Predictive Model for Hospital Readmission Among Patients in General Medicine
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Abstract
Abstract
Previous investigations into hospital readmission have primarily concentrated on specific patient conditions or demographics, often culminating in intricate prediction models. This study endeavors to pinpoint predictors of early hospital readmission within a heterogeneous patient population and to formulate and validate a simplified model for identifying individuals at an elevated risk of readmission. A prospective observational cohort study was conducted, involving 13,135 patients discharged from general medicine services across six academic medical centers. Participants were randomly divided into derivation (n=8,744) and validation (n=4,391) cohorts. Readmissions were identified through administrative data and 30-day post-discharge telephone follow-up. Patient-level factors were categorized into sociodemographic factors, social support, health condition, and healthcare utilization. Logistic regression analysis was employed to pinpoint significant predictors of unplanned readmission within 30 days of discharge, and a scoring system was devised to estimate readmission risk. Approximately 21% of patients experienced readmission in each cohort. 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 ≥30 points identified 6% of patients with a readmission risk of approximately 36% in each cohort. The model's discrimination was fair, with a c-statistic of 0.58 and 0.65 for the derivation and validation cohorts, respectively. Certain patient characteristics, readily available shortly after admission, can effectively identify a subgroup of individuals at increased risk of early readmission. This information has the potential to guide the targeted use of interventions aimed at preventing readmissions.