%0 Journal Article
%@ 2561-7605
%I 
%V 6
%N 
%P e51844
%T Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis
%A Abbott,Ethan E
%A Oh,Wonsuk
%A Dai,Yang
%A Feuer,Cole
%A Chan,Lili
%A Carr,Brendan G
%A Nadkarni,Girish N
%K out-of-hospital-cardiac arrest
%K machine learning
%K social determinants of health
%K SDOH
%K cluster
%K cardiac
%K heart
%K cardiology
%K myocardial
%K phenotype
%K phenotypes
%K subphenotype
%K subphenotypes
%K mortality
%K death
%K survive
%K survival
%K survivor
%K survivors
%K retrospective
%K observational
%K cohort
%K algorithm
%K algorithms
%K k-means
%K clustering
%K association
%K associations
%D 2023
%7 6.12.2023
%9 
%J JMIR Aging
%G English
%X 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. 
%M 38059569
%R 10.2196/51844
%U https://aging.jmir.org/2023/1/e51844
%U https://doi.org/10.2196/51844
%U http://www.ncbi.nlm.nih.gov/pubmed/38059569