Scott T. Micek, PHARMD

Rapid response systems (RRSs) have been developed to identify and treat deteriorating patients on general hospital units.[1] The most commonly proposed approach to the problem of identifying and stabilizing deteriorating hospitalized patients includes some combination of an early warning system to detect the deterioration and an RRS to deal with it. We previously demonstrated that a relatively simple hospital‐specific prediction model employing routine laboratory values and vital sign data is capable of predicting clinical deterioration, the need for intensive care unit (ICU) transfer, and hospital mortality in patients admitted to general medicine units.[2, 3, 4, 5, 6]

Hospital readmissions within 30 days of hospital discharge occur often and are difficult to predict. Starting in 2013, readmission penalties have been applied to specific conditions in the United States (acute myocardial infarction, heart failure, and pneumonia), with the expectation that additional conditions will be added to this group in years to come.[7, 8] Unfortunately, interventions developed to date have not been universally successful in preventing hospital readmissions for various medical conditions and patient types.[9] One potential explanation for this is the inability to reliably predict which patients are at risk for readmission to better target preventative interventions. Predictors of hospital readmission can be disease specific, such as the presence of multivessel disease in patients hospitalized with myocardial infarction,[10] or more general, such as lack of available medical follow‐up postdischarge.[11] Therefore, we performed a study to determine whether the occurrence of automated clinical deterioration alerts (CDAs) predicted 30‐day hospital readmission.

METHODS

RESULTS

The final cohort had 3015 patients with a mean age of 57.5 17.5 years and 47.8% males. The most common reasons for hospital admission were infection or sepsis syndrome including pneumonia and urinary tract infections (23.6%), congestive heart failure or other cardiac conditions (18.4%), respiratory distress including chronic obstructive pulmonary disease (16.2%), acute or chronic renal failure (9.7%), gastrointestinal disorders (8.4%), and diabetes mellitus management (7.4%). Overall, there were 567 (18.8%) patients who were readmitted within 30 days of their hospital discharge date.

Table 2 shows the characteristics of patients readmitted within 30 days and of patients not requiring hospital readmission within 30 days. Patients requiring hospital readmission within 30 days were younger and had significantly more comorbidities as manifested by significantly greater Charlson scores and individual comorbidities including coronary artery disease, congestive heart disease, peripheral vascular disease, connective tissue disease, cirrhosis, diabetes mellitus with end‐organ complications, renal failure, and metastatic cancer. Patients with a 30‐day readmission had significantly longer duration of hospitalization, more emergency department visits in the 6 months prior to the index hospitalization, lower minimum hemoglobin measurements, higher minimum serum creatinine values, and were more likely to have Medicare or Medicaid insurance compared to patients without a 30‐day readmission.

There were 1141 (34.4%) patients that triggered a CDA. Patients triggering a CDA were significantly more likely to have a 30‐day readmission compared to those who did not trigger a CDA (23.6% vs 15.9%; P < 0.001). Patients triggering a CDA were also significantly more likely to be readmitted within 60 days (31.7% vs 22.1%; P < 0.001) and 90 days (35.8% vs 26.2%; P < 0.001) compared to patients who did not trigger a CDA. Multiple logistic regression identified the triggering of a CDA to be independently associated with 30‐day readmission (OR: 1.40; 95% CI: 1.26‐1.55; P = 0.001) (Table 3). Other independent predictors of 30‐day readmission were: an emergency department visit in the previous 6 months, increasing age in 1‐year increments, presence of connective tissue disease, diabetes mellitus with end‐organ complications, chronic renal disease, cirrhosis, and metastatic cancer (Hosmer‐Lemeshow goodness of fit test, 0.363). Figure 1 reveals the ROC curves for the logistic regression model (Table 3) with and without the CDA variable. As the ROC curves document, the 2 models had similar sensitivity for the entire range of specificities. Reflecting this, the area under the ROC curve for the model inclusive of the CDA variable equaled 0.675 (95% CI: 0.649‐0.700), whereas the area under the ROC curve for the model excluding the CDA variable equaled 0.658 (95% CI: 0.632‐0.684).

Figure 1

DISCUSSION

We demonstrated that the occurrence of an automated CDA is associated with increased risk for 30‐day hospital readmission. However, the addition of the CDA variable to the other variables identified to be independently associated with 30‐day readmission (Table 3) did not significantly add to the overall predictive accuracy of the derived logistic regression model. Other investigators have previously attempted to develop automated predictors of hospital readmission. Amarasingham et al. developed a real‐time electronic predictive model that identifies hospitalized heart failure patients at high risk for readmission or death from clinical and nonclinical risk factors present on admission.[13] Their electronic model demonstrated good discrimination for 30‐day mortality and readmission and performed as well, or better than, models developed by the Center for Medicaid and Medicare Services and the Acute Decompensated Heart Failure Registry. Similarly, Baillie et al. developed an automated prediction model that was effectively integrated into an existing EMR and identified patients on admission who were at risk for readmission within 30 days of discharge.[14] Our automated CDA differs from these previous risk predictors by surveying patients throughout their hospital stay as opposed to identifying risk for readmission at a single time point.

Several limitations of our study should be recognized. First, this was a noninterventional study aimed at examining the ability of CDAs to predict hospital readmission. Future studies are needed to assess whether the use of enhanced readmission prediction algorithms can be utilized to avert hospital readmissions. Second, the data derive from a single center, and this necessarily limits the generalizability of our findings. As such, our results may not reflect what one might see at other institutions. For example, Barnes‐Jewish Hospital has a regional referral pattern that includes community hospitals, regional long‐term acute care hospitals, nursing homes, and chronic wound, dialysis, and infusion clinics. This may explain, in part, the relatively high rate of hospital readmission observed in our cohort. Third, there is the possibility that CDAs were associated with readmission by chance given the number of potential predictor variables examined. The importance of CDAs as a determinant of rehospitalization requires confirmation in other independent populations. Fourth, it is likely that we did not capture all hospital readmissions, primarily those occurring outside of our hospital system. Therefore, we may have underestimated the actual rates of readmission for this cohort. Finally, we cannot be certain that all important predictors of hospital readmission were captured in this study.

The development of an accurate real‐time early warning system has the potential to identify patients at risk for various adverse outcomes including clinical deterioration, hospital death, and postdischarge readmission. By identifying patients at greatest risk for readmission, valuable healthcare resources can be better targeted to such populations. Our findings suggest that existing readmission predictors may suboptimally risk‐stratify patients, and it may be important to include additional clinical variables if pay for performance and other across‐institution comparisons are to be fair to institutions that care for more seriously ill patients. The variables identified as predictors of 30‐day hospital readmission in our study, with the exception of a CDA, are all readily identifiable clinical characteristics. The modest incremental value of a CDA to these clinical characteristics suggests that they would suffice for the identification of patients at high risk for hospital readmission. This is especially important for safety‐net institutions not routinely employing automated CDAs. These safety‐net hospitals provide a disproportionate level of care for patients who otherwise would have difficulty obtaining inpatient medical care and disproportionately carry the greatest burden of hospital readmissions.[15]

Rapid response systems (RRSs) have been developed to identify and treat deteriorating patients on general hospital units.[1] The most commonly proposed approach to the problem of identifying and stabilizing deteriorating hospitalized patients includes some combination of an early warning system to detect the deterioration and an RRS to deal with it. We previously demonstrated that a relatively simple hospital‐specific prediction model employing routine laboratory values and vital sign data is capable of predicting clinical deterioration, the need for intensive care unit (ICU) transfer, and hospital mortality in patients admitted to general medicine units.[2, 3, 4, 5, 6]

Hospital readmissions within 30 days of hospital discharge occur often and are difficult to predict. Starting in 2013, readmission penalties have been applied to specific conditions in the United States (acute myocardial infarction, heart failure, and pneumonia), with the expectation that additional conditions will be added to this group in years to come.[7, 8] Unfortunately, interventions developed to date have not been universally successful in preventing hospital readmissions for various medical conditions and patient types.[9] One potential explanation for this is the inability to reliably predict which patients are at risk for readmission to better target preventative interventions. Predictors of hospital readmission can be disease specific, such as the presence of multivessel disease in patients hospitalized with myocardial infarction,[10] or more general, such as lack of available medical follow‐up postdischarge.[11] Therefore, we performed a study to determine whether the occurrence of automated clinical deterioration alerts (CDAs) predicted 30‐day hospital readmission.

METHODS

RESULTS

The final cohort had 3015 patients with a mean age of 57.5 17.5 years and 47.8% males. The most common reasons for hospital admission were infection or sepsis syndrome including pneumonia and urinary tract infections (23.6%), congestive heart failure or other cardiac conditions (18.4%), respiratory distress including chronic obstructive pulmonary disease (16.2%), acute or chronic renal failure (9.7%), gastrointestinal disorders (8.4%), and diabetes mellitus management (7.4%). Overall, there were 567 (18.8%) patients who were readmitted within 30 days of their hospital discharge date.

Table 2 shows the characteristics of patients readmitted within 30 days and of patients not requiring hospital readmission within 30 days. Patients requiring hospital readmission within 30 days were younger and had significantly more comorbidities as manifested by significantly greater Charlson scores and individual comorbidities including coronary artery disease, congestive heart disease, peripheral vascular disease, connective tissue disease, cirrhosis, diabetes mellitus with end‐organ complications, renal failure, and metastatic cancer. Patients with a 30‐day readmission had significantly longer duration of hospitalization, more emergency department visits in the 6 months prior to the index hospitalization, lower minimum hemoglobin measurements, higher minimum serum creatinine values, and were more likely to have Medicare or Medicaid insurance compared to patients without a 30‐day readmission.

There were 1141 (34.4%) patients that triggered a CDA. Patients triggering a CDA were significantly more likely to have a 30‐day readmission compared to those who did not trigger a CDA (23.6% vs 15.9%; P < 0.001). Patients triggering a CDA were also significantly more likely to be readmitted within 60 days (31.7% vs 22.1%; P < 0.001) and 90 days (35.8% vs 26.2%; P < 0.001) compared to patients who did not trigger a CDA. Multiple logistic regression identified the triggering of a CDA to be independently associated with 30‐day readmission (OR: 1.40; 95% CI: 1.26‐1.55; P = 0.001) (Table 3). Other independent predictors of 30‐day readmission were: an emergency department visit in the previous 6 months, increasing age in 1‐year increments, presence of connective tissue disease, diabetes mellitus with end‐organ complications, chronic renal disease, cirrhosis, and metastatic cancer (Hosmer‐Lemeshow goodness of fit test, 0.363). Figure 1 reveals the ROC curves for the logistic regression model (Table 3) with and without the CDA variable. As the ROC curves document, the 2 models had similar sensitivity for the entire range of specificities. Reflecting this, the area under the ROC curve for the model inclusive of the CDA variable equaled 0.675 (95% CI: 0.649‐0.700), whereas the area under the ROC curve for the model excluding the CDA variable equaled 0.658 (95% CI: 0.632‐0.684).

Figure 1

DISCUSSION

We demonstrated that the occurrence of an automated CDA is associated with increased risk for 30‐day hospital readmission. However, the addition of the CDA variable to the other variables identified to be independently associated with 30‐day readmission (Table 3) did not significantly add to the overall predictive accuracy of the derived logistic regression model. Other investigators have previously attempted to develop automated predictors of hospital readmission. Amarasingham et al. developed a real‐time electronic predictive model that identifies hospitalized heart failure patients at high risk for readmission or death from clinical and nonclinical risk factors present on admission.[13] Their electronic model demonstrated good discrimination for 30‐day mortality and readmission and performed as well, or better than, models developed by the Center for Medicaid and Medicare Services and the Acute Decompensated Heart Failure Registry. Similarly, Baillie et al. developed an automated prediction model that was effectively integrated into an existing EMR and identified patients on admission who were at risk for readmission within 30 days of discharge.[14] Our automated CDA differs from these previous risk predictors by surveying patients throughout their hospital stay as opposed to identifying risk for readmission at a single time point.

Several limitations of our study should be recognized. First, this was a noninterventional study aimed at examining the ability of CDAs to predict hospital readmission. Future studies are needed to assess whether the use of enhanced readmission prediction algorithms can be utilized to avert hospital readmissions. Second, the data derive from a single center, and this necessarily limits the generalizability of our findings. As such, our results may not reflect what one might see at other institutions. For example, Barnes‐Jewish Hospital has a regional referral pattern that includes community hospitals, regional long‐term acute care hospitals, nursing homes, and chronic wound, dialysis, and infusion clinics. This may explain, in part, the relatively high rate of hospital readmission observed in our cohort. Third, there is the possibility that CDAs were associated with readmission by chance given the number of potential predictor variables examined. The importance of CDAs as a determinant of rehospitalization requires confirmation in other independent populations. Fourth, it is likely that we did not capture all hospital readmissions, primarily those occurring outside of our hospital system. Therefore, we may have underestimated the actual rates of readmission for this cohort. Finally, we cannot be certain that all important predictors of hospital readmission were captured in this study.

The development of an accurate real‐time early warning system has the potential to identify patients at risk for various adverse outcomes including clinical deterioration, hospital death, and postdischarge readmission. By identifying patients at greatest risk for readmission, valuable healthcare resources can be better targeted to such populations. Our findings suggest that existing readmission predictors may suboptimally risk‐stratify patients, and it may be important to include additional clinical variables if pay for performance and other across‐institution comparisons are to be fair to institutions that care for more seriously ill patients. The variables identified as predictors of 30‐day hospital readmission in our study, with the exception of a CDA, are all readily identifiable clinical characteristics. The modest incremental value of a CDA to these clinical characteristics suggests that they would suffice for the identification of patients at high risk for hospital readmission. This is especially important for safety‐net institutions not routinely employing automated CDAs. These safety‐net hospitals provide a disproportionate level of care for patients who otherwise would have difficulty obtaining inpatient medical care and disproportionately carry the greatest burden of hospital readmissions.[15]

Rapid response systems (RRSs) have been developed to identify and treat deteriorating patients on general hospital units.[1] The most commonly proposed approach to the problem of identifying and stabilizing deteriorating hospitalized patients includes some combination of an early warning system to detect the deterioration and an RRS to deal with it. We previously demonstrated that a relatively simple hospital‐specific prediction model employing routine laboratory values and vital sign data is capable of predicting clinical deterioration, the need for intensive care unit (ICU) transfer, and hospital mortality in patients admitted to general medicine units.[2, 3, 4, 5, 6]

Hospital readmissions within 30 days of hospital discharge occur often and are difficult to predict. Starting in 2013, readmission penalties have been applied to specific conditions in the United States (acute myocardial infarction, heart failure, and pneumonia), with the expectation that additional conditions will be added to this group in years to come.[7, 8] Unfortunately, interventions developed to date have not been universally successful in preventing hospital readmissions for various medical conditions and patient types.[9] One potential explanation for this is the inability to reliably predict which patients are at risk for readmission to better target preventative interventions. Predictors of hospital readmission can be disease specific, such as the presence of multivessel disease in patients hospitalized with myocardial infarction,[10] or more general, such as lack of available medical follow‐up postdischarge.[11] Therefore, we performed a study to determine whether the occurrence of automated clinical deterioration alerts (CDAs) predicted 30‐day hospital readmission.

METHODS

RESULTS

The final cohort had 3015 patients with a mean age of 57.5 17.5 years and 47.8% males. The most common reasons for hospital admission were infection or sepsis syndrome including pneumonia and urinary tract infections (23.6%), congestive heart failure or other cardiac conditions (18.4%), respiratory distress including chronic obstructive pulmonary disease (16.2%), acute or chronic renal failure (9.7%), gastrointestinal disorders (8.4%), and diabetes mellitus management (7.4%). Overall, there were 567 (18.8%) patients who were readmitted within 30 days of their hospital discharge date.

Table 2 shows the characteristics of patients readmitted within 30 days and of patients not requiring hospital readmission within 30 days. Patients requiring hospital readmission within 30 days were younger and had significantly more comorbidities as manifested by significantly greater Charlson scores and individual comorbidities including coronary artery disease, congestive heart disease, peripheral vascular disease, connective tissue disease, cirrhosis, diabetes mellitus with end‐organ complications, renal failure, and metastatic cancer. Patients with a 30‐day readmission had significantly longer duration of hospitalization, more emergency department visits in the 6 months prior to the index hospitalization, lower minimum hemoglobin measurements, higher minimum serum creatinine values, and were more likely to have Medicare or Medicaid insurance compared to patients without a 30‐day readmission.

