.1999;14:8287.
  8. Crane JA.Patient comprehension of doctor‐patient communication on discharge from the emergency department.J Emerg Med.1997;15(1):17.
  9. Gandhi TK,Burstin HR,Cook EF, et al.Drug complications in outpatients.J Gen Intern Med.2000;15:149154.
  10. Manson A.Language concordance as a determinant of patient compliance and emergency room use in patients with asthma.Med Care.1988;26(12):11191128.
  11. John‐Baptiste A,Naglie G,Tomlinson G,et al.The effect of English language proficiency on length of stay and in‐hospital mortality.J Gen Intern Med.2004;19:221228.
  12. Divi C,Koss RG,Schmaltz SP,Loeb JM.Language proficiency and adverse events in US hospitals: a pilot study.Int J Qual Health Care.2007;19(2):6067.
  13. Auerbach AD,Katz R,Pantilat SZ, et al.Factors associated with discussion of care plans and code status at the time of hospital admission: results from the Multicenter Hospitalist Study.J Hosp Med.2008;3(6):437445.
  14. Vasilevskis EE,Meltzer D,Schnipper J, et al.Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non‐hospitalists.J Gen Intern Med.2008;23(9):13991406.
  15. Charlson ME,Pompei P,Ales KL,MacKenzie CR.A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40(5):373383.
  16. AHRQ. Healthcare Cost and Utilizlation Project: Tools 132(3):191200.
  17. Bessaoud F,Daures JP,Molinari N.Free knot splines for logistic models and threshold selection.Comput Methods Programs Biomed.2005;77(1):19.
  18. Boucher KM,Slattery ML,Berry TD,Quesenberry C,Anderson K.Statistical methods in epidemiology: a comparison of statistical methods to analyze dose‐response and trend analysis in epidemiologic studies.J Clin Epidemiol.1998;51(12):12231233.
  19. The Care Transitions Project. Health Care Policy and Research, Practitioner Tools. Available at: http://www.caretransitions.org/practitioner_tools.asp.
  20. AHRQ. Improving safety at the point of care. Available at: http://www.ahrq.gov/qual/pips. Accessed January2010.
  21. Karliner L,Jacobs E,Chen A,Mutha S.Do professional interpreters improve clinical care for patients with limited english proficiency? A systematic review of the literature.Health Serv Res.2007;42(2):727754.
  22. Jacobs EA,Sadowski LS,Rathouz PJ.The impact of an enhanced interpreter service intervention on hospital costs and patient satisfaction.J Gen Intern Med.2007;22 Suppl 2:306311.
  23. Grubbs V,Bibbins‐Domingo K,Fernandez A,Chattopadhyay A,Bindman AB.Acute myocardial infarction length of stay and hospital mortality are not associated with language preference.J Gen Intern Med.2008;23(2):190194.
  24. Campbell SE,Seymour DG,Primrose WR.A systematic literature review of factors affecting outcome in older medical patients admitted to hospital.Age Ageing.2004;33(2):110–115.
  25. Fan JS,Kao WF,Yen DH,Wang LM,Huang CI,Lee CH.Risk factors and prognostic predictors of unexpected intensive care unit admission within 3 days after ED discharge.Am J Emerg Med.2007;25(9):10091014.
  26. Howie‐Esquivel J,Dracup K.Effect of gender, ethnicity, pulmonary disease, and symptom stability on rehospitalization in patients with heart failure.Am J Cardiol.2007;100(7):11391144.
  27. Kind AJ,Smith MA,Frytak JR,Finch MD.Bouncing back: patterns and predictors of complicated transitions 30 days after hospitalization for acute ischemic stroke.J Am Geriatr Soc.2007;55(3):365373.
  28. Karliner L,Napoles‐Springer A,Schillinger D,Bibbins‐Domingo K,Perez‐Stable E.Identification of limited English proficient patients in clinical care.J Gen Intern Med.2008;23(10):15551560.
  29. Hasnain‐Wynia R,Yonek J,Pierce D,Kang R,Greising C.Hospital langague services for patients with limited English proficiency: results from a national survey.Health Research October2006.
  30. Wilson‐Stronks A,Galvez E.Hospitals, Language, and Culture: a Snapshot of the Nation. The Joint Commission and The California Endowment;2007.
  31. Cooper LA,Roter DL,Bone LR, et al.A randomized controlled trial of interventions to enhance patient‐physician partnership, patient adherence and high blood pressure control among ethnic minorities and poor persons: study protocol NCT00123045.Implement Sci.2009;4:7.
  32. Napoles AM,Gregorich SE,Santoyo‐Olsson J,O'Brien H,Stewart AL.Interpersonal processes of care and patient satisfaction: do associations differ by race, ethnicity, and language?Health Serv Res.2009;44(4):13261344.
  33. Street RL,O'Malley KJ,Cooper LA,Haidet P.Understanding concordance in patient‐physician relationships: personal and ethnic dimensions of shared identity.Ann Fam Med.2008;6(3):198–205.
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Journal of Hospital Medicine - 5(5)
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Influence of language barriers on outcomes of hospital care for general medicine inpatients
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Influence of language barriers on outcomes of hospital care for general medicine inpatients
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Hemoglobin Levels in Hospitalized Patients

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Hemoglobin concentration variations over time in general medical inpatients

Studies of the red blood cell mass performed in hospitalized patients were first done in the early 1970s.1 Thereafter, a decrease in the hemoglobin concentration ([Hb]) and its potential causes have been further reported, especially in critically ill patients.25 The decrease in [Hb] can occur as a result of blood draws for diagnostic testing; blood loss associated with invasive procedures and bleeding; occult gastrointestinal bleeding; hemolysis; shortening of red cell survival; iron, folic acid or cobalamine deficiencies; renal, liver or endocrine failure; hemodilution associated with fluid therapy; and the so‐called anemia of inflammation (AI).68 The latter would be a consequence of a blunted response of the bone marrow due to several factors such as inadequate secretion of erythropoietin, inhibition of the proliferation and differentiation of the erythroid precursors of the bone marrow and an hepcidin‐mediated functional iron deficiency.810

As mentioned, most studies were done in critically ill patients and scarce information is available about general ward admitted patients (GWAP) with less severe illnesses. In this scenario, some of the proposed mechanisms may have a less clear role. It is widely accepted that [Hb] may decrease without overt bleeding in GWAP. However, given the lack of information on this matter, laboratory controls and invasive procedures are undertaken to determine its potential causes.

The purpose of the present study is to describe [Hb] variation over time in nonbleeding GWAP, to estimate the proportion of patients with [Hb] decreases 1.5 g/dL, and to evaluate possible related variables.

Materials and Methods

A 16‐week (September 2004‐January 2005) prospective observational study was conducted in Internal Medicine GWAP at 2 Buenos Aires teaching hospitals.

All consecutive patients older than 16 years were evaluated. Patients admitted for the following reasons were excluded: bleeding, trauma, surgery, invasive procedures associated with blood loss (biopsies, biliary drainage, endovascular therapeutic procedures, and chest tubes), blood transfusions, anemia, chemotherapy, and acute renal failure.

Patients with a bleeding history, chemotherapy or radiation therapy within two months prior to admission, patients on dialysis and patients with current oncologic or hematologic disease were excluded, as well as those with length of stay less than 3 days, or with less than 2 [Hb] or hematocrit (HCT) measurements.

Patients were followed until discharge, death or transfer to a critical care unit. Patients were withdrawn from the study if they presented with bleeding or hemolysis, underwent red blood cell transfusion or therapy affecting hemoglobin levels (iron, chemotherapy or erythropoietin) or if either a surgical or invasive procedure associated with blood loss was performed.

Data were collected from patients' medical records and additional information was obtained from treating physicians and patients by the authors (AL, NC, SM, AN, MH). Standardized case report forms were completed during the hospitalization including: age, sex, admissions in the previous 3 months (readmissions) and whether or not the patient lived in a nursing home. Patients were categorized according to discharge diagnosis as reported by Nguyen et al.4 with modifications related to our general ward population. Upon admission Katz daily activity index (ADL),11, 12 APACHE II acute physiology score (APS),13, 14 and Charlson comorbidity score (Charlson)15, 16 were assessed. In all cases the [Hb] and HCT values were registered on admission and on days 3, 6, 10 and prior to discharge as well as any other determination required by the treating physician. Anemia was defined as [Hb] 13 g/dL for men and 12 g/dL for women.17 Based on previous reports a 1.5 g/dL decrease in [Hb] and of 4.5 points in HCT compared to admission values were considered a significant fall.1, 4 Acute renal failure was defined as an increase in creatinine level 0.5 mg/dL or a 50% increase from baseline.18, 19

Every procedure without significant blood loss (PWSBL) such as venous catheter placement, thoracentesis, lumbar punction, paracentesis, skin biopsy, arthrocentesis, and diagnostic angiogram was also recorded. The study was approved by the Hospital's ethical committee.

Data analysis and processing were performed with Excel 2000 and Stata version 8.0 (Stata Corp; USA). For continuous variables, results were expressed by the mean value its standard deviation (SD), and compared with Student's t‐test. The chi‐square test was used to compare categorical variables. A survival curve was developed with the Kaplan‐Meier method to analyze the time to a significant fall in [Hb]. Finally, Cox proportional hazard modeling was performed to assess the association between a significant fall in [Hb] and other variables. We accepted P < 0,05 as significant.

Results

A total of 338 patients were admitted to the Internal Medicine Inpatient Services in the study period. Thirty‐nine percent (131) of these patients were included. Exclusion criteria were: diagnosis at admission (n = 95, 45.9%), past medical history (n = 56, 27%) and length of stay less than 3 days, or less than 2 determinations of [Hb] or HCT (n = 56, 27%). Data collection was discontinued for the following reasons: discharge (81.7%), death (6.9%), surgery or procedures associated with blood loss (5.3%), transfer to a critical care unit (3%), transfusions (2.3%), and chemotherapy (0.8%). The baseline characteristics of the study patients are shown in Table 1.

Demographic Data of 131 Patients
 n%Mean (SD)MedianMin/Max
  • Abbreviations: ADL, Katz daily activity index; APS, APACHE II acute physiology score; CHARLSON, Charlson comorbidity score; [Hb], Hemoglobin concentration; PWSBL, procedure without significant blood loss; SD, Standard deviation.

Age, years  71.9 (17.4)7718/97
18‐40118.4   
41‐601612.2   
61‐805239.7   
>805239.7   
Gender     
Female7557.2   
Lenght of stay (days)  7 (4.8)63/28
APS  4.9 (4.2)40/22
0‐47154.2   
5‐83627.5   
>82418.3   
ADL  4.5 (2.3)60/6
0‐23325.2   
3‐5118.4   
68766.4   
CHARLSON  2.2 (2.3)20/11
03224.4   
13224.4   
22216.8   
31813.7   
>32720.6   
Readmissions2821.4   
PWSBL1410.7   
Anemia at admission6348.1   
[Hb] at admission  12.5 (1.7)12.58.6/17
[Hb] at admission males  12.8 (1.9)12.68.7/17
[Hb] at admission females  12.3(1.5)12.38.6/15.5

Diagnoses at discharge were divided into the following categories: infections (25.2%), electrolyte disturbances (7.6%), cardiac diseases (9.9%), neurologic (19.1%), respiratory (16.8%), gastrointestinal (10.7%), and other diagnosis (10.7%).

No evidence of bleeding was found in the included patients. Bleeding was only observed in 4 of the initially evaluated patients who were excluded since they failed to meet the required number of [Hb] determinations before bleeding.

A mean decrease in [Hb] of 0.71 g/dL was found between admission and discharge day (P < 0.001; 95% CI, 0.47‐0.97). Mean nadir [Hb] was 1.45 g/dL lower than admission [Hb] (P < 0.001; 95% CI, 1.24‐1.67). Mean nadir day occurred between hospitalization days 3 and 4. Mean [Hb] at discharge was 11.8 1.8 g/dL. This value is higher than the mean concentration at nadir (0.74 g/dL P < 0.001; 95% CI, 0.60‐0.97) (Figure 1).

Figure 1
Box and whisker plot of changes in hemoglobin concentration during hospital stay. The line within the box denotes the median and the box spans the interquartile range. Whiskers extend from the 10th to 90th percentiles.

Table 2 shows the rate of patients with a decrease in the [Hb] for different cutoff levels. Forty‐five percent of the study population (59 patients) had a significant fall in [Hb] during hospitalization. This was observed on day 2 in 50% of the patients. Likewise, a significant fall in HCT value was found in 42.7% of patients. If [Hb] decrease during hospitalization is analyzed as a proportion of [Hb] at admission, 52.7% of patients had a 10% or greater [Hb] decrease during their hospital stay.

Proportion of Patients With a Nadir Fall in the [Hb] for Different Cutoff Points
[Hb] fall (g/dL)0.511.522.533.544.5
% of patients80.960.345.028.217.69.95.33.82.3

Using Kaplan‐Meier analysis, the estimated proportion showing no significant fall of [Hb] was 0.63 (95% CI, 0.55‐0.71) on day 3 and 0.48 (95% CI, 0.29‐0.67) on day 10. By day 15, only 3.8% of the initially included patients were still hospitalized and showed no decrease in [Hb] 1.5 g/dl. These patients maintained their [Hb] stable for the rest of the follow up period (Figure 2).

Figure 2
Kaplan Meier plot showing the proportion of patient without a fall in the hemoglobin concentration ≥1.5 g/dL.

In the univariate analysis, comparing patients that experienced a decrease in [Hb] 1.5 g/dL with those who did not, significant differences were only found in the following variables: length of stay, APS, anemia at admission, [Hb] at admission, and infectious, gastrointestinal or cardiac diseases at discharge diagnosis (Table 3).

Univariate Analysis
 Patients with a significant fallPatients without a significant fallP Value
  • NOTE: Continuous variables are expressed by the mean value and its standard deviation (SD). Categorical variables are expressed by the number of cases and the percentage within its category.

  • Abbreviations: ADL, Katz daily activity index; APS, APACHE II acute physiology score; CHARLSON, Charlson comorbidity score; [Hb], Hemoglobin concentration; PWSBL, procedure without significant blood loss.

n59 (45%)72 (55%) 
Age, years73.15 (18.7)70.83 (16.2)0.448
Gender, female32 (54.2%)43 (59.7%)0.527
Length of stay (days)8.30 (5.6)5.91 (3.7)<0.007
APS6.13 (4.5)3.97 (3.7)<0.004
ADL4.33 (2.5)4.68 (2.1)0.410
CHARLSON2.03 (1.8)2.37 (2.5)0.382
Nurse home residents4 (6.8%)3 (4.2%)0.700
Readmissions11 (18.6%)17 (23.6%)0.490
PWSBL6 (10.2%)8 (11.1%)0.862
Anemia at admission20 (33.9%)43 (59.7%)<0.004
[Hb] at admission13.09 (1.7)12.01 (1.5)<0.001
Diagnosis at discharge   
Infectious20 (33.9%)13 (18.1%)<0.05
Respiratory8 (13.6%)14 (19.4%)0.370
Neurologic9 (15.2%)16 (22.2%)0.312
Gastrointestinal11 (18.6%)3 (4.2%)<0.01
Cardiac2 (3.4%)11(15.3%)<0.05
Electrolyte disturbances6 (10.2%)4 (5.6%)0.512
Others3 (5.1%)11 (15.3%)0.087

In the Cox proportional hazard model adjusting for the variables shown in Table 4, a significant independent direct association was found between a significant fall in [Hb] during hospital stay and APS, [Hb] at admission, and either diagnosis of infectious or gastrointestinal disease at discharge. Similar results were found if a significant fall was redefined as a 12% decrease in [Hb] or as a 4.5 point decrease in HCT from baseline.

Cox Proportional Hazard Model
VariableHRRP Value95% CI
  • Abbreviations: ADL, Katz daily activity index; APS, APACHE II acute physiology score; CHARLSON, Charlson comorbidity score; CI, confidence interval; electrolyte dist, electrolyte disturbances; [Hb], hemoglobin concentration; HRR, hazards relative ratio; PWSBL, procedure without significant blood loss.

APS1.070.0071.02‐1.12
ADL1.110.1320.97‐1.27
Charlson0.880.1210.75‐1.03
Nurse home resident1.520.3610.62‐3.72
PWSBL0.670.3900.27‐1.66
Readmission1.140.7100.57‐2.29
Female sex0.980.9440.57‐1.69
Age1.390.0980.94‐2.07
[Hb] at admission1.270.0051.07‐1.51
Diagnosis at discharge   
Infectious2.700.0151.21‐6.05
Neurologic1.420.4570.57‐3.55
Gastrointestinal3.740.0021.62‐8.64
Cardiac0.410.2890.08‐2.12
Electrolyte dist.2.080.1760.72‐6.05
Others0.950.9460.24‐3.81

Discussion

This study describes the variation of [Hb] and HCT values in GWAP without bleeding or other obvious medical conditions associated with a decrease in the red blood cell mass. As previously described,4, 20, 21 we found that [Hb] decreases during hospital stay are frequently observed. The mean [Hb] fall from admission was 1.45 g/dL, and was mainly recorded between hospitalization days 3 and 4. In approximately half of the study population, a 1.5 g/dL decrease in [Hb] was observed. In the survival analysis, 40% and 55% of patients are expected to present such a fall on day 4 and 12 respectively. In accordance with prior reports4, 21 a greater decrease in [Hb] occurred in the first days of hospitalization, and a high proportion of patients were already anemic at the time of admission.

The following variables were associated with a decrease in the [Hb] during hospitalization: higher APS score, higher [Hb] at admission, and diagnosis of infectious or gastrointestinal disease at discharge. Even though several mechanisms seem to contribute to the decrease in [Hb] during hospitalization our data suggest that 1 of these factors seems to be the severity of the disease, as previously proposed by Nguyen et al.4 This observation is supported by the association found in our study between [Hb] decrease and APS. No association was found with Charlson, ADL, and being a nursing home resident. These variables, which have not been previously analyzed, seem to indicate that patients with chronic illnesses are not more likely to have decreases in [Hb] during hospitalization.

