Identifying Potentially Avoidable Readmissions: A Medication‐Based 15‐Day Readmission Risk Stratification Algorithm

Background Stratifying patients according to 15-day readmission risk would be useful in identifying those who may benefit from targeted interventions during and/or following hospital discharge that are designed to reduce the likelihood of readmission. Methods A prediction model was derived via a case-control analysis of patients discharged from a tertiary hospital in Singapore using multivariate logistic regression. The model was validated in two independent external cohorts separated temporally and geographically. Model discrimination was assessed using the C-statistic while calibration was assessed using the Hosmer-Lemeshow χ2 and the Brier score statistics. Results A total of 1291 patients were included with 670, 101, and 520 patients in the derivation, temporal, and geographical validation cohorts, respectively. Age (OR: 1.02, 95% CI: 1.01 – 1.03, p = 0.008), anemia (OR: 2.08, 95% CI: 1.15 – 8.05, p = 0.015), malignancy (OR: 3.37, 95% CI: 1.16 – 9.80, p = 0.026), peptic ulcer disease (OR: 3.05, 95% CI: 1.12 – 8.26, p = 0.029), chronic obstructive pulmonary disease (OR: 3.16, 95% CI: 1.24 – 8.05, p = 0.016), number of discharge medications (OR: 1.06, 95% CI: 1.01 – 1.12, p = 0.026), discharge to nursing homes (OR: 3.57, 95% CI: 1.57 – 8.34, p = 0.003), and premature discharge against medical advice (OR: 5.05, 95% C.I: 1.20 – 21.23, p = 0.027) were independent predictors of 15-day readmission risk. The model demonstrated reasonable discrimination on the temporal and geographical validation cohorts with C-statistic of 0.65 and 0.64, respectively. Model miscalibration was observed in both validation cohorts. Conclusion A 15-day readmission risk prediction model is proposed and externally validated. The model facilitates the targeting of interventions for patients who are at high risk of an early readmission. This article is protected by copyright. All rights reserved.