Assessing and Augmenting Predictive Models for Hospital Readmissions With Novel Variables in an Urban Safety-net Population

Abstract

Background: The performance of existing predictive models of readmissions, such as the LACE, LACE+, and Epic models, is not established in urban safety-net populations. We assessed previously validated predictive models of readmission performance in a socially complex, urban safety-net population, and if augmentation with additional variables such as the Area Deprivation Index, mental health diagnoses, and housing access improves prediction. Through the addition of new variables, we introduce the LACE-social determinants of health (SDH) model.

Methods: This retrospective cohort study included adult admissions from July 1, 2016, to June 30, 2018, at a single urban safety-net health system, assessing the performance of the LACE, LACE+, and Epic models in predicting 30-day, unplanned rehospitalization. The LACE-SDH development is presented through logistic regression. Predictive model performance was compared using C-statistics.

Results: A total of 16,540 patients met the inclusion criteria. Within the validation cohort (n=8314), the Epic model performed the best (C-statistic=0.71, P<0.05), compared with LACE-SDH (0.67), LACE (0.65), and LACE+ (0.61). The variables most associated with readmissions were (odds ratio, 95% confidence interval) against medical advice discharge (3.19, 2.28-4.45), mental health diagnosis (2.06, 1.72-2.47), and health care utilization (1.94, 1.47-2.55).

Conclusions: The Epic model performed the best in our sample but requires the use of the Epic Electronic Health Record. The LACE-SDH performed significantly better than the LACE and LACE+ models when applied to a safety-net population, demonstrating the importance of accounting for socioeconomic stressors, mental health, and health care utilization in assessing readmission risk in urban safety-net patients.