Orly Tonkikh, MA

A continuing focus on readmission prevention as a means to improve quality and reduce waste and costs has led to abundant research on the identification of factors that put older patients at risk for readmission.[1, 2] Recently, research has focused on the development of prediction tools that are based on information from electronic health records (EHR) and that enable early, at‐admission, identification of patients at high risk for readmission.[3, 4, 5] Through such identification, patients are already targeted for inclusion in readmission‐prevention interventions early in their hospital stay.[6, 7] Yet, the ability to rely on these early‐identification tools is contingent on the stability of risk during the hospital stay. Of the in‐hospital factors that can affect readmission, change in functioning has been identified as a potentially major contributor, especially in older patients.[8]

Older adults' deterioration in functioning is a common, troubling phenomenon. Approximately 20% of patients older than 70 years and hospitalized for a medical illness deteriorate in functioning during the hospitalization.[9] Recent evidence points to the contribution of functioning to 30‐day readmission risk, with studies showing that pre‐ or posthospitalization functional impairment is associated with a 1.5 to 3 times greater likelihood of readmission.[10, 11, 12, 13] Whether in‐hospital changes in functioning add to patients' baseline risk, however, is unknown. The only readmission study that examined functional decline during hospitalization was a retrospective study performed in a population of older adults receiving rehabilitation services and therefore may not be generalizable to acutely hospitalized adults.[14] To fill the current knowledge gap, we examined whether in‐hospital functional decline significantly contributes to at‐admission readmission risk factors in its ability to accurately identify high readmission risk in older adults hospitalized in internal medicine units.

METHODS

RESULTS

Table 1 presents the baseline and in‐hospital characteristics of participants with and without readmissions. The sample includes 559 community‐dwelling older adults (49% men) aged 70 to 98 years (mean age 79 years). One‐third (36%) of the participants were fully independent in ADL at admission. Eighty‐five (15.2%) patients were readmitted within 30 days of discharge. Participants who were readmitted had lower at‐admission ADL levels; had 1 more hospitalization in the previous year; and were more likely to have HF, CRF, arrhythmia, and malignancy, and to be at risk of malnutrition, than those who were not readmitted. Participants who were readmitted were more likely to suffer from functional decline during the index hospitalization. No significant differences were found in living arrangements or in at‐admission mental and cognitive status.

Multivariate analysis (Table 2) shows that higher at‐admission mBI score was associated with lower odds of readmission (OR for 1‐unit increase: 0.99, 95% CI: 0.98‐0.99). Other predictors of higher readmission risk were: high or medium at‐admission risk of malnutrition, malignancy, CRF, each additional hospitalization during the previous year, and lower albumin levels. Severity of illness and demographic characteristics were not significantly associated with readmission.

The at‐discharge model that combined the baseline model and in‐hospital risk factors showed that in‐hospital (from admission to discharge) ADL decline was significantly associated with readmission, as a 10‐point decrease in the mBI from admission to discharge was associated with 1.32 (95% CI: 1.02‐1.72) greater odds of readmission. LOS was not significantly associated with readmission, after controlling for baseline health status and in‐hospital ADL decline. All other predictors did not markedly change from the baseline to the at‐discharge model either in significance levels or in magnitude.

The discriminatory power of the baseline model was good (C statistic=0.81). Adding ADL decline and LOS did not change the discriminatory power of the model (C statistic=0.81). The P value of the Hosmer‐Lemeshow test equaled 0.67 for the baseline model and 0.48 for the at‐discharge model, indicating good calibration of both models. The P values for the regression coefficients of bootstrap inference assessing the relationship between the at‐admission and in‐hospital ADL decline odds of readmission remained stable (P < 0.05).

