Beyond Composite Indices: Comprehensive Social Determinants Improve Heart Failure Readmission Prediction
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.
Authors
- Joyce C. Ho (ORCID: https://orcid.org/0000-0001-9168-3916)
- Rodrigo M. Carrillo‐Larco (ORCID: https://orcid.org/0000-0002-2090-1856)
- Shivani Anil Patel (ORCID: https://orcid.org/0000-0003-0082-5857)
- Aniruddha Deshpande (ORCID: https://orcid.org/0000-0003-0427-241X)
- Chase M. Fensore (ORCID: https://orcid.org/0009-0007-6761-1809)
Institutions
- Emory University (US)
Publication Details
- Journal
- Journal of the American Heart Association
- Published
- 2026-03-28
- DOI
- https://doi.org/10.1161/jaha.125.043735
- Primary Topic
- Heart Failure Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00