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Joint modeling of social determinants and clinical factors defines subphenotypes in out-of-hospital cardiac arrest survival
Ethan Abbott;
Wonsuk Oh;
Yang Dai;
Cole Feuer;
Lili Chan;
Brendan G Carr;
Girish N. Nadkarni
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 one year.
Methods:
We utilized a retrospective observational OHCA cohort identified from Medicare claims data, including area level SDOH features and hospital level datasets. We applied k-means clustering algorithms to identify subphenotypes of beneficiaries who had survived an OHCA and examined associations of outcomes by subphenotype.
Results:
27,028 unique beneficiaries survived to discharge after OHCA. We derived 4 distinct subphenotypes, finding subphenotype 1 with the highest unadjusted mortality (53.8%) and subphenotype 4 with low mortality (31.7%). Jointly modeling of these features demonstrated an increased hazard of death for subphenotypes 1-3 but not for subphenotype 4 when compared to reference.We identified four distinct subphenotypes with differences in outcomes by clinical and area level SDOH features for OHCA.
Conclusions:
Further work is needed to determine if individual or other SDOH domains are specifically tied to long-term survival after OHCA.
Citation
Please cite as:
Abbott E, Oh W, Dai Y, Feuer C, Chan L, Carr BG, Nadkarni GN
Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis