Christopher S. Johnson, MPH

Pneumonia remains a significant problem in the United States, both in terms of mortality as well as cost to the healthcare system. Each year, over 1 million patients are hospitalized for pneumonia, with costs conservatively estimated at more than $7 billion in 2010.[1] One contributing factor for these costs is that pneumonia is the second most frequent reason for rehospitalization.[2] Rehospitalization after admission for pneumonia is now used as a marker for quality of care.

Given the cost and adverse outcomes of pneumonia, it is important to examine early rehospitalization to better understand what factors play a role. Studies have examined factors associated with readmission following an initial pneumonia‐related hospitalization. Adamuz et al. showed that additional healthcare visits and rehospitalizations within 30 days of discharge from the hospital were common and were associated with worsening of signs or symptoms of pneumonia and/or comorbidities.[3] Other studies demonstrated that instability on hospital discharge, as well as treatment failure, were associated with increased risk of rehospitalization.[4, 5] Jasti et al. found rehospitalizations following pneumonia were usually comorbidity related, primarily cardiopulmonary and/or neurologic disease, rather than due to the index pneumonia.[6] Many of these studies, and others looking at age, activity of daily living score, socioeconomic status, and comorbidity characteristics, were performed in relatively small cohorts. Predictors of rehospitalization have not been studied in a large cohort of patients in an integrated healthcare system.

Our study looks at factors not addressed in prior studies that have used administrative claims data to identify factors associated with early readmission. We also evaluated these admission risk factors in a veteran population, whereas prior studies have primarily focused on those who receive Medicare. The purpose of this study was to examine predictors of early (30 days) readmission in the Veterans Affairs (VA) healthcare system for patients age 65 years and older hospitalized for pneumonia. Our a priori hypothesis was that comorbid illnesses, such as congestive heart failure and chronic obstructive pulmonary disease, and patients with high medical complexity, such as high number of medications and/or prior hospitalizations and nursing home residence, are the primary factors associated with increased risk of rehospitalization.

METHODS

For this national cohort study, we used data from the VA healthcare system administrative and clinical databases that serve as repositories of clinical data from more than 150 VA hospitals and 850 outpatient clinics throughout the United States. The institutional review boards of the University of Texas Health Science Center at San Antonio and VA North Texas Health Care System approved this study. Details regarding the study design and methods were previously published.[7]

RESULTS

Predictors of 30‐Day Readmission

All variables listed in Table 1 were evaluated. The derivation logistic regression model is shown in Table 2, and only includes predictors associated with rehospitalization. Antibiotic concordance and time in the ICU were evaluated and did not predict readmission. Variables that were significantly associated with rehospitalization in the models included age, marital status, the number of emergency department clinic visits a year prior, prior admission within 90 days, number of nonpharmacy clinic visits in a year prior, and hospital length of stay. The increasing age and number of emergency department and clinic visits were associated with higher odds of readmission. The longer the length of stay and those patients who were married had a lower odds of subsequent readmission. Other variables included were the presence of chronic renal disease, prior malignancy, nursing home residence, congestive heart failure, and prior use of oral corticosteroids. The C statistics for the derivation and validation models were 0.615 and 0.613, respectively. There were no significant differences in any of the variables between the derivation and validation cohorts.

Table 2.Multivariable Predictors of 30‐Day Readmission (Derivation Cohort)