Classification of patients into risk categories by the baseline model and the discharge model (Table 3) shows that identifying patients in the top‐tier category (20th highest percentile) according to information available before or at admission does not detect 6/111 (5.4%) of patients who would have been categorized as highest‐risk if information on ADL decline had been incorporated in the predictive algorithm. Additional partitioning of the top fifth group into 2 tiers (8089th and 9099th percentiles) shows that selection of patients in the top 10% of the baseline risk score would not have detected 7/55 (12.7%) patients who would have been identified as high risk at discharge (data not shown).

DISCUSSION

To our knowledge, ours is the first empirical test of the simultaneous role of functioning along the hospitalization course in explaining readmission risk.[8] Our results show that at‐admission lower functional status and in‐hospital functional decline are significant predictors of early unplanned readmission in older adults, beyond other well‐known risk factors.

The major purpose of this study was to examine whether at‐admission data can be used to detect high‐risk patients for potential inclusion in readmission prevention interventions, or whether changes in ADL occurring during the index hospitalization could affect patients' risk, therefore necessitating an additional assessment at discharge. Our results show that some patients would not have been detected at admission, as their in‐hospital ADL decline affects their at‐discharge risk. Nonetheless, this is a small group (only 5% of patients if a targeting threshold of the highest 20% risk is used). Our findings also show that information on ADL decline during the index hospitalization does not contribute to the accuracy of readmission‐risk prediction in a model that utilizes data on prior hospitalizations, baseline nutritional and functional status, and chronic morbidity (CRF and malignancy). Our results are consistent with previous studies showing the association between baseline,[11, 13, 32] or at‐admission[13] functional status and readmission. However, these studies did not analyze the related contribution of in‐hospital functioning to readmission risk, which was recently suggested as a feature that may significantly affect readmission risk, especially in older patients.[8]

Our findings are also congruent with those of a study in which LOS was not significantly associated with readmission in an elderly population.[33] Our null finding can be explained by the broad set of pre‐ and at‐admission variables, such as nutritional and functional status as well as in‐hospital functional decline, included in our model, making LOS a less significant contributor than in more parsimonious models.[28]

Our results also show that malnutrition contributes to readmission risk beyond other well‐known risk factors. Previous studies showed that malnutrition in the elderly is associated with early readmission.[11, 34] These studies, however, did not examine other well‐known risk factors, such as previous hospitalizations, which were tested in our study, precluding identification of the contribution of malnutrition beyond other well‐known risks.

Our findings should be interpreted in light of several limitations. First, the functional, nutritional, and cognitive data were collected from participants' self‐reports, which are prone to recall bias. Nonetheless, self‐report is often used in large‐scale studies, which preclude actual performance measurement.[21] Second, our sample is of adults aged 70 years or older, and may not be representative of the 65 and older population, which is the target population for many readmission reduction interventions.[35] Yet, participants were from a relatively high‐functioning group of patients who were discharged to their homes, thus may resemble the over age 65 years group. Moreover, these inclusion criteria may have affected their readmission rates, which at 15% are lower than the average reported in other older adult populations.[36] Nonetheless, a more heterogenic sample (in terms of baseline functional status) is needed to address the association between in‐hospital functional change and readmissions as well as the discrimination of the model. Third, the attrition rate (16%) might impact the predictive ability of the models, as patients dropped‐out from the study might have had higher in‐hospital deterioration. However, no significant differences between study sample and dropped‐out patients in the wide range of baseline characteristics except for age and baseline albumin levels were found. Fourth, the unique characteristics of the Israeli healthcare system may affect study's generalizability. The high hospital‐bed occupancy rate, stretched to the limit at 99%, which is much higher than in other developed countries,[37] may affect readmission rates and risk. Nonetheless, our findings may be of relevance to other populations and healthcare systems, as variables included in our model have been previously shown to affect readmission risk in other settings,[4, 6] and the percent of in‐hospital ADL decline is similar to that reported by others.[9] Future studies should examine the significance of in‐hospital functioning in other older adult populations, such as greater mix of baseline functioning and myocardial infarction, HF, and COPD patients, that have been emphasized for readmission prevention by the Centers for Medicare and Medicaid Services.

