Key result
Subphenotype 4 linked to ~47% lower 1-year mortality risk among OHCA survivors.
Why the study?
Machine learning clustering to understand interactions of complex social and clinical variables via integrative subphenotypes had not been studied in out-of-hospital cardiac arrest.
Does machine learning clustering of clinical and social determinants of health identify subphenotypes with different 1-year mortality in out-of-hospital cardiac arrest survivors?
Observational (n=27,028)
Yes
Does machine learning clustering of clinical and social determinants of health identify subphenotypes with different 1-year mortality in out-of-hospital cardiac arrest survivors?
Effect estimate: HR 0.53 (95% CI 0.50-0.57)
Unsupervised machine learning clustering of clinical and social determinants of health identified four distinct subphenotypes of out-of-hospital cardiac arrest survivors with significantly different 1-year mortality risks.
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.
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Abbott et al. (2023) conducted an observational in Out-of-hospital cardiac arrest (OHCA) survivors (n=27,028). Subphenotype 4 (moderate SDOH, high interhospital transfer) vs. All other subphenotypes (reference) was evaluated on Mortality at 1 year (HR 0.53, 95% CI 0.50-0.57). Among Medicare beneficiaries surviving out-of-hospital cardiac arrest, subphenotype 4 was associated with a decreased hazard of death at 1 year (HR 0.53) compared to other subphenotypes.
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