Hospital readmissions are recognized as both a significant contributor to health care costs and a putative indicator of healthcare quality.1, 2 Older medical patients with chronic medical comorbidities are at particularly high risk for hospital readmission3 and attendant risks of hospitalization.4 Many intervention strategies have been used in trials to reduce readmissions in such patients. Single interventions such as case management,5 care coordination,6 and self‐management7 have been disappointing. There is emerging evidence to support complex, multidisciplinary interventions which include outreach and support in the early post‐hospital period, especially in heart failure patients,8 but also in medical patients with a range of conditions.9 However, such interventions are resource intensive and it remains uncertain which patients may benefit most from interventions.

Although there are many studies of risk factors for hospital admission and readmission, few studies have reported predictors of recurrent readmission.1012 Patients with 2 or more recent hospitalizations are readily identifiable and have a substantially increased risk of hospital readmission compared to patients with only 1 recent hospitalization.10, 11, 1315 These patients may have a unique risk factor profile, and may be a group which may particularly benefit from complex interventions,16 but no previous study has specifically examined risk factors in this high‐risk group.

Previous studies of readmission predictors have largely focussed on demographic and disease characteristics which are not amenable to intervention at individual level. The results of such studies may determine a population at increased risk, but do not inform an intervention strategy.14 Psychological and behavioral factors such as depression and anxiety, perceptions of health, and adherence patterns may also contribute to hospitalizations.17, 18 However, the role of these factors in repeated admissions of medical patients has been poorly studied.

The aim of this study was to describe the association of a wider range of biological, functional, and psychosocial variables with the risk of unplanned hospital readmission within 6 months in medical patients with 2 or more recent hospitalizations. There was a particular emphasis on risk factors which might be amenable to intervention.

Methods

Results

Active screening over 12 months identified 1194 new admissions with a documented hospitalization in the previous 6 months. Of these, 85 were discharged prior to clinical review, 227 were aged less than 50 years, 16 died in hospital, and 153 had been screened previously in the study, leaving 713 individual patients for eligibility screening. Screening identified 328 of 713 (46.0%) patients eligible to participate in the study, who were approached for consent. Of these eligible patients, 190 of 328 (57.9%) agreed to participate but 48 of 190 (25%) did not complete posthospital follow‐up, leaving a total of 142 participants. Patient eligibility, consent, and follow‐up are detailed in Figure 1.

Figure 1

Demographic and disease characteristics of the study participants are shown in Table 1. The 48 participants without follow‐up data appeared similar to those with full data, and 25 (52.1%) of these participants without follow‐up data had an unplanned admission within 6 months.

By 6 months, 55 of 142 participants with follow‐up data (38.7%) had had a total of 102 unplanned admissions to the study hospital. Of these, 42 of 55 (76%) were readmitted to internal medicine. Of the 55 participants with an unplanned readmission, 30 had only 1 unplanned readmission, 9 had 2, and 16 had 3 or more unplanned readmissions within 6 months of the index hospitalization.

During 6 month follow‐up of all 142 participants, there were also 97 planned (scheduled) admissions, 56 (58%) of which occurred in the group with an unplanned admission. Thus the 55 participants with an unplanned readmission accounted for a total of 1055 hospital bed‐days (mean 19.2 days per patient over 6 months follow‐up) while the 87 participants without an unplanned readmission used only 147 bed‐days (mean 1.7 days per patient over 6 months).

Bivariate analysis of the association of unplanned readmissions with the study variables is detailed in Table 2. BMI showed a nonlinear relationship with readmission, with a higher risk apparent at each end of the distribution. Depressive symptoms were also associated with a higher risk of readmission.

Age, sex, number of previous admissions, and discharge supports were not significantly different between the 2 groups. There was no difference in length of the index hospital stay: median length of stay was 6 days (interquartile range [IQR] 3‐14 days) in the readmitted group and 7 (IQR 4‐10 days) in the non‐readmitted group. There was a trend to higher mean number of medications in the readmitted group (9.4 vs. 8.8, P = 0.21).

The strongest predictor of readmission was the presence of a chronic disease diagnosis. Patterns of readmission for each primary clinical diagnosis are shown in Table 3. Chronic comorbidities including heart failure, chronic renal failure, and diabetes were associated with a higher risk of readmission (Table 4). Median comorbidity score was 3 (IQR 1‐5) in the readmitted group compared to 2 (IQR 1‐3) in the nonreadmitted group (P = 0.02).

On the basis of these findings and the literature, a multivariate binary logistic regression model for unplanned admission within 6 months was fitted, including chronic disease diagnosis, comorbiditiy score, BMI, functional status, and GDS as explanatory variables, and adjusting for the potential confounders of age and length of stay (as a severity surrogate). The model is shown in Table 5, and demonstrates a significant association between readmission and chronic conditions, BMI, and depressive symptoms. The area under the receiving operating curve was 0.73.

Discussion

This study demonstrates a number of important findings. First, 39% of this group of participants went on to further unplanned hospital readmissions in the ensuing 6 months, demonstrating the high risk in this group with more than 1 recent hospital admission. However, within this group, the risk of readmission was not related to the frequency of admission within the previous year, consistent with several previous studies.29, 30 These finding suggest that 1 or more recent previous admissions identified at the time of a medical admission is an effective identifier of high risk patients. Subgroup analysis of a recent discharge intervention study in medical patients suggests that this high risk group may particularly benefit from such an intervention.16

Second, the study describes important predictors of readmission which may inform novel interventions. The BMI showed a significant nonlinear relationship with readmission, with an increased risk both above and below the normal weight range. Almost half the group was overweight or obese, with a 2‐fold risk compared to normal weight patients. While underweight was less prevalent, it carried a markedly increased probability of readmission. Limited previous studies support the association of nutritional status and unplanned readmission.31, 32 Malnutrition may be a marker of disease stage or severity, or may be associated with other unmeasured social determinants which increase readmission risk. However, malnutrition itself may reduce physiological resilience and predispose to higher health care needs. There are no published trials of posthospital nutritional intervention programs for reducing readmission rates in general medical patients.

The risk of readmission was also increased in participants with depressive symptoms, consistent with several previous studies.3336 This effect was independent of illness type and comorbidity. Depression is increasingly recognized as an important independent predictor of a range of important outcomes in older medical patients, including posthospital functional decline,37 institutionalization and mortality.36, 38 Posthospital decline and poor self‐management might contribute to higher rehospitalization. There is some evidence that effective treatment of psychological comorbidities in medically ill patients may reduce readmissions.18, 39

Both the number and type of chronic conditions appear to be predictors of readmission in this high risk group, where there was a high baseline prevalence of chronic diseases such as heart failure, diabetes, renal impairment, and chronic lung disease which have been associated with higher readmission rates in a number of previous studies.13, 15, 30, 40 Almost all participants had one or more significant comorbid conditions in addition to their presenting complaint; single disease‐focused chronic disease management programs may not be an optimal solution in this group. Consistent with this comorbidity burden, most participants were prescribed a large number of medications. In keeping with other studies,17 about one‐third of participants reported reduced medication adherence but this was not associated with a higher readmission risk.

Like most previous studies in medical patients,10, 11, 15, 29, 30, 35, 36, 40, 41 there was no evidence of increasing readmission rates with age. Functional status impairment was not a significant predictor of readmission, probably reflecting selection of a patient subgroup with a high prevalence of disability and chronic disease. Satisfaction with social support was generally high, and not associated with readmission. This may reflect the emphasis on discharge planning and postacute social and functional support already occurring in usual care.

Measures of cognition and literacy were not associated with readmission. However, these were the items with the most missing data (see Table 2), which may have reduced our ability to detect an association. The study design excluded patients with significant cognitive or communication deficits who were unable to participate in detailed assessments. Such stringent eligibility criteria may be seen as a weakness of this study, reducing the generalizability of the findings. However, the study deliberately sampled a population of older adults suitable for a multifacetted posthospital management program, in order to inform specific intervention targets, and the eligibility criteria reflect these practical considerations. Although some previous studies have found that cognitive impairment is a predictor of readmission,10, 12 others have found no association.29, 30, 36, 40, 41

The main study weaknesses are the small sample size (reflected in the wide confidence intervals [CIs] in the multivariate analysis), and the relatively high rate of drop‐outs (25% of enrolments) for whom detailed posthospital data could not be collected. This problem reflects the age and burden of illness in the population under study. Readmission data were collected for all participants, and a similar rate of readmission was observed in patients with missing data (52% vs. 39%, P = 0.11). The heterogeneous patients mix may have concealed some important associations within individual diagnoses or other patient subgroups. This heterogeneity reflects the reality of the selected high risk subgroup, and the study deliberately avoided a disease‐specific focus for generalizability.

Conclusions

This study confirms the high rate of hospital readmission in medical patients who have already had a previous inpatient admission in the past 6 months. It shifts the emphasis from nonmodifiable disease and demographic predictors to consideration of common, nondisease specific factors which might have a plausible causative relationship with readmission and may be amenable to specific interventions. The population sampled had a high prevalence of chronic disease, and often multiple diseases. Nutritional status and depressive symptoms are emerging as important modifiers of disease course and mortality in the setting of several chronic diseases; this study also supports their potential contribution to increased hospital resource consumption in a high‐risk group. Posthospital programs which specifically address these factors in the context of optimal medical management of underlying chronic diseases have the potential to reduce hospital readmissions.

Hospital readmissions are recognized as both a significant contributor to health care costs and a putative indicator of healthcare quality.1, 2 Older medical patients with chronic medical comorbidities are at particularly high risk for hospital readmission3 and attendant risks of hospitalization.4 Many intervention strategies have been used in trials to reduce readmissions in such patients. Single interventions such as case management,5 care coordination,6 and self‐management7 have been disappointing. There is emerging evidence to support complex, multidisciplinary interventions which include outreach and support in the early post‐hospital period, especially in heart failure patients,8 but also in medical patients with a range of conditions.9 However, such interventions are resource intensive and it remains uncertain which patients may benefit most from interventions.

Although there are many studies of risk factors for hospital admission and readmission, few studies have reported predictors of recurrent readmission.1012 Patients with 2 or more recent hospitalizations are readily identifiable and have a substantially increased risk of hospital readmission compared to patients with only 1 recent hospitalization.10, 11, 1315 These patients may have a unique risk factor profile, and may be a group which may particularly benefit from complex interventions,16 but no previous study has specifically examined risk factors in this high‐risk group.

Previous studies of readmission predictors have largely focussed on demographic and disease characteristics which are not amenable to intervention at individual level. The results of such studies may determine a population at increased risk, but do not inform an intervention strategy.14 Psychological and behavioral factors such as depression and anxiety, perceptions of health, and adherence patterns may also contribute to hospitalizations.17, 18 However, the role of these factors in repeated admissions of medical patients has been poorly studied.