CONCLUSIONS

This study shows that although both functional status and functional decline are significant predictors of readmission, in‐hospital functional decline did not contribute to the discriminative ability of the model, beyond the risk factors known at admission: malnutrition, prior hospitalizations, and being previously diagnosed with CRF or malignancy. These findings call attention to the ability to predict readmission early in the index hospitalization, to enable early intervention in targeted high‐risk patients. Nonetheless, further at‐discharge functional assessment can detect additional patients whose readmission risk changes during the index hospitalization and who should be considered for inclusion in readmission reduction interventions. As suggested in previous prediction models,[3, 38] most of the at‐admission variables examined in this study, including patient‐reported measures such as functioning, are readily available in the EHR or during the at‐admission intake.[39, 40] In settings where these assessments are not routinely performed, their implementation should be considered. These tools could be used to potentially identify patients at high risk of readmission, and accordingly, address physical function as part of routine medical care and during the acute hospitalization, and tailor adequate follow‐up care after discharge.[11]

Disclosures: This work was supported by the Israeli Science Foundation (grant number 565/08); Clalit Health Services(grant number 04‐121/2010); and the Israel National Institute for Health Policy Research (thesis scholarship number 35/2012). The funding agencies had no role in the design and conduct of this study, the analysis or interpretation of the data, or the preparation of the manuscript. The authors report no conflicts of interest.

A continuing focus on readmission prevention as a means to improve quality and reduce waste and costs has led to abundant research on the identification of factors that put older patients at risk for readmission.[1, 2] Recently, research has focused on the development of prediction tools that are based on information from electronic health records (EHR) and that enable early, at‐admission, identification of patients at high risk for readmission.[3, 4, 5] Through such identification, patients are already targeted for inclusion in readmission‐prevention interventions early in their hospital stay.[6, 7] Yet, the ability to rely on these early‐identification tools is contingent on the stability of risk during the hospital stay. Of the in‐hospital factors that can affect readmission, change in functioning has been identified as a potentially major contributor, especially in older patients.[8]

Older adults' deterioration in functioning is a common, troubling phenomenon. Approximately 20% of patients older than 70 years and hospitalized for a medical illness deteriorate in functioning during the hospitalization.[9] Recent evidence points to the contribution of functioning to 30‐day readmission risk, with studies showing that pre‐ or posthospitalization functional impairment is associated with a 1.5 to 3 times greater likelihood of readmission.[10, 11, 12, 13] Whether in‐hospital changes in functioning add to patients' baseline risk, however, is unknown. The only readmission study that examined functional decline during hospitalization was a retrospective study performed in a population of older adults receiving rehabilitation services and therefore may not be generalizable to acutely hospitalized adults.[14] To fill the current knowledge gap, we examined whether in‐hospital functional decline significantly contributes to at‐admission readmission risk factors in its ability to accurately identify high readmission risk in older adults hospitalized in internal medicine units.

METHODS

RESULTS

Table 1 presents the baseline and in‐hospital characteristics of participants with and without readmissions. The sample includes 559 community‐dwelling older adults (49% men) aged 70 to 98 years (mean age 79 years). One‐third (36%) of the participants were fully independent in ADL at admission. Eighty‐five (15.2%) patients were readmitted within 30 days of discharge. Participants who were readmitted had lower at‐admission ADL levels; had 1 more hospitalization in the previous year; and were more likely to have HF, CRF, arrhythmia, and malignancy, and to be at risk of malnutrition, than those who were not readmitted. Participants who were readmitted were more likely to suffer from functional decline during the index hospitalization. No significant differences were found in living arrangements or in at‐admission mental and cognitive status.

Multivariate analysis (Table 2) shows that higher at‐admission mBI score was associated with lower odds of readmission (OR for 1‐unit increase: 0.99, 95% CI: 0.98‐0.99). Other predictors of higher readmission risk were: high or medium at‐admission risk of malnutrition, malignancy, CRF, each additional hospitalization during the previous year, and lower albumin levels. Severity of illness and demographic characteristics were not significantly associated with readmission.

The at‐discharge model that combined the baseline model and in‐hospital risk factors showed that in‐hospital (from admission to discharge) ADL decline was significantly associated with readmission, as a 10‐point decrease in the mBI from admission to discharge was associated with 1.32 (95% CI: 1.02‐1.72) greater odds of readmission. LOS was not significantly associated with readmission, after controlling for baseline health status and in‐hospital ADL decline. All other predictors did not markedly change from the baseline to the at‐discharge model either in significance levels or in magnitude.

The discriminatory power of the baseline model was good (C statistic=0.81). Adding ADL decline and LOS did not change the discriminatory power of the model (C statistic=0.81). The P value of the Hosmer‐Lemeshow test equaled 0.67 for the baseline model and 0.48 for the at‐discharge model, indicating good calibration of both models. The P values for the regression coefficients of bootstrap inference assessing the relationship between the at‐admission and in‐hospital ADL decline odds of readmission remained stable (P < 0.05).

Classification of patients into risk categories by the baseline model and the discharge model (Table 3) shows that identifying patients in the top‐tier category (20th highest percentile) according to information available before or at admission does not detect 6/111 (5.4%) of patients who would have been categorized as highest‐risk if information on ADL decline had been incorporated in the predictive algorithm. Additional partitioning of the top fifth group into 2 tiers (8089th and 9099th percentiles) shows that selection of patients in the top 10% of the baseline risk score would not have detected 7/55 (12.7%) patients who would have been identified as high risk at discharge (data not shown).

DISCUSSION

To our knowledge, ours is the first empirical test of the simultaneous role of functioning along the hospitalization course in explaining readmission risk.[8] Our results show that at‐admission lower functional status and in‐hospital functional decline are significant predictors of early unplanned readmission in older adults, beyond other well‐known risk factors.

The major purpose of this study was to examine whether at‐admission data can be used to detect high‐risk patients for potential inclusion in readmission prevention interventions, or whether changes in ADL occurring during the index hospitalization could affect patients' risk, therefore necessitating an additional assessment at discharge. Our results show that some patients would not have been detected at admission, as their in‐hospital ADL decline affects their at‐discharge risk. Nonetheless, this is a small group (only 5% of patients if a targeting threshold of the highest 20% risk is used). Our findings also show that information on ADL decline during the index hospitalization does not contribute to the accuracy of readmission‐risk prediction in a model that utilizes data on prior hospitalizations, baseline nutritional and functional status, and chronic morbidity (CRF and malignancy). Our results are consistent with previous studies showing the association between baseline,[11, 13, 32] or at‐admission[13] functional status and readmission. However, these studies did not analyze the related contribution of in‐hospital functioning to readmission risk, which was recently suggested as a feature that may significantly affect readmission risk, especially in older patients.[8]

Our findings are also congruent with those of a study in which LOS was not significantly associated with readmission in an elderly population.[33] Our null finding can be explained by the broad set of pre‐ and at‐admission variables, such as nutritional and functional status as well as in‐hospital functional decline, included in our model, making LOS a less significant contributor than in more parsimonious models.[28]

Our results also show that malnutrition contributes to readmission risk beyond other well‐known risk factors. Previous studies showed that malnutrition in the elderly is associated with early readmission.[11, 34] These studies, however, did not examine other well‐known risk factors, such as previous hospitalizations, which were tested in our study, precluding identification of the contribution of malnutrition beyond other well‐known risks.

