Abstract

BackgroundTo reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions. In this study, we aimed to derive and validate a prediction model including several novel markers of hospitalization severity, and compare the model with the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past 6 months), an established risk stratification tool.MethodThis was a retrospective cohort study of all patients ≥ 21 years of age, who were admitted to a tertiary hospital in Singapore from January 1, 2013 through May 31, 2015. Data were extracted from the hospital’s electronic health records. The outcome was defined as unplanned readmissions within 30 days of discharge from the index hospitalization. Candidate predictive variables were broadly grouped into five categories: Patient demographics, social determinants of health, past healthcare utilization, medical comorbidities, and markers of hospitalization severity. Multivariable logistic regression was used to predict the outcome, and receiver operating characteristic analysis was performed to compare our model with the LACE index.Results74,102 cases were enrolled for analysis. Of these, 11,492 patient cases (15.5%) were readmitted within 30 days of discharge. A total of fifteen predictive variables were strongly associated with the risk of 30-day readmissions, including number of emergency department visits in the past 6 months, Charlson Comorbidity Index, markers of hospitalization severity such as ‘requiring inpatient dialysis during index admission, and ‘treatment with intravenous furosemide 40 milligrams or more’ during index admission. Our predictive model outperformed the LACE index by achieving larger area under the curve values: 0.78 (95% confidence interval [CI]: 0.77–0.79) versus 0.70 (95% CI: 0.69–0.71).ConclusionSeveral factors are important for the risk of 30-day readmissions, including proxy markers of hospitalization severity.

Highlights

  • Preventing unnecessary readmissions is one of the principal challenges facing health systems worldwide

  • It has been suggested that reducing unnecessary hospital readmissions first require adequate risk stratification to identify patients at highest risk for readmission, followed by effective intervention programs targeted at modifiable risk factors [2]

  • In this study we have shown that our predictive model incorporating markers of hospitalization severity and social determinants of health significantly outperformed the LACE index in the receiver operating characteristic (ROC) analysis

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Summary

Introduction

Preventing unnecessary readmissions is one of the principal challenges facing health systems worldwide. It has been suggested that reducing unnecessary hospital readmissions first require adequate risk stratification to identify patients at highest risk for readmission, followed by effective intervention programs targeted at modifiable risk factors [2]. Since 2012, hospitals in the United States with excessive 30-day readmission rates for acute myocardial infarction, pneumonia and heart failure are penalized by the Centre for Medicare and Medicaid [4]. To reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions.

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