Pneumonia results in some 1.2 million hospital admissions each year in the United States, is the second leading cause of hospitalization among patients over 65, and accounts for more than $10 billion annually in hospital expenditures.1, 2 As a result of complex demographic and clinical forces, including an aging population, increasing prevalence of comorbidities, and changes in antimicrobial resistance patterns, between the periods 1988 to 1990 and 2000 to 2002 the number of patients hospitalized for pneumonia grew by 20%, and pneumonia was the leading infectious cause of death.3, 4

Given its public health significance, pneumonia has been the subject of intensive quality measurement and improvement efforts for well over a decade. Two of the largest initiatives are the Centers for Medicare & Medicaid Services (CMS) National Pneumonia Project and The Joint Commission ORYX program.5, 6 These efforts have largely entailed measuring hospital performance on pneumonia‐specific processes of care, such as whether blood oxygen levels were assessed, whether blood cultures were drawn before antibiotic treatment was initiated, the choice and timing of antibiotics, and smoking cessation counseling and vaccination at the time of discharge. While measuring processes of care (especially when they are based on sound evidence), can provide insights about quality, and can help guide hospital improvement efforts, these measures necessarily focus on a narrow spectrum of the overall care provided. Outcomes can complement process measures by directing attention to the results of care, which are influenced by both measured and unmeasured factors, and which may be more relevant from the patient's perspective.79

In 2008 CMS expanded its public reporting initiatives by adding risk‐standardized hospital mortality rates for pneumonia to the Hospital Compare website (http://www.hospitalcompare.hhs.gov/).10 Readmission rates were added in 2009. We sought to examine patterns of hospital and regional performance for patients with pneumonia as reflected in 30‐day risk‐standardized readmission and mortality rates. Our report complements the June 2010 annual release of data on the Hospital Compare website. CMS also reports 30‐day risk‐standardized mortality and readmission for acute myocardial infarction and heart failure; a description of the 2010 reporting results for those measures are described elsewhere.

Methods

Results

Hospital‐Specific Risk‐Standardized 30‐Day Mortality and Readmission Rates

Of the 1,118,583 patients included in the mortality analysis 129,444 (11.6%) died within 30 days of hospital admission. The median (Q1, Q3) hospital 30‐day risk‐standardized mortality rate was 11.1% (10.0%, 12.3%), and ranged from 6.7% to 20.9% (Table 1, Figure 1). Hospitals at the 10th percentile had 30‐day risk‐standardized mortality rates of 9.0% while for those at the 90th percentile of performance the rate was 13.5%. The odds of all‐cause mortality for a patient treated at a hospital that was one standard deviation above the national average was 1.68 times higher than that of a patient treated at a hospital that was one standard deviation below the national average.

Figure 1

Table 1.Risk‐Standardized Hospital 30‐Day Pneumonia Mortality and Readmission Rates

	Mortality	Readmission
Abbreviation: SD, standard deviation.
Patients (n)	1118583	1161817
Hospitals (n)	4788	4813
Patient age, years, median (Q1, Q3)	81 (74,86)	80 (74,86)
Nonwhite, %	11.1	11.1
Hospital case volume, median (Q1, Q3)	168 (77,323)	174 (79,334)
Risk‐standardized hospital rate, mean (SD)	11.2 (1.2)	18.3 (0.9)
Minimum	6.7	13.6
1st percentile	7.5	14.9
5th percentile	8.5	15.8
10th percentile	9.0	16.4
25th percentile	10.0	17.2
Median	11.1	18.2
75th percentile	12.3	19.2
90th percentile	13.5	20.4
95th percentile	14.4	21.1
99th percentile	16.1	22.8
Maximum	20.9	26.7
Model fit statistics
c‐Statistic	0.72	0.63
Intrahospital Correlation	0.07	0.03

For the 3‐year period 2006 to 2009, 222 (4.7%) hospitals were categorized as having a mortality rate that was better than the national average, 3968 (83.7%) were no different than the national average, 221 (4.6%) were worse and 332 (7.0%) did not meet the minimum case threshold.

Among the 1,161,817 patients included in the readmission analysis 212,638 (18.3%) were readmitted within 30 days of hospital discharge. The median (Q1,Q3) 30‐day risk‐standardized readmission rate was 18.2% (17.2%, 19.2%) and ranged from 13.6% to 26.7% (Table 1, Figure 2). Hospitals at the 10th percentile had 30‐day risk‐standardized readmission rates of 16.4% while for those at the 90th percentile of performance the rate was 20.4%. The odds of all‐cause readmission for a patient treated at a hospital that was one standard deviation above the national average was 1.40 times higher than the odds of all‐cause readmission if treated at a hospital that was one standard deviation below the national average.

Figure 2

For the 3‐year period 2006 to 2009, 64 (1.3%) hospitals were categorized as having a readmission rate that was better than the national average, 4203 (88.2%) were no different than the national average, 163 (3.4%) were worse and 333 (7.0%) had less than 25 cases and were therefore not categorized.

While risk‐standardized readmission rates were substantially higher than risk‐standardized mortality rates, mortality rates varied more. For example, the top 10% of hospitals had a relative mortality rate that was 33% lower than those in the bottom 10%, as compared with just a 20% relative difference for readmission rates. The coefficient of variation, a normalized measure of dispersion that unlike the standard deviation is independent of the population mean, was 10.7 for risk‐standardized mortality rates and 4.9 for readmission rates.

Regional Risk‐Standardized 30‐Day Mortality and Readmission Rates

Figures 3 and 4 show the distribution of 30‐day risk‐standardized mortality and readmission rates among hospital referral regions by quintile. Highest mortality regions were found across the entire country, including parts of Northern New England, the Mid and South Atlantic, East and the West South Central, East and West North Central, and the Mountain and Pacific regions of the West. The lowest mortality rates were observed in Southern New England, parts of the Mid and South Atlantic, East and West South Central, and parts of the Mountain and Pacific regions of the West (Figure 3).

Figure 3

Figure 4

Readmission rates were higher in the eastern portions of the US (including the Northeast, Mid and South Atlantic, East South Central) as well as the East North Central, and small parts of the West North Central portions of the Midwest and in Central California. The lowest readmission rates were observed in the West (Mountain and Pacific regions), parts of the Midwest (East and West North Central) and small pockets within the South and Northeast (Figure 4).

Discussion

In this 3‐year analysis of patient, hospital, and regional outcomes we observed that pneumonia in the elderly remains a highly morbid illness, with a 30‐day mortality rate of approximately 11.6%. More notably we observed that risk‐standardized mortality rates, and to a lesser extent readmission rates, vary significantly across hospitals and regions. Finally, we observed that readmission rates, but not mortality rates, show strong geographic concentration.

These findings suggest possible opportunities to save or extend the lives of a substantial number of Americans, and to reduce the burden of rehospitalization on patients and families, if low performing institutions were able to achieve the performance of those with better outcomes. Additionally, because readmission is so common (nearly 1 in 5 patients), efforts to reduce overall health care spending should focus on this large potential source of savings.19 In this regard, impending changes in payment to hospitals around readmissions will change incentives for hospitals and physicians that may ultimately lead to lower readmission rates.20

Previous analyses of the quality of hospital care for patients with pneumonia have focused on the percentage of eligible patients who received guideline‐recommended antibiotics within a specified time frame (4 or 8 hours), and vaccination prior to hospital discharge.21, 22 These studies have highlighted large differences across hospitals and states in the percentage receiving recommended care. In contrast, the focus of this study was to compare risk‐standardized outcomes of care at the nation's hospitals and across its regions. This effort was guided by the notion that the measurement of care outcomes is an important complement to process measurement because outcomes represent a more holistic assessment of care, that an outcomes focus offers hospitals greater autonomy in terms of what processes to improve, and that outcomes are ultimately more meaningful to patients than the technical aspects of how the outcomes were achieved. In contrast to these earlier process‐oriented efforts, the magnitude of the differences we observed in mortality and readmission rates across hospitals was not nearly as large.

A recent analysis of the outcomes of care for patients with heart failure and acute myocardial infarction also found significant variation in both hospital and regional mortality and readmission rates.23 The relative differences in risk‐standardized hospital mortality rates across the 10th to 90th percentiles of hospital performance was 25% for acute myocardial infarction, and 39% for heart failure. By contrast, we found that the difference in risk‐standardized hospital mortality rates across the 10th to 90th percentiles in pneumonia was an even greater 50% (13.5% vs. 9.0%). Similar to the findings in acute myocardial infarction and heart failure, we observed that risk‐standardized mortality rates varied more so than did readmission rates.

Our study has a number of limitations. First, the analysis was restricted to Medicare patients only, and our findings may not be generalizable to younger patients. Second, our risk‐adjustment methods relied on claims data, not clinical information abstracted from charts. Nevertheless, we assessed comorbidities using all physician and hospital claims from the year prior to the index admission. Additionally our mortality and readmission models were validated against those based on medical record data and the outputs of the 2 approaches were highly correlated.15, 24, 25 Our study was restricted to patients with a principal diagnosis of pneumonia, and we therefore did not include those whose principal diagnosis was sepsis or respiratory failure and who had a secondary diagnosis of pneumonia. While this decision was made to reduce the risk of misclassifying complications of care as the reason for admission, we acknowledge that this is likely to have limited our study to patients with less severe disease, and may have introduced bias related to differences in hospital coding practices regarding the use of sepsis and respiratory failure codes. While we excluded patients with 1 day length of stay from the mortality analysis to reduce the risk of including patients in the measure who did not actually have pneumonia, we did not exclude them from the readmission analysis because very short length of stay may be a risk factor for readmission. An additional limitation of our study is that our findings are primarily descriptive, and we did not attempt to explain the sources of the variation we observed. For example, we did not examine the extent to which these differences might be explained by differences in adherence to process measures across hospitals or regions. However, if the experience in acute myocardial infarction can serve as a guide, then it is unlikely that more than a small fraction of the observed variation in outcomes can be attributed to factors such as antibiotic timing or selection.26 Additionally, we cannot explain why readmission rates were more geographically distributed than mortality rates, however it is possible that this may be related to the supply of physicians or hospital beds.27 Finally, some have argued that mortality and readmission rates do not necessarily reflect the very quality they intend to measure.2830

The outcomes of patients with pneumonia appear to be significantly influenced by both the hospital and region where they receive care. Efforts to improve population level outcomes might be informed by studying the practices of hospitals and regions that consistently achieve high levels of performance.31

Pneumonia results in some 1.2 million hospital admissions each year in the United States, is the second leading cause of hospitalization among patients over 65, and accounts for more than $10 billion annually in hospital expenditures.1, 2 As a result of complex demographic and clinical forces, including an aging population, increasing prevalence of comorbidities, and changes in antimicrobial resistance patterns, between the periods 1988 to 1990 and 2000 to 2002 the number of patients hospitalized for pneumonia grew by 20%, and pneumonia was the leading infectious cause of death.3, 4

Given its public health significance, pneumonia has been the subject of intensive quality measurement and improvement efforts for well over a decade. Two of the largest initiatives are the Centers for Medicare & Medicaid Services (CMS) National Pneumonia Project and The Joint Commission ORYX program.5, 6 These efforts have largely entailed measuring hospital performance on pneumonia‐specific processes of care, such as whether blood oxygen levels were assessed, whether blood cultures were drawn before antibiotic treatment was initiated, the choice and timing of antibiotics, and smoking cessation counseling and vaccination at the time of discharge. While measuring processes of care (especially when they are based on sound evidence), can provide insights about quality, and can help guide hospital improvement efforts, these measures necessarily focus on a narrow spectrum of the overall care provided. Outcomes can complement process measures by directing attention to the results of care, which are influenced by both measured and unmeasured factors, and which may be more relevant from the patient's perspective.79

In 2008 CMS expanded its public reporting initiatives by adding risk‐standardized hospital mortality rates for pneumonia to the Hospital Compare website (http://www.hospitalcompare.hhs.gov/).10 Readmission rates were added in 2009. We sought to examine patterns of hospital and regional performance for patients with pneumonia as reflected in 30‐day risk‐standardized readmission and mortality rates. Our report complements the June 2010 annual release of data on the Hospital Compare website. CMS also reports 30‐day risk‐standardized mortality and readmission for acute myocardial infarction and heart failure; a description of the 2010 reporting results for those measures are described elsewhere.

Methods

Results

Hospital‐Specific Risk‐Standardized 30‐Day Mortality and Readmission Rates

Of the 1,118,583 patients included in the mortality analysis 129,444 (11.6%) died within 30 days of hospital admission. The median (Q1, Q3) hospital 30‐day risk‐standardized mortality rate was 11.1% (10.0%, 12.3%), and ranged from 6.7% to 20.9% (Table 1, Figure 1). Hospitals at the 10th percentile had 30‐day risk‐standardized mortality rates of 9.0% while for those at the 90th percentile of performance the rate was 13.5%. The odds of all‐cause mortality for a patient treated at a hospital that was one standard deviation above the national average was 1.68 times higher than that of a patient treated at a hospital that was one standard deviation below the national average.

Figure 1

Table 1.Risk‐Standardized Hospital 30‐Day Pneumonia Mortality and Readmission Rates

	Mortality	Readmission
Abbreviation: SD, standard deviation.
Patients (n)	1118583	1161817
Hospitals (n)	4788	4813
Patient age, years, median (Q1, Q3)	81 (74,86)	80 (74,86)
Nonwhite, %	11.1	11.1
Hospital case volume, median (Q1, Q3)	168 (77,323)	174 (79,334)
Risk‐standardized hospital rate, mean (SD)	11.2 (1.2)	18.3 (0.9)
Minimum	6.7	13.6
1st percentile	7.5	14.9
5th percentile	8.5	15.8
10th percentile	9.0	16.4
25th percentile	10.0	17.2
Median	11.1	18.2
75th percentile	12.3	19.2
90th percentile	13.5	20.4
95th percentile	14.4	21.1
99th percentile	16.1	22.8
Maximum	20.9	26.7
Model fit statistics
c‐Statistic	0.72	0.63
Intrahospital Correlation	0.07	0.03

For the 3‐year period 2006 to 2009, 222 (4.7%) hospitals were categorized as having a mortality rate that was better than the national average, 3968 (83.7%) were no different than the national average, 221 (4.6%) were worse and 332 (7.0%) did not meet the minimum case threshold.

Among the 1,161,817 patients included in the readmission analysis 212,638 (18.3%) were readmitted within 30 days of hospital discharge. The median (Q1,Q3) 30‐day risk‐standardized readmission rate was 18.2% (17.2%, 19.2%) and ranged from 13.6% to 26.7% (Table 1, Figure 2). Hospitals at the 10th percentile had 30‐day risk‐standardized readmission rates of 16.4% while for those at the 90th percentile of performance the rate was 20.4%. The odds of all‐cause readmission for a patient treated at a hospital that was one standard deviation above the national average was 1.40 times higher than the odds of all‐cause readmission if treated at a hospital that was one standard deviation below the national average.

Figure 2

For the 3‐year period 2006 to 2009, 64 (1.3%) hospitals were categorized as having a readmission rate that was better than the national average, 4203 (88.2%) were no different than the national average, 163 (3.4%) were worse and 333 (7.0%) had less than 25 cases and were therefore not categorized.

While risk‐standardized readmission rates were substantially higher than risk‐standardized mortality rates, mortality rates varied more. For example, the top 10% of hospitals had a relative mortality rate that was 33% lower than those in the bottom 10%, as compared with just a 20% relative difference for readmission rates. The coefficient of variation, a normalized measure of dispersion that unlike the standard deviation is independent of the population mean, was 10.7 for risk‐standardized mortality rates and 4.9 for readmission rates.

Regional Risk‐Standardized 30‐Day Mortality and Readmission Rates

Figures 3 and 4 show the distribution of 30‐day risk‐standardized mortality and readmission rates among hospital referral regions by quintile. Highest mortality regions were found across the entire country, including parts of Northern New England, the Mid and South Atlantic, East and the West South Central, East and West North Central, and the Mountain and Pacific regions of the West. The lowest mortality rates were observed in Southern New England, parts of the Mid and South Atlantic, East and West South Central, and parts of the Mountain and Pacific regions of the West (Figure 3).

Figure 3

Figure 4

Readmission rates were higher in the eastern portions of the US (including the Northeast, Mid and South Atlantic, East South Central) as well as the East North Central, and small parts of the West North Central portions of the Midwest and in Central California. The lowest readmission rates were observed in the West (Mountain and Pacific regions), parts of the Midwest (East and West North Central) and small pockets within the South and Northeast (Figure 4).

Discussion

In this 3‐year analysis of patient, hospital, and regional outcomes we observed that pneumonia in the elderly remains a highly morbid illness, with a 30‐day mortality rate of approximately 11.6%. More notably we observed that risk‐standardized mortality rates, and to a lesser extent readmission rates, vary significantly across hospitals and regions. Finally, we observed that readmission rates, but not mortality rates, show strong geographic concentration.

These findings suggest possible opportunities to save or extend the lives of a substantial number of Americans, and to reduce the burden of rehospitalization on patients and families, if low performing institutions were able to achieve the performance of those with better outcomes. Additionally, because readmission is so common (nearly 1 in 5 patients), efforts to reduce overall health care spending should focus on this large potential source of savings.19 In this regard, impending changes in payment to hospitals around readmissions will change incentives for hospitals and physicians that may ultimately lead to lower readmission rates.20

Previous analyses of the quality of hospital care for patients with pneumonia have focused on the percentage of eligible patients who received guideline‐recommended antibiotics within a specified time frame (4 or 8 hours), and vaccination prior to hospital discharge.21, 22 These studies have highlighted large differences across hospitals and states in the percentage receiving recommended care. In contrast, the focus of this study was to compare risk‐standardized outcomes of care at the nation's hospitals and across its regions. This effort was guided by the notion that the measurement of care outcomes is an important complement to process measurement because outcomes represent a more holistic assessment of care, that an outcomes focus offers hospitals greater autonomy in terms of what processes to improve, and that outcomes are ultimately more meaningful to patients than the technical aspects of how the outcomes were achieved. In contrast to these earlier process‐oriented efforts, the magnitude of the differences we observed in mortality and readmission rates across hospitals was not nearly as large.

A recent analysis of the outcomes of care for patients with heart failure and acute myocardial infarction also found significant variation in both hospital and regional mortality and readmission rates.23 The relative differences in risk‐standardized hospital mortality rates across the 10th to 90th percentiles of hospital performance was 25% for acute myocardial infarction, and 39% for heart failure. By contrast, we found that the difference in risk‐standardized hospital mortality rates across the 10th to 90th percentiles in pneumonia was an even greater 50% (13.5% vs. 9.0%). Similar to the findings in acute myocardial infarction and heart failure, we observed that risk‐standardized mortality rates varied more so than did readmission rates.

Our study has a number of limitations. First, the analysis was restricted to Medicare patients only, and our findings may not be generalizable to younger patients. Second, our risk‐adjustment methods relied on claims data, not clinical information abstracted from charts. Nevertheless, we assessed comorbidities using all physician and hospital claims from the year prior to the index admission. Additionally our mortality and readmission models were validated against those based on medical record data and the outputs of the 2 approaches were highly correlated.15, 24, 25 Our study was restricted to patients with a principal diagnosis of pneumonia, and we therefore did not include those whose principal diagnosis was sepsis or respiratory failure and who had a secondary diagnosis of pneumonia. While this decision was made to reduce the risk of misclassifying complications of care as the reason for admission, we acknowledge that this is likely to have limited our study to patients with less severe disease, and may have introduced bias related to differences in hospital coding practices regarding the use of sepsis and respiratory failure codes. While we excluded patients with 1 day length of stay from the mortality analysis to reduce the risk of including patients in the measure who did not actually have pneumonia, we did not exclude them from the readmission analysis because very short length of stay may be a risk factor for readmission. An additional limitation of our study is that our findings are primarily descriptive, and we did not attempt to explain the sources of the variation we observed. For example, we did not examine the extent to which these differences might be explained by differences in adherence to process measures across hospitals or regions. However, if the experience in acute myocardial infarction can serve as a guide, then it is unlikely that more than a small fraction of the observed variation in outcomes can be attributed to factors such as antibiotic timing or selection.26 Additionally, we cannot explain why readmission rates were more geographically distributed than mortality rates, however it is possible that this may be related to the supply of physicians or hospital beds.27 Finally, some have argued that mortality and readmission rates do not necessarily reflect the very quality they intend to measure.2830

The outcomes of patients with pneumonia appear to be significantly influenced by both the hospital and region where they receive care. Efforts to improve population level outcomes might be informed by studying the practices of hospitals and regions that consistently achieve high levels of performance.31

Pneumonia results in some 1.2 million hospital admissions each year in the United States, is the second leading cause of hospitalization among patients over 65, and accounts for more than $10 billion annually in hospital expenditures.1, 2 As a result of complex demographic and clinical forces, including an aging population, increasing prevalence of comorbidities, and changes in antimicrobial resistance patterns, between the periods 1988 to 1990 and 2000 to 2002 the number of patients hospitalized for pneumonia grew by 20%, and pneumonia was the leading infectious cause of death.3, 4

Given its public health significance, pneumonia has been the subject of intensive quality measurement and improvement efforts for well over a decade. Two of the largest initiatives are the Centers for Medicare & Medicaid Services (CMS) National Pneumonia Project and The Joint Commission ORYX program.5, 6 These efforts have largely entailed measuring hospital performance on pneumonia‐specific processes of care, such as whether blood oxygen levels were assessed, whether blood cultures were drawn before antibiotic treatment was initiated, the choice and timing of antibiotics, and smoking cessation counseling and vaccination at the time of discharge. While measuring processes of care (especially when they are based on sound evidence), can provide insights about quality, and can help guide hospital improvement efforts, these measures necessarily focus on a narrow spectrum of the overall care provided. Outcomes can complement process measures by directing attention to the results of care, which are influenced by both measured and unmeasured factors, and which may be more relevant from the patient's perspective.79

In 2008 CMS expanded its public reporting initiatives by adding risk‐standardized hospital mortality rates for pneumonia to the Hospital Compare website (http://www.hospitalcompare.hhs.gov/).10 Readmission rates were added in 2009. We sought to examine patterns of hospital and regional performance for patients with pneumonia as reflected in 30‐day risk‐standardized readmission and mortality rates. Our report complements the June 2010 annual release of data on the Hospital Compare website. CMS also reports 30‐day risk‐standardized mortality and readmission for acute myocardial infarction and heart failure; a description of the 2010 reporting results for those measures are described elsewhere.

Methods

Results

Hospital‐Specific Risk‐Standardized 30‐Day Mortality and Readmission Rates

Of the 1,118,583 patients included in the mortality analysis 129,444 (11.6%) died within 30 days of hospital admission. The median (Q1, Q3) hospital 30‐day risk‐standardized mortality rate was 11.1% (10.0%, 12.3%), and ranged from 6.7% to 20.9% (Table 1, Figure 1). Hospitals at the 10th percentile had 30‐day risk‐standardized mortality rates of 9.0% while for those at the 90th percentile of performance the rate was 13.5%. The odds of all‐cause mortality for a patient treated at a hospital that was one standard deviation above the national average was 1.68 times higher than that of a patient treated at a hospital that was one standard deviation below the national average.

Figure 1

Table 1.Risk‐Standardized Hospital 30‐Day Pneumonia Mortality and Readmission Rates

	Mortality	Readmission
Abbreviation: SD, standard deviation.
Patients (n)	1118583	1161817
Hospitals (n)	4788	4813
Patient age, years, median (Q1, Q3)	81 (74,86)	80 (74,86)
Nonwhite, %	11.1	11.1
Hospital case volume, median (Q1, Q3)	168 (77,323)	174 (79,334)
Risk‐standardized hospital rate, mean (SD)	11.2 (1.2)	18.3 (0.9)
Minimum	6.7	13.6
1st percentile	7.5	14.9
5th percentile	8.5	15.8
10th percentile	9.0	16.4
25th percentile	10.0	17.2
Median	11.1	18.2
75th percentile	12.3	19.2
90th percentile	13.5	20.4
95th percentile	14.4	21.1
99th percentile	16.1	22.8
Maximum	20.9	26.7
Model fit statistics
c‐Statistic	0.72	0.63
Intrahospital Correlation	0.07	0.03

For the 3‐year period 2006 to 2009, 222 (4.7%) hospitals were categorized as having a mortality rate that was better than the national average, 3968 (83.7%) were no different than the national average, 221 (4.6%) were worse and 332 (7.0%) did not meet the minimum case threshold.

Among the 1,161,817 patients included in the readmission analysis 212,638 (18.3%) were readmitted within 30 days of hospital discharge. The median (Q1,Q3) 30‐day risk‐standardized readmission rate was 18.2% (17.2%, 19.2%) and ranged from 13.6% to 26.7% (Table 1, Figure 2). Hospitals at the 10th percentile had 30‐day risk‐standardized readmission rates of 16.4% while for those at the 90th percentile of performance the rate was 20.4%. The odds of all‐cause readmission for a patient treated at a hospital that was one standard deviation above the national average was 1.40 times higher than the odds of all‐cause readmission if treated at a hospital that was one standard deviation below the national average.

Figure 2

For the 3‐year period 2006 to 2009, 64 (1.3%) hospitals were categorized as having a readmission rate that was better than the national average, 4203 (88.2%) were no different than the national average, 163 (3.4%) were worse and 333 (7.0%) had less than 25 cases and were therefore not categorized.

While risk‐standardized readmission rates were substantially higher than risk‐standardized mortality rates, mortality rates varied more. For example, the top 10% of hospitals had a relative mortality rate that was 33% lower than those in the bottom 10%, as compared with just a 20% relative difference for readmission rates. The coefficient of variation, a normalized measure of dispersion that unlike the standard deviation is independent of the population mean, was 10.7 for risk‐standardized mortality rates and 4.9 for readmission rates.

Regional Risk‐Standardized 30‐Day Mortality and Readmission Rates

Figures 3 and 4 show the distribution of 30‐day risk‐standardized mortality and readmission rates among hospital referral regions by quintile. Highest mortality regions were found across the entire country, including parts of Northern New England, the Mid and South Atlantic, East and the West South Central, East and West North Central, and the Mountain and Pacific regions of the West. The lowest mortality rates were observed in Southern New England, parts of the Mid and South Atlantic, East and West South Central, and parts of the Mountain and Pacific regions of the West (Figure 3).

Figure 3

Figure 4

Readmission rates were higher in the eastern portions of the US (including the Northeast, Mid and South Atlantic, East South Central) as well as the East North Central, and small parts of the West North Central portions of the Midwest and in Central California. The lowest readmission rates were observed in the West (Mountain and Pacific regions), parts of the Midwest (East and West North Central) and small pockets within the South and Northeast (Figure 4).

Discussion

In this 3‐year analysis of patient, hospital, and regional outcomes we observed that pneumonia in the elderly remains a highly morbid illness, with a 30‐day mortality rate of approximately 11.6%. More notably we observed that risk‐standardized mortality rates, and to a lesser extent readmission rates, vary significantly across hospitals and regions. Finally, we observed that readmission rates, but not mortality rates, show strong geographic concentration.

These findings suggest possible opportunities to save or extend the lives of a substantial number of Americans, and to reduce the burden of rehospitalization on patients and families, if low performing institutions were able to achieve the performance of those with better outcomes. Additionally, because readmission is so common (nearly 1 in 5 patients), efforts to reduce overall health care spending should focus on this large potential source of savings.19 In this regard, impending changes in payment to hospitals around readmissions will change incentives for hospitals and physicians that may ultimately lead to lower readmission rates.20

Previous analyses of the quality of hospital care for patients with pneumonia have focused on the percentage of eligible patients who received guideline‐recommended antibiotics within a specified time frame (4 or 8 hours), and vaccination prior to hospital discharge.21, 22 These studies have highlighted large differences across hospitals and states in the percentage receiving recommended care. In contrast, the focus of this study was to compare risk‐standardized outcomes of care at the nation's hospitals and across its regions. This effort was guided by the notion that the measurement of care outcomes is an important complement to process measurement because outcomes represent a more holistic assessment of care, that an outcomes focus offers hospitals greater autonomy in terms of what processes to improve, and that outcomes are ultimately more meaningful to patients than the technical aspects of how the outcomes were achieved. In contrast to these earlier process‐oriented efforts, the magnitude of the differences we observed in mortality and readmission rates across hospitals was not nearly as large.

A recent analysis of the outcomes of care for patients with heart failure and acute myocardial infarction also found significant variation in both hospital and regional mortality and readmission rates.23 The relative differences in risk‐standardized hospital mortality rates across the 10th to 90th percentiles of hospital performance was 25% for acute myocardial infarction, and 39% for heart failure. By contrast, we found that the difference in risk‐standardized hospital mortality rates across the 10th to 90th percentiles in pneumonia was an even greater 50% (13.5% vs. 9.0%). Similar to the findings in acute myocardial infarction and heart failure, we observed that risk‐standardized mortality rates varied more so than did readmission rates.

Our study has a number of limitations. First, the analysis was restricted to Medicare patients only, and our findings may not be generalizable to younger patients. Second, our risk‐adjustment methods relied on claims data, not clinical information abstracted from charts. Nevertheless, we assessed comorbidities using all physician and hospital claims from the year prior to the index admission. Additionally our mortality and readmission models were validated against those based on medical record data and the outputs of the 2 approaches were highly correlated.15, 24, 25 Our study was restricted to patients with a principal diagnosis of pneumonia, and we therefore did not include those whose principal diagnosis was sepsis or respiratory failure and who had a secondary diagnosis of pneumonia. While this decision was made to reduce the risk of misclassifying complications of care as the reason for admission, we acknowledge that this is likely to have limited our study to patients with less severe disease, and may have introduced bias related to differences in hospital coding practices regarding the use of sepsis and respiratory failure codes. While we excluded patients with 1 day length of stay from the mortality analysis to reduce the risk of including patients in the measure who did not actually have pneumonia, we did not exclude them from the readmission analysis because very short length of stay may be a risk factor for readmission. An additional limitation of our study is that our findings are primarily descriptive, and we did not attempt to explain the sources of the variation we observed. For example, we did not examine the extent to which these differences might be explained by differences in adherence to process measures across hospitals or regions. However, if the experience in acute myocardial infarction can serve as a guide, then it is unlikely that more than a small fraction of the observed variation in outcomes can be attributed to factors such as antibiotic timing or selection.26 Additionally, we cannot explain why readmission rates were more geographically distributed than mortality rates, however it is possible that this may be related to the supply of physicians or hospital beds.27 Finally, some have argued that mortality and readmission rates do not necessarily reflect the very quality they intend to measure.2830

The outcomes of patients with pneumonia appear to be significantly influenced by both the hospital and region where they receive care. Efforts to improve population level outcomes might be informed by studying the practices of hospitals and regions that consistently achieve high levels of performance.31

Technological advances drive medical providers to specialize through the need for proficiency around increasingly focused areas of expertise.1 But the benefits of specialization are attained only by balancing the advantages of increasing expertise and the costs of coordinating care that must be borne as specialization increases.2 Integrating experts into modern medical delivery systems requires attention to the coordinating mechanisms that govern team‐based care.3

Coordination, defined as the management of task interdependencies,4 is a central component and a useful measure of teamwork.5 Several studies demonstrate the patient‐level impact of coordination among providers.69 Gittell et al.8 demonstrated that orthopedic hospitals whose staff had better relational coordination (RC) measures had shorter lengths of stay and better post‐operative pain control for patients undergoing surgery. In medical intensive care units (ICUs), Wheelan et al.9 showed that staff members of units with lower mortality rates perceived their teams as functioning at higher stages of group development and perceived their team members as less dependent and more trusting.

Communication is the cornerstone of effective team coordination.10, 11 As such, practice model interventions that facilitate frequent communication of higher quality are associated with lower error rates10 and better teamwork.11 The use of hospitalists, for example, is shown to capitalize on this advantage by improving coordination through physician availability that facilitates communication and relational interactions among hospital‐based staff.12 While system‐level interventions such as this have received significant attention from experts in organizations, empirical studies that explore the contribution of team member characteristics to overall coordination are lacking.13

Inpatient comanagement services offer a unique model for studying teamwork. While the label is used to describe a variety of arrangements,1416 comanagement broadly describes a practice model wherein providers of various specialties deliver direct care to patients, in contrast to the traditional generalist‐consultant model in which specialists lend expertise.17 Many recent comanagement practices involve hospitalists in partnership with surgeons in the care of patients with concurrent medical and surgical needs,18 but similar arrangements between hospitalists and medical subspecialists are being adopted in some medical centers for the care of complex patients with conditions such as heart failure, cancer, stroke, and solid organ transplantations. Coordination among providers has not been studied in this context.

The goals of this study are: (1) to measure the input of individual providers to the overall coordination of care on a highly interdependent medical comanagement service, (2) to characterize high and low coordinators, and (3) to explore the relationship between coordination and patient outcomes. The main hypothesis is that the quality of team coordination is determined partly by the attributes of its members such that their individual contributions to the coordination of care affect the outcomes of vulnerable hospitalized patients.

Materials and Methods

Results

All 32 providers (100%) completed the Baseline Survey and participated in the Repeated Surveys of which 177/224 (79%) were completed. The median number of surveys that contributed to the calculation of individual RC and the mean RC by provider type are summarized in Table 1.

Of the 119 patients managed on the service, the mean age (standard deviation [SD]) was 55 (14) years and 48% were women. Of the 201 hospitalizations, there were 13 floor‐to‐ICU transfers and 5 in‐hospital deaths, however, we excluded from the analysis 1 death of a patient who was admitted under inpatient hospice status.

Discussion

By adapting Gittell's RC instrument to focus on individual providers, we found that their characteristic attributes such as preference for particular management styles, leadership quality, and patient ownership are associated with their externally perceived contributions to the overall coordination of care. In an unadjusted analysis, we also observed an intriguing trend towards more frequent major hospital complications when the worst coordinators of each physician type were on service.

Existing evidence22, 23 mostly summarized in a recent RAND Health report shows a weak association between clinical teamwork quality and patient mortality. While our data also support this association, it does so with limitations. Most importantly, the small sample size limited our ability to rigorously account for potential confounders that may have contributed to this apparent association. Further studies may better address whether or not bad outcomes are indeed associated with poor coordinators in highly interdependent clinical teams. In addition to confounding, the small sample size of providers makes the analysis vulnerable to type 1 errors. We addressed this issue by intensively surveying providers repeatedly to achieve a high resolution of the coordination and management structure measures from each comanaged team. The potential for omitted variables and reverse causality in that the coordination scores may be negatively influenced by particularly complex patients and bad outcomes remains a valid concern. We addressed this by confirming the stability of provider RC over time and excluding the RC data from rotations with a bad outcome, but the negative perception of an individual tied to past bad outcomes may persist beyond a particular rotation. Survey responses are subject to recall and hindsight biases, which we attempted to minimize by surveying respondents immediately after each team rotation. Finally, all of our findings may be not be generalizable to other comanagement settings. However, the important correlations between coordination and quality have been observed in other contexts.24, 25

In our study, in‐hospital deaths and ICU transfers are treated as consequences of uncoordinated care. This interpretation may be problematic for circumstances when death is inevitable no matter how well coordinated the care, or when transfer to a higher level of care is appropriate. The rationale for grouping the 2 events into 1 composite bad outcome is based on the assumption that both death and the escalation of care can be delayed to an extent, if not wholly prevented, with the coordinated utilization of a modern hospital's resources. The attribution of these events to poor coordinators may indicate the unraveling of coordination that normally must be maintained to help patients overcome decompensating events that are particularly common in the course of patients with severe liver diseases. Due to the exploratory nature of this analysis, additional studies are necessary to fully characterize the relationship between care coordination and care transfers.

An important implication of this study is that the communication skill and ethical disposition of each individual provider is relevant to the coordination that is sought in multi‐provider teams. Training medical professionals to be better team members may have direct impact on the patients they serve. Our finding about patient ownership suggests that commitment to patients in the framework of care is not merely tradition but a characteristic of competent physicians. Moreover, physicians' commitment to patients is a possible factor, not just in achieving patients' satisfaction, but in securing better outcomes. To that end, the teaching of this and other humanistic principles must remain a vital part of medical education at all levels of training.

Several implications about team leadership and hierarchy are apparent from the data. Findings around the perceived assignment of responsibilities show that high coordinator hepatologists acknowledge the advantages of overlapping task boundaries to prevent critical tasks from being missed and risking bad outcomes. High RC hepatologists in our study adopted a more participatory than supervisory role which presumably facilitated better coordination by transmitting organizational goals to other team members. The function of a comanaged team is likely to be enhanced by a fluid assignment of roles to better handle tasks with high uncertainty. Accordingly, comanagement models of care may not be appropriate in settings where tasks are not interdependent.26 Inherent hierarchy appears to be a feature of well coordinated teams. One possible interpretation of our data is that hospitalists who yield the leadership role to the hepatologist are perceived to be better coordinators and that those who insist on exerting more influence in team decisions are perceived to be poor coordinators.

Existing evidence around care coordination predicts that comanagement designs improve provider coordination through stage‐based and site‐based specialization.12 However, the mechanisms that mediate coordination and patient outcomes are not clear. Moreover, the mechanisms of coordinating multi‐disciplinary teams may be specific to each clinical setting. The role of individual provider characteristics on coordination deserves more attention. Similarly, the impact of organizational culture under which favorable provider characteristics thrive is unknown. Finally, a detailed exposition of patient ownership and the role patients play in affecting the coordination of healthcare resources needs further exploration.

Technological advances drive medical providers to specialize through the need for proficiency around increasingly focused areas of expertise.1 But the benefits of specialization are attained only by balancing the advantages of increasing expertise and the costs of coordinating care that must be borne as specialization increases.2 Integrating experts into modern medical delivery systems requires attention to the coordinating mechanisms that govern team‐based care.3

Coordination, defined as the management of task interdependencies,4 is a central component and a useful measure of teamwork.5 Several studies demonstrate the patient‐level impact of coordination among providers.69 Gittell et al.8 demonstrated that orthopedic hospitals whose staff had better relational coordination (RC) measures had shorter lengths of stay and better post‐operative pain control for patients undergoing surgery. In medical intensive care units (ICUs), Wheelan et al.9 showed that staff members of units with lower mortality rates perceived their teams as functioning at higher stages of group development and perceived their team members as less dependent and more trusting.

Communication is the cornerstone of effective team coordination.10, 11 As such, practice model interventions that facilitate frequent communication of higher quality are associated with lower error rates10 and better teamwork.11 The use of hospitalists, for example, is shown to capitalize on this advantage by improving coordination through physician availability that facilitates communication and relational interactions among hospital‐based staff.12 While system‐level interventions such as this have received significant attention from experts in organizations, empirical studies that explore the contribution of team member characteristics to overall coordination are lacking.13

Inpatient comanagement services offer a unique model for studying teamwork. While the label is used to describe a variety of arrangements,1416 comanagement broadly describes a practice model wherein providers of various specialties deliver direct care to patients, in contrast to the traditional generalist‐consultant model in which specialists lend expertise.17 Many recent comanagement practices involve hospitalists in partnership with surgeons in the care of patients with concurrent medical and surgical needs,18 but similar arrangements between hospitalists and medical subspecialists are being adopted in some medical centers for the care of complex patients with conditions such as heart failure, cancer, stroke, and solid organ transplantations. Coordination among providers has not been studied in this context.

The goals of this study are: (1) to measure the input of individual providers to the overall coordination of care on a highly interdependent medical comanagement service, (2) to characterize high and low coordinators, and (3) to explore the relationship between coordination and patient outcomes. The main hypothesis is that the quality of team coordination is determined partly by the attributes of its members such that their individual contributions to the coordination of care affect the outcomes of vulnerable hospitalized patients.

Materials and Methods

Results

All 32 providers (100%) completed the Baseline Survey and participated in the Repeated Surveys of which 177/224 (79%) were completed. The median number of surveys that contributed to the calculation of individual RC and the mean RC by provider type are summarized in Table 1.

Of the 119 patients managed on the service, the mean age (standard deviation [SD]) was 55 (14) years and 48% were women. Of the 201 hospitalizations, there were 13 floor‐to‐ICU transfers and 5 in‐hospital deaths, however, we excluded from the analysis 1 death of a patient who was admitted under inpatient hospice status.

Discussion

By adapting Gittell's RC instrument to focus on individual providers, we found that their characteristic attributes such as preference for particular management styles, leadership quality, and patient ownership are associated with their externally perceived contributions to the overall coordination of care. In an unadjusted analysis, we also observed an intriguing trend towards more frequent major hospital complications when the worst coordinators of each physician type were on service.

Existing evidence22, 23 mostly summarized in a recent RAND Health report shows a weak association between clinical teamwork quality and patient mortality. While our data also support this association, it does so with limitations. Most importantly, the small sample size limited our ability to rigorously account for potential confounders that may have contributed to this apparent association. Further studies may better address whether or not bad outcomes are indeed associated with poor coordinators in highly interdependent clinical teams. In addition to confounding, the small sample size of providers makes the analysis vulnerable to type 1 errors. We addressed this issue by intensively surveying providers repeatedly to achieve a high resolution of the coordination and management structure measures from each comanaged team. The potential for omitted variables and reverse causality in that the coordination scores may be negatively influenced by particularly complex patients and bad outcomes remains a valid concern. We addressed this by confirming the stability of provider RC over time and excluding the RC data from rotations with a bad outcome, but the negative perception of an individual tied to past bad outcomes may persist beyond a particular rotation. Survey responses are subject to recall and hindsight biases, which we attempted to minimize by surveying respondents immediately after each team rotation. Finally, all of our findings may be not be generalizable to other comanagement settings. However, the important correlations between coordination and quality have been observed in other contexts.24, 25

In our study, in‐hospital deaths and ICU transfers are treated as consequences of uncoordinated care. This interpretation may be problematic for circumstances when death is inevitable no matter how well coordinated the care, or when transfer to a higher level of care is appropriate. The rationale for grouping the 2 events into 1 composite bad outcome is based on the assumption that both death and the escalation of care can be delayed to an extent, if not wholly prevented, with the coordinated utilization of a modern hospital's resources. The attribution of these events to poor coordinators may indicate the unraveling of coordination that normally must be maintained to help patients overcome decompensating events that are particularly common in the course of patients with severe liver diseases. Due to the exploratory nature of this analysis, additional studies are necessary to fully characterize the relationship between care coordination and care transfers.

An important implication of this study is that the communication skill and ethical disposition of each individual provider is relevant to the coordination that is sought in multi‐provider teams. Training medical professionals to be better team members may have direct impact on the patients they serve. Our finding about patient ownership suggests that commitment to patients in the framework of care is not merely tradition but a characteristic of competent physicians. Moreover, physicians' commitment to patients is a possible factor, not just in achieving patients' satisfaction, but in securing better outcomes. To that end, the teaching of this and other humanistic principles must remain a vital part of medical education at all levels of training.

Several implications about team leadership and hierarchy are apparent from the data. Findings around the perceived assignment of responsibilities show that high coordinator hepatologists acknowledge the advantages of overlapping task boundaries to prevent critical tasks from being missed and risking bad outcomes. High RC hepatologists in our study adopted a more participatory than supervisory role which presumably facilitated better coordination by transmitting organizational goals to other team members. The function of a comanaged team is likely to be enhanced by a fluid assignment of roles to better handle tasks with high uncertainty. Accordingly, comanagement models of care may not be appropriate in settings where tasks are not interdependent.26 Inherent hierarchy appears to be a feature of well coordinated teams. One possible interpretation of our data is that hospitalists who yield the leadership role to the hepatologist are perceived to be better coordinators and that those who insist on exerting more influence in team decisions are perceived to be poor coordinators.

Existing evidence around care coordination predicts that comanagement designs improve provider coordination through stage‐based and site‐based specialization.12 However, the mechanisms that mediate coordination and patient outcomes are not clear. Moreover, the mechanisms of coordinating multi‐disciplinary teams may be specific to each clinical setting. The role of individual provider characteristics on coordination deserves more attention. Similarly, the impact of organizational culture under which favorable provider characteristics thrive is unknown. Finally, a detailed exposition of patient ownership and the role patients play in affecting the coordination of healthcare resources needs further exploration.

Technological advances drive medical providers to specialize through the need for proficiency around increasingly focused areas of expertise.1 But the benefits of specialization are attained only by balancing the advantages of increasing expertise and the costs of coordinating care that must be borne as specialization increases.2 Integrating experts into modern medical delivery systems requires attention to the coordinating mechanisms that govern team‐based care.3

Coordination, defined as the management of task interdependencies,4 is a central component and a useful measure of teamwork.5 Several studies demonstrate the patient‐level impact of coordination among providers.69 Gittell et al.8 demonstrated that orthopedic hospitals whose staff had better relational coordination (RC) measures had shorter lengths of stay and better post‐operative pain control for patients undergoing surgery. In medical intensive care units (ICUs), Wheelan et al.9 showed that staff members of units with lower mortality rates perceived their teams as functioning at higher stages of group development and perceived their team members as less dependent and more trusting.

Communication is the cornerstone of effective team coordination.10, 11 As such, practice model interventions that facilitate frequent communication of higher quality are associated with lower error rates10 and better teamwork.11 The use of hospitalists, for example, is shown to capitalize on this advantage by improving coordination through physician availability that facilitates communication and relational interactions among hospital‐based staff.12 While system‐level interventions such as this have received significant attention from experts in organizations, empirical studies that explore the contribution of team member characteristics to overall coordination are lacking.13

Inpatient comanagement services offer a unique model for studying teamwork. While the label is used to describe a variety of arrangements,1416 comanagement broadly describes a practice model wherein providers of various specialties deliver direct care to patients, in contrast to the traditional generalist‐consultant model in which specialists lend expertise.17 Many recent comanagement practices involve hospitalists in partnership with surgeons in the care of patients with concurrent medical and surgical needs,18 but similar arrangements between hospitalists and medical subspecialists are being adopted in some medical centers for the care of complex patients with conditions such as heart failure, cancer, stroke, and solid organ transplantations. Coordination among providers has not been studied in this context.

The goals of this study are: (1) to measure the input of individual providers to the overall coordination of care on a highly interdependent medical comanagement service, (2) to characterize high and low coordinators, and (3) to explore the relationship between coordination and patient outcomes. The main hypothesis is that the quality of team coordination is determined partly by the attributes of its members such that their individual contributions to the coordination of care affect the outcomes of vulnerable hospitalized patients.

Materials and Methods

Results

All 32 providers (100%) completed the Baseline Survey and participated in the Repeated Surveys of which 177/224 (79%) were completed. The median number of surveys that contributed to the calculation of individual RC and the mean RC by provider type are summarized in Table 1.

Of the 119 patients managed on the service, the mean age (standard deviation [SD]) was 55 (14) years and 48% were women. Of the 201 hospitalizations, there were 13 floor‐to‐ICU transfers and 5 in‐hospital deaths, however, we excluded from the analysis 1 death of a patient who was admitted under inpatient hospice status.

Discussion

By adapting Gittell's RC instrument to focus on individual providers, we found that their characteristic attributes such as preference for particular management styles, leadership quality, and patient ownership are associated with their externally perceived contributions to the overall coordination of care. In an unadjusted analysis, we also observed an intriguing trend towards more frequent major hospital complications when the worst coordinators of each physician type were on service.

Existing evidence22, 23 mostly summarized in a recent RAND Health report shows a weak association between clinical teamwork quality and patient mortality. While our data also support this association, it does so with limitations. Most importantly, the small sample size limited our ability to rigorously account for potential confounders that may have contributed to this apparent association. Further studies may better address whether or not bad outcomes are indeed associated with poor coordinators in highly interdependent clinical teams. In addition to confounding, the small sample size of providers makes the analysis vulnerable to type 1 errors. We addressed this issue by intensively surveying providers repeatedly to achieve a high resolution of the coordination and management structure measures from each comanaged team. The potential for omitted variables and reverse causality in that the coordination scores may be negatively influenced by particularly complex patients and bad outcomes remains a valid concern. We addressed this by confirming the stability of provider RC over time and excluding the RC data from rotations with a bad outcome, but the negative perception of an individual tied to past bad outcomes may persist beyond a particular rotation. Survey responses are subject to recall and hindsight biases, which we attempted to minimize by surveying respondents immediately after each team rotation. Finally, all of our findings may be not be generalizable to other comanagement settings. However, the important correlations between coordination and quality have been observed in other contexts.24, 25

In our study, in‐hospital deaths and ICU transfers are treated as consequences of uncoordinated care. This interpretation may be problematic for circumstances when death is inevitable no matter how well coordinated the care, or when transfer to a higher level of care is appropriate. The rationale for grouping the 2 events into 1 composite bad outcome is based on the assumption that both death and the escalation of care can be delayed to an extent, if not wholly prevented, with the coordinated utilization of a modern hospital's resources. The attribution of these events to poor coordinators may indicate the unraveling of coordination that normally must be maintained to help patients overcome decompensating events that are particularly common in the course of patients with severe liver diseases. Due to the exploratory nature of this analysis, additional studies are necessary to fully characterize the relationship between care coordination and care transfers.

An important implication of this study is that the communication skill and ethical disposition of each individual provider is relevant to the coordination that is sought in multi‐provider teams. Training medical professionals to be better team members may have direct impact on the patients they serve. Our finding about patient ownership suggests that commitment to patients in the framework of care is not merely tradition but a characteristic of competent physicians. Moreover, physicians' commitment to patients is a possible factor, not just in achieving patients' satisfaction, but in securing better outcomes. To that end, the teaching of this and other humanistic principles must remain a vital part of medical education at all levels of training.

Several implications about team leadership and hierarchy are apparent from the data. Findings around the perceived assignment of responsibilities show that high coordinator hepatologists acknowledge the advantages of overlapping task boundaries to prevent critical tasks from being missed and risking bad outcomes. High RC hepatologists in our study adopted a more participatory than supervisory role which presumably facilitated better coordination by transmitting organizational goals to other team members. The function of a comanaged team is likely to be enhanced by a fluid assignment of roles to better handle tasks with high uncertainty. Accordingly, comanagement models of care may not be appropriate in settings where tasks are not interdependent.26 Inherent hierarchy appears to be a feature of well coordinated teams. One possible interpretation of our data is that hospitalists who yield the leadership role to the hepatologist are perceived to be better coordinators and that those who insist on exerting more influence in team decisions are perceived to be poor coordinators.

Existing evidence around care coordination predicts that comanagement designs improve provider coordination through stage‐based and site‐based specialization.12 However, the mechanisms that mediate coordination and patient outcomes are not clear. Moreover, the mechanisms of coordinating multi‐disciplinary teams may be specific to each clinical setting. The role of individual provider characteristics on coordination deserves more attention. Similarly, the impact of organizational culture under which favorable provider characteristics thrive is unknown. Finally, a detailed exposition of patient ownership and the role patients play in affecting the coordination of healthcare resources needs further exploration.

A 49‐year‐old woman with history of rheumatic fever necessitating mechanical mitral valve replacement and a cerebrovascular accident of presumed embolic etiology presented with several months of progressive fatigue, weakness, arthralgias, and myalgias. After an extensive workup, a rheumatologist in the community diagnosed her with systemic lupus erythematosus and dermatomyositis. The patient refused therapy with corticosteroids and disease‐modifying agents, citing concerns of adverse effects. She consulted a naturopathic clinician, who gave her Rejuvenator Pills, Super Booster pill, Genesis Juice, and alkaline water (Table 1).

Several weeks later, the patient developed dusky erythematous plaques on her anterior and posterior trunk, face, and proximal extremities. Over the next several weeks, she became progressively weak until she was ultimately bedbound. The plaques over her back began to denude. Upon admission to an outside hospital, she was diagnosed with warfarin‐related skin necrosis, superinfected decubitus ulcers, and severe anemia. She refused blood transfusion, and was discharged home with clindamycin and iron. After her clinical status deteriorated over the subsequent week, she arrived at our hospital by ambulance.

In addition to the herbal medications she had recently started, she had been taking warfarin, furosemide, nitroglycerin via skin patch, and aspirin for over 10 years. On exam, she was febrile, tachycardic, hypotensive, and toxic‐appearing. Conjunctivitis was absent. Her mucous membranes were dry, with easily removable white and yellowish deposits on the buccal mucosa. No lesions or ulcerations were present. Dermatologic exam demonstrated confluent scaly, violaceous erythematous patches and plaques covering 60% of the total body surface area with focal areas that were denuded. Large areas of denuded skin were present over the back, inframammary folds, and underneath her abdominal pannus (Figures 1 and 2). Nikolsky's sign was present. She was oriented to person only.

Figure 1

Figure 2

Initial laboratory studies were significant for the following: white blood cell count = 12,800 cells/mm³, hemoglobin = 7.3 g/dL, creatinine = 11.2 mg/dL, blood urea nitrogen = 136 mg/dL, and bicarbonate level = 15 mmol/L. She was admitted to the medical intensive care unit for presumed sepsis. Aggressive resuscitation and broad spectrum antibiotics were administered. A thorough workup for infection, including blood and urine cultures, chest radiography, and lumbar puncture, was unremarkable. Antinuclear antibodies (ANAs) were present in a 1:2560 titer; with a nucleolar and speckled pattern and cytoplasmic antibodies. Additional rheumatologic workup revealed positive anti‐Smith antibody and weakly positive antiribonuclear protein antibody. Pathology from a punch biopsy performed by a dermatology consultant on hospital day 2 demonstrated full‐thickness skin necrosis with scant perivascular infiltrate. While the patient's family had disposed of the pill containers, they had kept several pills. These were sent for analysis, which did not reveal contamination with heavy metals or allopathic medications.

The patient was ultimately diagnosed with TEN and systemic lupus erythematosus with overlap syndrome, and intravenous methylprednisolone was administered. Broad‐spectrum antibiotics were administered for 48 hours, but stopped after workup for infection proved unrevealing. Wound care was mupirocin ointment with petrolatum dressings twice daily as per the hospital's TEN protocol. The patient's course was complicated by acidosis requiring hemodialysis and several tonic‐clonic seizures, a result of presumed lupus cerebritis due to rapidly progressive lesions on serial magnetic resonance images (MRIs) with a negative lumbar puncture. Renal biopsy demonstrated acute tubular necrosis and collapsing glomerulopathy. The patient ultimately recovered, and was discharged to a rehabilitation facility. In follow‐up several months later, she had healing skin with residual dyspigmentation and normal renal function. She was ambulatory and fully oriented, but complained of persistent memory difficulties.

Discussion

While use of complementary or alternative medicine (CAM) is widespread, physicians often underestimate the prevalence of CAM use in their patients. Only one‐half of primary care practitioners are aware of the risk for serious adverse reactions from CAMs.1 This case demonstrates the need for hospitalists to obtain a thorough medication history, including probing for CAM use, when evaluating a new patient. The delayed diagnosis of TEN, whether due to failure to elicit CAM use or recognize the clinical presentation, delayed appropriate treatment by a week and this patient developed potentially lethal complications.

Stevens‐Johnson syndrome (SJS) and TEN lie on a spectrum of disorders involving separation of the epidermis from the dermis when tension is applied to the skin, associated with mucositis, conjunctivitis, and generalized toxicity. The rash is dusky and erythematous, and Nikolsky's sign (separation of the epidermis from the dermis with tension applied to the skin) is present. These entities most commonly develop secondary to medications or infections. Most reactions occur within 60 days of drug initiation. The rash progressives over 1 to 15 days, and the rate of healing is variable. The overall mortality is 30% and is predicted by the SCORTEN system, which incorporates laboratory data, patient history, and the extent of skin breakdown.2 Treatment is primarily supportive; the use of corticosteroids, nonsteroidal immunosuppressive agents, intravenous immunoglobulin (IVIG), or plasmapheresis remains controversial.3

Case reports have described the development of SJS or TEN with CAM use. For example, 1 patient repeatedly developed SJS, with each episode occurring after exposure to an herbal medication containing red clover, burdock, queen's delight, poke root, prickly ash, sassafras bark, and passion flower.4 Similar to our case, identifying the exact agent responsible for TEN was impossible due to large numbers of herbal medications combined into a single pill. SJS and TEN are not limited to Western herbal medicines. Traditional Chinese medications are one of the most common causes of SJS and TEN in East Asia,5 although adulteration with allopathic medications is common in this setting. Ayurvedic medications,6 an ophiopogonis‐containing health drink,7 ginseng,8 and Gingko biloba9, 10 have also been implicated.

Conclusions

This case demonstrates the difficulty in making a diagnosis of CAM‐induced toxicity and identifying the likely agent responsible. Hospitalists must have a high index of suspicion of CAM‐associated toxicity to make this diagnosis, especially when admitting patients who may not volunteer CAM use without direct questioning.

A 49‐year‐old woman with history of rheumatic fever necessitating mechanical mitral valve replacement and a cerebrovascular accident of presumed embolic etiology presented with several months of progressive fatigue, weakness, arthralgias, and myalgias. After an extensive workup, a rheumatologist in the community diagnosed her with systemic lupus erythematosus and dermatomyositis. The patient refused therapy with corticosteroids and disease‐modifying agents, citing concerns of adverse effects. She consulted a naturopathic clinician, who gave her Rejuvenator Pills, Super Booster pill, Genesis Juice, and alkaline water (Table 1).

Several weeks later, the patient developed dusky erythematous plaques on her anterior and posterior trunk, face, and proximal extremities. Over the next several weeks, she became progressively weak until she was ultimately bedbound. The plaques over her back began to denude. Upon admission to an outside hospital, she was diagnosed with warfarin‐related skin necrosis, superinfected decubitus ulcers, and severe anemia. She refused blood transfusion, and was discharged home with clindamycin and iron. After her clinical status deteriorated over the subsequent week, she arrived at our hospital by ambulance.

In addition to the herbal medications she had recently started, she had been taking warfarin, furosemide, nitroglycerin via skin patch, and aspirin for over 10 years. On exam, she was febrile, tachycardic, hypotensive, and toxic‐appearing. Conjunctivitis was absent. Her mucous membranes were dry, with easily removable white and yellowish deposits on the buccal mucosa. No lesions or ulcerations were present. Dermatologic exam demonstrated confluent scaly, violaceous erythematous patches and plaques covering 60% of the total body surface area with focal areas that were denuded. Large areas of denuded skin were present over the back, inframammary folds, and underneath her abdominal pannus (Figures 1 and 2). Nikolsky's sign was present. She was oriented to person only.

Figure 1

Figure 2

Initial laboratory studies were significant for the following: white blood cell count = 12,800 cells/mm³, hemoglobin = 7.3 g/dL, creatinine = 11.2 mg/dL, blood urea nitrogen = 136 mg/dL, and bicarbonate level = 15 mmol/L. She was admitted to the medical intensive care unit for presumed sepsis. Aggressive resuscitation and broad spectrum antibiotics were administered. A thorough workup for infection, including blood and urine cultures, chest radiography, and lumbar puncture, was unremarkable. Antinuclear antibodies (ANAs) were present in a 1:2560 titer; with a nucleolar and speckled pattern and cytoplasmic antibodies. Additional rheumatologic workup revealed positive anti‐Smith antibody and weakly positive antiribonuclear protein antibody. Pathology from a punch biopsy performed by a dermatology consultant on hospital day 2 demonstrated full‐thickness skin necrosis with scant perivascular infiltrate. While the patient's family had disposed of the pill containers, they had kept several pills. These were sent for analysis, which did not reveal contamination with heavy metals or allopathic medications.

The patient was ultimately diagnosed with TEN and systemic lupus erythematosus with overlap syndrome, and intravenous methylprednisolone was administered. Broad‐spectrum antibiotics were administered for 48 hours, but stopped after workup for infection proved unrevealing. Wound care was mupirocin ointment with petrolatum dressings twice daily as per the hospital's TEN protocol. The patient's course was complicated by acidosis requiring hemodialysis and several tonic‐clonic seizures, a result of presumed lupus cerebritis due to rapidly progressive lesions on serial magnetic resonance images (MRIs) with a negative lumbar puncture. Renal biopsy demonstrated acute tubular necrosis and collapsing glomerulopathy. The patient ultimately recovered, and was discharged to a rehabilitation facility. In follow‐up several months later, she had healing skin with residual dyspigmentation and normal renal function. She was ambulatory and fully oriented, but complained of persistent memory difficulties.

Discussion

While use of complementary or alternative medicine (CAM) is widespread, physicians often underestimate the prevalence of CAM use in their patients. Only one‐half of primary care practitioners are aware of the risk for serious adverse reactions from CAMs.1 This case demonstrates the need for hospitalists to obtain a thorough medication history, including probing for CAM use, when evaluating a new patient. The delayed diagnosis of TEN, whether due to failure to elicit CAM use or recognize the clinical presentation, delayed appropriate treatment by a week and this patient developed potentially lethal complications.

Stevens‐Johnson syndrome (SJS) and TEN lie on a spectrum of disorders involving separation of the epidermis from the dermis when tension is applied to the skin, associated with mucositis, conjunctivitis, and generalized toxicity. The rash is dusky and erythematous, and Nikolsky's sign (separation of the epidermis from the dermis with tension applied to the skin) is present. These entities most commonly develop secondary to medications or infections. Most reactions occur within 60 days of drug initiation. The rash progressives over 1 to 15 days, and the rate of healing is variable. The overall mortality is 30% and is predicted by the SCORTEN system, which incorporates laboratory data, patient history, and the extent of skin breakdown.2 Treatment is primarily supportive; the use of corticosteroids, nonsteroidal immunosuppressive agents, intravenous immunoglobulin (IVIG), or plasmapheresis remains controversial.3

Case reports have described the development of SJS or TEN with CAM use. For example, 1 patient repeatedly developed SJS, with each episode occurring after exposure to an herbal medication containing red clover, burdock, queen's delight, poke root, prickly ash, sassafras bark, and passion flower.4 Similar to our case, identifying the exact agent responsible for TEN was impossible due to large numbers of herbal medications combined into a single pill. SJS and TEN are not limited to Western herbal medicines. Traditional Chinese medications are one of the most common causes of SJS and TEN in East Asia,5 although adulteration with allopathic medications is common in this setting. Ayurvedic medications,6 an ophiopogonis‐containing health drink,7 ginseng,8 and Gingko biloba9, 10 have also been implicated.

Conclusions

This case demonstrates the difficulty in making a diagnosis of CAM‐induced toxicity and identifying the likely agent responsible. Hospitalists must have a high index of suspicion of CAM‐associated toxicity to make this diagnosis, especially when admitting patients who may not volunteer CAM use without direct questioning.

A 49‐year‐old woman with history of rheumatic fever necessitating mechanical mitral valve replacement and a cerebrovascular accident of presumed embolic etiology presented with several months of progressive fatigue, weakness, arthralgias, and myalgias. After an extensive workup, a rheumatologist in the community diagnosed her with systemic lupus erythematosus and dermatomyositis. The patient refused therapy with corticosteroids and disease‐modifying agents, citing concerns of adverse effects. She consulted a naturopathic clinician, who gave her Rejuvenator Pills, Super Booster pill, Genesis Juice, and alkaline water (Table 1).

Several weeks later, the patient developed dusky erythematous plaques on her anterior and posterior trunk, face, and proximal extremities. Over the next several weeks, she became progressively weak until she was ultimately bedbound. The plaques over her back began to denude. Upon admission to an outside hospital, she was diagnosed with warfarin‐related skin necrosis, superinfected decubitus ulcers, and severe anemia. She refused blood transfusion, and was discharged home with clindamycin and iron. After her clinical status deteriorated over the subsequent week, she arrived at our hospital by ambulance.

In addition to the herbal medications she had recently started, she had been taking warfarin, furosemide, nitroglycerin via skin patch, and aspirin for over 10 years. On exam, she was febrile, tachycardic, hypotensive, and toxic‐appearing. Conjunctivitis was absent. Her mucous membranes were dry, with easily removable white and yellowish deposits on the buccal mucosa. No lesions or ulcerations were present. Dermatologic exam demonstrated confluent scaly, violaceous erythematous patches and plaques covering 60% of the total body surface area with focal areas that were denuded. Large areas of denuded skin were present over the back, inframammary folds, and underneath her abdominal pannus (Figures 1 and 2). Nikolsky's sign was present. She was oriented to person only.

Figure 1

Figure 2

Initial laboratory studies were significant for the following: white blood cell count = 12,800 cells/mm³, hemoglobin = 7.3 g/dL, creatinine = 11.2 mg/dL, blood urea nitrogen = 136 mg/dL, and bicarbonate level = 15 mmol/L. She was admitted to the medical intensive care unit for presumed sepsis. Aggressive resuscitation and broad spectrum antibiotics were administered. A thorough workup for infection, including blood and urine cultures, chest radiography, and lumbar puncture, was unremarkable. Antinuclear antibodies (ANAs) were present in a 1:2560 titer; with a nucleolar and speckled pattern and cytoplasmic antibodies. Additional rheumatologic workup revealed positive anti‐Smith antibody and weakly positive antiribonuclear protein antibody. Pathology from a punch biopsy performed by a dermatology consultant on hospital day 2 demonstrated full‐thickness skin necrosis with scant perivascular infiltrate. While the patient's family had disposed of the pill containers, they had kept several pills. These were sent for analysis, which did not reveal contamination with heavy metals or allopathic medications.

The patient was ultimately diagnosed with TEN and systemic lupus erythematosus with overlap syndrome, and intravenous methylprednisolone was administered. Broad‐spectrum antibiotics were administered for 48 hours, but stopped after workup for infection proved unrevealing. Wound care was mupirocin ointment with petrolatum dressings twice daily as per the hospital's TEN protocol. The patient's course was complicated by acidosis requiring hemodialysis and several tonic‐clonic seizures, a result of presumed lupus cerebritis due to rapidly progressive lesions on serial magnetic resonance images (MRIs) with a negative lumbar puncture. Renal biopsy demonstrated acute tubular necrosis and collapsing glomerulopathy. The patient ultimately recovered, and was discharged to a rehabilitation facility. In follow‐up several months later, she had healing skin with residual dyspigmentation and normal renal function. She was ambulatory and fully oriented, but complained of persistent memory difficulties.

Discussion

While use of complementary or alternative medicine (CAM) is widespread, physicians often underestimate the prevalence of CAM use in their patients. Only one‐half of primary care practitioners are aware of the risk for serious adverse reactions from CAMs.1 This case demonstrates the need for hospitalists to obtain a thorough medication history, including probing for CAM use, when evaluating a new patient. The delayed diagnosis of TEN, whether due to failure to elicit CAM use or recognize the clinical presentation, delayed appropriate treatment by a week and this patient developed potentially lethal complications.

Stevens‐Johnson syndrome (SJS) and TEN lie on a spectrum of disorders involving separation of the epidermis from the dermis when tension is applied to the skin, associated with mucositis, conjunctivitis, and generalized toxicity. The rash is dusky and erythematous, and Nikolsky's sign (separation of the epidermis from the dermis with tension applied to the skin) is present. These entities most commonly develop secondary to medications or infections. Most reactions occur within 60 days of drug initiation. The rash progressives over 1 to 15 days, and the rate of healing is variable. The overall mortality is 30% and is predicted by the SCORTEN system, which incorporates laboratory data, patient history, and the extent of skin breakdown.2 Treatment is primarily supportive; the use of corticosteroids, nonsteroidal immunosuppressive agents, intravenous immunoglobulin (IVIG), or plasmapheresis remains controversial.3

Case reports have described the development of SJS or TEN with CAM use. For example, 1 patient repeatedly developed SJS, with each episode occurring after exposure to an herbal medication containing red clover, burdock, queen's delight, poke root, prickly ash, sassafras bark, and passion flower.4 Similar to our case, identifying the exact agent responsible for TEN was impossible due to large numbers of herbal medications combined into a single pill. SJS and TEN are not limited to Western herbal medicines. Traditional Chinese medications are one of the most common causes of SJS and TEN in East Asia,5 although adulteration with allopathic medications is common in this setting. Ayurvedic medications,6 an ophiopogonis‐containing health drink,7 ginseng,8 and Gingko biloba9, 10 have also been implicated.

Conclusions

This case demonstrates the difficulty in making a diagnosis of CAM‐induced toxicity and identifying the likely agent responsible. Hospitalists must have a high index of suspicion of CAM‐associated toxicity to make this diagnosis, especially when admitting patients who may not volunteer CAM use without direct questioning.

A 49‐year‐old man with a history of hypertension presented to our hospital with a 2‐week history of sharp pain in the right upper abdomen and right lower chest radiating to the back. The patient reported a few days of fevers, chills, drenching night sweats, shortness of breath, malaise, and fatigue. He denied recent travel. Vital signs were temperature 38.4C, blood pressure 119/74 mmHg, heart rate 95 beats/minutes, respiratory rate 16 breaths/minutes, and oxygen saturation 96% on 5 L nasal cannula. Physical examination revealed poor dentition, right upper abdominal quadrant tenderness, and dullness to percussion over the right lung base.

Initial labs showed white blood count (WBC) 22,540/mm³, alkaline phosphatase 280 units/L, bilirubin 1.1 mg/dL, aspartate aminotransferase (AST) 28 units/L, alanine aminotransferase (ALT) 33 units/L. Blood cultures were negative. An human immunodeficiency virus (HIV)1/HIV2 antibody screen was negative. Computed tomography (CT) of the chest demonstrated a large cystic lesion in the diaphragmatic dome of the liver with multiple lesions in the right lobe of the liver. Elevation of the right hemidiaphragm and moderate right pleural effusion were noted. CT abdomen showed multiple areas of fluid collection within the liver suspicious for liver abscesses (see Figure 1). Multiple gallstones were seen within gallbladder with a large stone in the region of the gallbladder neck vs. cystic duct without evidence of extrahepatic biliary dilatation. There was mild distention of the appendix with minimal soft tissue stranding.