Our findings should be interpreted in light of several limitations. First, the functional, nutritional, and cognitive data were collected from participants' self‐reports, which are prone to recall bias. Nonetheless, self‐report is often used in large‐scale studies, which preclude actual performance measurement.[21] Second, our sample is of adults aged 70 years or older, and may not be representative of the 65 and older population, which is the target population for many readmission reduction interventions.[35] Yet, participants were from a relatively high‐functioning group of patients who were discharged to their homes, thus may resemble the over age 65 years group. Moreover, these inclusion criteria may have affected their readmission rates, which at 15% are lower than the average reported in other older adult populations.[36] Nonetheless, a more heterogenic sample (in terms of baseline functional status) is needed to address the association between in‐hospital functional change and readmissions as well as the discrimination of the model. Third, the attrition rate (16%) might impact the predictive ability of the models, as patients dropped‐out from the study might have had higher in‐hospital deterioration. However, no significant differences between study sample and dropped‐out patients in the wide range of baseline characteristics except for age and baseline albumin levels were found. Fourth, the unique characteristics of the Israeli healthcare system may affect study's generalizability. The high hospital‐bed occupancy rate, stretched to the limit at 99%, which is much higher than in other developed countries,[37] may affect readmission rates and risk. Nonetheless, our findings may be of relevance to other populations and healthcare systems, as variables included in our model have been previously shown to affect readmission risk in other settings,[4, 6] and the percent of in‐hospital ADL decline is similar to that reported by others.[9] Future studies should examine the significance of in‐hospital functioning in other older adult populations, such as greater mix of baseline functioning and myocardial infarction, HF, and COPD patients, that have been emphasized for readmission prevention by the Centers for Medicare and Medicaid Services.

CONCLUSIONS

This study shows that although both functional status and functional decline are significant predictors of readmission, in‐hospital functional decline did not contribute to the discriminative ability of the model, beyond the risk factors known at admission: malnutrition, prior hospitalizations, and being previously diagnosed with CRF or malignancy. These findings call attention to the ability to predict readmission early in the index hospitalization, to enable early intervention in targeted high‐risk patients. Nonetheless, further at‐discharge functional assessment can detect additional patients whose readmission risk changes during the index hospitalization and who should be considered for inclusion in readmission reduction interventions. As suggested in previous prediction models,[3, 38] most of the at‐admission variables examined in this study, including patient‐reported measures such as functioning, are readily available in the EHR or during the at‐admission intake.[39, 40] In settings where these assessments are not routinely performed, their implementation should be considered. These tools could be used to potentially identify patients at high risk of readmission, and accordingly, address physical function as part of routine medical care and during the acute hospitalization, and tailor adequate follow‐up care after discharge.[11]

Disclosures: This work was supported by the Israeli Science Foundation (grant number 565/08); Clalit Health Services(grant number 04‐121/2010); and the Israel National Institute for Health Policy Research (thesis scholarship number 35/2012). The funding agencies had no role in the design and conduct of this study, the analysis or interpretation of the data, or the preparation of the manuscript. The authors report no conflicts of interest.

A continuing focus on readmission prevention as a means to improve quality and reduce waste and costs has led to abundant research on the identification of factors that put older patients at risk for readmission.[1, 2] Recently, research has focused on the development of prediction tools that are based on information from electronic health records (EHR) and that enable early, at‐admission, identification of patients at high risk for readmission.[3, 4, 5] Through such identification, patients are already targeted for inclusion in readmission‐prevention interventions early in their hospital stay.[6, 7] Yet, the ability to rely on these early‐identification tools is contingent on the stability of risk during the hospital stay. Of the in‐hospital factors that can affect readmission, change in functioning has been identified as a potentially major contributor, especially in older patients.[8]

Older adults' deterioration in functioning is a common, troubling phenomenon. Approximately 20% of patients older than 70 years and hospitalized for a medical illness deteriorate in functioning during the hospitalization.[9] Recent evidence points to the contribution of functioning to 30‐day readmission risk, with studies showing that pre‐ or posthospitalization functional impairment is associated with a 1.5 to 3 times greater likelihood of readmission.[10, 11, 12, 13] Whether in‐hospital changes in functioning add to patients' baseline risk, however, is unknown. The only readmission study that examined functional decline during hospitalization was a retrospective study performed in a population of older adults receiving rehabilitation services and therefore may not be generalizable to acutely hospitalized adults.[14] To fill the current knowledge gap, we examined whether in‐hospital functional decline significantly contributes to at‐admission readmission risk factors in its ability to accurately identify high readmission risk in older adults hospitalized in internal medicine units.

