{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T04:04:28Z","timestamp":1747454668990,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685960","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,15]]},"abstract":"<jats:p>This study develops a CPR prediction model using initial admission data in the emergency department. We retrospectively analyzed EMR data from Severance Hospital (2018-2022), excluding patients aged under 18 or without initial vital signs and KTAS, and those with DNR orders. We developed and compared two models: one using patient information and initial vital signs and another incorporating KTAS. The model with KTAS showed significantly better predictive performance. Our findings suggest that combining vital signs and KTAS can effectively predict CPR, helping to prevent cardiac arrest.<\/jats:p>","DOI":"10.3233\/shti250414","type":"book-chapter","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:56:20Z","timestamp":1747385780000},"source":"Crossref","is-referenced-by-count":0,"title":["Improving CPR Predictive Model in ED: The Role of Initial Data and KTAS"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4534-9375","authenticated-orcid":false,"given":"Sungsoo","family":"Hong","sequence":"first","affiliation":[{"name":"AITRICS Inc., 218 Teheran-ro, Gangnam-gu, Seoul, Republic of Korea, 06221"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1249-7945","authenticated-orcid":false,"given":"Heejung","family":"Hyun","sequence":"additional","affiliation":[{"name":"AITRICS Inc., 218 Teheran-ro, Gangnam-gu, Seoul, Republic of Korea, 06221"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2797-238X","authenticated-orcid":false,"given":"Sungjun","family":"Hong","sequence":"additional","affiliation":[{"name":"Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, Republic of Korea, 06351"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Intelligent Health Systems \u2013 From Technology to Data and Knowledge"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250414","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:56:20Z","timestamp":1747385780000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"ISBN":["9781643685960"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250414","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,15]]}}}