{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T06:57:55Z","timestamp":1776149875994,"version":"3.50.1"},"reference-count":39,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T00:00:00Z","timestamp":1653609600000},"content-version":"vor","delay-in-days":146,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acute Medicine &amp; Surgery"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:sec><jats:title>Aim<\/jats:title><jats:p>We aimed to identify subphenotypes among patients with out\u2010of\u2010hospital cardiac arrest (OHCA) with initial non\u2010shockable rhythm by applying machine learning latent class analysis and examining the associations between subphenotypes and neurological outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study was a retrospective analysis within a multi\u2010institutional prospective observational cohort study of OHCA patients in Osaka, Japan (the CRITICAL study). The data of adult OHCA patients with medical causes and initial non\u2010shockable rhythm presenting with OHCA between 2012 and 2016 were included in machine learning latent class analysis models, which identified subphenotypes, and patients who presented in 2017 were included in a dataset validating the subphenotypes. We investigated associations between subphenotypes and 30\u2010day neurological outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among the 12,594 patients in the CRITICAL study database, 4,849 were included in the dataset used to classify subphenotypes (median age: 75\u2009years, 60.2% male), and 1,465 were included in the validation dataset (median age: 76\u2009years, 59.0% male). Latent class analysis identified four subphenotypes. Odds ratios and 95% confidence intervals for a favorable 30\u2010day neurological outcome among patients with these subphenotypes, using group 4 for comparison, were as follows; group 1, 0.01 (0.001\u20130.046); group 2, 0.097 (0.051\u20130.171); and group 3, 0.175 (0.073\u20130.358). Associations between subphenotypes and 30\u2010day neurological outcomes were validated using the validation dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We identified four subphenotypes of OHCA patients with initial non\u2010shockable rhythm. These patient subgroups presented with different characteristics associated with 30\u2010day survival and neurological outcomes.<\/jats:p><\/jats:sec>","DOI":"10.1002\/ams2.760","type":"journal-article","created":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T10:44:46Z","timestamp":1653648286000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Clustering out\u2010of\u2010hospital cardiac arrest patients with non\u2010shockable rhythm by machine learning latent class analysis"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2266-476X","authenticated-orcid":false,"given":"Yohei","family":"Okada","sequence":"first","affiliation":[{"name":"Department of Preventive Services, School of Public Health Kyoto University  Kyoto Japan"},{"name":"Department of Primary Care and Emergency Medicine, Graduate School of Medicine Kyoto University  Kyoto 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