Impact of social determinants of health on improving the LACE index for 30-day unplanned readmission prediction.

ABSTRACT:

Objective

Early and accurate prediction of patients at risk of readmission is key to reducing costs and improving outcomes. LACE is a widely used score to predict 30-day readmissions. We examine whether adding social determinants of health (SDOH) to LACE can improve its predictive performance.

Methods

This is a retrospective study that included all inpatient encounters in the state of Maryland in 2019. We constructed predictive models by fitting Logistic Regression (LR) on LACE and different sets of SDOH predictors. We used the area under the curve (AUC) to evaluate discrimination and SHapley Additive exPlanations values to assess feature importance.

Results

Our study population included 316 558 patients of whom 35 431 (11.19%) patients were readmitted after 30 days.

SUBMITTER: Belouali A 

PROVIDER: S-EPMC9185729 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature