Abstract 12965: Race-Specific Models to Predict In-Hospital Mortality in Patients With Heart Failure Using Machine Learning: The American Heart Association Get With the Guidelines Registry

• Published in: Circulation (New York, N.Y.) N.Y.), 2021-11, Vol.144 (Suppl_1), p.A12965-A12965
• Main Authors: Pandey, Ambarish, Segar, Matthew, Hall, Jennifer, Navar, Ann M, Powell-wiley, Tiffany, Morris, Alanna A, Kao, David P, Fonarow, Gregg C, Hernandez, Rosalba, Ibrahim, Nasrien E, Rutan, Christine, Stevens, Laura
• Format: Article
• Language: English
• ISSN: 0009-7322; 1524-4539
• DOI: 10.1161/circ.144.suppl_1.12965

BackgroundPrior prognostic models for acute decompensated HF (ADHF) have incorporated race as a covariate may not have completely account for the social and biological factors that underlie the racial disparities in outcomes. We developed race-specific models to improve risk prediction and better identify race-specific predictors of mortality in patients with ADHF using machine learning (ML) methods. MethodsRace-specific ML-models for in-hospital mortality were developed and validated in ADHF patients enrolled in the GWTG-HF registry between 2007 to 2020 (14,586 Black and 50,351 Non-Black adults) using over 40 candidate variables. External validation was performed among participants from the ARIC study with ADHF (n=1,115 Black and 2,028 Non-Black adults). The discrimination and calibration was compared with a previously validated risk score and cohort-specific logistic regression (LR) models. The changes in model reclassification performance with addition of zip-code level data of socioeconomic (SES) parameters was also assessed. ResultsIn the GWTG-HF cohort, the ML models had superior performance in both Black and Non-Black participants compared with the traditional GWTG-HF risk score (Table). The superior performance of the ML-model was also observed in the external validation cohort as compared with the other LR models (Table). Addition of measures of SES did not meaningfully change the performance but improved the reclassification metrics of the ML-models with net up-classification of risk in Black [NRI = 0.21 (0.07, 0.35)] and down-classification in Non-Black patients [NRI = -0.08 (-0.12, -0.04)]. SES was responsible for 21.7% of the total attributable mortality risk in Black adults compared to 0.4% in Non-Black adults. ConclusionRace-specific, ML-based mortality models demonstrated superior performance when compared to traditional HF mortality risk models. Socioeconomic distress is an important contributor to risk in Black adults.