{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T02:32:31Z","timestamp":1791599551134,"version":"4.3.4"},"reference-count":34,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Aging"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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).<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n 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\u2013level 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.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/51844","type":"journal-article","created":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T01:04:48Z","timestamp":1698541488000},"page":"e51844-e51844","source":"Crossref","is-referenced-by-count":4,"title":["Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis"],"prefix":"10.2196","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3295-7996","authenticated-orcid":false,"given":"Ethan E","family":"Abbott","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0874-5026","authenticated-orcid":false,"given":"Wonsuk","family":"Oh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1982-1477","authenticated-orcid":false,"given":"Yang","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3679-0380","authenticated-orcid":false,"given":"Cole","family":"Feuer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4300-5760","authenticated-orcid":false,"given":"Lili","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9147-9701","authenticated-orcid":false,"given":"Brendan G","family":"Carr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6319-4314","authenticated-orcid":false,"given":"Girish N","family":"Nadkarni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2023,12,6]]},"reference":[{"key":"R1","unstructured":"2020 annual report. 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