There were 1141 (34.4%) patients that triggered a CDA. Patients triggering a CDA were significantly more likely to have a 30‐day readmission compared to those who did not trigger a CDA (23.6% vs 15.9%; P < 0.001). Patients triggering a CDA were also significantly more likely to be readmitted within 60 days (31.7% vs 22.1%; P < 0.001) and 90 days (35.8% vs 26.2%; P < 0.001) compared to patients who did not trigger a CDA. Multiple logistic regression identified the triggering of a CDA to be independently associated with 30‐day readmission (OR: 1.40; 95% CI: 1.26‐1.55; P = 0.001) (Table 3). Other independent predictors of 30‐day readmission were: an emergency department visit in the previous 6 months, increasing age in 1‐year increments, presence of connective tissue disease, diabetes mellitus with end‐organ complications, chronic renal disease, cirrhosis, and metastatic cancer (Hosmer‐Lemeshow goodness of fit test, 0.363). Figure 1 reveals the ROC curves for the logistic regression model (Table 3) with and without the CDA variable. As the ROC curves document, the 2 models had similar sensitivity for the entire range of specificities. Reflecting this, the area under the ROC curve for the model inclusive of the CDA variable equaled 0.675 (95% CI: 0.649‐0.700), whereas the area under the ROC curve for the model excluding the CDA variable equaled 0.658 (95% CI: 0.632‐0.684).

Figure 1

DISCUSSION

We demonstrated that the occurrence of an automated CDA is associated with increased risk for 30‐day hospital readmission. However, the addition of the CDA variable to the other variables identified to be independently associated with 30‐day readmission (Table 3) did not significantly add to the overall predictive accuracy of the derived logistic regression model. Other investigators have previously attempted to develop automated predictors of hospital readmission. Amarasingham et al. developed a real‐time electronic predictive model that identifies hospitalized heart failure patients at high risk for readmission or death from clinical and nonclinical risk factors present on admission.[13] Their electronic model demonstrated good discrimination for 30‐day mortality and readmission and performed as well, or better than, models developed by the Center for Medicaid and Medicare Services and the Acute Decompensated Heart Failure Registry. Similarly, Baillie et al. developed an automated prediction model that was effectively integrated into an existing EMR and identified patients on admission who were at risk for readmission within 30 days of discharge.[14] Our automated CDA differs from these previous risk predictors by surveying patients throughout their hospital stay as opposed to identifying risk for readmission at a single time point.

Several limitations of our study should be recognized. First, this was a noninterventional study aimed at examining the ability of CDAs to predict hospital readmission. Future studies are needed to assess whether the use of enhanced readmission prediction algorithms can be utilized to avert hospital readmissions. Second, the data derive from a single center, and this necessarily limits the generalizability of our findings. As such, our results may not reflect what one might see at other institutions. For example, Barnes‐Jewish Hospital has a regional referral pattern that includes community hospitals, regional long‐term acute care hospitals, nursing homes, and chronic wound, dialysis, and infusion clinics. This may explain, in part, the relatively high rate of hospital readmission observed in our cohort. Third, there is the possibility that CDAs were associated with readmission by chance given the number of potential predictor variables examined. The importance of CDAs as a determinant of rehospitalization requires confirmation in other independent populations. Fourth, it is likely that we did not capture all hospital readmissions, primarily those occurring outside of our hospital system. Therefore, we may have underestimated the actual rates of readmission for this cohort. Finally, we cannot be certain that all important predictors of hospital readmission were captured in this study.

The development of an accurate real‐time early warning system has the potential to identify patients at risk for various adverse outcomes including clinical deterioration, hospital death, and postdischarge readmission. By identifying patients at greatest risk for readmission, valuable healthcare resources can be better targeted to such populations. Our findings suggest that existing readmission predictors may suboptimally risk‐stratify patients, and it may be important to include additional clinical variables if pay for performance and other across‐institution comparisons are to be fair to institutions that care for more seriously ill patients. The variables identified as predictors of 30‐day hospital readmission in our study, with the exception of a CDA, are all readily identifiable clinical characteristics. The modest incremental value of a CDA to these clinical characteristics suggests that they would suffice for the identification of patients at high risk for hospital readmission. This is especially important for safety‐net institutions not routinely employing automated CDAs. These safety‐net hospitals provide a disproportionate level of care for patients who otherwise would have difficulty obtaining inpatient medical care and disproportionately carry the greatest burden of hospital readmissions.[15]

Despite its decreasing mortality, sepsis remains a leading reason for intensive care unit (ICU) admission and is associated with crude mortality in excess of 25%.[1, 2] In the United States there are between 660,000 and 750,000 sepsis hospitalizations annually, with the direct costs surpassing $24 billion.[3, 4, 5] As mortality rates have begun to fall, attention has shifted to issues of morbidity and recovery, the intermediate and longer‐term consequences associated with survivorship, and how interventions made while the patient is acutely ill in the ICU alter later health outcomes.[3, 5, 6, 7, 8]

One area of particular interest is the need for healthcare utilization following an acute admission for sepsis, and specifically rehospitalization within 30 days of discharge. This outcome is important not just from the perspective of the patient's well‐being, but also from the point of view of healthcare financing. Through the establishment of Hospital Readmission Reduction Program, the Centers for Medicare and Medicaid Services have sharply reduced reimbursement to hospitals for excessive rates of 30‐day readmissions.[9]

For sepsis, little is known about such readmissions, and even less about how to prevent them. A handful of studies suggest that this rate is between 5% and 26%.[10, 11, 12, 13] Whereas some of these studies looked at some of the factors that impact readmissions,[11, 12] none examined the potential contribution of microbiology of sepsis to this outcome.

To explore these questions, we conducted a single‐center retrospective cohort study among critically ill patients admitted to the ICU with severe culture‐positive sepsis and/or septic shock and determined the rate of early posthospital discharge readmission. In addition, we sought to elucidate predictors of subsequent readmission.

METHODS

RESULTS

Among the 1697 patients with severe sepsis or septic shock who were discharged alive from the hospital, 543 (32.0%) required a rehospitalization within 30 days. There were no differences in age or gender distribution between the groups (Table 1). All comorbidities examined were more prevalent among those with a 30‐day readmission than among those without, with the median Charlson comorbidity score reflecting this imbalance (5 vs 4, P<0.001). Similarly, most of the HCA risk factors were more prevalent among the readmitted group than the comparator group, with HCA sepsis among 94.2% of the former and 90.7% of the latter (P = 0.014).

During the index hospitalization, 589 patients (34.7%) suffered from septic shock requiring vasopressors; this did not impact the 30‐day readmission risk (Table 1). Commensurately, markers of severity of acute illness (APACHE II score, mechanical ventilation, peak white blood cell count) did not differ between the groups. With respect to the primary source of sepsis, urine was less, whereas central nervous system was more likely among those readmitted within 30 days. Similarly, there was a significant imbalance between the groups in the prevalence of AKI (Table 1). Specifically, those who did require a readmission were slightly less likely to have sustained no AKI (RIFLE: None; 14.9% vs 18.5%, P = 0.073). Those requiring readmission were also less likely to be in the category RIFLE: Risk (20.6% vs 26.5%, P = 0.009). The direction of this disparity was reversed for the Injury and Failure categories. No differences between groups were seen among those with categories Loss and end‐stage kidney disease (ESKD) (Table 1).

The microbiology of sepsis did not differ in most respects between the 30‐day readmission groups, save for several organisms (Table 2). Most strikingly, those who required a readmission were more likely than those who did not to be infected with Bacteroides spp, Candida spp, an MDR or an ESBL organism (Table 2). As for the outcomes of the index hospitalization, those with a repeat admission had a longer overall and postonset of sepsis initial hospital length of stay, and were less likely to be discharged either home without home health care or transferred to another hospital at the end of their index hospitalization (Table 3).

In a logistic regression model, 5 factors emerged as predictors of 30‐day readmission (Table 4). Having RIFLE: Injury or RIFLE: Failure carried an approximately 2‐fold increase in the odds of 30‐day rehospitalization (odds ratio: 1.95, 95% confidence interval: 1.302.93, P = 0.001) relative to having a RIFLE: None or RIFLE: Risk. Although having strong association with this outcome, harboring an ESBL organism or Bacteroides spp were both relatively infrequent events (3.3% ESBL and 3.2% Bacteroides spp). Infection with Escherichia coli and urine as the source of sepsis both appeared to be significantly protective against a readmission (Table 4). The model's discrimination was moderate (AUROC = 0.653) and its calibration adequate (Hosmer‐Lemeshow P = 0.907). (See Supporting Information, Appendix 1, in the online version of this article for the steps in the development of the final model.)

DISCUSSION

In this single‐center retrospective cohort study, nearly one‐third of survivors of culture‐positive severe sepsis or septic shock required a rehospitalization within 30 days of discharge from their index admission. Factors that contributed to a higher odds of rehospitalization were having mild‐to‐moderate AKI (RIFLE: Injury or RIFLE: Failure) and infection with ESBL organisms or Bacteroides spp, whereas urine as the source of sepsis and E coli as the pathogen appeared to be protective.