Patients with higher [Hb] at admission had a greater [Hb] decrease during their hospital stay. This finding could suggest that the mechanism related to this decrease had less effect on patients with lower [Hb]. This is similar to that observed in the anemia of chronic diseases where [Hb] does not usually fall to extreme values. Our analysis reveals a greater decrease during the first days of hospitalization. Given the high rate of patients anemic at admission, it is possible that the decrease in [Hb] had begun prior to admission.

Previous papers describe a low prevalence of cyanocobalamine (2%), folic acid (2%), and iron (9%) deficiency in patients admitted to critical care units.3 In these patients an inadequate bone marrow response has been proposed as a mechanism for [Hb] decrease, a phenomenon that has been called AI or anemia of critical illness.8, 2228 Inflammatory response associated with acute disease causes this hypoproliferative anemia through 3 different pathways: relative erythropoietin deficit, a direct inhibition of the erythropoiesis in the bone marrow through different mediators (ie, interleukin [IL]‐1, IL‐6, tumor necrosis factor [TNF]) and a functional iron deficit. This relative iron deficiency is produced mainly by the IL‐6 induced overexpression of the hepcidin gene in hepatocytes. Hepcidin causes impaired intestinal iron absorption and an inadequate delivery of iron from the iron‐recycling macrophages to the erythroid precursors in the bone marrow.8, 9

AI could explain some of our findings, such as the greater decrease observed in the first hospital days when inflamatory mediators levels are expected to be higher. The association between APS and infectious disease diagnosis and a greater [Hb] decrease may also be explained by this mechanism. Nonetheless, the daily bone marrow production of red blood cells is small compared with the circulating red mass cell and therefore it is necessary to have a better explanation on how AI could be associated with this rapid decrease in the [Hb]. Rice et al.30, 31 have explored some mechanisms leading to a rapid decrease in the red mass cell related to acute variations in erytropoietin level. A role of this mechanism and other could be hypothesized.29

Overt bleeding was not found in our study population and procedures without significant blood loss (PWSBL) were infrequent, therefore it is unlikely that they could have had an impact on the decrease of [Hb]. The influence of other variables, such as blood volume drawn for diagnostic testing and occult blood losses (especially from the gastrointestinal tract) was not investigated. Parenteral hydration and hemodilution neither were evaluated in our study nor were extensively described in the literature. We think this last mentioned mechanism plays a role in the large [Hb] decrease during the first days of hospitalization. However a lack of association was mentioned in the only study that evaluated this issue in critically ill patients. Intravascular hemolysis has been cited as an infrequent event in these patients.2, 4

Only patients without a clear explanation for their [Hb] decrease were included because they represent a matter of concern for hospitalists and other treating physicians. Therefore 60% of the initially evaluated patients were excluded, since they were admitted for conditions likely to cause a decrease in [Hb]. Nevertheless, it seems likely that the mechanisms involved in the included patients may play a role in those with an obvious cause for [Hb] variation as well. Accordingly, the decrease of [Hb] expected in patients admitted with disorders known to cause a decrease in the red blood cell mass would be greater than the 1 observed in our study population.

This [Hb] drop during hospitalization may be clinically relevant in a number of ways: it could cause the attending physicians to order costly and invasive studies, it could have prognostic value as it occurs in patients admitted with myocardial infarction, and it could trigger an acute coronary syndrome.20 We observed an association between [Hb] drop and a higher APS in our patients. This score has been validated as a prognostic factor in previous studies. However, it is not possible to conclude that this [Hb] drop is associated with a worse prognosis.

Our study had several limitations. The sample's heterogeneity inherent to our general ward population could have altered our results and their generalization. To overcome this bias we used a categorization system based on discharge diagnosis. However, this particular system has several limitations. The small sample size and the absence of follow up data after discharge limited our capacity to detect any prognostic significance of [Hb] decrease. In the present study, the total decrease of [Hb] may have been underestimated because the onset of the acute illness precedes hospital admission by a variable time. Finally, a relatively small sample size limited our ability to detect other possible significant predictors of [Hb] decrease. However, this study was not designed to assess the mechanisms associated with the decrease of [Hb], but rather to establish its occurrence, measure it and explore related variables.

These results may be useful for further studies to evaluate [Hb] variation in admitted patients and its relation to other variables, such as bone marrow production, oxygen use, erythrocyte survival, nutritional deficiencies, and erythropoietin and inflammatory mediators levels.

In conclusion, our general medical inpatients had a mean 1.45 g/dL [Hb] decrease during hospitalization, which was greater in the first days of hospitalization, even though an evident cause was not present. These findings would help attending physicians in general wards make a rational and efficient approach when dealing with patients' decrease in [Hb].

Acknowledgements

The authors thank Jorge Lopez Camelo and Hugo Krupitzki for statistical advice and Valeria Melia for helping in the preparation of this manuscript.

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  25. Corwin HL.Anemia and blood transfusion in the critically ill patient: role of erythropoietin.Crit Care.2004;8(Suppl 2):S42S44.
  26. Eckart KU.Anemia of critical illness – implications for understanding and treating rHuEPO resistance.Nephrol Dial Transplant.2002;17(Suppl 5):S48S55.
  27. Corwin HL,Surgenor SD,Gettinger A.Transfusion practice in the critically ill.Crit Care Med.2003;31(Suppl):S668S671.
  28. van de Wiel A.Anemia in critically ill patients.Eur J Inter Med.2004;15(8):481486.
  29. Alfrey CP,Rice L,Udden M,Leach‐Huntoon CS.Neocytolysis: a physiologic down‐regulator of red blood cell mass.Lancet.1997;349:13891390.
  30. Rice L,Alfrey CP,Driscoll T,Whitley CE,Hachey DL,Suki W.Neocytolysis contributes to the anemia of renal disease.Am J Kidney Dis.1999;33:5962.
  31. Rice L,Ruiz W,Driscoll T, et al.Neocytolysis on descent from altitude: a newly recognized mechanism for the control of red cell mass.Ann Intern Med.2001;134:652656.
  32. Smoller BR,Kruskall MS.Phlebotomy for diagnostic laboratory test in adults. Pattern of use and effect on transfusion requirements.N Engl J Med.1986;314:12331235.
Article PDF
Issue
Journal of Hospital Medicine - 5(5)
Page Number
283-288
Legacy Keywords
anemia, critical illness, hemoglobin, iatrogenic anemia, internal medicine inpatients
Sections
Article PDF
Article PDF

Studies of the red blood cell mass performed in hospitalized patients were first done in the early 1970s.1 Thereafter, a decrease in the hemoglobin concentration ([Hb]) and its potential causes have been further reported, especially in critically ill patients.25 The decrease in [Hb] can occur as a result of blood draws for diagnostic testing; blood loss associated with invasive procedures and bleeding; occult gastrointestinal bleeding; hemolysis; shortening of red cell survival; iron, folic acid or cobalamine deficiencies; renal, liver or endocrine failure; hemodilution associated with fluid therapy; and the so‐called anemia of inflammation (AI).68 The latter would be a consequence of a blunted response of the bone marrow due to several factors such as inadequate secretion of erythropoietin, inhibition of the proliferation and differentiation of the erythroid precursors of the bone marrow and an hepcidin‐mediated functional iron deficiency.810

As mentioned, most studies were done in critically ill patients and scarce information is available about general ward admitted patients (GWAP) with less severe illnesses. In this scenario, some of the proposed mechanisms may have a less clear role. It is widely accepted that [Hb] may decrease without overt bleeding in GWAP. However, given the lack of information on this matter, laboratory controls and invasive procedures are undertaken to determine its potential causes.

The purpose of the present study is to describe [Hb] variation over time in nonbleeding GWAP, to estimate the proportion of patients with [Hb] decreases 1.5 g/dL, and to evaluate possible related variables.

Materials and Methods

A 16‐week (September 2004‐January 2005) prospective observational study was conducted in Internal Medicine GWAP at 2 Buenos Aires teaching hospitals.

All consecutive patients older than 16 years were evaluated. Patients admitted for the following reasons were excluded: bleeding, trauma, surgery, invasive procedures associated with blood loss (biopsies, biliary drainage, endovascular therapeutic procedures, and chest tubes), blood transfusions, anemia, chemotherapy, and acute renal failure.

Patients with a bleeding history, chemotherapy or radiation therapy within two months prior to admission, patients on dialysis and patients with current oncologic or hematologic disease were excluded, as well as those with length of stay less than 3 days, or with less than 2 [Hb] or hematocrit (HCT) measurements.

Patients were followed until discharge, death or transfer to a critical care unit. Patients were withdrawn from the study if they presented with bleeding or hemolysis, underwent red blood cell transfusion or therapy affecting hemoglobin levels (iron, chemotherapy or erythropoietin) or if either a surgical or invasive procedure associated with blood loss was performed.