Predictors	OR	95% CI		P Value
		Lower	Upper
NOTE: Abbreviations: AIDS, acquired immune deficiency syndrome; CI, confidence interval; OR, odds ratio. a Includes only predictors from Table 1 that predicted readmission. b Reference group includes native Hawaiian, American Indian, black, Asian, and unknown. c Reference group includes non‐Hispanic white, non‐Hispanic black, Asian, American Indian, and unknown. d Reference group includes never married, separated/divorced, widowed, and unknown.
Sociodemographics
Age at admission	1.01	1.00	1.02	<0.01
White	1.14	1.02	1.28	0.02
Nonwhiteb	1.00
Hispanic	1.26	1.07	1.47	0.01
Not Hispanicc	1.00
Married	0.89	0.82	0.96	<0.01
Not marriedd	1.00
Nursing home residence	1.68	1.18	2.39	<0.01
Comorbid illnesses
Peptic ulcer disease	1.24	1.02	1.50	0.03
Chronic renal disease	1.18	1.06	1.33	<0.01
Any prior malignancy	1.17	1.06	1.28	<0.01
Metastatic solid tumor	1.20	0.98	1.47	0.08
AIDS	1.99	1.06	3.74	0.03
Index admission factors
Heart failure on initial admission	1.18	1.07	1.29	<0.01
Length of stay (per day)	0.97	0.97	0.98	<0.01
Medication history
Cardiovascular drug count 90 days prior	1.03	1.00	1.06	0.02
Oral corticosteroids 90 days prior	1.16	1.06	1.27	<0.01
Prior medical utilization
Number of emergency department visits within 1 year	1.04	1.02	1.06	<0.01
Number of specialty clinic visits within 1 year	1.01	1.00	1.02	0.01
Prior hospital admission within 90 days	1.60	1.46	1.75	<0.01
Number of nonpharmacy clinic visits within 1 year	1.00	1.00	1.01	<0.01

Figure 1 shows that the derivation and validation models are similar in percent rehospitalization group in all severity groups.

Figure 1

DISCUSSION

Sociodemographics and comorbidities, which are host factors, were significantly associated with rehospitalization of elderly patients admitted for pneumonia and length of hospital stay, the only pneumonia‐specific variable associated with readmission.[6, 14, 15, 16] Higher rates of all types of prior utilization (inpatient, outpatient, emergency department visits) were also associated with higher risk of rehospitalization. Our findings highlight the role that host factors play in rehospitalization of elderly patients with pneumonia and that there are few potentially modifiable targets to help reduce readmissions after pneumonia.

These findings may indicate 2 things: patients in this cohort are sicker than the general population at baseline and therefore require more medical care, and/or they are being evaluated by more providers increasing the chance they will be readmitted. For the former, an example is the association of prior use of oral corticosteroids with rehospitalization, which may demonstrate poorly controlled comorbidities (eg, chronic obstructive lung disease with recent exacerbation) that require steroid use. Given the VA's integrated healthcare system, greater access to primary care providers may lead to more opportunities for patients to voice their complaints, which can lead to more readmissions among severely ill patients. Weinberger et al. found an increase in hospital readmissions with increased access to primary care.[17] The association of readmission and length of stay is less likely related to pneumonia‐related clinical instabilities on discharge, but rather to the ability to address other potential issues related to the patient's underlying comorbidities.[5]

Our study presents a model looking at rehospitalization of patients with pneumonia, including factors not addressed in a prior publication by Lindenauer et al., the model Centers for Medicare and Medicaid Services (CMS) uses to predict early rehospitalization.[18] Lindenauer's model included 39 variables (of which 37 were clinical variables), including vertebral fractures, other injuries, other gastrointestinal disorders, other urinary tract disorders, as well as demographic and comorbidity factors that were similar to some of those included in our study. Their 30‐day rehospitalization medical record and administrative models had a C statistic of 0.59 and 0.63, respectively. A major difference between our presented model and the latter are the data on utilization of medical care. Despite these additional data, the resulting C statistics of 0.615 and 0.613 were not qualitatively better.

The similar C statistics demonstrates that factors playing a significant role in determining rehospitalization have yet to be identified, and it is not clear if researchers will be able to identify potentially modifiable risk factors for rehospitalization. Examples of other potential factors that should be examined in future studies include family/community support, quality of the transition of care back to their residence, and quality of inpatient care. Calvillo‐King et al. found a broad range of social factors that potentially affect the risk of postdischarge readmission for chronic heart failure.[19] For pneumonia, there were few studies that assessed the risk of social factors on readmission, and these were most commonly rather broad categories such as race/ethnicity or nursing home residence. In the publications that studied social factors and readmissions, there were inconsistent associations between hospital readmission with lower education, low income, and unemployment. Future articles may benefit from taking these additional factors into account.

Potential limitations to our study include the overwhelming majority of patients being male and in a VA population, which may limit generalizability. In addition, no data were available on the quality of the transition of care. Furthermore, limitations include the age of the data and the lack of evaluation of outpatient follow‐up and antibiotics on discharge. Though our data are up to 10 years old, this study evaluates risk factors that are still relevant. Finally, some variables noted in the CMS model were not evaluated in our study, such as protein‐calorie malnutrition, skin ulcers, and fracture history. Given these differences, we were unable to directly compare the performance of the 2 models.

Despite examining novel factors related to prior healthcare utilization, our model still performed suboptimally in identifying subjects at risk for rehospitalization after pneumonia. Future studies need to examine other social factors and patient frailty that may increase the risk of readmissions, as well as examine the impact of clinical stability on discharge. It remains to be determined if readmissions after pneumonia are truly preventable or mostly attributable to the patient's prior health status.

Disclosures: The project described was supported by grant number R01NR010828 from the National Institute of Nursing Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Nursing Research or the National Institutes of Health. This material is the result of work supported with resources and the use of facilities at the VA North Texas Health Care System. Funding agencies had no role in conducting the study, or role in the preparation, review, or approval of the manuscript. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.

Pneumonia remains a significant problem in the United States, both in terms of mortality as well as cost to the healthcare system. Each year, over 1 million patients are hospitalized for pneumonia, with costs conservatively estimated at more than $7 billion in 2010.[1] One contributing factor for these costs is that pneumonia is the second most frequent reason for rehospitalization.[2] Rehospitalization after admission for pneumonia is now used as a marker for quality of care.

Given the cost and adverse outcomes of pneumonia, it is important to examine early rehospitalization to better understand what factors play a role. Studies have examined factors associated with readmission following an initial pneumonia‐related hospitalization. Adamuz et al. showed that additional healthcare visits and rehospitalizations within 30 days of discharge from the hospital were common and were associated with worsening of signs or symptoms of pneumonia and/or comorbidities.[3] Other studies demonstrated that instability on hospital discharge, as well as treatment failure, were associated with increased risk of rehospitalization.[4, 5] Jasti et al. found rehospitalizations following pneumonia were usually comorbidity related, primarily cardiopulmonary and/or neurologic disease, rather than due to the index pneumonia.[6] Many of these studies, and others looking at age, activity of daily living score, socioeconomic status, and comorbidity characteristics, were performed in relatively small cohorts. Predictors of rehospitalization have not been studied in a large cohort of patients in an integrated healthcare system.

Our study looks at factors not addressed in prior studies that have used administrative claims data to identify factors associated with early readmission. We also evaluated these admission risk factors in a veteran population, whereas prior studies have primarily focused on those who receive Medicare. The purpose of this study was to examine predictors of early (30 days) readmission in the Veterans Affairs (VA) healthcare system for patients age 65 years and older hospitalized for pneumonia. Our a priori hypothesis was that comorbid illnesses, such as congestive heart failure and chronic obstructive pulmonary disease, and patients with high medical complexity, such as high number of medications and/or prior hospitalizations and nursing home residence, are the primary factors associated with increased risk of rehospitalization.

METHODS

For this national cohort study, we used data from the VA healthcare system administrative and clinical databases that serve as repositories of clinical data from more than 150 VA hospitals and 850 outpatient clinics throughout the United States. The institutional review boards of the University of Texas Health Science Center at San Antonio and VA North Texas Health Care System approved this study. Details regarding the study design and methods were previously published.[7]

RESULTS

Predictors of 30‐Day Readmission

All variables listed in Table 1 were evaluated. The derivation logistic regression model is shown in Table 2, and only includes predictors associated with rehospitalization. Antibiotic concordance and time in the ICU were evaluated and did not predict readmission. Variables that were significantly associated with rehospitalization in the models included age, marital status, the number of emergency department clinic visits a year prior, prior admission within 90 days, number of nonpharmacy clinic visits in a year prior, and hospital length of stay. The increasing age and number of emergency department and clinic visits were associated with higher odds of readmission. The longer the length of stay and those patients who were married had a lower odds of subsequent readmission. Other variables included were the presence of chronic renal disease, prior malignancy, nursing home residence, congestive heart failure, and prior use of oral corticosteroids. The C statistics for the derivation and validation models were 0.615 and 0.613, respectively. There were no significant differences in any of the variables between the derivation and validation cohorts.

Table 2.Multivariable Predictors of 30‐Day Readmission (Derivation Cohort)

Predictors	OR	95% CI		P Value
		Lower	Upper
NOTE: Abbreviations: AIDS, acquired immune deficiency syndrome; CI, confidence interval; OR, odds ratio. a Includes only predictors from Table 1 that predicted readmission. b Reference group includes native Hawaiian, American Indian, black, Asian, and unknown. c Reference group includes non‐Hispanic white, non‐Hispanic black, Asian, American Indian, and unknown. d Reference group includes never married, separated/divorced, widowed, and unknown.
Sociodemographics
Age at admission	1.01	1.00	1.02	<0.01
White	1.14	1.02	1.28	0.02
Nonwhiteb	1.00
Hispanic	1.26	1.07	1.47	0.01
Not Hispanicc	1.00
Married	0.89	0.82	0.96	<0.01
Not marriedd	1.00
Nursing home residence	1.68	1.18	2.39	<0.01
Comorbid illnesses
Peptic ulcer disease	1.24	1.02	1.50	0.03
Chronic renal disease	1.18	1.06	1.33	<0.01
Any prior malignancy	1.17	1.06	1.28	<0.01
Metastatic solid tumor	1.20	0.98	1.47	0.08
AIDS	1.99	1.06	3.74	0.03
Index admission factors
Heart failure on initial admission	1.18	1.07	1.29	<0.01
Length of stay (per day)	0.97	0.97	0.98	<0.01
Medication history
Cardiovascular drug count 90 days prior	1.03	1.00	1.06	0.02
Oral corticosteroids 90 days prior	1.16	1.06	1.27	<0.01
Prior medical utilization
Number of emergency department visits within 1 year	1.04	1.02	1.06	<0.01
Number of specialty clinic visits within 1 year	1.01	1.00	1.02	0.01
Prior hospital admission within 90 days	1.60	1.46	1.75	<0.01
Number of nonpharmacy clinic visits within 1 year	1.00	1.00	1.01	<0.01

Figure 1 shows that the derivation and validation models are similar in percent rehospitalization group in all severity groups.

Figure 1

DISCUSSION

Sociodemographics and comorbidities, which are host factors, were significantly associated with rehospitalization of elderly patients admitted for pneumonia and length of hospital stay, the only pneumonia‐specific variable associated with readmission.[6, 14, 15, 16] Higher rates of all types of prior utilization (inpatient, outpatient, emergency department visits) were also associated with higher risk of rehospitalization. Our findings highlight the role that host factors play in rehospitalization of elderly patients with pneumonia and that there are few potentially modifiable targets to help reduce readmissions after pneumonia.

These findings may indicate 2 things: patients in this cohort are sicker than the general population at baseline and therefore require more medical care, and/or they are being evaluated by more providers increasing the chance they will be readmitted. For the former, an example is the association of prior use of oral corticosteroids with rehospitalization, which may demonstrate poorly controlled comorbidities (eg, chronic obstructive lung disease with recent exacerbation) that require steroid use. Given the VA's integrated healthcare system, greater access to primary care providers may lead to more opportunities for patients to voice their complaints, which can lead to more readmissions among severely ill patients. Weinberger et al. found an increase in hospital readmissions with increased access to primary care.[17] The association of readmission and length of stay is less likely related to pneumonia‐related clinical instabilities on discharge, but rather to the ability to address other potential issues related to the patient's underlying comorbidities.[5]

Our study presents a model looking at rehospitalization of patients with pneumonia, including factors not addressed in a prior publication by Lindenauer et al., the model Centers for Medicare and Medicaid Services (CMS) uses to predict early rehospitalization.[18] Lindenauer's model included 39 variables (of which 37 were clinical variables), including vertebral fractures, other injuries, other gastrointestinal disorders, other urinary tract disorders, as well as demographic and comorbidity factors that were similar to some of those included in our study. Their 30‐day rehospitalization medical record and administrative models had a C statistic of 0.59 and 0.63, respectively. A major difference between our presented model and the latter are the data on utilization of medical care. Despite these additional data, the resulting C statistics of 0.615 and 0.613 were not qualitatively better.

The similar C statistics demonstrates that factors playing a significant role in determining rehospitalization have yet to be identified, and it is not clear if researchers will be able to identify potentially modifiable risk factors for rehospitalization. Examples of other potential factors that should be examined in future studies include family/community support, quality of the transition of care back to their residence, and quality of inpatient care. Calvillo‐King et al. found a broad range of social factors that potentially affect the risk of postdischarge readmission for chronic heart failure.[19] For pneumonia, there were few studies that assessed the risk of social factors on readmission, and these were most commonly rather broad categories such as race/ethnicity or nursing home residence. In the publications that studied social factors and readmissions, there were inconsistent associations between hospital readmission with lower education, low income, and unemployment. Future articles may benefit from taking these additional factors into account.

Potential limitations to our study include the overwhelming majority of patients being male and in a VA population, which may limit generalizability. In addition, no data were available on the quality of the transition of care. Furthermore, limitations include the age of the data and the lack of evaluation of outpatient follow‐up and antibiotics on discharge. Though our data are up to 10 years old, this study evaluates risk factors that are still relevant. Finally, some variables noted in the CMS model were not evaluated in our study, such as protein‐calorie malnutrition, skin ulcers, and fracture history. Given these differences, we were unable to directly compare the performance of the 2 models.

Despite examining novel factors related to prior healthcare utilization, our model still performed suboptimally in identifying subjects at risk for rehospitalization after pneumonia. Future studies need to examine other social factors and patient frailty that may increase the risk of readmissions, as well as examine the impact of clinical stability on discharge. It remains to be determined if readmissions after pneumonia are truly preventable or mostly attributable to the patient's prior health status.

Disclosures: The project described was supported by grant number R01NR010828 from the National Institute of Nursing Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Nursing Research or the National Institutes of Health. This material is the result of work supported with resources and the use of facilities at the VA North Texas Health Care System. Funding agencies had no role in conducting the study, or role in the preparation, review, or approval of the manuscript. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.

Pneumonia remains a significant problem in the United States, both in terms of mortality as well as cost to the healthcare system. Each year, over 1 million patients are hospitalized for pneumonia, with costs conservatively estimated at more than $7 billion in 2010.[1] One contributing factor for these costs is that pneumonia is the second most frequent reason for rehospitalization.[2] Rehospitalization after admission for pneumonia is now used as a marker for quality of care.

Given the cost and adverse outcomes of pneumonia, it is important to examine early rehospitalization to better understand what factors play a role. Studies have examined factors associated with readmission following an initial pneumonia‐related hospitalization. Adamuz et al. showed that additional healthcare visits and rehospitalizations within 30 days of discharge from the hospital were common and were associated with worsening of signs or symptoms of pneumonia and/or comorbidities.[3] Other studies demonstrated that instability on hospital discharge, as well as treatment failure, were associated with increased risk of rehospitalization.[4, 5] Jasti et al. found rehospitalizations following pneumonia were usually comorbidity related, primarily cardiopulmonary and/or neurologic disease, rather than due to the index pneumonia.[6] Many of these studies, and others looking at age, activity of daily living score, socioeconomic status, and comorbidity characteristics, were performed in relatively small cohorts. Predictors of rehospitalization have not been studied in a large cohort of patients in an integrated healthcare system.

Our study looks at factors not addressed in prior studies that have used administrative claims data to identify factors associated with early readmission. We also evaluated these admission risk factors in a veteran population, whereas prior studies have primarily focused on those who receive Medicare. The purpose of this study was to examine predictors of early (30 days) readmission in the Veterans Affairs (VA) healthcare system for patients age 65 years and older hospitalized for pneumonia. Our a priori hypothesis was that comorbid illnesses, such as congestive heart failure and chronic obstructive pulmonary disease, and patients with high medical complexity, such as high number of medications and/or prior hospitalizations and nursing home residence, are the primary factors associated with increased risk of rehospitalization.

METHODS

For this national cohort study, we used data from the VA healthcare system administrative and clinical databases that serve as repositories of clinical data from more than 150 VA hospitals and 850 outpatient clinics throughout the United States. The institutional review boards of the University of Texas Health Science Center at San Antonio and VA North Texas Health Care System approved this study. Details regarding the study design and methods were previously published.[7]

RESULTS

Predictors of 30‐Day Readmission

All variables listed in Table 1 were evaluated. The derivation logistic regression model is shown in Table 2, and only includes predictors associated with rehospitalization. Antibiotic concordance and time in the ICU were evaluated and did not predict readmission. Variables that were significantly associated with rehospitalization in the models included age, marital status, the number of emergency department clinic visits a year prior, prior admission within 90 days, number of nonpharmacy clinic visits in a year prior, and hospital length of stay. The increasing age and number of emergency department and clinic visits were associated with higher odds of readmission. The longer the length of stay and those patients who were married had a lower odds of subsequent readmission. Other variables included were the presence of chronic renal disease, prior malignancy, nursing home residence, congestive heart failure, and prior use of oral corticosteroids. The C statistics for the derivation and validation models were 0.615 and 0.613, respectively. There were no significant differences in any of the variables between the derivation and validation cohorts.

Table 2.Multivariable Predictors of 30‐Day Readmission (Derivation Cohort)

Predictors	OR	95% CI		P Value
		Lower	Upper
NOTE: Abbreviations: AIDS, acquired immune deficiency syndrome; CI, confidence interval; OR, odds ratio. a Includes only predictors from Table 1 that predicted readmission. b Reference group includes native Hawaiian, American Indian, black, Asian, and unknown. c Reference group includes non‐Hispanic white, non‐Hispanic black, Asian, American Indian, and unknown. d Reference group includes never married, separated/divorced, widowed, and unknown.
Sociodemographics
Age at admission	1.01	1.00	1.02	<0.01
White	1.14	1.02	1.28	0.02
Nonwhiteb	1.00
Hispanic	1.26	1.07	1.47	0.01
Not Hispanicc	1.00
Married	0.89	0.82	0.96	<0.01
Not marriedd	1.00
Nursing home residence	1.68	1.18	2.39	<0.01
Comorbid illnesses
Peptic ulcer disease	1.24	1.02	1.50	0.03
Chronic renal disease	1.18	1.06	1.33	<0.01
Any prior malignancy	1.17	1.06	1.28	<0.01
Metastatic solid tumor	1.20	0.98	1.47	0.08
AIDS	1.99	1.06	3.74	0.03
Index admission factors
Heart failure on initial admission	1.18	1.07	1.29	<0.01
Length of stay (per day)	0.97	0.97	0.98	<0.01
Medication history
Cardiovascular drug count 90 days prior	1.03	1.00	1.06	0.02
Oral corticosteroids 90 days prior	1.16	1.06	1.27	<0.01
Prior medical utilization
Number of emergency department visits within 1 year	1.04	1.02	1.06	<0.01
Number of specialty clinic visits within 1 year	1.01	1.00	1.02	0.01
Prior hospital admission within 90 days	1.60	1.46	1.75	<0.01
Number of nonpharmacy clinic visits within 1 year	1.00	1.00	1.01	<0.01

Figure 1 shows that the derivation and validation models are similar in percent rehospitalization group in all severity groups.

Figure 1

DISCUSSION

Sociodemographics and comorbidities, which are host factors, were significantly associated with rehospitalization of elderly patients admitted for pneumonia and length of hospital stay, the only pneumonia‐specific variable associated with readmission.[6, 14, 15, 16] Higher rates of all types of prior utilization (inpatient, outpatient, emergency department visits) were also associated with higher risk of rehospitalization. Our findings highlight the role that host factors play in rehospitalization of elderly patients with pneumonia and that there are few potentially modifiable targets to help reduce readmissions after pneumonia.

These findings may indicate 2 things: patients in this cohort are sicker than the general population at baseline and therefore require more medical care, and/or they are being evaluated by more providers increasing the chance they will be readmitted. For the former, an example is the association of prior use of oral corticosteroids with rehospitalization, which may demonstrate poorly controlled comorbidities (eg, chronic obstructive lung disease with recent exacerbation) that require steroid use. Given the VA's integrated healthcare system, greater access to primary care providers may lead to more opportunities for patients to voice their complaints, which can lead to more readmissions among severely ill patients. Weinberger et al. found an increase in hospital readmissions with increased access to primary care.[17] The association of readmission and length of stay is less likely related to pneumonia‐related clinical instabilities on discharge, but rather to the ability to address other potential issues related to the patient's underlying comorbidities.[5]

Our study presents a model looking at rehospitalization of patients with pneumonia, including factors not addressed in a prior publication by Lindenauer et al., the model Centers for Medicare and Medicaid Services (CMS) uses to predict early rehospitalization.[18] Lindenauer's model included 39 variables (of which 37 were clinical variables), including vertebral fractures, other injuries, other gastrointestinal disorders, other urinary tract disorders, as well as demographic and comorbidity factors that were similar to some of those included in our study. Their 30‐day rehospitalization medical record and administrative models had a C statistic of 0.59 and 0.63, respectively. A major difference between our presented model and the latter are the data on utilization of medical care. Despite these additional data, the resulting C statistics of 0.615 and 0.613 were not qualitatively better.

The similar C statistics demonstrates that factors playing a significant role in determining rehospitalization have yet to be identified, and it is not clear if researchers will be able to identify potentially modifiable risk factors for rehospitalization. Examples of other potential factors that should be examined in future studies include family/community support, quality of the transition of care back to their residence, and quality of inpatient care. Calvillo‐King et al. found a broad range of social factors that potentially affect the risk of postdischarge readmission for chronic heart failure.[19] For pneumonia, there were few studies that assessed the risk of social factors on readmission, and these were most commonly rather broad categories such as race/ethnicity or nursing home residence. In the publications that studied social factors and readmissions, there were inconsistent associations between hospital readmission with lower education, low income, and unemployment. Future articles may benefit from taking these additional factors into account.

Potential limitations to our study include the overwhelming majority of patients being male and in a VA population, which may limit generalizability. In addition, no data were available on the quality of the transition of care. Furthermore, limitations include the age of the data and the lack of evaluation of outpatient follow‐up and antibiotics on discharge. Though our data are up to 10 years old, this study evaluates risk factors that are still relevant. Finally, some variables noted in the CMS model were not evaluated in our study, such as protein‐calorie malnutrition, skin ulcers, and fracture history. Given these differences, we were unable to directly compare the performance of the 2 models.

Despite examining novel factors related to prior healthcare utilization, our model still performed suboptimally in identifying subjects at risk for rehospitalization after pneumonia. Future studies need to examine other social factors and patient frailty that may increase the risk of readmissions, as well as examine the impact of clinical stability on discharge. It remains to be determined if readmissions after pneumonia are truly preventable or mostly attributable to the patient's prior health status.

Disclosures: The project described was supported by grant number R01NR010828 from the National Institute of Nursing Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Nursing Research or the National Institutes of Health. This material is the result of work supported with resources and the use of facilities at the VA North Texas Health Care System. Funding agencies had no role in conducting the study, or role in the preparation, review, or approval of the manuscript. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.