A recent study by Hua and colleagues examining the New York Statewide Planning and Research Cooperative System for the years 2008 to 2010 noted a 16.2% overall rate of 30‐day rehospitalization among survivors of initial critical illness.[11] Just as we observed, Hua et al. concluded that development of AKI correlated with readmission. Because they relied on administrative data for their analysis, AKI was diagnosed when hemodialysis was utilized. Examining AKI using SCr changes, our findings add a layer of granularity to the relationship between AKI stages and early readmission. Specifically, we failed to detect any rise in the odds of rehospitalization when either very mild (RIFLE: Risk) or severe (RIFLE: Loss or RIFLE: ESKD) AKI was present. Only when either RIFLE: Injury or RIFLE: Failure developed did the odds of readmission rise. In addition to diverging definitions between our studies, differences in populations also likely yielded different results.[11] Although Hua et al. examined all admissions to the ICU regardless of the diagnosis or illness severity, our cohort consisted of only those ICU patients who survived culture‐positive severe sepsis/septic shock. Because AKI is a known risk factor for mortality in sepsis,[19] the potential for immortal time bias leaves a smaller pool of surviving patients with ESKD at risk for readmission. Regardless of the explanation, it may be prudent to focus on preventing AKI not only to improve survival, but also from the standpoint of diminishing the risk of an early readmission.

Four additional studies have examined the frequency of early readmissions among survivors of critical illness. Liu et al. noted 17.9% 30‐day rehospitalization rate among sepsis survivors.[12] Factors associated with the risk of early readmission included acute and chronic diseases burdens, index hospital LOS, and the need for the ICU in the index sepsis admission. In contrast to our cohort, all of whom were in the ICU during their index episode, less than two‐thirds of the entire population studied by Liu had required an ICU admission. Additionally, Liu's study did not specifically examine the potential impact of AKI or of microbiology on this outcome.

Prescott and coworkers examined healthcare utilization following an episode of severe sepsis.[13] Among other findings, they reported a 30‐day readmission rate of 26.5% among survivors. Although closer to our estimate, this study included all patients surviving a severe sepsis hospitalization, and not only those with a positive culture. These investigators did not examine predictors of readmission.[13]

Horkan et al. examined specifically whether there was an association between AKI and postdischarge outcomes, including 30‐day readmission risk, in a large cohort of patients who survived their critical illness.[20] In it they found that readmission risk ranged from 19% to 21%, depending on the extent of the AKI. Moreover, similar to our findings, they reported that in an adjusted analysis RIFLE: Injury and RIFLE: Failure were associated with a rise in the odds of a 30‐day rehospitalizaiton. In contrast to our study, Horkan et al. did detect an increase in the odds of this outcome associated with RIFLE: Risk. There are likely at least 3 reasons for this difference. First, we focused only on patients with severe sepsis or septic shock, whereas Horkan and colleagues included all critical illness survivors. Second, we were able to explore the impact of microbiology on this outcome. Third, Horkan's study included an order of magnitude more patients than did ours, thus making it more likely either to have the power to detect a true association that we may have lacked or to be more susceptible to type I error.

Finally, Goodwin and colleagues utilized 3 states' databases included in the Health Care and Utilization Project (HCUP) from the Agency for Healthcare Research and Quality to study frequency and risk factors for 30‐day readmission among survivors of severe sepsis.[21] Patients were identified based on the use of the severe sepsis (995.92) and septic shock (785.52). These authors found a 30‐day readmission rate of 26%. Although chronic renal disease, among several other factors, was associated with an increase in this risk, the data source did not permit these investigators to examine the impact of AKI on the outcomes. Similarly, HCUP data do not contain microbiology, a distinct difference from our analysis.

If clinicians are to pursue strategies to reduce the risk of an all‐cause 30‐day readmission, the key goal is not simply to identify all variables associated with readmission, but to focus on factors that are potentially modifiable. Although neither Hua nor Liu and their teams identified any additional factors that are potentially modifiable,[11, 12] in the present study, among the 5 factors we identified, the development of mild to moderate AKI during the index hospitalization may deserve stronger consideration for efforts at prevention. Although one cannot conclude automatically that preventing AKI in this population could mitigate some of the early rehospitalization risk, critically ill patients are frequently exposed to a multitude of nephrotoxic agents. Those caring for subjects with sepsis should reevaluate the risk‐benefit equation of these factors more cautiously and apply guideline‐recommended AKI prevention strategies more aggressively, particularly because a relatively minor change in SCr resulted in an excess risk of readmission.[22]

In addition to AKI, which is potentially modifiable, we identified several other clinical factors predictive of 30‐day readmission, which are admittedly not preventable. Thus, microbiology was predictive of this outcome, with E coli engendering fewer and Bacteroides spp and ESBL organisms more early rehospitalizations. Similarly, urine as the source of sepsis was associated with a lower risk for this endpoint.

Our study has a number of limitations. As a retrospective cohort, it is subject to bias, most notably a selection bias. Specifically, because the flagship hospital of the BJC HealthCare system is a referral center, it is possible that we did not capture all readmissions. However, generally, if a patient who receives healthcare within 1 of the BJC hospitals presents to a nonsystem hospital, that patient is nearly always transferred back into the integrated system because of issues of insurance coverage. Analysis of certain diagnosis‐related groups has indicated that 73% of all patients overall discharged from 4 of the large BJC system institutions who require a readmission within 30 days of discharge return to a BJC hospital (personal communication, Financial Analysis and Decision Support Department at BJC to Dr. Kollef May 12, 2015). Therefore, we may have misclassified the outcome in as many as 180 patients. The fact that our readmission rate was fully double that seen in Hua et al.'s and Liu et al.'s studies, and somewhat higher than that reported by Prescott et al., attests not only to the population differences, but also to the fact that we are unlikely to have missed a substantial percentage of readmissions.[11, 12, 13] Furthermore, to mitigate biases, we enrolled all consecutive patients meeting the predetermined criteria. Missing from our analysis are events that occurred between the index discharge and the readmission. Likewise, we were unable to obtain such potentially important variables as code status or outpatient mortality following discharge. These intervening factors, if included in subsequent studies, may increase the predictive power of the model. Because we relied on administrative coding to identify cases of severe sepsis and septic shock, it is possible that there is misclassification within our cohort. Recent studies indicate, however, that the Angus definition, used in our study, has high negative and positive predictive values for severe sepsis identification.[23] It is still possible that our cohort is skewed toward a more severely ill population, making our results less generalizable to the less severely ill septic patients.[24] The study was performed at a single healthcare system and included only cases of severe sepsis or septic shock that had a positive blood culture, and thus the findings may not be broadly generalizable either to patients without a positive blood culture or to institutions that do not resemble it.

In summary, we have demonstrated that survivors of culture‐positive severe sepsis or septic shock have a high rate of 30‐day rehospitalization. Because the US federal government's initiatives deem 30‐day readmissions to be a quality metric and penalize institutions with higher‐than average readmission rates, a high volume of critically ill patients with culture‐positive severe sepsis and septic shock may disproportionately put an institution at risk for such penalties. Unfortunately, not many of the determinants of readmission are amenable to prevention. As sepsis survival continues to improve, hospitals will need to concentrate their resources on coordinating care of these complex patients so as to improve both individual quality of life and the quality of care that they provide.

Despite its decreasing mortality, sepsis remains a leading reason for intensive care unit (ICU) admission and is associated with crude mortality in excess of 25%.[1, 2] In the United States there are between 660,000 and 750,000 sepsis hospitalizations annually, with the direct costs surpassing $24 billion.[3, 4, 5] As mortality rates have begun to fall, attention has shifted to issues of morbidity and recovery, the intermediate and longer‐term consequences associated with survivorship, and how interventions made while the patient is acutely ill in the ICU alter later health outcomes.[3, 5, 6, 7, 8]

One area of particular interest is the need for healthcare utilization following an acute admission for sepsis, and specifically rehospitalization within 30 days of discharge. This outcome is important not just from the perspective of the patient's well‐being, but also from the point of view of healthcare financing. Through the establishment of Hospital Readmission Reduction Program, the Centers for Medicare and Medicaid Services have sharply reduced reimbursement to hospitals for excessive rates of 30‐day readmissions.[9]

For sepsis, little is known about such readmissions, and even less about how to prevent them. A handful of studies suggest that this rate is between 5% and 26%.[10, 11, 12, 13] Whereas some of these studies looked at some of the factors that impact readmissions,[11, 12] none examined the potential contribution of microbiology of sepsis to this outcome.

To explore these questions, we conducted a single‐center retrospective cohort study among critically ill patients admitted to the ICU with severe culture‐positive sepsis and/or septic shock and determined the rate of early posthospital discharge readmission. In addition, we sought to elucidate predictors of subsequent readmission.

METHODS

RESULTS

Among the 1697 patients with severe sepsis or septic shock who were discharged alive from the hospital, 543 (32.0%) required a rehospitalization within 30 days. There were no differences in age or gender distribution between the groups (Table 1). All comorbidities examined were more prevalent among those with a 30‐day readmission than among those without, with the median Charlson comorbidity score reflecting this imbalance (5 vs 4, P<0.001). Similarly, most of the HCA risk factors were more prevalent among the readmitted group than the comparator group, with HCA sepsis among 94.2% of the former and 90.7% of the latter (P = 0.014).

During the index hospitalization, 589 patients (34.7%) suffered from septic shock requiring vasopressors; this did not impact the 30‐day readmission risk (Table 1). Commensurately, markers of severity of acute illness (APACHE II score, mechanical ventilation, peak white blood cell count) did not differ between the groups. With respect to the primary source of sepsis, urine was less, whereas central nervous system was more likely among those readmitted within 30 days. Similarly, there was a significant imbalance between the groups in the prevalence of AKI (Table 1). Specifically, those who did require a readmission were slightly less likely to have sustained no AKI (RIFLE: None; 14.9% vs 18.5%, P = 0.073). Those requiring readmission were also less likely to be in the category RIFLE: Risk (20.6% vs 26.5%, P = 0.009). The direction of this disparity was reversed for the Injury and Failure categories. No differences between groups were seen among those with categories Loss and end‐stage kidney disease (ESKD) (Table 1).

The microbiology of sepsis did not differ in most respects between the 30‐day readmission groups, save for several organisms (Table 2). Most strikingly, those who required a readmission were more likely than those who did not to be infected with Bacteroides spp, Candida spp, an MDR or an ESBL organism (Table 2). As for the outcomes of the index hospitalization, those with a repeat admission had a longer overall and postonset of sepsis initial hospital length of stay, and were less likely to be discharged either home without home health care or transferred to another hospital at the end of their index hospitalization (Table 3).

In a logistic regression model, 5 factors emerged as predictors of 30‐day readmission (Table 4). Having RIFLE: Injury or RIFLE: Failure carried an approximately 2‐fold increase in the odds of 30‐day rehospitalization (odds ratio: 1.95, 95% confidence interval: 1.302.93, P = 0.001) relative to having a RIFLE: None or RIFLE: Risk. Although having strong association with this outcome, harboring an ESBL organism or Bacteroides spp were both relatively infrequent events (3.3% ESBL and 3.2% Bacteroides spp). Infection with Escherichia coli and urine as the source of sepsis both appeared to be significantly protective against a readmission (Table 4). The model's discrimination was moderate (AUROC = 0.653) and its calibration adequate (Hosmer‐Lemeshow P = 0.907). (See Supporting Information, Appendix 1, in the online version of this article for the steps in the development of the final model.)

DISCUSSION

In this single‐center retrospective cohort study, nearly one‐third of survivors of culture‐positive severe sepsis or septic shock required a rehospitalization within 30 days of discharge from their index admission. Factors that contributed to a higher odds of rehospitalization were having mild‐to‐moderate AKI (RIFLE: Injury or RIFLE: Failure) and infection with ESBL organisms or Bacteroides spp, whereas urine as the source of sepsis and E coli as the pathogen appeared to be protective.

A recent study by Hua and colleagues examining the New York Statewide Planning and Research Cooperative System for the years 2008 to 2010 noted a 16.2% overall rate of 30‐day rehospitalization among survivors of initial critical illness.[11] Just as we observed, Hua et al. concluded that development of AKI correlated with readmission. Because they relied on administrative data for their analysis, AKI was diagnosed when hemodialysis was utilized. Examining AKI using SCr changes, our findings add a layer of granularity to the relationship between AKI stages and early readmission. Specifically, we failed to detect any rise in the odds of rehospitalization when either very mild (RIFLE: Risk) or severe (RIFLE: Loss or RIFLE: ESKD) AKI was present. Only when either RIFLE: Injury or RIFLE: Failure developed did the odds of readmission rise. In addition to diverging definitions between our studies, differences in populations also likely yielded different results.[11] Although Hua et al. examined all admissions to the ICU regardless of the diagnosis or illness severity, our cohort consisted of only those ICU patients who survived culture‐positive severe sepsis/septic shock. Because AKI is a known risk factor for mortality in sepsis,[19] the potential for immortal time bias leaves a smaller pool of surviving patients with ESKD at risk for readmission. Regardless of the explanation, it may be prudent to focus on preventing AKI not only to improve survival, but also from the standpoint of diminishing the risk of an early readmission.

Four additional studies have examined the frequency of early readmissions among survivors of critical illness. Liu et al. noted 17.9% 30‐day rehospitalization rate among sepsis survivors.[12] Factors associated with the risk of early readmission included acute and chronic diseases burdens, index hospital LOS, and the need for the ICU in the index sepsis admission. In contrast to our cohort, all of whom were in the ICU during their index episode, less than two‐thirds of the entire population studied by Liu had required an ICU admission. Additionally, Liu's study did not specifically examine the potential impact of AKI or of microbiology on this outcome.

Prescott and coworkers examined healthcare utilization following an episode of severe sepsis.[13] Among other findings, they reported a 30‐day readmission rate of 26.5% among survivors. Although closer to our estimate, this study included all patients surviving a severe sepsis hospitalization, and not only those with a positive culture. These investigators did not examine predictors of readmission.[13]

Horkan et al. examined specifically whether there was an association between AKI and postdischarge outcomes, including 30‐day readmission risk, in a large cohort of patients who survived their critical illness.[20] In it they found that readmission risk ranged from 19% to 21%, depending on the extent of the AKI. Moreover, similar to our findings, they reported that in an adjusted analysis RIFLE: Injury and RIFLE: Failure were associated with a rise in the odds of a 30‐day rehospitalizaiton. In contrast to our study, Horkan et al. did detect an increase in the odds of this outcome associated with RIFLE: Risk. There are likely at least 3 reasons for this difference. First, we focused only on patients with severe sepsis or septic shock, whereas Horkan and colleagues included all critical illness survivors. Second, we were able to explore the impact of microbiology on this outcome. Third, Horkan's study included an order of magnitude more patients than did ours, thus making it more likely either to have the power to detect a true association that we may have lacked or to be more susceptible to type I error.

Finally, Goodwin and colleagues utilized 3 states' databases included in the Health Care and Utilization Project (HCUP) from the Agency for Healthcare Research and Quality to study frequency and risk factors for 30‐day readmission among survivors of severe sepsis.[21] Patients were identified based on the use of the severe sepsis (995.92) and septic shock (785.52). These authors found a 30‐day readmission rate of 26%. Although chronic renal disease, among several other factors, was associated with an increase in this risk, the data source did not permit these investigators to examine the impact of AKI on the outcomes. Similarly, HCUP data do not contain microbiology, a distinct difference from our analysis.

If clinicians are to pursue strategies to reduce the risk of an all‐cause 30‐day readmission, the key goal is not simply to identify all variables associated with readmission, but to focus on factors that are potentially modifiable. Although neither Hua nor Liu and their teams identified any additional factors that are potentially modifiable,[11, 12] in the present study, among the 5 factors we identified, the development of mild to moderate AKI during the index hospitalization may deserve stronger consideration for efforts at prevention. Although one cannot conclude automatically that preventing AKI in this population could mitigate some of the early rehospitalization risk, critically ill patients are frequently exposed to a multitude of nephrotoxic agents. Those caring for subjects with sepsis should reevaluate the risk‐benefit equation of these factors more cautiously and apply guideline‐recommended AKI prevention strategies more aggressively, particularly because a relatively minor change in SCr resulted in an excess risk of readmission.[22]

In addition to AKI, which is potentially modifiable, we identified several other clinical factors predictive of 30‐day readmission, which are admittedly not preventable. Thus, microbiology was predictive of this outcome, with E coli engendering fewer and Bacteroides spp and ESBL organisms more early rehospitalizations. Similarly, urine as the source of sepsis was associated with a lower risk for this endpoint.

Our study has a number of limitations. As a retrospective cohort, it is subject to bias, most notably a selection bias. Specifically, because the flagship hospital of the BJC HealthCare system is a referral center, it is possible that we did not capture all readmissions. However, generally, if a patient who receives healthcare within 1 of the BJC hospitals presents to a nonsystem hospital, that patient is nearly always transferred back into the integrated system because of issues of insurance coverage. Analysis of certain diagnosis‐related groups has indicated that 73% of all patients overall discharged from 4 of the large BJC system institutions who require a readmission within 30 days of discharge return to a BJC hospital (personal communication, Financial Analysis and Decision Support Department at BJC to Dr. Kollef May 12, 2015). Therefore, we may have misclassified the outcome in as many as 180 patients. The fact that our readmission rate was fully double that seen in Hua et al.'s and Liu et al.'s studies, and somewhat higher than that reported by Prescott et al., attests not only to the population differences, but also to the fact that we are unlikely to have missed a substantial percentage of readmissions.[11, 12, 13] Furthermore, to mitigate biases, we enrolled all consecutive patients meeting the predetermined criteria. Missing from our analysis are events that occurred between the index discharge and the readmission. Likewise, we were unable to obtain such potentially important variables as code status or outpatient mortality following discharge. These intervening factors, if included in subsequent studies, may increase the predictive power of the model. Because we relied on administrative coding to identify cases of severe sepsis and septic shock, it is possible that there is misclassification within our cohort. Recent studies indicate, however, that the Angus definition, used in our study, has high negative and positive predictive values for severe sepsis identification.[23] It is still possible that our cohort is skewed toward a more severely ill population, making our results less generalizable to the less severely ill septic patients.[24] The study was performed at a single healthcare system and included only cases of severe sepsis or septic shock that had a positive blood culture, and thus the findings may not be broadly generalizable either to patients without a positive blood culture or to institutions that do not resemble it.

In summary, we have demonstrated that survivors of culture‐positive severe sepsis or septic shock have a high rate of 30‐day rehospitalization. Because the US federal government's initiatives deem 30‐day readmissions to be a quality metric and penalize institutions with higher‐than average readmission rates, a high volume of critically ill patients with culture‐positive severe sepsis and septic shock may disproportionately put an institution at risk for such penalties. Unfortunately, not many of the determinants of readmission are amenable to prevention. As sepsis survival continues to improve, hospitals will need to concentrate their resources on coordinating care of these complex patients so as to improve both individual quality of life and the quality of care that they provide.

Despite its decreasing mortality, sepsis remains a leading reason for intensive care unit (ICU) admission and is associated with crude mortality in excess of 25%.[1, 2] In the United States there are between 660,000 and 750,000 sepsis hospitalizations annually, with the direct costs surpassing $24 billion.[3, 4, 5] As mortality rates have begun to fall, attention has shifted to issues of morbidity and recovery, the intermediate and longer‐term consequences associated with survivorship, and how interventions made while the patient is acutely ill in the ICU alter later health outcomes.[3, 5, 6, 7, 8]

One area of particular interest is the need for healthcare utilization following an acute admission for sepsis, and specifically rehospitalization within 30 days of discharge. This outcome is important not just from the perspective of the patient's well‐being, but also from the point of view of healthcare financing. Through the establishment of Hospital Readmission Reduction Program, the Centers for Medicare and Medicaid Services have sharply reduced reimbursement to hospitals for excessive rates of 30‐day readmissions.[9]

For sepsis, little is known about such readmissions, and even less about how to prevent them. A handful of studies suggest that this rate is between 5% and 26%.[10, 11, 12, 13] Whereas some of these studies looked at some of the factors that impact readmissions,[11, 12] none examined the potential contribution of microbiology of sepsis to this outcome.

To explore these questions, we conducted a single‐center retrospective cohort study among critically ill patients admitted to the ICU with severe culture‐positive sepsis and/or septic shock and determined the rate of early posthospital discharge readmission. In addition, we sought to elucidate predictors of subsequent readmission.

METHODS

RESULTS

Among the 1697 patients with severe sepsis or septic shock who were discharged alive from the hospital, 543 (32.0%) required a rehospitalization within 30 days. There were no differences in age or gender distribution between the groups (Table 1). All comorbidities examined were more prevalent among those with a 30‐day readmission than among those without, with the median Charlson comorbidity score reflecting this imbalance (5 vs 4, P<0.001). Similarly, most of the HCA risk factors were more prevalent among the readmitted group than the comparator group, with HCA sepsis among 94.2% of the former and 90.7% of the latter (P = 0.014).

During the index hospitalization, 589 patients (34.7%) suffered from septic shock requiring vasopressors; this did not impact the 30‐day readmission risk (Table 1). Commensurately, markers of severity of acute illness (APACHE II score, mechanical ventilation, peak white blood cell count) did not differ between the groups. With respect to the primary source of sepsis, urine was less, whereas central nervous system was more likely among those readmitted within 30 days. Similarly, there was a significant imbalance between the groups in the prevalence of AKI (Table 1). Specifically, those who did require a readmission were slightly less likely to have sustained no AKI (RIFLE: None; 14.9% vs 18.5%, P = 0.073). Those requiring readmission were also less likely to be in the category RIFLE: Risk (20.6% vs 26.5%, P = 0.009). The direction of this disparity was reversed for the Injury and Failure categories. No differences between groups were seen among those with categories Loss and end‐stage kidney disease (ESKD) (Table 1).

The microbiology of sepsis did not differ in most respects between the 30‐day readmission groups, save for several organisms (Table 2). Most strikingly, those who required a readmission were more likely than those who did not to be infected with Bacteroides spp, Candida spp, an MDR or an ESBL organism (Table 2). As for the outcomes of the index hospitalization, those with a repeat admission had a longer overall and postonset of sepsis initial hospital length of stay, and were less likely to be discharged either home without home health care or transferred to another hospital at the end of their index hospitalization (Table 3).

In a logistic regression model, 5 factors emerged as predictors of 30‐day readmission (Table 4). Having RIFLE: Injury or RIFLE: Failure carried an approximately 2‐fold increase in the odds of 30‐day rehospitalization (odds ratio: 1.95, 95% confidence interval: 1.302.93, P = 0.001) relative to having a RIFLE: None or RIFLE: Risk. Although having strong association with this outcome, harboring an ESBL organism or Bacteroides spp were both relatively infrequent events (3.3% ESBL and 3.2% Bacteroides spp). Infection with Escherichia coli and urine as the source of sepsis both appeared to be significantly protective against a readmission (Table 4). The model's discrimination was moderate (AUROC = 0.653) and its calibration adequate (Hosmer‐Lemeshow P = 0.907). (See Supporting Information, Appendix 1, in the online version of this article for the steps in the development of the final model.)

DISCUSSION

In this single‐center retrospective cohort study, nearly one‐third of survivors of culture‐positive severe sepsis or septic shock required a rehospitalization within 30 days of discharge from their index admission. Factors that contributed to a higher odds of rehospitalization were having mild‐to‐moderate AKI (RIFLE: Injury or RIFLE: Failure) and infection with ESBL organisms or Bacteroides spp, whereas urine as the source of sepsis and E coli as the pathogen appeared to be protective.

A recent study by Hua and colleagues examining the New York Statewide Planning and Research Cooperative System for the years 2008 to 2010 noted a 16.2% overall rate of 30‐day rehospitalization among survivors of initial critical illness.[11] Just as we observed, Hua et al. concluded that development of AKI correlated with readmission. Because they relied on administrative data for their analysis, AKI was diagnosed when hemodialysis was utilized. Examining AKI using SCr changes, our findings add a layer of granularity to the relationship between AKI stages and early readmission. Specifically, we failed to detect any rise in the odds of rehospitalization when either very mild (RIFLE: Risk) or severe (RIFLE: Loss or RIFLE: ESKD) AKI was present. Only when either RIFLE: Injury or RIFLE: Failure developed did the odds of readmission rise. In addition to diverging definitions between our studies, differences in populations also likely yielded different results.[11] Although Hua et al. examined all admissions to the ICU regardless of the diagnosis or illness severity, our cohort consisted of only those ICU patients who survived culture‐positive severe sepsis/septic shock. Because AKI is a known risk factor for mortality in sepsis,[19] the potential for immortal time bias leaves a smaller pool of surviving patients with ESKD at risk for readmission. Regardless of the explanation, it may be prudent to focus on preventing AKI not only to improve survival, but also from the standpoint of diminishing the risk of an early readmission.

