Hospital Readmission in General Medicine Patients: A Prediction Model
Meltzer, David O.
Shaykevich, Shimon A.
Bell, Chaim M.
Kaboli, Peter J.
Wetterneck, Tosha B.
Arora, Vineet M.
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CitationHasan, Omar, David O. Meltzer, Shimon A. Shaykevich, Chaim M. Bell, Peter J. Kaboli, Andrew D. Auerbach, Tosha B. Wetterneck, Vineet M. Arora, James Zhang, and Jeffrey L. Schnipper. 2009. Hospital Readmission in General Medicine Patients: A Prediction Model. Journal of General Internal Medicine 25(3): 211-219.
AbstractBackground: Previous studies of hospital readmission have focused on specific conditions or populations and generated complex prediction models. Objective: To identify predictors of early hospital readmission in a diverse patient population and derive and validate a simple model for identifying patients at high readmission risk. Design: Prospective observational cohort study. Patients: Participants encompassed 10,946 patients discharged home from general medicine services at six academic medical centers and were randomly divided into derivation (n = 7,287) and validation (n = 3,659) cohorts. Measurements: We identified readmissions from administrative data and 30-day post-discharge telephone follow-up. Patient-level factors were grouped into four categories: sociodemographic factors, social support, health condition, and healthcare utilization. We performed logistic regression analysis to identify significant predictors of unplanned readmission within 30 days of discharge and developed a scoring system for estimating readmission risk. Results: Approximately 17.5% of patients were readmitted in each cohort. Among patients in the derivation cohort, seven factors emerged as significant predictors of early readmission: insurance status, marital status, having a regular physician, Charlson comorbidity index, SF12 physical component score, ≥1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of ≥25 points identified 5% of patients with a readmission risk of approximately 30% in each cohort. Model discrimination was fair with a c-statistic of 0.65 and 0.61 for the derivation and validation cohorts, respectively. Conclusions: Select patient characteristics easily available shortly after admission can be used to identify a subset of patients at elevated risk of early readmission. This information may guide the efficient use of interventions to prevent readmission.
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