TY - JOUR AU - Abbott, Ethan E AU - Oh, Wonsuk AU - Dai, Yang AU - Feuer, Cole AU - Chan, Lili AU - Carr, Brendan G AU - Nadkarni, Girish N PY - 2023 DA - 2023/12/6 TI - Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis JO - JMIR Aging SP - e51844 VL - 6 KW - out-of-hospital-cardiac arrest KW - machine learning KW - social determinants of health KW - SDOH KW - cluster KW - cardiac KW - heart KW - cardiology KW - myocardial KW - phenotype KW - phenotypes KW - subphenotype KW - subphenotypes KW - mortality KW - death KW - survive KW - survival KW - survivor KW - survivors KW - retrospective KW - observational KW - cohort KW - algorithm KW - algorithms KW - k-means KW - clustering KW - association KW - associations AB - Background: Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA). Objective: We conducted a cluster analysis for a cohort of OHCA survivors to examine the association of clinical and social factors for mortality at 1 year. Methods: We used a retrospective observational OHCA cohort identified from Medicare claims data, including area-level social determinants of health (SDOH) features and hospital-level data sets. We applied k-means clustering algorithms to identify subphenotypes of beneficiaries who had survived an OHCA and examined associations of outcomes by subphenotype. Results: We identified 27,028 unique beneficiaries who survived to discharge after OHCA. We derived 4 distinct subphenotypes. Subphenotype 1 included a distribution of more urban, female, and Black beneficiaries with the least robust area-level SDOH measures and the highest 1-year mortality (2375/4417, 53.8%). Subphenotype 2 was characterized by a greater distribution of male, White beneficiaries and had the strongest zip code–level SDOH measures, with 1-year mortality at 49.9% (4577/9165). Subphenotype 3 had the highest rates of cardiac catheterization at 34.7% (1342/3866) and the greatest distribution with a driving distance to the index OHCA hospital from their primary residence >16.1 km at 85.4% (8179/9580); more were also discharged to a skilled nursing facility after index hospitalization. Subphenotype 4 had moderate median household income at US $51,659.50 (IQR US $41,295 to $67,081) and moderate to high median unemployment at 5.5% (IQR 4.2%-7.1%), with the lowest 1-year mortality (1207/3866, 31.2%). Joint modeling of these features demonstrated an increased hazard of death for subphenotypes 1 to 3 but not for subphenotype 4 when compared to reference. Conclusions: We identified 4 distinct subphenotypes with differences in outcomes by clinical and area-level SDOH features for OHCA. Further work is needed to determine if individual or other SDOH domains are specifically tied to long-term survival after OHCA. SN - 2561-7605 UR - https://aging.jmir.org/2023/1/e51844 UR - https://doi.org/10.2196/51844 UR - http://www.ncbi.nlm.nih.gov/pubmed/38059569 DO - 10.2196/51844 ID - info:doi/10.2196/51844 ER -