Four additional studies have examined the frequency of early readmissions among survivors of critical illness. Liu et al. noted 17.9% 30‐day rehospitalization rate among sepsis survivors.[12] Factors associated with the risk of early readmission included acute and chronic diseases burdens, index hospital LOS, and the need for the ICU in the index sepsis admission. In contrast to our cohort, all of whom were in the ICU during their index episode, less than two‐thirds of the entire population studied by Liu had required an ICU admission. Additionally, Liu's study did not specifically examine the potential impact of AKI or of microbiology on this outcome.

Prescott and coworkers examined healthcare utilization following an episode of severe sepsis.[13] Among other findings, they reported a 30‐day readmission rate of 26.5% among survivors. Although closer to our estimate, this study included all patients surviving a severe sepsis hospitalization, and not only those with a positive culture. These investigators did not examine predictors of readmission.[13]

Horkan et al. examined specifically whether there was an association between AKI and postdischarge outcomes, including 30‐day readmission risk, in a large cohort of patients who survived their critical illness.[20] In it they found that readmission risk ranged from 19% to 21%, depending on the extent of the AKI. Moreover, similar to our findings, they reported that in an adjusted analysis RIFLE: Injury and RIFLE: Failure were associated with a rise in the odds of a 30‐day rehospitalizaiton. In contrast to our study, Horkan et al. did detect an increase in the odds of this outcome associated with RIFLE: Risk. There are likely at least 3 reasons for this difference. First, we focused only on patients with severe sepsis or septic shock, whereas Horkan and colleagues included all critical illness survivors. Second, we were able to explore the impact of microbiology on this outcome. Third, Horkan's study included an order of magnitude more patients than did ours, thus making it more likely either to have the power to detect a true association that we may have lacked or to be more susceptible to type I error.

Finally, Goodwin and colleagues utilized 3 states' databases included in the Health Care and Utilization Project (HCUP) from the Agency for Healthcare Research and Quality to study frequency and risk factors for 30‐day readmission among survivors of severe sepsis.[21] Patients were identified based on the use of the severe sepsis (995.92) and septic shock (785.52). These authors found a 30‐day readmission rate of 26%. Although chronic renal disease, among several other factors, was associated with an increase in this risk, the data source did not permit these investigators to examine the impact of AKI on the outcomes. Similarly, HCUP data do not contain microbiology, a distinct difference from our analysis.

If clinicians are to pursue strategies to reduce the risk of an all‐cause 30‐day readmission, the key goal is not simply to identify all variables associated with readmission, but to focus on factors that are potentially modifiable. Although neither Hua nor Liu and their teams identified any additional factors that are potentially modifiable,[11, 12] in the present study, among the 5 factors we identified, the development of mild to moderate AKI during the index hospitalization may deserve stronger consideration for efforts at prevention. Although one cannot conclude automatically that preventing AKI in this population could mitigate some of the early rehospitalization risk, critically ill patients are frequently exposed to a multitude of nephrotoxic agents. Those caring for subjects with sepsis should reevaluate the risk‐benefit equation of these factors more cautiously and apply guideline‐recommended AKI prevention strategies more aggressively, particularly because a relatively minor change in SCr resulted in an excess risk of readmission.[22]

In addition to AKI, which is potentially modifiable, we identified several other clinical factors predictive of 30‐day readmission, which are admittedly not preventable. Thus, microbiology was predictive of this outcome, with E coli engendering fewer and Bacteroides spp and ESBL organisms more early rehospitalizations. Similarly, urine as the source of sepsis was associated with a lower risk for this endpoint.

Our study has a number of limitations. As a retrospective cohort, it is subject to bias, most notably a selection bias. Specifically, because the flagship hospital of the BJC HealthCare system is a referral center, it is possible that we did not capture all readmissions. However, generally, if a patient who receives healthcare within 1 of the BJC hospitals presents to a nonsystem hospital, that patient is nearly always transferred back into the integrated system because of issues of insurance coverage. Analysis of certain diagnosis‐related groups has indicated that 73% of all patients overall discharged from 4 of the large BJC system institutions who require a readmission within 30 days of discharge return to a BJC hospital (personal communication, Financial Analysis and Decision Support Department at BJC to Dr. Kollef May 12, 2015). Therefore, we may have misclassified the outcome in as many as 180 patients. The fact that our readmission rate was fully double that seen in Hua et al.'s and Liu et al.'s studies, and somewhat higher than that reported by Prescott et al., attests not only to the population differences, but also to the fact that we are unlikely to have missed a substantial percentage of readmissions.[11, 12, 13] Furthermore, to mitigate biases, we enrolled all consecutive patients meeting the predetermined criteria. Missing from our analysis are events that occurred between the index discharge and the readmission. Likewise, we were unable to obtain such potentially important variables as code status or outpatient mortality following discharge. These intervening factors, if included in subsequent studies, may increase the predictive power of the model. Because we relied on administrative coding to identify cases of severe sepsis and septic shock, it is possible that there is misclassification within our cohort. Recent studies indicate, however, that the Angus definition, used in our study, has high negative and positive predictive values for severe sepsis identification.[23] It is still possible that our cohort is skewed toward a more severely ill population, making our results less generalizable to the less severely ill septic patients.[24] The study was performed at a single healthcare system and included only cases of severe sepsis or septic shock that had a positive blood culture, and thus the findings may not be broadly generalizable either to patients without a positive blood culture or to institutions that do not resemble it.

In summary, we have demonstrated that survivors of culture‐positive severe sepsis or septic shock have a high rate of 30‐day rehospitalization. Because the US federal government's initiatives deem 30‐day readmissions to be a quality metric and penalize institutions with higher‐than average readmission rates, a high volume of critically ill patients with culture‐positive severe sepsis and septic shock may disproportionately put an institution at risk for such penalties. Unfortunately, not many of the determinants of readmission are amenable to prevention. As sepsis survival continues to improve, hospitals will need to concentrate their resources on coordinating care of these complex patients so as to improve both individual quality of life and the quality of care that they provide.

Patients deemed suitable for care on a general hospital unit are not expected to deteriorate; however, triage systems are not perfect, and some patients on general nursing units do develop critical illness during their hospitalization. Fortunately, there is mounting evidence that deteriorating patients exhibit measurable pathologic changes that could possibly be used to identify them prior to significant adverse outcomes, such as cardiac arrest.[1, 2, 3] Given the evidence that unplanned intensive care unit (ICU) transfers of patients on general units result in worse outcomes than more controlled ICU admissions,[1, 4, 5, 6] it is logical to assume that earlier identification of a deteriorating patient could provide a window of opportunity to prevent adverse outcomes.

The most commonly proposed systematic solution to the problem of identifying and stabilizing deteriorating patients on general hospital units includes some combination of an early warning system (EWS) to detect the deterioration and a rapid response team (RRT) to deal with it.[7, 8, 9, 10] We previously demonstrated that a relatively simple hospital‐specific method for generating EWS alerts derived from the electronic medical record (EMR) database is capable of predicting clinical deterioration and the need for ICU transfer, as well as hospital mortality, in non‐ICU patients admitted to general inpatient medicine units.[11, 12, 13, 14] However, our data also showed that simply providing the EWS alerts to these nursing units did not result in any demonstrable improvement in patient outcomes.[14] Therefore, we set out to determine whether linking real‐time EWS alerts to an intervention and notification of the RRT for patient evaluation could improve the outcomes of patients cared for on general inpatient units.

METHODS

RESULTS

Between January 15, 2013 and May 9, 2013, there were 4731 consecutive patients admitted to the 8 inpatient units and electronically screened as the base population for this investigation. Five hundred seventy‐one (12.1%) patients triggered an alert and were enrolled into the study (Figure 1). There were 286 patients assigned to the intervention group and 285 assigned to the control group. No patients were lost to follow‐up. Demographics, reason for hospital admission, and comorbidities of the 2 groups were similar (Table 1). The number of patients having a separate RRT call by the primary nursing team on the hospital units within 24 hours of generating an alert was greater for the intervention group but did not reach statistical significance (19.9% vs 16.5%; odds ratio: 1.260; 95% confidence interval [CI]: 0.8231.931). Table 2 provides the new diagnostic and therapeutic interventions initiated within 24 hours after a EWS alert was generated. Patients in the intervention group were significantly more likely to have their primary care team physician notified by an RRT nurse regarding medical condition issues and to have oximetry and telemetry started, whereas control patients were significantly more likely to have new antibiotic orders written within 24 hours of generating an alert.

