Beyond Composite Indices: Comprehensive Social Determinants Improve Heart Failure Readmission Prediction

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: AUROC improvement from 0.632 to 0.671 with expanded SDOH

• Primary Outcome: 30-day heart failure readmission prediction

• Nnt Or Sensitivity: Equalized odds ratio improved from 0.329 to 0.437 indicating better algorithmic fairness

• Confidence Interval: Not reported

Clinical Application

Requires significant data infrastructure and machine learning expertise; implementation challenging in routine clinical practice Limited direct applicability due to different healthcare system structure, SDOH measures, and population demographics; would require adaptation with Australian census and health data Heart failure patients in health systems with comprehensive EHR and area-level SDOH data

Abstract

BACKGROUND: Heart failure (HF) hospitalization readmissions are associated with a high mortality rate and strain the health care system. Both clinical factors and social determinants of health (SDOH) predict HF readmissions, but the optimal approach to incorporating area-level SDOH data remains unclear. METHODS: We merged census tract- and county-level SDOH measures with electronic health record data in a retrospective cohort of 33 579 Black and White patients with HF (Emory Healthcare, 2010-2018). Six combinations of electronic health record data with 752 area-level SDOH were evaluated using multiple machine learning models (logistic regression, random forest, XGBoost) to predict 30-day HF readmission. Models were assessed for predictive performance using area under the receiver operating characteristic curve and algorithmic fairness across race using the equalized odds ratio. RESULTS: Expanded SDOH predictor sets improved predictive performance and algorithmic fairness compared with traditional SDOH indices. The XGBoost model using expanded SDOH alongside clinical predictors provided better predictive performance (area under the receiver operating characteristic curve, 0.671) and improved algorithmic fairness across patient race (equalized odds ratio, 0.437) than models with traditional indices (area under the receiver operating characteristic curve, 0.632; equalized odds ratio, 0.329). Feature-importance analysis revealed that specific environmental predictors (housing cost burden, air quality) ranked among top predictors alongside clinical biomarkers. County-level SDOH outperformed census tract-level SDOH and matched prediction performance of clinical predictors alone. CONCLUSIONS: Including hundreds of individual SDOH indicators rather than traditional composite indices improves machine learning models' ability to predict 30-day HF readmissions. While performance gains are modest, inclusion of specific environmental factors may provide greater clinical utility and improved equity across racial groups than composite SDOH measures for guiding further research and preventive interventions.

References

1. 1.Fensore, C., Deshpande, A., Carrillo-Larco, R. M., Patel, S. A., & Ho, J. C. (2026). Beyond composite indices: Comprehensive social determinants improve heart failure readmission prediction. Journal of the American Heart Association, 15(7), e043735. https://doi.org/10.1161/JAHA.125.043735