METHODS

RESULTS

Table 1 presents the baseline and in‐hospital characteristics of participants with and without readmissions. The sample includes 559 community‐dwelling older adults (49% men) aged 70 to 98 years (mean age 79 years). One‐third (36%) of the participants were fully independent in ADL at admission. Eighty‐five (15.2%) patients were readmitted within 30 days of discharge. Participants who were readmitted had lower at‐admission ADL levels; had 1 more hospitalization in the previous year; and were more likely to have HF, CRF, arrhythmia, and malignancy, and to be at risk of malnutrition, than those who were not readmitted. Participants who were readmitted were more likely to suffer from functional decline during the index hospitalization. No significant differences were found in living arrangements or in at‐admission mental and cognitive status.

Multivariate analysis (Table 2) shows that higher at‐admission mBI score was associated with lower odds of readmission (OR for 1‐unit increase: 0.99, 95% CI: 0.98‐0.99). Other predictors of higher readmission risk were: high or medium at‐admission risk of malnutrition, malignancy, CRF, each additional hospitalization during the previous year, and lower albumin levels. Severity of illness and demographic characteristics were not significantly associated with readmission.

The at‐discharge model that combined the baseline model and in‐hospital risk factors showed that in‐hospital (from admission to discharge) ADL decline was significantly associated with readmission, as a 10‐point decrease in the mBI from admission to discharge was associated with 1.32 (95% CI: 1.02‐1.72) greater odds of readmission. LOS was not significantly associated with readmission, after controlling for baseline health status and in‐hospital ADL decline. All other predictors did not markedly change from the baseline to the at‐discharge model either in significance levels or in magnitude.

The discriminatory power of the baseline model was good (C statistic=0.81). Adding ADL decline and LOS did not change the discriminatory power of the model (C statistic=0.81). The P value of the Hosmer‐Lemeshow test equaled 0.67 for the baseline model and 0.48 for the at‐discharge model, indicating good calibration of both models. The P values for the regression coefficients of bootstrap inference assessing the relationship between the at‐admission and in‐hospital ADL decline odds of readmission remained stable (P < 0.05).

Classification of patients into risk categories by the baseline model and the discharge model (Table 3) shows that identifying patients in the top‐tier category (20th highest percentile) according to information available before or at admission does not detect 6/111 (5.4%) of patients who would have been categorized as highest‐risk if information on ADL decline had been incorporated in the predictive algorithm. Additional partitioning of the top fifth group into 2 tiers (8089th and 9099th percentiles) shows that selection of patients in the top 10% of the baseline risk score would not have detected 7/55 (12.7%) patients who would have been identified as high risk at discharge (data not shown).

DISCUSSION

To our knowledge, ours is the first empirical test of the simultaneous role of functioning along the hospitalization course in explaining readmission risk.[8] Our results show that at‐admission lower functional status and in‐hospital functional decline are significant predictors of early unplanned readmission in older adults, beyond other well‐known risk factors.

The major purpose of this study was to examine whether at‐admission data can be used to detect high‐risk patients for potential inclusion in readmission prevention interventions, or whether changes in ADL occurring during the index hospitalization could affect patients' risk, therefore necessitating an additional assessment at discharge. Our results show that some patients would not have been detected at admission, as their in‐hospital ADL decline affects their at‐discharge risk. Nonetheless, this is a small group (only 5% of patients if a targeting threshold of the highest 20% risk is used). Our findings also show that information on ADL decline during the index hospitalization does not contribute to the accuracy of readmission‐risk prediction in a model that utilizes data on prior hospitalizations, baseline nutritional and functional status, and chronic morbidity (CRF and malignancy). Our results are consistent with previous studies showing the association between baseline,[11, 13, 32] or at‐admission[13] functional status and readmission. However, these studies did not analyze the related contribution of in‐hospital functioning to readmission risk, which was recently suggested as a feature that may significantly affect readmission risk, especially in older patients.[8]

Our findings are also congruent with those of a study in which LOS was not significantly associated with readmission in an elderly population.[33] Our null finding can be explained by the broad set of pre‐ and at‐admission variables, such as nutritional and functional status as well as in‐hospital functional decline, included in our model, making LOS a less significant contributor than in more parsimonious models.[28]

Our results also show that malnutrition contributes to readmission risk beyond other well‐known risk factors. Previous studies showed that malnutrition in the elderly is associated with early readmission.[11, 34] These studies, however, did not examine other well‐known risk factors, such as previous hospitalizations, which were tested in our study, precluding identification of the contribution of malnutrition beyond other well‐known risks.

Our findings should be interpreted in light of several limitations. First, the functional, nutritional, and cognitive data were collected from participants' self‐reports, which are prone to recall bias. Nonetheless, self‐report is often used in large‐scale studies, which preclude actual performance measurement.[21] Second, our sample is of adults aged 70 years or older, and may not be representative of the 65 and older population, which is the target population for many readmission reduction interventions.[35] Yet, participants were from a relatively high‐functioning group of patients who were discharged to their homes, thus may resemble the over age 65 years group. Moreover, these inclusion criteria may have affected their readmission rates, which at 15% are lower than the average reported in other older adult populations.[36] Nonetheless, a more heterogenic sample (in terms of baseline functional status) is needed to address the association between in‐hospital functional change and readmissions as well as the discrimination of the model. Third, the attrition rate (16%) might impact the predictive ability of the models, as patients dropped‐out from the study might have had higher in‐hospital deterioration. However, no significant differences between study sample and dropped‐out patients in the wide range of baseline characteristics except for age and baseline albumin levels were found. Fourth, the unique characteristics of the Israeli healthcare system may affect study's generalizability. The high hospital‐bed occupancy rate, stretched to the limit at 99%, which is much higher than in other developed countries,[37] may affect readmission rates and risk. Nonetheless, our findings may be of relevance to other populations and healthcare systems, as variables included in our model have been previously shown to affect readmission risk in other settings,[4, 6] and the percent of in‐hospital ADL decline is similar to that reported by others.[9] Future studies should examine the significance of in‐hospital functioning in other older adult populations, such as greater mix of baseline functioning and myocardial infarction, HF, and COPD patients, that have been emphasized for readmission prevention by the Centers for Medicare and Medicaid Services.

CONCLUSIONS

This study shows that although both functional status and functional decline are significant predictors of readmission, in‐hospital functional decline did not contribute to the discriminative ability of the model, beyond the risk factors known at admission: malnutrition, prior hospitalizations, and being previously diagnosed with CRF or malignancy. These findings call attention to the ability to predict readmission early in the index hospitalization, to enable early intervention in targeted high‐risk patients. Nonetheless, further at‐discharge functional assessment can detect additional patients whose readmission risk changes during the index hospitalization and who should be considered for inclusion in readmission reduction interventions. As suggested in previous prediction models,[3, 38] most of the at‐admission variables examined in this study, including patient‐reported measures such as functioning, are readily available in the EHR or during the at‐admission intake.[39, 40] In settings where these assessments are not routinely performed, their implementation should be considered. These tools could be used to potentially identify patients at high risk of readmission, and accordingly, address physical function as part of routine medical care and during the acute hospitalization, and tailor adequate follow‐up care after discharge.[11]

Disclosures: This work was supported by the Israeli Science Foundation (grant number 565/08); Clalit Health Services(grant number 04‐121/2010); and the Israel National Institute for Health Policy Research (thesis scholarship number 35/2012). The funding agencies had no role in the design and conduct of this study, the analysis or interpretation of the data, or the preparation of the manuscript. The authors report no conflicts of interest.