Figure 1

Fifty‐one patients (17.8%) randomly assigned to the intervention group required ICU transfer compared with 52 of 285 patients (18.2%) in the control group (odds ratio: 0.972; 95% CI: 0.6351.490; P=0.898) (Table 3). Twenty‐one patients (7.3%) randomly assigned to the intervention group expired during their hospitalization compared with 22 of 285 patients (7.7%) in the control group (odds ratio: 0.947; 95%CI: 0.5091.764; P=0.865). Hospital length of stay was 8.49.5 days (median, 4.5 days; interquartile range, 2.311.4 days) for patients randomized to the intervention group and 9.411.1 days (median, 5.3 days; interquartile range, 3.211.2 days) for patients randomized to the control group (P=0.038). The ICU length of stay was 4.86.6 days (median, 2.9 days; interquartile range, 1.76.5 days) for patients randomized to the intervention group and 5.86.4 days (median, 2.9 days; interquartile range, 1.57.4) for patients randomized to the control group (P=0.812).The number of patients requiring transfer to a nursing home or long‐term acute care hospital was similar for patients in the intervention and control groups (26.9% vs 26.3%; odds ratio: 1.032; 95% CI: 0.7121.495; P=0.870). Similarly, the number of patients requiring hospital readmission before 30 days and 180 days, respectively, was similar for the 2 treatment groups (Table 3). For the combined study population, the EWS alerts were triggered 94138 hours (median, 27 hours; interquartile range, 7132 hours) prior to ICU transfer and 250204 hours (median200 hours; interquartile range, 54347 hours) prior to hospital mortality. The number of RRT calls for the 8 medicine units studied progressively increased from the start of the RRT program in 2009 through 2013 (121 in 2009, 194 in 2010, 298 in 2011, 415 in 2012, 415 in 2013; P<0.001 for the trend).

DISCUSSION

We demonstrated that a real‐time EWS alert sent to a RRT nurse was associated with a modest reduction in hospital length of stay, but similar rates of hospital mortality, ICU transfer, and subsequent need for placement in a long‐term care setting compared with usual care. We also found the number of RRT calls to have increased progressively from 2009 to the present on the study units examined.

Unplanned ICU transfers occurring as early as within 8 hours of hospitalization are relatively common and associated with increased mortality.[6] Bapoje et al. evaluated a total of 152 patients over 1 year who had unplanned ICU transfers.[19] The most common reason was worsening of the problem for which the patient was admitted (48%). Other investigators have also attempted to identify predictors for clinical deterioration resulting in unplanned ICU transfer that could be employed in an EWS.[20, 21] Organizations like the Institute for Healthcare Improvement have called for the development and routine implementation of EWSs to direct the activities of RRTs and improve outcomes.[22] However, a recent systematic review found that much of the evidence in support of EWSs and emergency response teams is of poor quality and lacking prospective randomized trials.[23]

Our earlier experience demonstrated that simply providing an alert to nursing units did not result in any demonstrable improvements in the outcomes of high‐risk patients identified by our EWS.[14] Previous investigations have also had difficulty in demonstrating consistent outcome improvements with the use of EWSs and RRTs.[24, 25, 26, 27, 28, 29, 30, 31, 32] As a result of mandates from quality improvement organizations, most US hospitals currently employ RRTs for emergent mobilization of resources when a clinically deteriorating patient is identified on a hospital ward.[33, 34] Linking RRT actions with a validated real‐time alert may represent a way of improving the overall effectiveness of such teams for monitoring general hospital units, short of having all hospitalized patients in units staffed and monitored to provide higher levels of supervision (eg, ICUs, step‐down units).[9, 35]

An alternative approach to preventing patient deterioration is to provide closer overall monitoring. This has been accomplished by employing nursing personnel to increase monitoring, or with the use of automated monitoring equipment. Bellomo et al. showed that the deployment of electronic automated vital sign monitors on general hospital units was associated with improved utilization of RRTs, increased patient survival, and decreased time for vital sign measurement and recording.[36] Laurens and Dwyer found that implementation of medical emergency teams (METs) to respond to predefined MET activation criteria as observed by hospital staff resulted in reduced hospital mortality and reduced need for ICU transfer.[37] However, other investigators have observed that imperfect implementation of nursing‐performed observational monitoring resulted in no demonstrable benefit, illustrating the limitations of this approach.[38] Our findings suggest that nursing care of patients on general hospital units may be enhanced with the use of an EWS alert sent to the RRT. This is supported by the observation that communications between the RRT and the primary care teams was greater as was the use of telemetry and oximetry in the intervention arm. Moreover, there appears to have been a learning effect for the nursing staff that occurred on our study units, as evidenced by the increased number of RRT calls that occurred between 2009 and 2013. This change in nursing practices on these units certainly made it more difficult for us to observe outcome differences in our current study with the prescribed intervention, reinforcing the notion that evaluating an already established practice is a difficult proposition.[39]

Our study has several important limitations. First, the EWS alert was developed and validated at Barnes‐Jewish Hospital.[11, 12, 13, 14] We cannot say whether this alert will perform similarly in another hospital. Second, the EWS alert only contains data from medical patients. Development and validation of EWS alerts for other hospitalized populations, including surgical and pediatric patients, are needed to make such systems more generalizable. Third, the primary clinical outcome employed for this trial was problematic. Transfer to an ICU may not be an optimal outcome variable, as it may be desirable to transfer alerted patients to an ICU, which can be perceived to represent a soft landing for such patients once an alert has been generated. A better measure could be 30‐day all‐cause mortality, which would not be subject to clinician biases. Finally, we could not specifically identify explanations for the greater use of antibiotics in the control group despite similar rates of infection for both study arms. Future studies should closely evaluate the ability of EWS alerts to alter specific therapies (eg, reduce antibiotic utilization).

In summary, we have demonstrated that an EWS alert linked to a RRT likely contributed to a modest reduction in hospital length of stay, but no reductions in hospital mortality and ICU transfer. These findings suggest that inpatient deterioration on general hospital units can be identified and linked to a specific intervention. Continued efforts are needed to identify and implement systems that will not only accurately identify high‐risk patients on general hospital units but also intervene to improve their outcomes. We are moving forward with the development of a 2‐tiered EWS utilizing both EMR data and real‐time streamed vital sign data, to determine if we can further improve the prediction of clinical deterioration and potentially intervene in a more clinically meaningful manner.

Patients deemed suitable for care on a general hospital unit are not expected to deteriorate; however, triage systems are not perfect, and some patients on general nursing units do develop critical illness during their hospitalization. Fortunately, there is mounting evidence that deteriorating patients exhibit measurable pathologic changes that could possibly be used to identify them prior to significant adverse outcomes, such as cardiac arrest.[1, 2, 3] Given the evidence that unplanned intensive care unit (ICU) transfers of patients on general units result in worse outcomes than more controlled ICU admissions,[1, 4, 5, 6] it is logical to assume that earlier identification of a deteriorating patient could provide a window of opportunity to prevent adverse outcomes.

The most commonly proposed systematic solution to the problem of identifying and stabilizing deteriorating patients on general hospital units includes some combination of an early warning system (EWS) to detect the deterioration and a rapid response team (RRT) to deal with it.[7, 8, 9, 10] We previously demonstrated that a relatively simple hospital‐specific method for generating EWS alerts derived from the electronic medical record (EMR) database is capable of predicting clinical deterioration and the need for ICU transfer, as well as hospital mortality, in non‐ICU patients admitted to general inpatient medicine units.[11, 12, 13, 14] However, our data also showed that simply providing the EWS alerts to these nursing units did not result in any demonstrable improvement in patient outcomes.[14] Therefore, we set out to determine whether linking real‐time EWS alerts to an intervention and notification of the RRT for patient evaluation could improve the outcomes of patients cared for on general inpatient units.

METHODS

RESULTS

Between January 15, 2013 and May 9, 2013, there were 4731 consecutive patients admitted to the 8 inpatient units and electronically screened as the base population for this investigation. Five hundred seventy‐one (12.1%) patients triggered an alert and were enrolled into the study (Figure 1). There were 286 patients assigned to the intervention group and 285 assigned to the control group. No patients were lost to follow‐up. Demographics, reason for hospital admission, and comorbidities of the 2 groups were similar (Table 1). The number of patients having a separate RRT call by the primary nursing team on the hospital units within 24 hours of generating an alert was greater for the intervention group but did not reach statistical significance (19.9% vs 16.5%; odds ratio: 1.260; 95% confidence interval [CI]: 0.8231.931). Table 2 provides the new diagnostic and therapeutic interventions initiated within 24 hours after a EWS alert was generated. Patients in the intervention group were significantly more likely to have their primary care team physician notified by an RRT nurse regarding medical condition issues and to have oximetry and telemetry started, whereas control patients were significantly more likely to have new antibiotic orders written within 24 hours of generating an alert.