The aim of this study was to describe the association of a wider range of biological, functional, and psychosocial variables with the risk of unplanned hospital readmission within 6 months in medical patients with 2 or more recent hospitalizations. There was a particular emphasis on risk factors which might be amenable to intervention.

Methods

Results

Active screening over 12 months identified 1194 new admissions with a documented hospitalization in the previous 6 months. Of these, 85 were discharged prior to clinical review, 227 were aged less than 50 years, 16 died in hospital, and 153 had been screened previously in the study, leaving 713 individual patients for eligibility screening. Screening identified 328 of 713 (46.0%) patients eligible to participate in the study, who were approached for consent. Of these eligible patients, 190 of 328 (57.9%) agreed to participate but 48 of 190 (25%) did not complete posthospital follow‐up, leaving a total of 142 participants. Patient eligibility, consent, and follow‐up are detailed in Figure 1.

Figure 1

Demographic and disease characteristics of the study participants are shown in Table 1. The 48 participants without follow‐up data appeared similar to those with full data, and 25 (52.1%) of these participants without follow‐up data had an unplanned admission within 6 months.

By 6 months, 55 of 142 participants with follow‐up data (38.7%) had had a total of 102 unplanned admissions to the study hospital. Of these, 42 of 55 (76%) were readmitted to internal medicine. Of the 55 participants with an unplanned readmission, 30 had only 1 unplanned readmission, 9 had 2, and 16 had 3 or more unplanned readmissions within 6 months of the index hospitalization.

During 6 month follow‐up of all 142 participants, there were also 97 planned (scheduled) admissions, 56 (58%) of which occurred in the group with an unplanned admission. Thus the 55 participants with an unplanned readmission accounted for a total of 1055 hospital bed‐days (mean 19.2 days per patient over 6 months follow‐up) while the 87 participants without an unplanned readmission used only 147 bed‐days (mean 1.7 days per patient over 6 months).

Bivariate analysis of the association of unplanned readmissions with the study variables is detailed in Table 2. BMI showed a nonlinear relationship with readmission, with a higher risk apparent at each end of the distribution. Depressive symptoms were also associated with a higher risk of readmission.

Age, sex, number of previous admissions, and discharge supports were not significantly different between the 2 groups. There was no difference in length of the index hospital stay: median length of stay was 6 days (interquartile range [IQR] 3‐14 days) in the readmitted group and 7 (IQR 4‐10 days) in the non‐readmitted group. There was a trend to higher mean number of medications in the readmitted group (9.4 vs. 8.8, P = 0.21).

The strongest predictor of readmission was the presence of a chronic disease diagnosis. Patterns of readmission for each primary clinical diagnosis are shown in Table 3. Chronic comorbidities including heart failure, chronic renal failure, and diabetes were associated with a higher risk of readmission (Table 4). Median comorbidity score was 3 (IQR 1‐5) in the readmitted group compared to 2 (IQR 1‐3) in the nonreadmitted group (P = 0.02).

On the basis of these findings and the literature, a multivariate binary logistic regression model for unplanned admission within 6 months was fitted, including chronic disease diagnosis, comorbiditiy score, BMI, functional status, and GDS as explanatory variables, and adjusting for the potential confounders of age and length of stay (as a severity surrogate). The model is shown in Table 5, and demonstrates a significant association between readmission and chronic conditions, BMI, and depressive symptoms. The area under the receiving operating curve was 0.73.

Discussion

This study demonstrates a number of important findings. First, 39% of this group of participants went on to further unplanned hospital readmissions in the ensuing 6 months, demonstrating the high risk in this group with more than 1 recent hospital admission. However, within this group, the risk of readmission was not related to the frequency of admission within the previous year, consistent with several previous studies.29, 30 These finding suggest that 1 or more recent previous admissions identified at the time of a medical admission is an effective identifier of high risk patients. Subgroup analysis of a recent discharge intervention study in medical patients suggests that this high risk group may particularly benefit from such an intervention.16

Second, the study describes important predictors of readmission which may inform novel interventions. The BMI showed a significant nonlinear relationship with readmission, with an increased risk both above and below the normal weight range. Almost half the group was overweight or obese, with a 2‐fold risk compared to normal weight patients. While underweight was less prevalent, it carried a markedly increased probability of readmission. Limited previous studies support the association of nutritional status and unplanned readmission.31, 32 Malnutrition may be a marker of disease stage or severity, or may be associated with other unmeasured social determinants which increase readmission risk. However, malnutrition itself may reduce physiological resilience and predispose to higher health care needs. There are no published trials of posthospital nutritional intervention programs for reducing readmission rates in general medical patients.

The risk of readmission was also increased in participants with depressive symptoms, consistent with several previous studies.3336 This effect was independent of illness type and comorbidity. Depression is increasingly recognized as an important independent predictor of a range of important outcomes in older medical patients, including posthospital functional decline,37 institutionalization and mortality.36, 38 Posthospital decline and poor self‐management might contribute to higher rehospitalization. There is some evidence that effective treatment of psychological comorbidities in medically ill patients may reduce readmissions.18, 39

Both the number and type of chronic conditions appear to be predictors of readmission in this high risk group, where there was a high baseline prevalence of chronic diseases such as heart failure, diabetes, renal impairment, and chronic lung disease which have been associated with higher readmission rates in a number of previous studies.13, 15, 30, 40 Almost all participants had one or more significant comorbid conditions in addition to their presenting complaint; single disease‐focused chronic disease management programs may not be an optimal solution in this group. Consistent with this comorbidity burden, most participants were prescribed a large number of medications. In keeping with other studies,17 about one‐third of participants reported reduced medication adherence but this was not associated with a higher readmission risk.