Figure 1

The patient underwent ultrasound‐guided drainage of the largest liver abscess. Cultures from the aspiration grew Fusobacterium nucleatum. The patient's stool studies for ova and parasites were negative. The patient was started on piperacillin/tazobactam and metronidazole, then switched to ertapenem. A hepatobiliary iminodiacetic acid (HIDA) scan confirmed cholecystitis, and the patient underwent open cholecystectomy. Pathology on the gallbladder returned as chronic cholecystitis with cholelithiasis. A full dental examination revealed possible periapical abscesses of teeth #12 and #30 and stringent daily oral hygiene was recommended. Tooth extraction was initially recommended but ultimately postponed. Plans were made for dental follow‐up.

With continued antibiotic treatment, the patient's fevers resolved and leukocytosis improved. A follow‐up CT abdomen/pelvis obtained on hospital day 10 showed a reduction in size of the multiple liver abscesses. There was also increased prominence of the appendix with mild stranding. The patient was taken for appendectomy. Pathology was consistent with acute appendicitis with focal fat necrosis. The patient was ultimately discharged with the plan being to continue ertapenem until radiographic resolution of all the abscesses was demonstrated.

Discussion

Pyogenic liver abscesses are infrequently encountered in the western population, but when present, result in significant morbidity and mortality.1 Mortality rates range from 6% to 31%, decreased from 100% mortality in the preantibiotic era.1 The leading cause of pyogenic liver abscesses has been in the past ascribed to ruptured appendicitis.2 However, biliary tract pathology is now the leading cause, accounting for 43% to 60% of cases.2 In addition, hematogenous seeding of infection from the oral cavity has been recognized in the literature as a potential source of infection in the development of pyogenic liver abscesses.2

The empiric treatment of pyogenic liver abscesses is intravenous broad‐spectrum antibiotics, most commonly metronidazole in combination with quinolones, aminoglycosides, third generation cephalosporins, carbapenems, piperacillin/tazobactam, ampicillin‐sulbactam, or amoxicillin/clavulanate.1 The optimal treatment course is controversial but suggested to include 2 weeks to 3 weeks of intravenous antibiotics followed by at least 3 weeks to 4 weeks of oral antibiotics.1

According to a study of 84 patients hospitalized with pyogenic liver abscesses of which 70 cases were cultured, the most typical organisms isolated from liver abscesses are Streptococcus spp. (40.5%), Escherichia coli (27.4%), Klebsiella spp. (14.3%), and anaerobic organisms (17.9%).1 The anaerobic Gram‐negative bacterium Fusobacterium nucleatum, known to play a role in periodontal disease, is an uncommon cause of liver abscesses: a review of the literature revealed only 14 cases of liver abscesses caused by Fusobacterium nucleatum, five cases of which occurred in patients with known immunodeficiency, and a retrospective study of 70 cases of liver abscesses revealed only 2 cases linked to this bacterium.1, 2 Though accounting for a minority of cases of pyogenic liver abscesses, it is commonly cited as a cause of liver abscesses resulting from spread of infection from the oral cavity. Four case reports have implicated severe dental disease or recent dental work in the development of pyogenic liver abscesses involving Fusobacterium nucleatum.2 For example, a literature search revealed a case report of a patient with a liver abscess due to Fusobacterium nucleatum resulting from hematogenous spread of infection from the oral cavity.2

Although Fusobacterium has rarely been reported in biliary culture from patients with cholangitis or gangrenous cholecystitis,3 this organism has been identified as a causative organism in appendicitis. In two separate studies of 41 children with appendicitis and 30 patients older than 12 years with gangrenous or perforated appendicitis, Fusobacterium nucleatum or Fusobacterium spp. were isolated in 44% and 33% of cases, respectively.4, 5 Nevertheless, the mechanism of appendicitis causing liver abscesses is thought to be by direct spread via the peritoneum after perforation.2 Thus, despite the isolation of this bacterium from appendectomy specimens, appendicitis is less likely the source of infection in this patient given that there is no evidence that appendiceal perforation occurred in this case.

Our patient was found to have dental abscesses, cholecystitis requiring cholecystectomy, and appendicitis requiring appendectomyall of which, to varying degrees, were plausible sources of infection by virtue of their known role in the development of pyogenic liver abscesses. Although periodontal disease was the likely source of Fusobacterium nucleatum infection, we could not exclude the leading causes of pyogenic liver abscesses, appendicitis and/or biliary tract disease. As a result, the patient underwent 2 surgeries and was counseled to maintain good oral hygiene in order to eliminate all persisting sources of infection.

This was an unusual case in which the question What is the source of infection? appears to have had multiple correct answers. We theorize that leaving any 1 of the 3 possible sources of infection in place could have led to treatment failure. This patient is a humbling reminder that not every clinical problem will have one clear solution. In such cases, all possible underlying conditions need to be managed appropriately to achieve the desired outcome.

A 49‐year‐old man with a history of hypertension presented to our hospital with a 2‐week history of sharp pain in the right upper abdomen and right lower chest radiating to the back. The patient reported a few days of fevers, chills, drenching night sweats, shortness of breath, malaise, and fatigue. He denied recent travel. Vital signs were temperature 38.4C, blood pressure 119/74 mmHg, heart rate 95 beats/minutes, respiratory rate 16 breaths/minutes, and oxygen saturation 96% on 5 L nasal cannula. Physical examination revealed poor dentition, right upper abdominal quadrant tenderness, and dullness to percussion over the right lung base.

Initial labs showed white blood count (WBC) 22,540/mm³, alkaline phosphatase 280 units/L, bilirubin 1.1 mg/dL, aspartate aminotransferase (AST) 28 units/L, alanine aminotransferase (ALT) 33 units/L. Blood cultures were negative. An human immunodeficiency virus (HIV)1/HIV2 antibody screen was negative. Computed tomography (CT) of the chest demonstrated a large cystic lesion in the diaphragmatic dome of the liver with multiple lesions in the right lobe of the liver. Elevation of the right hemidiaphragm and moderate right pleural effusion were noted. CT abdomen showed multiple areas of fluid collection within the liver suspicious for liver abscesses (see Figure 1). Multiple gallstones were seen within gallbladder with a large stone in the region of the gallbladder neck vs. cystic duct without evidence of extrahepatic biliary dilatation. There was mild distention of the appendix with minimal soft tissue stranding.

Figure 1

The patient underwent ultrasound‐guided drainage of the largest liver abscess. Cultures from the aspiration grew Fusobacterium nucleatum. The patient's stool studies for ova and parasites were negative. The patient was started on piperacillin/tazobactam and metronidazole, then switched to ertapenem. A hepatobiliary iminodiacetic acid (HIDA) scan confirmed cholecystitis, and the patient underwent open cholecystectomy. Pathology on the gallbladder returned as chronic cholecystitis with cholelithiasis. A full dental examination revealed possible periapical abscesses of teeth #12 and #30 and stringent daily oral hygiene was recommended. Tooth extraction was initially recommended but ultimately postponed. Plans were made for dental follow‐up.

With continued antibiotic treatment, the patient's fevers resolved and leukocytosis improved. A follow‐up CT abdomen/pelvis obtained on hospital day 10 showed a reduction in size of the multiple liver abscesses. There was also increased prominence of the appendix with mild stranding. The patient was taken for appendectomy. Pathology was consistent with acute appendicitis with focal fat necrosis. The patient was ultimately discharged with the plan being to continue ertapenem until radiographic resolution of all the abscesses was demonstrated.

Discussion

Pyogenic liver abscesses are infrequently encountered in the western population, but when present, result in significant morbidity and mortality.1 Mortality rates range from 6% to 31%, decreased from 100% mortality in the preantibiotic era.1 The leading cause of pyogenic liver abscesses has been in the past ascribed to ruptured appendicitis.2 However, biliary tract pathology is now the leading cause, accounting for 43% to 60% of cases.2 In addition, hematogenous seeding of infection from the oral cavity has been recognized in the literature as a potential source of infection in the development of pyogenic liver abscesses.2

The empiric treatment of pyogenic liver abscesses is intravenous broad‐spectrum antibiotics, most commonly metronidazole in combination with quinolones, aminoglycosides, third generation cephalosporins, carbapenems, piperacillin/tazobactam, ampicillin‐sulbactam, or amoxicillin/clavulanate.1 The optimal treatment course is controversial but suggested to include 2 weeks to 3 weeks of intravenous antibiotics followed by at least 3 weeks to 4 weeks of oral antibiotics.1

According to a study of 84 patients hospitalized with pyogenic liver abscesses of which 70 cases were cultured, the most typical organisms isolated from liver abscesses are Streptococcus spp. (40.5%), Escherichia coli (27.4%), Klebsiella spp. (14.3%), and anaerobic organisms (17.9%).1 The anaerobic Gram‐negative bacterium Fusobacterium nucleatum, known to play a role in periodontal disease, is an uncommon cause of liver abscesses: a review of the literature revealed only 14 cases of liver abscesses caused by Fusobacterium nucleatum, five cases of which occurred in patients with known immunodeficiency, and a retrospective study of 70 cases of liver abscesses revealed only 2 cases linked to this bacterium.1, 2 Though accounting for a minority of cases of pyogenic liver abscesses, it is commonly cited as a cause of liver abscesses resulting from spread of infection from the oral cavity. Four case reports have implicated severe dental disease or recent dental work in the development of pyogenic liver abscesses involving Fusobacterium nucleatum.2 For example, a literature search revealed a case report of a patient with a liver abscess due to Fusobacterium nucleatum resulting from hematogenous spread of infection from the oral cavity.2

Although Fusobacterium has rarely been reported in biliary culture from patients with cholangitis or gangrenous cholecystitis,3 this organism has been identified as a causative organism in appendicitis. In two separate studies of 41 children with appendicitis and 30 patients older than 12 years with gangrenous or perforated appendicitis, Fusobacterium nucleatum or Fusobacterium spp. were isolated in 44% and 33% of cases, respectively.4, 5 Nevertheless, the mechanism of appendicitis causing liver abscesses is thought to be by direct spread via the peritoneum after perforation.2 Thus, despite the isolation of this bacterium from appendectomy specimens, appendicitis is less likely the source of infection in this patient given that there is no evidence that appendiceal perforation occurred in this case.

Our patient was found to have dental abscesses, cholecystitis requiring cholecystectomy, and appendicitis requiring appendectomyall of which, to varying degrees, were plausible sources of infection by virtue of their known role in the development of pyogenic liver abscesses. Although periodontal disease was the likely source of Fusobacterium nucleatum infection, we could not exclude the leading causes of pyogenic liver abscesses, appendicitis and/or biliary tract disease. As a result, the patient underwent 2 surgeries and was counseled to maintain good oral hygiene in order to eliminate all persisting sources of infection.

This was an unusual case in which the question What is the source of infection? appears to have had multiple correct answers. We theorize that leaving any 1 of the 3 possible sources of infection in place could have led to treatment failure. This patient is a humbling reminder that not every clinical problem will have one clear solution. In such cases, all possible underlying conditions need to be managed appropriately to achieve the desired outcome.

A 49‐year‐old man with a history of hypertension presented to our hospital with a 2‐week history of sharp pain in the right upper abdomen and right lower chest radiating to the back. The patient reported a few days of fevers, chills, drenching night sweats, shortness of breath, malaise, and fatigue. He denied recent travel. Vital signs were temperature 38.4C, blood pressure 119/74 mmHg, heart rate 95 beats/minutes, respiratory rate 16 breaths/minutes, and oxygen saturation 96% on 5 L nasal cannula. Physical examination revealed poor dentition, right upper abdominal quadrant tenderness, and dullness to percussion over the right lung base.

Initial labs showed white blood count (WBC) 22,540/mm³, alkaline phosphatase 280 units/L, bilirubin 1.1 mg/dL, aspartate aminotransferase (AST) 28 units/L, alanine aminotransferase (ALT) 33 units/L. Blood cultures were negative. An human immunodeficiency virus (HIV)1/HIV2 antibody screen was negative. Computed tomography (CT) of the chest demonstrated a large cystic lesion in the diaphragmatic dome of the liver with multiple lesions in the right lobe of the liver. Elevation of the right hemidiaphragm and moderate right pleural effusion were noted. CT abdomen showed multiple areas of fluid collection within the liver suspicious for liver abscesses (see Figure 1). Multiple gallstones were seen within gallbladder with a large stone in the region of the gallbladder neck vs. cystic duct without evidence of extrahepatic biliary dilatation. There was mild distention of the appendix with minimal soft tissue stranding.

Figure 1

The patient underwent ultrasound‐guided drainage of the largest liver abscess. Cultures from the aspiration grew Fusobacterium nucleatum. The patient's stool studies for ova and parasites were negative. The patient was started on piperacillin/tazobactam and metronidazole, then switched to ertapenem. A hepatobiliary iminodiacetic acid (HIDA) scan confirmed cholecystitis, and the patient underwent open cholecystectomy. Pathology on the gallbladder returned as chronic cholecystitis with cholelithiasis. A full dental examination revealed possible periapical abscesses of teeth #12 and #30 and stringent daily oral hygiene was recommended. Tooth extraction was initially recommended but ultimately postponed. Plans were made for dental follow‐up.

With continued antibiotic treatment, the patient's fevers resolved and leukocytosis improved. A follow‐up CT abdomen/pelvis obtained on hospital day 10 showed a reduction in size of the multiple liver abscesses. There was also increased prominence of the appendix with mild stranding. The patient was taken for appendectomy. Pathology was consistent with acute appendicitis with focal fat necrosis. The patient was ultimately discharged with the plan being to continue ertapenem until radiographic resolution of all the abscesses was demonstrated.

