Hospital readmission in general medicine patients: a prediction model

Hasan, Omar; Meltzer, David O.; Shaykevich, Shimon A.; Bell, Chaim M.; Kaboli, Peter J.; Auerbach, Andrew D.; Wetterneck, Tosha B.; Arora, Vineet M.; Zhang, James; Schnipper, Jeffrey L.
March 2010
JGIM: Journal of General Internal Medicine;Mar2010, Vol. 25 Issue 3, p211
Academic Journal
journal article
Background: 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, >or=1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of >or=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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