Data were collected from patients' medical records and additional information was obtained from treating physicians and patients by the authors (AL, NC, SM, AN, MH). Standardized case report forms were completed during the hospitalization including: age, sex, admissions in the previous 3 months (readmissions) and whether or not the patient lived in a nursing home. Patients were categorized according to discharge diagnosis as reported by Nguyen et al.4 with modifications related to our general ward population. Upon admission Katz daily activity index (ADL),11, 12 APACHE II acute physiology score (APS),13, 14 and Charlson comorbidity score (Charlson)15, 16 were assessed. In all cases the [Hb] and HCT values were registered on admission and on days 3, 6, 10 and prior to discharge as well as any other determination required by the treating physician. Anemia was defined as [Hb] 13 g/dL for men and 12 g/dL for women.17 Based on previous reports a 1.5 g/dL decrease in [Hb] and of 4.5 points in HCT compared to admission values were considered a significant fall.1, 4 Acute renal failure was defined as an increase in creatinine level 0.5 mg/dL or a 50% increase from baseline.18, 19

Every procedure without significant blood loss (PWSBL) such as venous catheter placement, thoracentesis, lumbar punction, paracentesis, skin biopsy, arthrocentesis, and diagnostic angiogram was also recorded. The study was approved by the Hospital's ethical committee.

Data analysis and processing were performed with Excel 2000 and Stata version 8.0 (Stata Corp; USA). For continuous variables, results were expressed by the mean value its standard deviation (SD), and compared with Student's t‐test. The chi‐square test was used to compare categorical variables. A survival curve was developed with the Kaplan‐Meier method to analyze the time to a significant fall in [Hb]. Finally, Cox proportional hazard modeling was performed to assess the association between a significant fall in [Hb] and other variables. We accepted P < 0,05 as significant.

Results

A total of 338 patients were admitted to the Internal Medicine Inpatient Services in the study period. Thirty‐nine percent (131) of these patients were included. Exclusion criteria were: diagnosis at admission (n = 95, 45.9%), past medical history (n = 56, 27%) and length of stay less than 3 days, or less than 2 determinations of [Hb] or HCT (n = 56, 27%). Data collection was discontinued for the following reasons: discharge (81.7%), death (6.9%), surgery or procedures associated with blood loss (5.3%), transfer to a critical care unit (3%), transfusions (2.3%), and chemotherapy (0.8%). The baseline characteristics of the study patients are shown in Table 1.

Demographic Data of 131 Patients
 n%Mean (SD)MedianMin/Max
  • Abbreviations: ADL, Katz daily activity index; APS, APACHE II acute physiology score; CHARLSON, Charlson comorbidity score; [Hb], Hemoglobin concentration; PWSBL, procedure without significant blood loss; SD, Standard deviation.

Age, years  71.9 (17.4)7718/97
18‐40118.4   
41‐601612.2   
61‐805239.7   
>805239.7   
Gender     
Female7557.2   
Lenght of stay (days)  7 (4.8)63/28
APS  4.9 (4.2)40/22
0‐47154.2   
5‐83627.5   
>82418.3   
ADL  4.5 (2.3)60/6
0‐23325.2   
3‐5118.4   
68766.4   
CHARLSON  2.2 (2.3)20/11
03224.4   
13224.4   
22216.8   
31813.7   
>32720.6   
Readmissions2821.4   
PWSBL1410.7   
Anemia at admission6348.1   
[Hb] at admission  12.5 (1.7)12.58.6/17
[Hb] at admission males  12.8 (1.9)12.68.7/17
[Hb] at admission females  12.3(1.5)12.38.6/15.5

Diagnoses at discharge were divided into the following categories: infections (25.2%), electrolyte disturbances (7.6%), cardiac diseases (9.9%), neurologic (19.1%), respiratory (16.8%), gastrointestinal (10.7%), and other diagnosis (10.7%).

No evidence of bleeding was found in the included patients. Bleeding was only observed in 4 of the initially evaluated patients who were excluded since they failed to meet the required number of [Hb] determinations before bleeding.

A mean decrease in [Hb] of 0.71 g/dL was found between admission and discharge day (P < 0.001; 95% CI, 0.47‐0.97). Mean nadir [Hb] was 1.45 g/dL lower than admission [Hb] (P < 0.001; 95% CI, 1.24‐1.67). Mean nadir day occurred between hospitalization days 3 and 4. Mean [Hb] at discharge was 11.8 1.8 g/dL. This value is higher than the mean concentration at nadir (0.74 g/dL P < 0.001; 95% CI, 0.60‐0.97) (Figure 1).

Figure 1
Box and whisker plot of changes in hemoglobin concentration during hospital stay. The line within the box denotes the median and the box spans the interquartile range. Whiskers extend from the 10th to 90th percentiles.

Table 2 shows the rate of patients with a decrease in the [Hb] for different cutoff levels. Forty‐five percent of the study population (59 patients) had a significant fall in [Hb] during hospitalization. This was observed on day 2 in 50% of the patients. Likewise, a significant fall in HCT value was found in 42.7% of patients. If [Hb] decrease during hospitalization is analyzed as a proportion of [Hb] at admission, 52.7% of patients had a 10% or greater [Hb] decrease during their hospital stay.

Proportion of Patients With a Nadir Fall in the [Hb] for Different Cutoff Points
[Hb] fall (g/dL)0.511.522.533.544.5
% of patients80.960.345.028.217.69.95.33.82.3

Using Kaplan‐Meier analysis, the estimated proportion showing no significant fall of [Hb] was 0.63 (95% CI, 0.55‐0.71) on day 3 and 0.48 (95% CI, 0.29‐0.67) on day 10. By day 15, only 3.8% of the initially included patients were still hospitalized and showed no decrease in [Hb] 1.5 g/dl. These patients maintained their [Hb] stable for the rest of the follow up period (Figure 2).

Figure 2
Kaplan Meier plot showing the proportion of patient without a fall in the hemoglobin concentration ≥1.5 g/dL.

In the univariate analysis, comparing patients that experienced a decrease in [Hb] 1.5 g/dL with those who did not, significant differences were only found in the following variables: length of stay, APS, anemia at admission, [Hb] at admission, and infectious, gastrointestinal or cardiac diseases at discharge diagnosis (Table 3).

Univariate Analysis
 Patients with a significant fallPatients without a significant fallP Value
  • NOTE: Continuous variables are expressed by the mean value and its standard deviation (SD). Categorical variables are expressed by the number of cases and the percentage within its category.

  • Abbreviations: ADL, Katz daily activity index; APS, APACHE II acute physiology score; CHARLSON, Charlson comorbidity score; [Hb], Hemoglobin concentration; PWSBL, procedure without significant blood loss.

n59 (45%)72 (55%) 
Age, years73.15 (18.7)70.83 (16.2)0.448
Gender, female32 (54.2%)43 (59.7%)0.527
Length of stay (days)8.30 (5.6)5.91 (3.7)<0.007
APS6.13 (4.5)3.97 (3.7)<0.004
ADL4.33 (2.5)4.68 (2.1)0.410
CHARLSON2.03 (1.8)2.37 (2.5)0.382
Nurse home residents4 (6.8%)3 (4.2%)0.700
Readmissions11 (18.6%)17 (23.6%)0.490
PWSBL6 (10.2%)8 (11.1%)0.862
Anemia at admission20 (33.9%)43 (59.7%)<0.004
[Hb] at admission13.09 (1.7)12.01 (1.5)<0.001
Diagnosis at discharge   
Infectious20 (33.9%)13 (18.1%)<0.05
Respiratory8 (13.6%)14 (19.4%)0.370
Neurologic9 (15.2%)16 (22.2%)0.312
Gastrointestinal11 (18.6%)3 (4.2%)<0.01
Cardiac2 (3.4%)11(15.3%)<0.05
Electrolyte disturbances6 (10.2%)4 (5.6%)0.512
Others3 (5.1%)11 (15.3%)0.087

In the Cox proportional hazard model adjusting for the variables shown in Table 4, a significant independent direct association was found between a significant fall in [Hb] during hospital stay and APS, [Hb] at admission, and either diagnosis of infectious or gastrointestinal disease at discharge. Similar results were found if a significant fall was redefined as a 12% decrease in [Hb] or as a 4.5 point decrease in HCT from baseline.

Cox Proportional Hazard Model
VariableHRRP Value95% CI
  • Abbreviations: ADL, Katz daily activity index; APS, APACHE II acute physiology score; CHARLSON, Charlson comorbidity score; CI, confidence interval; electrolyte dist, electrolyte disturbances; [Hb], hemoglobin concentration; HRR, hazards relative ratio; PWSBL, procedure without significant blood loss.