Discussion

Pyogenic liver abscesses are infrequently encountered in the western population, but when present, result in significant morbidity and mortality.1 Mortality rates range from 6% to 31%, decreased from 100% mortality in the preantibiotic era.1 The leading cause of pyogenic liver abscesses has been in the past ascribed to ruptured appendicitis.2 However, biliary tract pathology is now the leading cause, accounting for 43% to 60% of cases.2 In addition, hematogenous seeding of infection from the oral cavity has been recognized in the literature as a potential source of infection in the development of pyogenic liver abscesses.2

The empiric treatment of pyogenic liver abscesses is intravenous broad‐spectrum antibiotics, most commonly metronidazole in combination with quinolones, aminoglycosides, third generation cephalosporins, carbapenems, piperacillin/tazobactam, ampicillin‐sulbactam, or amoxicillin/clavulanate.1 The optimal treatment course is controversial but suggested to include 2 weeks to 3 weeks of intravenous antibiotics followed by at least 3 weeks to 4 weeks of oral antibiotics.1

According to a study of 84 patients hospitalized with pyogenic liver abscesses of which 70 cases were cultured, the most typical organisms isolated from liver abscesses are Streptococcus spp. (40.5%), Escherichia coli (27.4%), Klebsiella spp. (14.3%), and anaerobic organisms (17.9%).1 The anaerobic Gram‐negative bacterium Fusobacterium nucleatum, known to play a role in periodontal disease, is an uncommon cause of liver abscesses: a review of the literature revealed only 14 cases of liver abscesses caused by Fusobacterium nucleatum, five cases of which occurred in patients with known immunodeficiency, and a retrospective study of 70 cases of liver abscesses revealed only 2 cases linked to this bacterium.1, 2 Though accounting for a minority of cases of pyogenic liver abscesses, it is commonly cited as a cause of liver abscesses resulting from spread of infection from the oral cavity. Four case reports have implicated severe dental disease or recent dental work in the development of pyogenic liver abscesses involving Fusobacterium nucleatum.2 For example, a literature search revealed a case report of a patient with a liver abscess due to Fusobacterium nucleatum resulting from hematogenous spread of infection from the oral cavity.2

Although Fusobacterium has rarely been reported in biliary culture from patients with cholangitis or gangrenous cholecystitis,3 this organism has been identified as a causative organism in appendicitis. In two separate studies of 41 children with appendicitis and 30 patients older than 12 years with gangrenous or perforated appendicitis, Fusobacterium nucleatum or Fusobacterium spp. were isolated in 44% and 33% of cases, respectively.4, 5 Nevertheless, the mechanism of appendicitis causing liver abscesses is thought to be by direct spread via the peritoneum after perforation.2 Thus, despite the isolation of this bacterium from appendectomy specimens, appendicitis is less likely the source of infection in this patient given that there is no evidence that appendiceal perforation occurred in this case.

Our patient was found to have dental abscesses, cholecystitis requiring cholecystectomy, and appendicitis requiring appendectomyall of which, to varying degrees, were plausible sources of infection by virtue of their known role in the development of pyogenic liver abscesses. Although periodontal disease was the likely source of Fusobacterium nucleatum infection, we could not exclude the leading causes of pyogenic liver abscesses, appendicitis and/or biliary tract disease. As a result, the patient underwent 2 surgeries and was counseled to maintain good oral hygiene in order to eliminate all persisting sources of infection.

This was an unusual case in which the question What is the source of infection? appears to have had multiple correct answers. We theorize that leaving any 1 of the 3 possible sources of infection in place could have led to treatment failure. This patient is a humbling reminder that not every clinical problem will have one clear solution. In such cases, all possible underlying conditions need to be managed appropriately to achieve the desired outcome.

Venous thromboembolism (VTE) is a major cause of morbidity and mortality in hospitalized patients.13 Major efforts are underway to increase appropriate VTE prophylaxis (VTEP)4 and adherence to VTEP guidelines are increasingly used as a quality of care measure. National 2008 VTEP guidelines suggest that all medical patients ill enough to require hospitalization, particularly those requiring admission to the Intensive Care Unit (ICU), have at least a moderate risk of developing VTE and prophylaxis is recommended.4 Hospitalized patients with end‐stage liver disease (ESLD), despite their coagulopathy, are known to be at risk for VTE48 and may be VTEP candidates.

Based on available literature, it is unknown whether pharmacologic VTEP should be utilized in acutely ill, hospitalized patients with ESLD, particularly in those admitted with variceal bleeding. These patients are at high risk for rebleeding, with the highest risk in the first 5 days.9 Early rebleeding, defined as recurrent bleeding within 6 weeks of initial bleed, declined from 47% in the 1980s to 13% by 2000 because of increased early endoscopic intervention and use of medications to prevent rebleeding.911 In multicenter cohort studies, D'Amico and De Franchis12 reported that 13% of patients with variceal bleeding had uncontrolled bleeding, rebleeding, or death within 5 days of admission while Bahmba et al.13 reported a 16% rate of rebleeding within 5 days. We are unaware of prior reports regarding the safety of VTEP in this high‐risk group of patients.

Objective

We sought to describe rebleeding in a series of 22 patients with ESLD admitted with variceal bleeding who received pharmacologic VTEP.

Methods

We identified all patients 18 years and older with upper gastrointestinal bleeding admitted to Harborview Medical Center, a 400‐bed urban county teaching hospital in Seattle, Washington, between January 1, 2003 and December 31, 2005 (Figure 1), just prior to medical center‐wide implementation of a VTEP guideline. Potential cases were identified using administrative data based on 8 discharge diagnoses (Supporting Information Appendix 1) and 10 procedure codes (Supporting Information Appendix 2).14 Inpatient pharmacy data indicating continuous octreotide infusion were used to refine the sample. At our institution, it is a standard of care to initiate octreotide in patients admitted with variceal bleeding. We excluded patients who did not have ESLD (defined as evidence of cirrhosis and associated complications including but not limited to ascites, encephalopathy, variceal bleeding, portal hypertension) documented in their problem list or past medical history and those with no variceal bleeding based on medical record review. We identified cases receiving pharmacologic VTEP, either subcutaneous unfractionated heparin (UFH) or low molecular weight heparin (LMWH), during hospitalization from pharmacy records.

Figure 1

We obtained demographic and clinical data from administrative billing systems, electronic and paper medical records, and inpatient pharmacy databases and verified transfusion data from the Puget Sound Blood Center. We abstracted esophagogastroduodenoscopy (EGD) findings indicating high risk of rebleeding including variceal grade and stigmata of recent bleeding such as red spots or wales.15, 16 Data were abstracted by the first 3 authors (AS, MS, KJ) and reviewed again by 2 authors (AS, KJ) blinded to the others' abstractions.

We calculated Model for ESLD (MELD) scores on admission. These scores correlate with 3 month mortality in ESLD.17 We tabulated 5 factors shown in some studies to predict bleeding including high International Normalized Ratio (INR) (>1.5), low hematocrit (25%), low platelet count (100,000 per microliter), active bleeding at EGD, and transfusion of four or more units of red cells within 24 hours of admission.1013

We defined rebleeding as a decrease in hematocrit of greater than 5 percentage points compared with postresuscitation hematocrit, transfusion of additional red cells more than 48 hours after initial resuscitation, repeat unscheduled EGD, or return to the ICU for therapies related to rebleeding.18 The University of Washington Human Subjects Board approved this study.

Results

Of 224 patients initially identified, 36 received pharmacologic VTEP. We excluded 14 who did not have ESLD (n = 1) or did not have a variceal bleed (n = 13). The remaining 22 patients form the sample described in Figure 1.

The median age of patients was 52 years (range 42‐85) and 77% were men (Table 1). Twenty‐one of 22 patients (95%) were initially admitted to the ICU; median length of stay was 8 days (range 4‐30). Median MELD score on admission was 15 (range 825). On EGD, the number of variceal columns ranged from 1 to 4; 17 patients (77%) had at least 3. A total of 15 patients (68%) had stigmata of recent bleeding and 16 (72%) underwent banding (range 16 bands). All patients had at least 1 bleeding risk factor (Table 1) of which the most common factors observed were initial transfusion of 4 or more units of red cells (50%, n = 11), INR > 1.5 (45%, n = 10), and hematocrit 25% (45%, n = 10).

A total of 12 patients (55%) received 5000 units of UFH every 8 hours, 8 (36%) received 5000 units UFH every 12 hours, and 2 (9%) received LMWH. VTEP was initiated as early as day of admission and as late as day 19. Median VTEP start date was hospital day 4. Median duration of of VTEP was 5 days.

Only 1 patient (4.5%) rebled after VTEP initiation. The patient received UFH every 8 hours starting on hospital day 6, and rebleeding occurred on day 9. Repeat EGD showed ulcers at banding sites. The patient was restarted on VTEP on hospital day 13 without recurrence of rebleeding. This patient had a MELD score of 24, initial INR >2, hematocrit 25%, had grade 3 varices and stigmata of recent bleeding on EGD, and received 4 units of packed red cells. These values are similar to those of the cohort as a whole (Table 1). This patient also was diagnosed with DVT while receiving VTEP on hospital day 15. This patient's coagulopathy was in the setting of terminal illness; the patient expired on hospital day 25.

One additional patient rebled prior to VTEP initiation on day 3 with repeat EGD showing a bleeding varix. This patient was nevertheless started on VTEP 4 days after rebleeding. Despite use of VTEP, this patient was diagnosed with DVT on hospital day 9 (and may well have had the DVT at the time of VTEP initiation). The patient was transitioned to therapeutic dose heparin which was tolerated without recurrence of rebleeding.

There were no other confirmed cases of DVT in this series. One additional patient underwent angiogram that showed no pulmonary embolism; 2 other patients underwent lower extremity ultrasounds that were negative for DVT.

Discussion

At our medical center, only a few inpatients with ESLD admitted with variceal bleed received VTEP. These patients were seemingly at high risk for bleeding and rebleeding given high MELD scores, variceal bleeding, and presence of at least one clinical factor suggesting bleeding risk, and in several cases 3 or more such factors.13, 18 Despite this, only 1 patient rebled while receiving VTEP. We captured rebleeding rates only during the index hospitalization. We therefore may underestimate early rebleeding rates.1013 Nevertheless, our inpatient data included complete coverage of the earliest period after the index bleeds and the period during which patients were exposed to VTEP, which should be the time of highest rebleeding risk related to VTEP exposure. Interestingly the patient who rebled while on VTEP was also diagnosed with VTE while on VTEP. Two patients (9%) in our sample were diagnosed with VTE.

This case series is limited by its small sample size, retrospective nature, single center observation, and perhaps especially by possible selection bias. We were unable to specifically quantify rebleeding risk. Several authors have identified individual factors associated with rebleeding,1013 these were tabulated for patients in this case series (Table 1) and all patients had at least 1 of these factors. Concurrent infection and hepatic vein pressure gradient have been shown to predict rebleeding;9, 19 we were unable to identify these factors in our data.

There was considerable variability in this case series in timing of VTEP initiation relative to initial bleed. We were unable to characterize provider or patient characteristics that may have influenced the decision to initiate VTEP and timing. The sample size was also too small to comment upon factors associated with choice of UFH versus LMWH and any potential differences in rebleeding risk between the 2. We also did not look at outcomes postindex hospitalization so we can not comment on the extended risk of rebleeding with VTEP after discharge. However, the risk of rebleeding is highest within the first 96 hours13 and all patients in this series were hospitalized at least 4 days. Nonetheless, we captured all patients with ESLD and variceal bleeding exposed to VTEP at a large center over a three‐year period and found rebleeding rates less than what might be expected.

Conclusions

Our observations suggest that some inpatients with ESLD and variceal bleeding may tolerate pharmacologic VTEP. In this small group of patients, VTEP was associated with an unexpectedly low incidence of rebleeding. While this case series does not support broad use of VTEP in this population, the lower‐than‐expected rates of rebleeding suggest that further study of the safety and effectiveness of pharmacologic VTEP in inpatient populations with ESLD may be warranted, particularly given the recommendations of recent national VTE prophylaxis guidelines.4

Venous thromboembolism (VTE) is a major cause of morbidity and mortality in hospitalized patients.13 Major efforts are underway to increase appropriate VTE prophylaxis (VTEP)4 and adherence to VTEP guidelines are increasingly used as a quality of care measure. National 2008 VTEP guidelines suggest that all medical patients ill enough to require hospitalization, particularly those requiring admission to the Intensive Care Unit (ICU), have at least a moderate risk of developing VTE and prophylaxis is recommended.4 Hospitalized patients with end‐stage liver disease (ESLD), despite their coagulopathy, are known to be at risk for VTE48 and may be VTEP candidates.

Based on available literature, it is unknown whether pharmacologic VTEP should be utilized in acutely ill, hospitalized patients with ESLD, particularly in those admitted with variceal bleeding. These patients are at high risk for rebleeding, with the highest risk in the first 5 days.9 Early rebleeding, defined as recurrent bleeding within 6 weeks of initial bleed, declined from 47% in the 1980s to 13% by 2000 because of increased early endoscopic intervention and use of medications to prevent rebleeding.911 In multicenter cohort studies, D'Amico and De Franchis12 reported that 13% of patients with variceal bleeding had uncontrolled bleeding, rebleeding, or death within 5 days of admission while Bahmba et al.13 reported a 16% rate of rebleeding within 5 days. We are unaware of prior reports regarding the safety of VTEP in this high‐risk group of patients.

Objective

We sought to describe rebleeding in a series of 22 patients with ESLD admitted with variceal bleeding who received pharmacologic VTEP.

Methods

We identified all patients 18 years and older with upper gastrointestinal bleeding admitted to Harborview Medical Center, a 400‐bed urban county teaching hospital in Seattle, Washington, between January 1, 2003 and December 31, 2005 (Figure 1), just prior to medical center‐wide implementation of a VTEP guideline. Potential cases were identified using administrative data based on 8 discharge diagnoses (Supporting Information Appendix 1) and 10 procedure codes (Supporting Information Appendix 2).14 Inpatient pharmacy data indicating continuous octreotide infusion were used to refine the sample. At our institution, it is a standard of care to initiate octreotide in patients admitted with variceal bleeding. We excluded patients who did not have ESLD (defined as evidence of cirrhosis and associated complications including but not limited to ascites, encephalopathy, variceal bleeding, portal hypertension) documented in their problem list or past medical history and those with no variceal bleeding based on medical record review. We identified cases receiving pharmacologic VTEP, either subcutaneous unfractionated heparin (UFH) or low molecular weight heparin (LMWH), during hospitalization from pharmacy records.