Like most previous studies in medical patients,10, 11, 15, 29, 30, 35, 36, 40, 41 there was no evidence of increasing readmission rates with age. Functional status impairment was not a significant predictor of readmission, probably reflecting selection of a patient subgroup with a high prevalence of disability and chronic disease. Satisfaction with social support was generally high, and not associated with readmission. This may reflect the emphasis on discharge planning and postacute social and functional support already occurring in usual care.

Measures of cognition and literacy were not associated with readmission. However, these were the items with the most missing data (see Table 2), which may have reduced our ability to detect an association. The study design excluded patients with significant cognitive or communication deficits who were unable to participate in detailed assessments. Such stringent eligibility criteria may be seen as a weakness of this study, reducing the generalizability of the findings. However, the study deliberately sampled a population of older adults suitable for a multifacetted posthospital management program, in order to inform specific intervention targets, and the eligibility criteria reflect these practical considerations. Although some previous studies have found that cognitive impairment is a predictor of readmission,10, 12 others have found no association.29, 30, 36, 40, 41

The main study weaknesses are the small sample size (reflected in the wide confidence intervals [CIs] in the multivariate analysis), and the relatively high rate of drop‐outs (25% of enrolments) for whom detailed posthospital data could not be collected. This problem reflects the age and burden of illness in the population under study. Readmission data were collected for all participants, and a similar rate of readmission was observed in patients with missing data (52% vs. 39%, P = 0.11). The heterogeneous patients mix may have concealed some important associations within individual diagnoses or other patient subgroups. This heterogeneity reflects the reality of the selected high risk subgroup, and the study deliberately avoided a disease‐specific focus for generalizability.

Conclusions

This study confirms the high rate of hospital readmission in medical patients who have already had a previous inpatient admission in the past 6 months. It shifts the emphasis from nonmodifiable disease and demographic predictors to consideration of common, nondisease specific factors which might have a plausible causative relationship with readmission and may be amenable to specific interventions. The population sampled had a high prevalence of chronic disease, and often multiple diseases. Nutritional status and depressive symptoms are emerging as important modifiers of disease course and mortality in the setting of several chronic diseases; this study also supports their potential contribution to increased hospital resource consumption in a high‐risk group. Posthospital programs which specifically address these factors in the context of optimal medical management of underlying chronic diseases have the potential to reduce hospital readmissions.

Hospital readmissions are recognized as both a significant contributor to health care costs and a putative indicator of healthcare quality.1, 2 Older medical patients with chronic medical comorbidities are at particularly high risk for hospital readmission3 and attendant risks of hospitalization.4 Many intervention strategies have been used in trials to reduce readmissions in such patients. Single interventions such as case management,5 care coordination,6 and self‐management7 have been disappointing. There is emerging evidence to support complex, multidisciplinary interventions which include outreach and support in the early post‐hospital period, especially in heart failure patients,8 but also in medical patients with a range of conditions.9 However, such interventions are resource intensive and it remains uncertain which patients may benefit most from interventions.

Although there are many studies of risk factors for hospital admission and readmission, few studies have reported predictors of recurrent readmission.1012 Patients with 2 or more recent hospitalizations are readily identifiable and have a substantially increased risk of hospital readmission compared to patients with only 1 recent hospitalization.10, 11, 1315 These patients may have a unique risk factor profile, and may be a group which may particularly benefit from complex interventions,16 but no previous study has specifically examined risk factors in this high‐risk group.

Previous studies of readmission predictors have largely focussed on demographic and disease characteristics which are not amenable to intervention at individual level. The results of such studies may determine a population at increased risk, but do not inform an intervention strategy.14 Psychological and behavioral factors such as depression and anxiety, perceptions of health, and adherence patterns may also contribute to hospitalizations.17, 18 However, the role of these factors in repeated admissions of medical patients has been poorly studied.

The aim of this study was to describe the association of a wider range of biological, functional, and psychosocial variables with the risk of unplanned hospital readmission within 6 months in medical patients with 2 or more recent hospitalizations. There was a particular emphasis on risk factors which might be amenable to intervention.

Methods

Results

Active screening over 12 months identified 1194 new admissions with a documented hospitalization in the previous 6 months. Of these, 85 were discharged prior to clinical review, 227 were aged less than 50 years, 16 died in hospital, and 153 had been screened previously in the study, leaving 713 individual patients for eligibility screening. Screening identified 328 of 713 (46.0%) patients eligible to participate in the study, who were approached for consent. Of these eligible patients, 190 of 328 (57.9%) agreed to participate but 48 of 190 (25%) did not complete posthospital follow‐up, leaving a total of 142 participants. Patient eligibility, consent, and follow‐up are detailed in Figure 1.

Figure 1

Demographic and disease characteristics of the study participants are shown in Table 1. The 48 participants without follow‐up data appeared similar to those with full data, and 25 (52.1%) of these participants without follow‐up data had an unplanned admission within 6 months.

By 6 months, 55 of 142 participants with follow‐up data (38.7%) had had a total of 102 unplanned admissions to the study hospital. Of these, 42 of 55 (76%) were readmitted to internal medicine. Of the 55 participants with an unplanned readmission, 30 had only 1 unplanned readmission, 9 had 2, and 16 had 3 or more unplanned readmissions within 6 months of the index hospitalization.

During 6 month follow‐up of all 142 participants, there were also 97 planned (scheduled) admissions, 56 (58%) of which occurred in the group with an unplanned admission. Thus the 55 participants with an unplanned readmission accounted for a total of 1055 hospital bed‐days (mean 19.2 days per patient over 6 months follow‐up) while the 87 participants without an unplanned readmission used only 147 bed‐days (mean 1.7 days per patient over 6 months).

Bivariate analysis of the association of unplanned readmissions with the study variables is detailed in Table 2. BMI showed a nonlinear relationship with readmission, with a higher risk apparent at each end of the distribution. Depressive symptoms were also associated with a higher risk of readmission.

Age, sex, number of previous admissions, and discharge supports were not significantly different between the 2 groups. There was no difference in length of the index hospital stay: median length of stay was 6 days (interquartile range [IQR] 3‐14 days) in the readmitted group and 7 (IQR 4‐10 days) in the non‐readmitted group. There was a trend to higher mean number of medications in the readmitted group (9.4 vs. 8.8, P = 0.21).

The strongest predictor of readmission was the presence of a chronic disease diagnosis. Patterns of readmission for each primary clinical diagnosis are shown in Table 3. Chronic comorbidities including heart failure, chronic renal failure, and diabetes were associated with a higher risk of readmission (Table 4). Median comorbidity score was 3 (IQR 1‐5) in the readmitted group compared to 2 (IQR 1‐3) in the nonreadmitted group (P = 0.02).

On the basis of these findings and the literature, a multivariate binary logistic regression model for unplanned admission within 6 months was fitted, including chronic disease diagnosis, comorbiditiy score, BMI, functional status, and GDS as explanatory variables, and adjusting for the potential confounders of age and length of stay (as a severity surrogate). The model is shown in Table 5, and demonstrates a significant association between readmission and chronic conditions, BMI, and depressive symptoms. The area under the receiving operating curve was 0.73.

Discussion

This study demonstrates a number of important findings. First, 39% of this group of participants went on to further unplanned hospital readmissions in the ensuing 6 months, demonstrating the high risk in this group with more than 1 recent hospital admission. However, within this group, the risk of readmission was not related to the frequency of admission within the previous year, consistent with several previous studies.29, 30 These finding suggest that 1 or more recent previous admissions identified at the time of a medical admission is an effective identifier of high risk patients. Subgroup analysis of a recent discharge intervention study in medical patients suggests that this high risk group may particularly benefit from such an intervention.16

Second, the study describes important predictors of readmission which may inform novel interventions. The BMI showed a significant nonlinear relationship with readmission, with an increased risk both above and below the normal weight range. Almost half the group was overweight or obese, with a 2‐fold risk compared to normal weight patients. While underweight was less prevalent, it carried a markedly increased probability of readmission. Limited previous studies support the association of nutritional status and unplanned readmission.31, 32 Malnutrition may be a marker of disease stage or severity, or may be associated with other unmeasured social determinants which increase readmission risk. However, malnutrition itself may reduce physiological resilience and predispose to higher health care needs. There are no published trials of posthospital nutritional intervention programs for reducing readmission rates in general medical patients.

The risk of readmission was also increased in participants with depressive symptoms, consistent with several previous studies.3336 This effect was independent of illness type and comorbidity. Depression is increasingly recognized as an important independent predictor of a range of important outcomes in older medical patients, including posthospital functional decline,37 institutionalization and mortality.36, 38 Posthospital decline and poor self‐management might contribute to higher rehospitalization. There is some evidence that effective treatment of psychological comorbidities in medically ill patients may reduce readmissions.18, 39

Both the number and type of chronic conditions appear to be predictors of readmission in this high risk group, where there was a high baseline prevalence of chronic diseases such as heart failure, diabetes, renal impairment, and chronic lung disease which have been associated with higher readmission rates in a number of previous studies.13, 15, 30, 40 Almost all participants had one or more significant comorbid conditions in addition to their presenting complaint; single disease‐focused chronic disease management programs may not be an optimal solution in this group. Consistent with this comorbidity burden, most participants were prescribed a large number of medications. In keeping with other studies,17 about one‐third of participants reported reduced medication adherence but this was not associated with a higher readmission risk.

Like most previous studies in medical patients,10, 11, 15, 29, 30, 35, 36, 40, 41 there was no evidence of increasing readmission rates with age. Functional status impairment was not a significant predictor of readmission, probably reflecting selection of a patient subgroup with a high prevalence of disability and chronic disease. Satisfaction with social support was generally high, and not associated with readmission. This may reflect the emphasis on discharge planning and postacute social and functional support already occurring in usual care.

Measures of cognition and literacy were not associated with readmission. However, these were the items with the most missing data (see Table 2), which may have reduced our ability to detect an association. The study design excluded patients with significant cognitive or communication deficits who were unable to participate in detailed assessments. Such stringent eligibility criteria may be seen as a weakness of this study, reducing the generalizability of the findings. However, the study deliberately sampled a population of older adults suitable for a multifacetted posthospital management program, in order to inform specific intervention targets, and the eligibility criteria reflect these practical considerations. Although some previous studies have found that cognitive impairment is a predictor of readmission,10, 12 others have found no association.29, 30, 36, 40, 41

The main study weaknesses are the small sample size (reflected in the wide confidence intervals [CIs] in the multivariate analysis), and the relatively high rate of drop‐outs (25% of enrolments) for whom detailed posthospital data could not be collected. This problem reflects the age and burden of illness in the population under study. Readmission data were collected for all participants, and a similar rate of readmission was observed in patients with missing data (52% vs. 39%, P = 0.11). The heterogeneous patients mix may have concealed some important associations within individual diagnoses or other patient subgroups. This heterogeneity reflects the reality of the selected high risk subgroup, and the study deliberately avoided a disease‐specific focus for generalizability.

Conclusions

This study confirms the high rate of hospital readmission in medical patients who have already had a previous inpatient admission in the past 6 months. It shifts the emphasis from nonmodifiable disease and demographic predictors to consideration of common, nondisease specific factors which might have a plausible causative relationship with readmission and may be amenable to specific interventions. The population sampled had a high prevalence of chronic disease, and often multiple diseases. Nutritional status and depressive symptoms are emerging as important modifiers of disease course and mortality in the setting of several chronic diseases; this study also supports their potential contribution to increased hospital resource consumption in a high‐risk group. Posthospital programs which specifically address these factors in the context of optimal medical management of underlying chronic diseases have the potential to reduce hospital readmissions.

Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5

To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.

To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.

Methods

Results

Discussion

In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.

Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31

Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.

Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.

Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.

While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.

This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.

Conclusions

Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.

Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5

To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.

To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.

Methods

Results

Discussion

In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.

Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31

Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.

Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.

Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.

While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.

This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.

Conclusions

Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.

Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5

To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.

To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.

Methods

Results

Discussion

In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.

Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31

Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.

Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.

Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.

While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.

This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.

Conclusions

Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.

A 27‐year‐old woman with a history of asthma presented to her primary care physician (PCP) with a sore throat which began after attending a party where she shared alcoholic beverages with friends. She denied any high‐risk sexual behavior. Her PCP prescribed azithromycin and methylprednisolone empirically for tonsillitis. The throat pain subsided, but in the next several days she experienced increased weakness, lethargy, poor appetite, and chills, and she returned to her PCP for reevaluation.

Two months prior she had been treated at a walk‐in clinic with a course of penicillin for a presumed streptococcal pharyngitis. Her symptoms resolved until her current presentation.

In a young woman with 2 episodes of pharyngitis in 2 months followed by an acute systemic illness, one must consider an immunocompromised state such as human immunodeficiency virus (HIV), hematologic malignancy, or autoimmune diseases. Weakness, lethargy, anorexia, and chills in the setting of pharyngitis suggest a local process in the neck, most likely infection associated with systemic toxicity. As neck abscess and bacteremia warrant early consideration, the physical examination should focus on the neck and oropharynx, as well as neurologic exam to evaluate for bacterial spreading into the central nervous system. In addition to routine laboratory studies, a chest x‐ray (CXR) would be appropriate as upper respiratory infections may be complicated by pneumonia and present with signs and symptoms of systemic illness.

On examination by her PCP, her temperature was 99.2F and her blood pressure was 118/68. She had bilateral oropharyngeal erythema without exudates and bilateral tonsillar and anterior triangle lymphadenopathy (LAD). An oropharyngeal rapid Streptococcal antigen detection test was negative, but a Monospot test was positive for heterophile antibodies. Azithromycin and methylprednisolone were discontinued, and the patient was informed she most likely had Epstein Barr Virus (EBV) infection.

The following day, the complete blood count results returned. The platelet count was 50 K/L and the white blood cell (WBC) count was 13.0 K/L. The patient stated she had developed right‐sided flank pain upon deep inspiration and used her albuterol inhaler with minimal relief. She continued to have fever, decreased appetite, chest and abdominal pain, and difficulty swallowing due to odynophagia. She was instructed to go to the emergency department (ED) for further evaluation. In the ED, she denied any shortness of breath, but reported a slight cough and right‐sided abdominal pain.

Acute tonsillar pharyngitis and fever, as well as systemic symptoms of fatigue and abdominal pain along with positive heterophile screen are highly suggestive of EBV infection in this young female. The episode of pharyngitis 2 months prior remains unexplained and may be unrelated. Right‐sided pleuritic pain and abdominal pain may be related to EBV hepatitis. Odynophagia is consistent with EBV infection as well. Profound lethargy, however, is not a common presenting feature in mononucleosis unless infected patients are profoundly dehydrated due to inability to swallow. Her pain symptoms may be secondary to other signs of EBV infection, such as hepatomegaly, splenomegaly, ascites, and/or right pleural effusion. A history of rash should be investigated. Initial assessment in this acutely ill patient should focus on evaluation for the presence of severe sepsis and for a primary source of infection. Given the severity of her illness, I would consider early computed tomography (CT) of her chest, abdomen, and pelvis, as well as CT of the neck to exclude a possibility of peritonsillar abscess. The complaint of chills indicates a possible bacteremia, so coverage with broad‐spectrum antibiotics is indicated. Symptomatic relief with acetaminophen and intravenous fluid rehydration is appropriate.

On exam, temperature was 101.9F, blood pressure was 111/74, heart rate was 140 beats per minute, respiratory rate was 18 per minute, and oxygen saturation was 99% on room air. She appeared drowsy, but answered questions appropriately. She had bilateral swollen tonsils, as well as anterior and posterior cervical adenopathy, with tenderness greater on the left side. Her chest exam had slightly diminished breath sounds at the bases bilaterally. Heart rhythm was regular, and there were no murmurs appreciated. On abdominal exam, she was tender to palpation in both right‐upper and left‐upper quadrants, without obvious hepatosplenomegaly. There were no petechiae noted on her skin.

The WBC was 17.6 K/L, with 89% neutrophils and 5% lymphocytes, platelet count was 22 K/L, and hemoglobin was 13.8 g/dL. A D‐dimer test was elevated at 1344 ng/mL. Peripheral blood smear showed thrombocytopenia and neutrophilia, but demonstrated no schistocytes. The serum potassium was 3.2 mEq/L, bicarbonate was 29 mEq/L, blood urea nitrogen was 15 mg/dL and the creatinine was 1.29 mg/dL. Transaminases were within normal limits, but total bilirubin was 1.8 mg/dL. Her urinalysis was normal. Blood cultures were sent. A CXR showed bibasilar consolidations and pleural effusions (Figure 1). A CT of the chest with contrast was obtained that showed multiple confluent and patchy foci of consolidation in the lung bases, with trace bilateral pleural effusions (Figure 2). A CT of the abdomen showed a spleen at the upper limits of normal in size, measuring 13 cm in length, but was otherwise normal.

Figure 1

Figure 2

Leukocytosis with lymphopenia is not consistent with EBV infection and another process needs to be considered. This patient meets criteria for sepsis syndrome and should receive broad spectrum antibiotics, such as vancomycin and piperacillin‐tazobactam immediately after the blood cultures are sent, in addition to further evaluation to determine the source of sepsis. Depending on her mental status response to initial measures such as acetaminophen and hydration, one should consider a lumbar puncture, which would require platelet transfusion and may therefore not be done immediately. HIV serology should be performed, since acute retroviral syndrome can mimic this presentation. With neck tenderness that is more localized to her left side, a CT of her neck to evaluate for an abscess may be helpful.

She was admitted for presumed community‐acquired pneumonia complicating an upper respiratory tract infection. Her pharyngitis was thought to be of viral etiology. Moxifloxacin was started and intravenous fluids were administered. She was started on prednisone 60 mg daily for presumed immune‐mediated thrombocytopenia related to EBV infection. An HIV antibody test and quantitative polymerase chain reaction (PCR) were both negative. The EBV immunoglobulin G (IgG) titer was positive (>1:10), but the IgM titer was negative. Her mental status improved after starting moxifloxacin and fluids. Her creatinine and bilirubin normalized to 0.97 mg/dL and 0.8 mg/dL respectively. She continued to have a tender left‐sided submandibular swelling. Blood cultures grew Gram‐negative bacilli in 2 anaerobic bottles.

I am uncomfortable with moxifloxacin as initial empiric therapy because at presentation she had sepsis syndrome as well as a suspected immunocompromised state. In addition, moxifloxacin would not be adequate coverage for anaerobic organisms if a peritonsillar abscess was involved. At this point, she needs a CT of her neck to look for a focus of infection which may require surgical management and, if negative, further imaging such as a tagged white blood scan to identify the source of the anaerobes.

Moxifloxacin was switched to piperacillin‐tazobactam and prednisone was discontinued. By day 4 of hospitalization her platelet count had risen to 261 K/L. Her WBC continued to rise to a peak of 21.5 K/L and she continued to have fevers and diffuse pains, although her repeat blood cultures were negative. She continued to have tenderness of the cervical lymph nodes, left greater than right. A repeat CXR showed patchy air space disease bilaterally and pleural effusions, both of which had progressed compared with the prior film. Clindamycin was empirically added to her antibiotic regimen in light of her progressing pneumonia and evidence of anaerobic infection. A repeat CT scan of her chest revealed multiple nodular opacities scattered throughout the lung fields, some of which were cavitary, predominating in the lung bases. The CT scan of her neck revealed a left peritonsillar abscess and phlegmon in the left retropharyngeal and deep neck area along the sternocleidomastoid and internal jugular vein (IJV). There also was noted a large thrombus within the left IJV extending superiorly to involve the jugular bulb, sigmoid sinus, and distal left transverse sinus; and inferiorly to near the origin of the brachiocephalic vein (see Figure 3). An echocardiogram did not reveal any vegetations.

Figure 3

The combination of recent pharyngitis, septic pulmonary emboli, and IJV thrombosis is consistent with a diagnosis of Lemierre's syndrome (LS). This is a life threatening condition, even if diagnosis is made early and appropriate treatment is started. The most likely causative agent is Fusobacterium necrophorum. In this case it was important to realize that clinical presentation was not consistent with EBV infection, even though heterophile screen was positive. Early initiation of broad spectrum antibiotics as well as CT scan of the neck would have been appropriate.

The diagnosis of LS was made. The blood culture speciation revealed Fusobacterium nucleatum, which was too fastidious to perform antimicrobial sensitivities. Her symptoms improved significantly with the addition of clindamycin to piperacillin‐tazobactam, which was postulated to be the result of bacterial beta‐lactamase activity mitigating the efficacy of piperacillin‐tazobactam. Thoracentesis of her pleural effusion did not reveal an empyema. Due to her large thrombus burden, she was started on anticoagulation with heparin and transitioned to outpatient coumadin. She was switched to metronidazole as a single agent antibiotic for 6 weeks, and on outpatient follow‐up was doing well.

Commentary

LS was described by Dr. Andre Lemierre in 1936.6 The syndrome consists of a primary oropharyngeal infection, thrombosis of the IJV, bacteremia, and septic metastatic foci, usually involving the lungs.1, 2 LS is a form of necrobacillosis, which is a systemic infection resulting from F. necrophorum.3, 4 In classic LS, the initial pharyngitis is usually a tonsillar or peritonsillar abscess, and is followed by intense fever and rigors after 4 days to 2 weeks.1, 3 This is followed by a unilateral painful submaxillary LAD and IJV thrombophlebitisthe cord sign.2 Finally, bacteremia and distant metastatic pyogenic abscesses develop.1 (see Table 1).

With the advent of antibiotics, LS is now rare with an incidence of 0.9 per million persons per year. In Lemierre's time, the disease was fulminant and led to death within 2 weeks, but in the antibiotic age the mortality rate is 4.9%.1, 3 The median age of an LS patient is 19 years, with a higher incidence in males.1, 35 Although in the literature it is referred to as the forgotten disease, there is evidence the incidence is increasing.3, 4, 6, 8

There are variations of classic LS. Bacteremia may occur much later than the initial pharyngitis, the disease may be less aggressive, the thrombus may be in the external jugular vein, or there may be no identified thrombus.3, 4, 8 In fact, a thrombus is only identified in 36% of cases.9 The primary infection may be a head and neck infection that is not pharyngitis, such as an odontogenic infection,4 or may not be identified.10 Despite variations, the fundamentals of diagnosis are prior head and neck infection, presumed thrombophlebitis and bacteremia, and evidence of septic metastatic foci.

The genus Fusobacterium comprises anaerobic, nonspore forming gram negative bacilli.1, 35, 11 F. necrophorum and F. nucleatum are 2 species within this genus. F. nucleatum causes the majority of reported human bacteremias by Fusobacterium species, but it is F. necrophorum that is most associated with anaerobic oropharyngeal infections, thrombocytopenia, clot formation, and LS.35, 8, 9

It is unknown if Fusobacterium species directly cause the sore throat, or rather are bystanders which thrive once a favorable anaerobic environment is created via endotoxins and exotoxins.35 A break in oral mucosa via trauma or coinfection with bacteria/viruses (especially EBV) is also thought to play a role with infection.2, 3, 5 One‐third of LS cases have coinfection with other oropharyngeal flora. Thus, one must reexamine the anaerobic blood cultures after an organism has been identified in suspect cases.3, 4

There is an increased association of LS with EBV infection, likely due to viral‐induced and steroid‐induced immunosuppression.24 False positive heterophile tests are reported with LS, so the specific antibody tests for EBV must be checked.3, 4

Once thrombophlebitis occurs, the bacteria can metastasize to distant sites. In 80% to 92% of LS cases, the metastatic complication is a pleuro‐pulmonary infection, consisting of septic pulmonary emboli, empyema, and pleural effusions, but extra‐pulmonary lesions occur.1, 3, 9, 12 Abdominal pain usually results from abdominal microabscesses or thrombophlebitis.4 Mild renal impairment and abnormal liver function tests are common.3, 4 Cranial nerve palsies and Horner's syndrome are rare and indicate carotid sheath involvement.3, 12 An elevated C‐reactive protein can distinguish bacterial from uncomplicated viral pharyngitis.3, 4 Also, rigors are unusual in tonsillitis, and their presence indicate bacterial entry into the circulation.3

CXRs may reveal the pulmonary septic emboli. Ultrasound of the IJV is inexpensive and noninvasive, but may have limited sensitivity for an acute thrombus. CT scan allows increased visualization of anatomy, but can have decreased sensitivity and specificity for thrombosis.3 Magnetic resonance imaging (MRI) is recommended if LS results from mastoiditis, to exclude an intracerebral vein thrombosis.9

Antibiotics have both dramatically decreased the incidence of LS and improved its prognosis. The recent rise in incidence may be due to a renewed interest in restricting the use of antibiotics in cases of pharyngitis, as well as an increased use of macrolides, to which F. necrophorum is frequently resistant.3 Decreased tonsillectomies may also have a role, as LS is more common with retained tonsils.1, 3

No trials have evaluated the optimal antibiotic regimen. Fusobacterium species are sensitive to penicillin, but 23% have beta‐lactamase activity as reported clinically by several authors.3, 5 F. necrophorum is also sensitive to metronidazole, ticarcillin‐clavulanate, cefoxitin, amoxicillin‐clavulanate, imipenem, and clindamycin. There is a high resistance to macrolides and gentamicin, and the activity of tetracyclines is poor. For treatment, most authors suggest a carbapenem, a penicillin/beta‐lactamase inhibitor combination, or metronidazole. Clindamycin has weaker bactericidal activity than metronidazole or imipenem. Metronidazole is preferred because of its activity against all Fusobacterium species, good penetration into tissues, bactericidal activity, low minimum inhibitory concentration, and ability to achieve high concentration in the cerebrospinal fluid if meningitis occurs. An effective regimen is metronidazole with a penicillinase‐resistant penicillin to cover for mixed coinfection with streptococci or staphylococci.3, 4, 12 A 6‐week antibiotic course is given for adequate penetration into the protective fibrin clots.4

Reports have shown good outcomes both with and without the use of anticoagulation.3, 4, 8 Support for anticoagulation is extrapolated from experience with septic pelvic thrombophlebitis, in which anticoagulation results in more rapid resolution of symptoms.13 Given the lack of firm evidence in cases of LS, anticoagulation is typically reserved for poor clinical response despite 2 to 3 days of antibiotic therapy or propagation of thromboses into the cavernous sinus. It is generally given for 3 months.4, 13

Prior to the antibiotic era, surgical ligation or excision of the IJV was done without clear benefit. Today, surgery is reserved for cases of continued septic emboli or extension of thrombus despite aggressive medical therapy.3 If mediastinitis develops, then surgical intervention is essential.4

Lemierre stated that the symptoms and signs of LS are so characteristic that it permits diagnosis before bacteriological examination.1 However, today it may go unrecognized by physicians until a blood culture shows anaerobes or Fusobacterium species. For a young patient admitted with pneumonia preceded by pharyngitis, hospitalists must remain vigilant for the presence of LS.