@Article{info:doi/10.2196/51844,
author="Abbott, Ethan E
and Oh, Wonsuk
and Dai, Yang
and Feuer, Cole
and Chan, Lili
and Carr, Brendan G
and Nadkarni, Girish N",
title="Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis",
journal="JMIR Aging",
year="2023",
month="Dec",
day="6",
volume="6",
pages="e51844",
keywords="out-of-hospital-cardiac arrest; machine learning; social determinants of health; SDOH; cluster; cardiac; heart; cardiology; myocardial; phenotype; phenotypes; subphenotype; subphenotypes; mortality; death; survive; survival; survivor; survivors; retrospective; observational; cohort; algorithm; algorithms; k-means; clustering; association; associations",
abstract="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. ",
issn="2561-7605",
doi="10.2196/51844",
url="https://aging.jmir.org/2023/1/e51844",
url="https://doi.org/10.2196/51844",
url="http://www.ncbi.nlm.nih.gov/pubmed/38059569"
}