APS1.070.0071.02‐1.12
ADL1.110.1320.97‐1.27
Charlson0.880.1210.75‐1.03
Nurse home resident1.520.3610.62‐3.72
PWSBL0.670.3900.27‐1.66
Readmission1.140.7100.57‐2.29
Female sex0.980.9440.57‐1.69
Age1.390.0980.94‐2.07
[Hb] at admission1.270.0051.07‐1.51
Diagnosis at discharge   
Infectious2.700.0151.21‐6.05
Neurologic1.420.4570.57‐3.55
Gastrointestinal3.740.0021.62‐8.64
Cardiac0.410.2890.08‐2.12
Electrolyte dist.2.080.1760.72‐6.05
Others0.950.9460.24‐3.81

Discussion

This study describes the variation of [Hb] and HCT values in GWAP without bleeding or other obvious medical conditions associated with a decrease in the red blood cell mass. As previously described,4, 20, 21 we found that [Hb] decreases during hospital stay are frequently observed. The mean [Hb] fall from admission was 1.45 g/dL, and was mainly recorded between hospitalization days 3 and 4. In approximately half of the study population, a 1.5 g/dL decrease in [Hb] was observed. In the survival analysis, 40% and 55% of patients are expected to present such a fall on day 4 and 12 respectively. In accordance with prior reports4, 21 a greater decrease in [Hb] occurred in the first days of hospitalization, and a high proportion of patients were already anemic at the time of admission.

The following variables were associated with a decrease in the [Hb] during hospitalization: higher APS score, higher [Hb] at admission, and diagnosis of infectious or gastrointestinal disease at discharge. Even though several mechanisms seem to contribute to the decrease in [Hb] during hospitalization our data suggest that 1 of these factors seems to be the severity of the disease, as previously proposed by Nguyen et al.4 This observation is supported by the association found in our study between [Hb] decrease and APS. No association was found with Charlson, ADL, and being a nursing home resident. These variables, which have not been previously analyzed, seem to indicate that patients with chronic illnesses are not more likely to have decreases in [Hb] during hospitalization.

Patients with higher [Hb] at admission had a greater [Hb] decrease during their hospital stay. This finding could suggest that the mechanism related to this decrease had less effect on patients with lower [Hb]. This is similar to that observed in the anemia of chronic diseases where [Hb] does not usually fall to extreme values. Our analysis reveals a greater decrease during the first days of hospitalization. Given the high rate of patients anemic at admission, it is possible that the decrease in [Hb] had begun prior to admission.

Previous papers describe a low prevalence of cyanocobalamine (2%), folic acid (2%), and iron (9%) deficiency in patients admitted to critical care units.3 In these patients an inadequate bone marrow response has been proposed as a mechanism for [Hb] decrease, a phenomenon that has been called AI or anemia of critical illness.8, 2228 Inflammatory response associated with acute disease causes this hypoproliferative anemia through 3 different pathways: relative erythropoietin deficit, a direct inhibition of the erythropoiesis in the bone marrow through different mediators (ie, interleukin [IL]‐1, IL‐6, tumor necrosis factor [TNF]) and a functional iron deficit. This relative iron deficiency is produced mainly by the IL‐6 induced overexpression of the hepcidin gene in hepatocytes. Hepcidin causes impaired intestinal iron absorption and an inadequate delivery of iron from the iron‐recycling macrophages to the erythroid precursors in the bone marrow.8, 9

AI could explain some of our findings, such as the greater decrease observed in the first hospital days when inflamatory mediators levels are expected to be higher. The association between APS and infectious disease diagnosis and a greater [Hb] decrease may also be explained by this mechanism. Nonetheless, the daily bone marrow production of red blood cells is small compared with the circulating red mass cell and therefore it is necessary to have a better explanation on how AI could be associated with this rapid decrease in the [Hb]. Rice et al.30, 31 have explored some mechanisms leading to a rapid decrease in the red mass cell related to acute variations in erytropoietin level. A role of this mechanism and other could be hypothesized.29

Overt bleeding was not found in our study population and procedures without significant blood loss (PWSBL) were infrequent, therefore it is unlikely that they could have had an impact on the decrease of [Hb]. The influence of other variables, such as blood volume drawn for diagnostic testing and occult blood losses (especially from the gastrointestinal tract) was not investigated. Parenteral hydration and hemodilution neither were evaluated in our study nor were extensively described in the literature. We think this last mentioned mechanism plays a role in the large [Hb] decrease during the first days of hospitalization. However a lack of association was mentioned in the only study that evaluated this issue in critically ill patients. Intravascular hemolysis has been cited as an infrequent event in these patients.2, 4

Only patients without a clear explanation for their [Hb] decrease were included because they represent a matter of concern for hospitalists and other treating physicians. Therefore 60% of the initially evaluated patients were excluded, since they were admitted for conditions likely to cause a decrease in [Hb]. Nevertheless, it seems likely that the mechanisms involved in the included patients may play a role in those with an obvious cause for [Hb] variation as well. Accordingly, the decrease of [Hb] expected in patients admitted with disorders known to cause a decrease in the red blood cell mass would be greater than the 1 observed in our study population.

This [Hb] drop during hospitalization may be clinically relevant in a number of ways: it could cause the attending physicians to order costly and invasive studies, it could have prognostic value as it occurs in patients admitted with myocardial infarction, and it could trigger an acute coronary syndrome.20 We observed an association between [Hb] drop and a higher APS in our patients. This score has been validated as a prognostic factor in previous studies. However, it is not possible to conclude that this [Hb] drop is associated with a worse prognosis.

Our study had several limitations. The sample's heterogeneity inherent to our general ward population could have altered our results and their generalization. To overcome this bias we used a categorization system based on discharge diagnosis. However, this particular system has several limitations. The small sample size and the absence of follow up data after discharge limited our capacity to detect any prognostic significance of [Hb] decrease. In the present study, the total decrease of [Hb] may have been underestimated because the onset of the acute illness precedes hospital admission by a variable time. Finally, a relatively small sample size limited our ability to detect other possible significant predictors of [Hb] decrease. However, this study was not designed to assess the mechanisms associated with the decrease of [Hb], but rather to establish its occurrence, measure it and explore related variables.

These results may be useful for further studies to evaluate [Hb] variation in admitted patients and its relation to other variables, such as bone marrow production, oxygen use, erythrocyte survival, nutritional deficiencies, and erythropoietin and inflammatory mediators levels.

In conclusion, our general medical inpatients had a mean 1.45 g/dL [Hb] decrease during hospitalization, which was greater in the first days of hospitalization, even though an evident cause was not present. These findings would help attending physicians in general wards make a rational and efficient approach when dealing with patients' decrease in [Hb].

Acknowledgements

The authors thank Jorge Lopez Camelo and Hugo Krupitzki for statistical advice and Valeria Melia for helping in the preparation of this manuscript.

Studies of the red blood cell mass performed in hospitalized patients were first done in the early 1970s.1 Thereafter, a decrease in the hemoglobin concentration ([Hb]) and its potential causes have been further reported, especially in critically ill patients.25 The decrease in [Hb] can occur as a result of blood draws for diagnostic testing; blood loss associated with invasive procedures and bleeding; occult gastrointestinal bleeding; hemolysis; shortening of red cell survival; iron, folic acid or cobalamine deficiencies; renal, liver or endocrine failure; hemodilution associated with fluid therapy; and the so‐called anemia of inflammation (AI).68 The latter would be a consequence of a blunted response of the bone marrow due to several factors such as inadequate secretion of erythropoietin, inhibition of the proliferation and differentiation of the erythroid precursors of the bone marrow and an hepcidin‐mediated functional iron deficiency.810

As mentioned, most studies were done in critically ill patients and scarce information is available about general ward admitted patients (GWAP) with less severe illnesses. In this scenario, some of the proposed mechanisms may have a less clear role. It is widely accepted that [Hb] may decrease without overt bleeding in GWAP. However, given the lack of information on this matter, laboratory controls and invasive procedures are undertaken to determine its potential causes.

The purpose of the present study is to describe [Hb] variation over time in nonbleeding GWAP, to estimate the proportion of patients with [Hb] decreases 1.5 g/dL, and to evaluate possible related variables.

Materials and Methods

A 16‐week (September 2004‐January 2005) prospective observational study was conducted in Internal Medicine GWAP at 2 Buenos Aires teaching hospitals.

All consecutive patients older than 16 years were evaluated. Patients admitted for the following reasons were excluded: bleeding, trauma, surgery, invasive procedures associated with blood loss (biopsies, biliary drainage, endovascular therapeutic procedures, and chest tubes), blood transfusions, anemia, chemotherapy, and acute renal failure.

Patients with a bleeding history, chemotherapy or radiation therapy within two months prior to admission, patients on dialysis and patients with current oncologic or hematologic disease were excluded, as well as those with length of stay less than 3 days, or with less than 2 [Hb] or hematocrit (HCT) measurements.

Patients were followed until discharge, death or transfer to a critical care unit. Patients were withdrawn from the study if they presented with bleeding or hemolysis, underwent red blood cell transfusion or therapy affecting hemoglobin levels (iron, chemotherapy or erythropoietin) or if either a surgical or invasive procedure associated with blood loss was performed.

Data were collected from patients' medical records and additional information was obtained from treating physicians and patients by the authors (AL, NC, SM, AN, MH). Standardized case report forms were completed during the hospitalization including: age, sex, admissions in the previous 3 months (readmissions) and whether or not the patient lived in a nursing home. Patients were categorized according to discharge diagnosis as reported by Nguyen et al.4 with modifications related to our general ward population. Upon admission Katz daily activity index (ADL),11, 12 APACHE II acute physiology score (APS),13, 14 and Charlson comorbidity score (Charlson)15, 16 were assessed. In all cases the [Hb] and HCT values were registered on admission and on days 3, 6, 10 and prior to discharge as well as any other determination required by the treating physician. Anemia was defined as [Hb] 13 g/dL for men and 12 g/dL for women.17 Based on previous reports a 1.5 g/dL decrease in [Hb] and of 4.5 points in HCT compared to admission values were considered a significant fall.1, 4 Acute renal failure was defined as an increase in creatinine level 0.5 mg/dL or a 50% increase from baseline.18, 19

Every procedure without significant blood loss (PWSBL) such as venous catheter placement, thoracentesis, lumbar punction, paracentesis, skin biopsy, arthrocentesis, and diagnostic angiogram was also recorded. The study was approved by the Hospital's ethical committee.

Data analysis and processing were performed with Excel 2000 and Stata version 8.0 (Stata Corp; USA). For continuous variables, results were expressed by the mean value its standard deviation (SD), and compared with Student's t‐test. The chi‐square test was used to compare categorical variables. A survival curve was developed with the Kaplan‐Meier method to analyze the time to a significant fall in [Hb]. Finally, Cox proportional hazard modeling was performed to assess the association between a significant fall in [Hb] and other variables. We accepted P < 0,05 as significant.

Results

A total of 338 patients were admitted to the Internal Medicine Inpatient Services in the study period. Thirty‐nine percent (131) of these patients were included. Exclusion criteria were: diagnosis at admission (n = 95, 45.9%), past medical history (n = 56, 27%) and length of stay less than 3 days, or less than 2 determinations of [Hb] or HCT (n = 56, 27%). Data collection was discontinued for the following reasons: discharge (81.7%), death (6.9%), surgery or procedures associated with blood loss (5.3%), transfer to a critical care unit (3%), transfusions (2.3%), and chemotherapy (0.8%). The baseline characteristics of the study patients are shown in Table 1.

Demographic Data of 131 Patients
 n%Mean (SD)MedianMin/Max
  • Abbreviations: ADL, Katz daily activity index; APS, APACHE II acute physiology score; CHARLSON, Charlson comorbidity score; [Hb], Hemoglobin concentration; PWSBL, procedure without significant blood loss; SD, Standard deviation.

Age, years  71.9 (17.4)7718/97
18‐40118.4   
41‐601612.2   
61‐805239.7   
>805239.7   
Gender     
Female7557.2   
Lenght of stay (days)  7 (4.8)63/28
APS  4.9 (4.2)40/22
0‐47154.2   
5‐83627.5   
>82418.3   
ADL  4.5 (2.3)60/6
0‐23325.2   
3‐5118.4   
68766.4   
CHARLSON  2.2 (2.3)20/11
03224.4   
13224.4   
22216.8   
31813.7   
>32720.6   
Readmissions2821.4   
PWSBL1410.7   
Anemia at admission6348.1   
[Hb] at admission  12.5 (1.7)12.58.6/17
[Hb] at admission males  12.8 (1.9)12.68.7/17
[Hb] at admission females  12.3(1.5)12.38.6/15.5

Diagnoses at discharge were divided into the following categories: infections (25.2%), electrolyte disturbances (7.6%), cardiac diseases (9.9%), neurologic (19.1%), respiratory (16.8%), gastrointestinal (10.7%), and other diagnosis (10.7%).

No evidence of bleeding was found in the included patients. Bleeding was only observed in 4 of the initially evaluated patients who were excluded since they failed to meet the required number of [Hb] determinations before bleeding.

A mean decrease in [Hb] of 0.71 g/dL was found between admission and discharge day (P < 0.001; 95% CI, 0.47‐0.97). Mean nadir [Hb] was 1.45 g/dL lower than admission [Hb] (P < 0.001; 95% CI, 1.24‐1.67). Mean nadir day occurred between hospitalization days 3 and 4. Mean [Hb] at discharge was 11.8 1.8 g/dL. This value is higher than the mean concentration at nadir (0.74 g/dL P < 0.001; 95% CI, 0.60‐0.97) (Figure 1).

Figure 1
Box and whisker plot of changes in hemoglobin concentration during hospital stay. The line within the box denotes the median and the box spans the interquartile range. Whiskers extend from the 10th to 90th percentiles.

Table 2 shows the rate of patients with a decrease in the [Hb] for different cutoff levels. Forty‐five percent of the study population (59 patients) had a significant fall in [Hb] during hospitalization. This was observed on day 2 in 50% of the patients. Likewise, a significant fall in HCT value was found in 42.7% of patients. If [Hb] decrease during hospitalization is analyzed as a proportion of [Hb] at admission, 52.7% of patients had a 10% or greater [Hb] decrease during their hospital stay.

Proportion of Patients With a Nadir Fall in the [Hb] for Different Cutoff Points
[Hb] fall (g/dL)0.511.522.533.544.5
% of patients80.960.345.028.217.69.95.33.82.3

Using Kaplan‐Meier analysis, the estimated proportion showing no significant fall of [Hb] was 0.63 (95% CI, 0.55‐0.71) on day 3 and 0.48 (95% CI, 0.29‐0.67) on day 10. By day 15, only 3.8% of the initially included patients were still hospitalized and showed no decrease in [Hb] 1.5 g/dl. These patients maintained their [Hb] stable for the rest of the follow up period (Figure 2).

Figure 2
Kaplan Meier plot showing the proportion of patient without a fall in the hemoglobin concentration ≥1.5 g/dL.

In the univariate analysis, comparing patients that experienced a decrease in [Hb] 1.5 g/dL with those who did not, significant differences were only found in the following variables: length of stay, APS, anemia at admission, [Hb] at admission, and infectious, gastrointestinal or cardiac diseases at discharge diagnosis (Table 3).

Univariate Analysis
 Patients with a significant fallPatients without a significant fallP Value
  • NOTE: Continuous variables are expressed by the mean value and its standard deviation (SD). Categorical variables are expressed by the number of cases and the percentage within its category.

  • Abbreviations: ADL, Katz daily activity index; APS, APACHE II acute physiology score; CHARLSON, Charlson comorbidity score; [Hb], Hemoglobin concentration; PWSBL, procedure without significant blood loss.

n59 (45%)72 (55%) 
Age, years73.15 (18.7)70.83 (16.2)0.448
Gender, female32 (54.2%)43 (59.7%)0.527
Length of stay (days)8.30 (5.6)5.91 (3.7)<0.007
APS6.13 (4.5)3.97 (3.7)<0.004
ADL4.33 (2.5)4.68 (2.1)0.410
CHARLSON2.03 (1.8)2.37 (2.5)0.382
Nurse home residents4 (6.8%)3 (4.2%)0.700
Readmissions11 (18.6%)17 (23.6%)0.490
PWSBL6 (10.2%)8 (11.1%)0.862
Anemia at admission20 (33.9%)43 (59.7%)<0.004
[Hb] at admission13.09 (1.7)12.01 (1.5)<0.001
Diagnosis at discharge   
Infectious20 (33.9%)13 (18.1%)<0.05
Respiratory8 (13.6%)14 (19.4%)0.370
Neurologic9 (15.2%)16 (22.2%)0.312
Gastrointestinal11 (18.6%)3 (4.2%)<0.01
Cardiac2 (3.4%)11(15.3%)<0.05
Electrolyte disturbances6 (10.2%)4 (5.6%)0.512
Others3 (5.1%)11 (15.3%)0.087

In the Cox proportional hazard model adjusting for the variables shown in Table 4, a significant independent direct association was found between a significant fall in [Hb] during hospital stay and APS, [Hb] at admission, and either diagnosis of infectious or gastrointestinal disease at discharge. Similar results were found if a significant fall was redefined as a 12% decrease in [Hb] or as a 4.5 point decrease in HCT from baseline.

Cox Proportional Hazard Model
VariableHRRP Value95% CI
  • Abbreviations: ADL, Katz daily activity index; APS, APACHE II acute physiology score; CHARLSON, Charlson comorbidity score; CI, confidence interval; electrolyte dist, electrolyte disturbances; [Hb], hemoglobin concentration; HRR, hazards relative ratio; PWSBL, procedure without significant blood loss.

APS1.070.0071.02‐1.12
ADL1.110.1320.97‐1.27
Charlson0.880.1210.75‐1.03
Nurse home resident1.520.3610.62‐3.72
PWSBL0.670.3900.27‐1.66
Readmission1.140.7100.57‐2.29
Female sex0.980.9440.57‐1.69
Age1.390.0980.94‐2.07
[Hb] at admission1.270.0051.07‐1.51
Diagnosis at discharge   
Infectious2.700.0151.21‐6.05
Neurologic1.420.4570.57‐3.55
Gastrointestinal3.740.0021.62‐8.64
Cardiac0.410.2890.08‐2.12
Electrolyte dist.2.080.1760.72‐6.05
Others0.950.9460.24‐3.81

Discussion

This study describes the variation of [Hb] and HCT values in GWAP without bleeding or other obvious medical conditions associated with a decrease in the red blood cell mass. As previously described,4, 20, 21 we found that [Hb] decreases during hospital stay are frequently observed. The mean [Hb] fall from admission was 1.45 g/dL, and was mainly recorded between hospitalization days 3 and 4. In approximately half of the study population, a 1.5 g/dL decrease in [Hb] was observed. In the survival analysis, 40% and 55% of patients are expected to present such a fall on day 4 and 12 respectively. In accordance with prior reports4, 21 a greater decrease in [Hb] occurred in the first days of hospitalization, and a high proportion of patients were already anemic at the time of admission.

The following variables were associated with a decrease in the [Hb] during hospitalization: higher APS score, higher [Hb] at admission, and diagnosis of infectious or gastrointestinal disease at discharge. Even though several mechanisms seem to contribute to the decrease in [Hb] during hospitalization our data suggest that 1 of these factors seems to be the severity of the disease, as previously proposed by Nguyen et al.4 This observation is supported by the association found in our study between [Hb] decrease and APS. No association was found with Charlson, ADL, and being a nursing home resident. These variables, which have not been previously analyzed, seem to indicate that patients with chronic illnesses are not more likely to have decreases in [Hb] during hospitalization.

Patients with higher [Hb] at admission had a greater [Hb] decrease during their hospital stay. This finding could suggest that the mechanism related to this decrease had less effect on patients with lower [Hb]. This is similar to that observed in the anemia of chronic diseases where [Hb] does not usually fall to extreme values. Our analysis reveals a greater decrease during the first days of hospitalization. Given the high rate of patients anemic at admission, it is possible that the decrease in [Hb] had begun prior to admission.

Previous papers describe a low prevalence of cyanocobalamine (2%), folic acid (2%), and iron (9%) deficiency in patients admitted to critical care units.3 In these patients an inadequate bone marrow response has been proposed as a mechanism for [Hb] decrease, a phenomenon that has been called AI or anemia of critical illness.8, 2228 Inflammatory response associated with acute disease causes this hypoproliferative anemia through 3 different pathways: relative erythropoietin deficit, a direct inhibition of the erythropoiesis in the bone marrow through different mediators (ie, interleukin [IL]‐1, IL‐6, tumor necrosis factor [TNF]) and a functional iron deficit. This relative iron deficiency is produced mainly by the IL‐6 induced overexpression of the hepcidin gene in hepatocytes. Hepcidin causes impaired intestinal iron absorption and an inadequate delivery of iron from the iron‐recycling macrophages to the erythroid precursors in the bone marrow.8, 9

AI could explain some of our findings, such as the greater decrease observed in the first hospital days when inflamatory mediators levels are expected to be higher. The association between APS and infectious disease diagnosis and a greater [Hb] decrease may also be explained by this mechanism. Nonetheless, the daily bone marrow production of red blood cells is small compared with the circulating red mass cell and therefore it is necessary to have a better explanation on how AI could be associated with this rapid decrease in the [Hb]. Rice et al.30, 31 have explored some mechanisms leading to a rapid decrease in the red mass cell related to acute variations in erytropoietin level. A role of this mechanism and other could be hypothesized.29

Overt bleeding was not found in our study population and procedures without significant blood loss (PWSBL) were infrequent, therefore it is unlikely that they could have had an impact on the decrease of [Hb]. The influence of other variables, such as blood volume drawn for diagnostic testing and occult blood losses (especially from the gastrointestinal tract) was not investigated. Parenteral hydration and hemodilution neither were evaluated in our study nor were extensively described in the literature. We think this last mentioned mechanism plays a role in the large [Hb] decrease during the first days of hospitalization. However a lack of association was mentioned in the only study that evaluated this issue in critically ill patients. Intravascular hemolysis has been cited as an infrequent event in these patients.2, 4

Only patients without a clear explanation for their [Hb] decrease were included because they represent a matter of concern for hospitalists and other treating physicians. Therefore 60% of the initially evaluated patients were excluded, since they were admitted for conditions likely to cause a decrease in [Hb]. Nevertheless, it seems likely that the mechanisms involved in the included patients may play a role in those with an obvious cause for [Hb] variation as well. Accordingly, the decrease of [Hb] expected in patients admitted with disorders known to cause a decrease in the red blood cell mass would be greater than the 1 observed in our study population.

This [Hb] drop during hospitalization may be clinically relevant in a number of ways: it could cause the attending physicians to order costly and invasive studies, it could have prognostic value as it occurs in patients admitted with myocardial infarction, and it could trigger an acute coronary syndrome.20 We observed an association between [Hb] drop and a higher APS in our patients. This score has been validated as a prognostic factor in previous studies. However, it is not possible to conclude that this [Hb] drop is associated with a worse prognosis.

Our study had several limitations. The sample's heterogeneity inherent to our general ward population could have altered our results and their generalization. To overcome this bias we used a categorization system based on discharge diagnosis. However, this particular system has several limitations. The small sample size and the absence of follow up data after discharge limited our capacity to detect any prognostic significance of [Hb] decrease. In the present study, the total decrease of [Hb] may have been underestimated because the onset of the acute illness precedes hospital admission by a variable time. Finally, a relatively small sample size limited our ability to detect other possible significant predictors of [Hb] decrease. However, this study was not designed to assess the mechanisms associated with the decrease of [Hb], but rather to establish its occurrence, measure it and explore related variables.

These results may be useful for further studies to evaluate [Hb] variation in admitted patients and its relation to other variables, such as bone marrow production, oxygen use, erythrocyte survival, nutritional deficiencies, and erythropoietin and inflammatory mediators levels.

In conclusion, our general medical inpatients had a mean 1.45 g/dL [Hb] decrease during hospitalization, which was greater in the first days of hospitalization, even though an evident cause was not present. These findings would help attending physicians in general wards make a rational and efficient approach when dealing with patients' decrease in [Hb].

Acknowledgements

The authors thank Jorge Lopez Camelo and Hugo Krupitzki for statistical advice and Valeria Melia for helping in the preparation of this manuscript.