Figure 1

We obtained demographic and clinical data from administrative billing systems, electronic and paper medical records, and inpatient pharmacy databases and verified transfusion data from the Puget Sound Blood Center. We abstracted esophagogastroduodenoscopy (EGD) findings indicating high risk of rebleeding including variceal grade and stigmata of recent bleeding such as red spots or wales.15, 16 Data were abstracted by the first 3 authors (AS, MS, KJ) and reviewed again by 2 authors (AS, KJ) blinded to the others' abstractions.

We calculated Model for ESLD (MELD) scores on admission. These scores correlate with 3 month mortality in ESLD.17 We tabulated 5 factors shown in some studies to predict bleeding including high International Normalized Ratio (INR) (>1.5), low hematocrit (25%), low platelet count (100,000 per microliter), active bleeding at EGD, and transfusion of four or more units of red cells within 24 hours of admission.1013

We defined rebleeding as a decrease in hematocrit of greater than 5 percentage points compared with postresuscitation hematocrit, transfusion of additional red cells more than 48 hours after initial resuscitation, repeat unscheduled EGD, or return to the ICU for therapies related to rebleeding.18 The University of Washington Human Subjects Board approved this study.

Results

Of 224 patients initially identified, 36 received pharmacologic VTEP. We excluded 14 who did not have ESLD (n = 1) or did not have a variceal bleed (n = 13). The remaining 22 patients form the sample described in Figure 1.

The median age of patients was 52 years (range 42‐85) and 77% were men (Table 1). Twenty‐one of 22 patients (95%) were initially admitted to the ICU; median length of stay was 8 days (range 4‐30). Median MELD score on admission was 15 (range 825). On EGD, the number of variceal columns ranged from 1 to 4; 17 patients (77%) had at least 3. A total of 15 patients (68%) had stigmata of recent bleeding and 16 (72%) underwent banding (range 16 bands). All patients had at least 1 bleeding risk factor (Table 1) of which the most common factors observed were initial transfusion of 4 or more units of red cells (50%, n = 11), INR > 1.5 (45%, n = 10), and hematocrit 25% (45%, n = 10).

A total of 12 patients (55%) received 5000 units of UFH every 8 hours, 8 (36%) received 5000 units UFH every 12 hours, and 2 (9%) received LMWH. VTEP was initiated as early as day of admission and as late as day 19. Median VTEP start date was hospital day 4. Median duration of of VTEP was 5 days.

Only 1 patient (4.5%) rebled after VTEP initiation. The patient received UFH every 8 hours starting on hospital day 6, and rebleeding occurred on day 9. Repeat EGD showed ulcers at banding sites. The patient was restarted on VTEP on hospital day 13 without recurrence of rebleeding. This patient had a MELD score of 24, initial INR >2, hematocrit 25%, had grade 3 varices and stigmata of recent bleeding on EGD, and received 4 units of packed red cells. These values are similar to those of the cohort as a whole (Table 1). This patient also was diagnosed with DVT while receiving VTEP on hospital day 15. This patient's coagulopathy was in the setting of terminal illness; the patient expired on hospital day 25.

One additional patient rebled prior to VTEP initiation on day 3 with repeat EGD showing a bleeding varix. This patient was nevertheless started on VTEP 4 days after rebleeding. Despite use of VTEP, this patient was diagnosed with DVT on hospital day 9 (and may well have had the DVT at the time of VTEP initiation). The patient was transitioned to therapeutic dose heparin which was tolerated without recurrence of rebleeding.

There were no other confirmed cases of DVT in this series. One additional patient underwent angiogram that showed no pulmonary embolism; 2 other patients underwent lower extremity ultrasounds that were negative for DVT.

Discussion

At our medical center, only a few inpatients with ESLD admitted with variceal bleed received VTEP. These patients were seemingly at high risk for bleeding and rebleeding given high MELD scores, variceal bleeding, and presence of at least one clinical factor suggesting bleeding risk, and in several cases 3 or more such factors.13, 18 Despite this, only 1 patient rebled while receiving VTEP. We captured rebleeding rates only during the index hospitalization. We therefore may underestimate early rebleeding rates.1013 Nevertheless, our inpatient data included complete coverage of the earliest period after the index bleeds and the period during which patients were exposed to VTEP, which should be the time of highest rebleeding risk related to VTEP exposure. Interestingly the patient who rebled while on VTEP was also diagnosed with VTE while on VTEP. Two patients (9%) in our sample were diagnosed with VTE.

This case series is limited by its small sample size, retrospective nature, single center observation, and perhaps especially by possible selection bias. We were unable to specifically quantify rebleeding risk. Several authors have identified individual factors associated with rebleeding,1013 these were tabulated for patients in this case series (Table 1) and all patients had at least 1 of these factors. Concurrent infection and hepatic vein pressure gradient have been shown to predict rebleeding;9, 19 we were unable to identify these factors in our data.

There was considerable variability in this case series in timing of VTEP initiation relative to initial bleed. We were unable to characterize provider or patient characteristics that may have influenced the decision to initiate VTEP and timing. The sample size was also too small to comment upon factors associated with choice of UFH versus LMWH and any potential differences in rebleeding risk between the 2. We also did not look at outcomes postindex hospitalization so we can not comment on the extended risk of rebleeding with VTEP after discharge. However, the risk of rebleeding is highest within the first 96 hours13 and all patients in this series were hospitalized at least 4 days. Nonetheless, we captured all patients with ESLD and variceal bleeding exposed to VTEP at a large center over a three‐year period and found rebleeding rates less than what might be expected.

Conclusions

Our observations suggest that some inpatients with ESLD and variceal bleeding may tolerate pharmacologic VTEP. In this small group of patients, VTEP was associated with an unexpectedly low incidence of rebleeding. While this case series does not support broad use of VTEP in this population, the lower‐than‐expected rates of rebleeding suggest that further study of the safety and effectiveness of pharmacologic VTEP in inpatient populations with ESLD may be warranted, particularly given the recommendations of recent national VTE prophylaxis guidelines.4

Venous thromboembolism (VTE) is a major cause of morbidity and mortality in hospitalized patients.13 Major efforts are underway to increase appropriate VTE prophylaxis (VTEP)4 and adherence to VTEP guidelines are increasingly used as a quality of care measure. National 2008 VTEP guidelines suggest that all medical patients ill enough to require hospitalization, particularly those requiring admission to the Intensive Care Unit (ICU), have at least a moderate risk of developing VTE and prophylaxis is recommended.4 Hospitalized patients with end‐stage liver disease (ESLD), despite their coagulopathy, are known to be at risk for VTE48 and may be VTEP candidates.

Based on available literature, it is unknown whether pharmacologic VTEP should be utilized in acutely ill, hospitalized patients with ESLD, particularly in those admitted with variceal bleeding. These patients are at high risk for rebleeding, with the highest risk in the first 5 days.9 Early rebleeding, defined as recurrent bleeding within 6 weeks of initial bleed, declined from 47% in the 1980s to 13% by 2000 because of increased early endoscopic intervention and use of medications to prevent rebleeding.911 In multicenter cohort studies, D'Amico and De Franchis12 reported that 13% of patients with variceal bleeding had uncontrolled bleeding, rebleeding, or death within 5 days of admission while Bahmba et al.13 reported a 16% rate of rebleeding within 5 days. We are unaware of prior reports regarding the safety of VTEP in this high‐risk group of patients.

Objective

We sought to describe rebleeding in a series of 22 patients with ESLD admitted with variceal bleeding who received pharmacologic VTEP.

Methods

We identified all patients 18 years and older with upper gastrointestinal bleeding admitted to Harborview Medical Center, a 400‐bed urban county teaching hospital in Seattle, Washington, between January 1, 2003 and December 31, 2005 (Figure 1), just prior to medical center‐wide implementation of a VTEP guideline. Potential cases were identified using administrative data based on 8 discharge diagnoses (Supporting Information Appendix 1) and 10 procedure codes (Supporting Information Appendix 2).14 Inpatient pharmacy data indicating continuous octreotide infusion were used to refine the sample. At our institution, it is a standard of care to initiate octreotide in patients admitted with variceal bleeding. We excluded patients who did not have ESLD (defined as evidence of cirrhosis and associated complications including but not limited to ascites, encephalopathy, variceal bleeding, portal hypertension) documented in their problem list or past medical history and those with no variceal bleeding based on medical record review. We identified cases receiving pharmacologic VTEP, either subcutaneous unfractionated heparin (UFH) or low molecular weight heparin (LMWH), during hospitalization from pharmacy records.

Figure 1

We obtained demographic and clinical data from administrative billing systems, electronic and paper medical records, and inpatient pharmacy databases and verified transfusion data from the Puget Sound Blood Center. We abstracted esophagogastroduodenoscopy (EGD) findings indicating high risk of rebleeding including variceal grade and stigmata of recent bleeding such as red spots or wales.15, 16 Data were abstracted by the first 3 authors (AS, MS, KJ) and reviewed again by 2 authors (AS, KJ) blinded to the others' abstractions.

We calculated Model for ESLD (MELD) scores on admission. These scores correlate with 3 month mortality in ESLD.17 We tabulated 5 factors shown in some studies to predict bleeding including high International Normalized Ratio (INR) (>1.5), low hematocrit (25%), low platelet count (100,000 per microliter), active bleeding at EGD, and transfusion of four or more units of red cells within 24 hours of admission.1013

We defined rebleeding as a decrease in hematocrit of greater than 5 percentage points compared with postresuscitation hematocrit, transfusion of additional red cells more than 48 hours after initial resuscitation, repeat unscheduled EGD, or return to the ICU for therapies related to rebleeding.18 The University of Washington Human Subjects Board approved this study.

Results

Of 224 patients initially identified, 36 received pharmacologic VTEP. We excluded 14 who did not have ESLD (n = 1) or did not have a variceal bleed (n = 13). The remaining 22 patients form the sample described in Figure 1.

The median age of patients was 52 years (range 42‐85) and 77% were men (Table 1). Twenty‐one of 22 patients (95%) were initially admitted to the ICU; median length of stay was 8 days (range 4‐30). Median MELD score on admission was 15 (range 825). On EGD, the number of variceal columns ranged from 1 to 4; 17 patients (77%) had at least 3. A total of 15 patients (68%) had stigmata of recent bleeding and 16 (72%) underwent banding (range 16 bands). All patients had at least 1 bleeding risk factor (Table 1) of which the most common factors observed were initial transfusion of 4 or more units of red cells (50%, n = 11), INR > 1.5 (45%, n = 10), and hematocrit 25% (45%, n = 10).

A total of 12 patients (55%) received 5000 units of UFH every 8 hours, 8 (36%) received 5000 units UFH every 12 hours, and 2 (9%) received LMWH. VTEP was initiated as early as day of admission and as late as day 19. Median VTEP start date was hospital day 4. Median duration of of VTEP was 5 days.

Only 1 patient (4.5%) rebled after VTEP initiation. The patient received UFH every 8 hours starting on hospital day 6, and rebleeding occurred on day 9. Repeat EGD showed ulcers at banding sites. The patient was restarted on VTEP on hospital day 13 without recurrence of rebleeding. This patient had a MELD score of 24, initial INR >2, hematocrit 25%, had grade 3 varices and stigmata of recent bleeding on EGD, and received 4 units of packed red cells. These values are similar to those of the cohort as a whole (Table 1). This patient also was diagnosed with DVT while receiving VTEP on hospital day 15. This patient's coagulopathy was in the setting of terminal illness; the patient expired on hospital day 25.

One additional patient rebled prior to VTEP initiation on day 3 with repeat EGD showing a bleeding varix. This patient was nevertheless started on VTEP 4 days after rebleeding. Despite use of VTEP, this patient was diagnosed with DVT on hospital day 9 (and may well have had the DVT at the time of VTEP initiation). The patient was transitioned to therapeutic dose heparin which was tolerated without recurrence of rebleeding.

There were no other confirmed cases of DVT in this series. One additional patient underwent angiogram that showed no pulmonary embolism; 2 other patients underwent lower extremity ultrasounds that were negative for DVT.

Discussion

At our medical center, only a few inpatients with ESLD admitted with variceal bleed received VTEP. These patients were seemingly at high risk for bleeding and rebleeding given high MELD scores, variceal bleeding, and presence of at least one clinical factor suggesting bleeding risk, and in several cases 3 or more such factors.13, 18 Despite this, only 1 patient rebled while receiving VTEP. We captured rebleeding rates only during the index hospitalization. We therefore may underestimate early rebleeding rates.1013 Nevertheless, our inpatient data included complete coverage of the earliest period after the index bleeds and the period during which patients were exposed to VTEP, which should be the time of highest rebleeding risk related to VTEP exposure. Interestingly the patient who rebled while on VTEP was also diagnosed with VTE while on VTEP. Two patients (9%) in our sample were diagnosed with VTE.

This case series is limited by its small sample size, retrospective nature, single center observation, and perhaps especially by possible selection bias. We were unable to specifically quantify rebleeding risk. Several authors have identified individual factors associated with rebleeding,1013 these were tabulated for patients in this case series (Table 1) and all patients had at least 1 of these factors. Concurrent infection and hepatic vein pressure gradient have been shown to predict rebleeding;9, 19 we were unable to identify these factors in our data.

There was considerable variability in this case series in timing of VTEP initiation relative to initial bleed. We were unable to characterize provider or patient characteristics that may have influenced the decision to initiate VTEP and timing. The sample size was also too small to comment upon factors associated with choice of UFH versus LMWH and any potential differences in rebleeding risk between the 2. We also did not look at outcomes postindex hospitalization so we can not comment on the extended risk of rebleeding with VTEP after discharge. However, the risk of rebleeding is highest within the first 96 hours13 and all patients in this series were hospitalized at least 4 days. Nonetheless, we captured all patients with ESLD and variceal bleeding exposed to VTEP at a large center over a three‐year period and found rebleeding rates less than what might be expected.

Conclusions

Our observations suggest that some inpatients with ESLD and variceal bleeding may tolerate pharmacologic VTEP. In this small group of patients, VTEP was associated with an unexpectedly low incidence of rebleeding. While this case series does not support broad use of VTEP in this population, the lower‐than‐expected rates of rebleeding suggest that further study of the safety and effectiveness of pharmacologic VTEP in inpatient populations with ESLD may be warranted, particularly given the recommendations of recent national VTE prophylaxis guidelines.4