Inclusion of social determinants of health improves sepsis readmission prediction models

Abstract

Objective: Sepsis has a high rate of 30-day unplanned readmissions. Predictive modeling has been suggested as a tool to identify high-risk patients. However, existing sepsis readmission models have low predictive value and most predictive factors in such models are not actionable. Materials and Methods: Data from patients enrolled in the AllofUs Research Program cohort from 35 hospitals were used to develop a multicenter validated sepsis-related unplanned readmission model that incorporates clinical and social determinants of health (SDH) to predict 30-day unplanned readmissions. Sepsis cases were identified using concepts represented in the Observational Medical Outcomes Partnership. The dataset included over 60 clinical/laboratory features and over 100 SDH features. Results: Incorporation of SDH factors into our model of clinical and demographic features improves model area under the receiver operating characteristic curve (AUC) significantly (from 0.75 to 0.80; P