References
  1. Eyster E,Bernene J.Nosocomial anemia.JAMA.1973;223(1):7374.
  2. von Ahsen N,Muller C,Serke S,Frei U,Eckardt KU.Important role of nondiagnostic blood loss and blunted erythropoietic response in the anemia of medical intensive care patients.Crit Care Med.1999;27(12):26302639.
  3. Rodriguez RM,Corwin HL,Gettinger A,Corwin MJ,Gubler D,Pearl RG.Nutritional deficiencies and blunted erythropoietin response as causes of the anemia of critical illness.J Crit Care.2001;16(1):3641.
  4. Nguyen BV,Bota DP,Melot C,Vincent JL.Time course of hemoglobin concentrations in nonbleeding intensive care unit patients.Crit Care Med.2003;31(2):406410.
  5. Corwin HL,Rodriguez RM,Pearl RG, et al.Erithropoietin response in critically ill patients.Crit Care Med.1997;25(Suppl1):a82.
  6. van Iperen CE,van de Wiel A,Marx JJ.Acute event‐related anaemia.Br J Haematol.2001;115(4):739743.
  7. van Iperen CE,Gaillard CA,Kraaijenhagen RJ,Braam BG,Marx JJ,van de Wiel A.Response of erythropoiesis and iron metabolism to recombinant human erythropoietin in intensive care unit patients.Crit Care Med.2000;28(8):27732778.
  8. Adamson JW.The anemia of inflammation/malignancy: mechanisms and management.Hematology Am Soc Hematol Educ Program.2008:159165.
  9. Fleming MD.The regulation of hepcidin and its effects on systemic and cellular iron metabolism.Hematology Am Soc Hematol Educ Program.2008:151158.
  10. Rogiers P,Zhang H,Leeman M, et al.,Erythropoietin response is blunted in critically ill patients.Intensive Care Med.1997;23:159162.
  11. Katz S,Ford AB,Moskowitz RW,Jackson BA,Jaffe MW.Studies of illness in the aged. The index of ADL: standardized measure of biological and psychosocial function.JAMA.1963;185:914919.
  12. Katz S,Down TD,Cash HR,Progress in the development of the index of ADL.Gerontologist.1970;10:2030.
  13. Covinsky KE,Justice AC,Rosenthal GE,Palmer RM,Landefeld S.Measuring prognosis and case mix in hospitalized elders. The importance of functional status.J Gen Intern Med.1997;12:203208.
  14. Knauss W,Draper E,Wagner D,Zimmerman J.APACHE II: A severity of disease classification system.Crit Care Med.1985;12(10):818829.
  15. Charlson ME,Pompei P,Ales KL,Mac Kenzie CR.A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chron Dis.1997;40:373383.
  16. de Groot V,Beckerman H,Lankhorst GJ,Bouter LM.How to measure comorbidity: a critical review of available methods.J Clin Epidemiol.2003;56:221229.
  17. Blanc B,Finch CA,Hallberg L, et al.Nutritional anaemias. Report of a WHO Scientific Group.World Health Organ Tech Rep Ser.1968;405:140.
  18. Hsu CY,Bates DW,Kuperman GJ,Curhan GC.Relationship between hematocrit and renal function in men and women.Kidney Int.2001;59(2):725731.
  19. Hsu CY,McCulloch CE,Curhan GC.Epidemiology of anemia associated with chronic renal insufficiency among adults in the United States: results from the Third National Health and Nutrition Examination Survey.J Am Soc Nephrol.2002;13(2):504510.
  20. Doron A,Suleiman M,Agmon Y, et al.,Changes in haemoglobin levels during hospital course and long‐term outcome after myocardial infarction.Eur Heart J.2007;28(11):12891296.
  21. Thavendiranathan P,Bagai A,Ebidia A,Detsky A,Choudhry N.Do blood test cause anemia in hospitalized patients?J Gen Intern Med.2005;20:520524.
  22. Scharte M,Fink M.Red blood cell physiology in critical illness.Crit Care Med.2003;31(12 Suppl)( ):S651S657.
  23. Shander A.Anemia in the critically ill.Crit Care Clin.2004;20:159178.
  24. Corwin HL,Krantz SB.Anemia of the critically ill: acute anemia of chronic disease.Crit Care Med.2000;28(8):30983099.
  25. Corwin HL.Anemia and blood transfusion in the critically ill patient: role of erythropoietin.Crit Care.2004;8(Suppl 2):S42S44.
  26. Eckart KU.Anemia of critical illness – implications for understanding and treating rHuEPO resistance.Nephrol Dial Transplant.2002;17(Suppl 5):S48S55.
  27. Corwin HL,Surgenor SD,Gettinger A.Transfusion practice in the critically ill.Crit Care Med.2003;31(Suppl):S668S671.
  28. van de Wiel A.Anemia in critically ill patients.Eur J Inter Med.2004;15(8):481486.
  29. Alfrey CP,Rice L,Udden M,Leach‐Huntoon CS.Neocytolysis: a physiologic down‐regulator of red blood cell mass.Lancet.1997;349:13891390.
  30. Rice L,Alfrey CP,Driscoll T,Whitley CE,Hachey DL,Suki W.Neocytolysis contributes to the anemia of renal disease.Am J Kidney Dis.1999;33:5962.
  31. Rice L,Ruiz W,Driscoll T, et al.Neocytolysis on descent from altitude: a newly recognized mechanism for the control of red cell mass.Ann Intern Med.2001;134:652656.
  32. Smoller BR,Kruskall MS.Phlebotomy for diagnostic laboratory test in adults. Pattern of use and effect on transfusion requirements.N Engl J Med.1986;314:12331235.
References
  1. Eyster E,Bernene J.Nosocomial anemia.JAMA.1973;223(1):7374.
  2. von Ahsen N,Muller C,Serke S,Frei U,Eckardt KU.Important role of nondiagnostic blood loss and blunted erythropoietic response in the anemia of medical intensive care patients.Crit Care Med.1999;27(12):26302639.
  3. Rodriguez RM,Corwin HL,Gettinger A,Corwin MJ,Gubler D,Pearl RG.Nutritional deficiencies and blunted erythropoietin response as causes of the anemia of critical illness.J Crit Care.2001;16(1):3641.
  4. Nguyen BV,Bota DP,Melot C,Vincent JL.Time course of hemoglobin concentrations in nonbleeding intensive care unit patients.Crit Care Med.2003;31(2):406410.
  5. Corwin HL,Rodriguez RM,Pearl RG, et al.Erithropoietin response in critically ill patients.Crit Care Med.1997;25(Suppl1):a82.
  6. van Iperen CE,van de Wiel A,Marx JJ.Acute event‐related anaemia.Br J Haematol.2001;115(4):739743.
  7. van Iperen CE,Gaillard CA,Kraaijenhagen RJ,Braam BG,Marx JJ,van de Wiel A.Response of erythropoiesis and iron metabolism to recombinant human erythropoietin in intensive care unit patients.Crit Care Med.2000;28(8):27732778.
  8. Adamson JW.The anemia of inflammation/malignancy: mechanisms and management.Hematology Am Soc Hematol Educ Program.2008:159165.
  9. Fleming MD.The regulation of hepcidin and its effects on systemic and cellular iron metabolism.Hematology Am Soc Hematol Educ Program.2008:151158.
  10. Rogiers P,Zhang H,Leeman M, et al.,Erythropoietin response is blunted in critically ill patients.Intensive Care Med.1997;23:159162.
  11. Katz S,Ford AB,Moskowitz RW,Jackson BA,Jaffe MW.Studies of illness in the aged. The index of ADL: standardized measure of biological and psychosocial function.JAMA.1963;185:914919.
  12. Katz S,Down TD,Cash HR,Progress in the development of the index of ADL.Gerontologist.1970;10:2030.
  13. Covinsky KE,Justice AC,Rosenthal GE,Palmer RM,Landefeld S.Measuring prognosis and case mix in hospitalized elders. The importance of functional status.J Gen Intern Med.1997;12:203208.
  14. Knauss W,Draper E,Wagner D,Zimmerman J.APACHE II: A severity of disease classification system.Crit Care Med.1985;12(10):818829.
  15. Charlson ME,Pompei P,Ales KL,Mac Kenzie CR.A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chron Dis.1997;40:373383.
  16. de Groot V,Beckerman H,Lankhorst GJ,Bouter LM.How to measure comorbidity: a critical review of available methods.J Clin Epidemiol.2003;56:221229.
  17. Blanc B,Finch CA,Hallberg L, et al.Nutritional anaemias. Report of a WHO Scientific Group.World Health Organ Tech Rep Ser.1968;405:140.
  18. Hsu CY,Bates DW,Kuperman GJ,Curhan GC.Relationship between hematocrit and renal function in men and women.Kidney Int.2001;59(2):725731.
  19. Hsu CY,McCulloch CE,Curhan GC.Epidemiology of anemia associated with chronic renal insufficiency among adults in the United States: results from the Third National Health and Nutrition Examination Survey.J Am Soc Nephrol.2002;13(2):504510.
  20. Doron A,Suleiman M,Agmon Y, et al.,Changes in haemoglobin levels during hospital course and long‐term outcome after myocardial infarction.Eur Heart J.2007;28(11):12891296.
  21. Thavendiranathan P,Bagai A,Ebidia A,Detsky A,Choudhry N.Do blood test cause anemia in hospitalized patients?J Gen Intern Med.2005;20:520524.
  22. Scharte M,Fink M.Red blood cell physiology in critical illness.Crit Care Med.2003;31(12 Suppl)( ):S651S657.
  23. Shander A.Anemia in the critically ill.Crit Care Clin.2004;20:159178.
  24. Corwin HL,Krantz SB.Anemia of the critically ill: acute anemia of chronic disease.Crit Care Med.2000;28(8):30983099.
  25. Corwin HL.Anemia and blood transfusion in the critically ill patient: role of erythropoietin.Crit Care.2004;8(Suppl 2):S42S44.
  26. Eckart KU.Anemia of critical illness – implications for understanding and treating rHuEPO resistance.Nephrol Dial Transplant.2002;17(Suppl 5):S48S55.
  27. Corwin HL,Surgenor SD,Gettinger A.Transfusion practice in the critically ill.Crit Care Med.2003;31(Suppl):S668S671.
  28. van de Wiel A.Anemia in critically ill patients.Eur J Inter Med.2004;15(8):481486.
  29. Alfrey CP,Rice L,Udden M,Leach‐Huntoon CS.Neocytolysis: a physiologic down‐regulator of red blood cell mass.Lancet.1997;349:13891390.
  30. Rice L,Alfrey CP,Driscoll T,Whitley CE,Hachey DL,Suki W.Neocytolysis contributes to the anemia of renal disease.Am J Kidney Dis.1999;33:5962.
  31. Rice L,Ruiz W,Driscoll T, et al.Neocytolysis on descent from altitude: a newly recognized mechanism for the control of red cell mass.Ann Intern Med.2001;134:652656.
  32. Smoller BR,Kruskall MS.Phlebotomy for diagnostic laboratory test in adults. Pattern of use and effect on transfusion requirements.N Engl J Med.1986;314:12331235.
Issue
Journal of Hospital Medicine - 5(5)
Issue
Journal of Hospital Medicine - 5(5)
Page Number
283-288
Page Number
283-288
Article Type
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Hemoglobin concentration variations over time in general medical inpatients
Display Headline
Hemoglobin concentration variations over time in general medical inpatients
Legacy Keywords
anemia, critical illness, hemoglobin, iatrogenic anemia, internal medicine inpatients
Legacy Keywords
anemia, critical illness, hemoglobin, iatrogenic anemia, internal medicine inpatients
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Department of Internal Medicine, Center for Medical Education and Clinical Research (CEMIC), Buenos Aires, Argentina;
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