{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:35:08Z","timestamp":1740202508927,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>Frailty is an important outcome predictor in older patients. We randomly sampled 12,000 veterans with heart failure diagnosed in 2010. The topic modeling method was applied to identify frailty-related topics from the clinical notes in the electronic medical records. The frailty topics were classified into five deficit areas including physical functioning (PF), role-physical (RP), general health (GH), social functioning (SF), and mental health (MH). We experimented with different covariates and four different frailty measures: individual frailty topics, number of distinct frailty topics, a dichotomous deficit category, and the number of distinct deficits, respectively. A total of 8,531 (71.1%) patients had at least one frailty topic. The prevalence of GH, PF, MH, SF, and RP deficits were 89.0%, 61.3%, 56.9%, 40.6%, and 9.5%, respectively. PF deficits (yes\/no) and the number of distinct deficits were the most consistent, significant predictors of adverse outcomes of rehosptalization or death.<\/jats:p>","DOI":"10.3233\/978-1-61499-830-3-327","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T15:10:01Z","timestamp":1740150601000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting Adverse Outcomes in Heart Failure Patients Using Different Frailty Status Measures"],"prefix":"10.3233","author":[{"family":"Cheng Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Shao Yijun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Weir Charlene R.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Shah Rashmee U.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Bray Bruce E.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Garvin Jennifer H.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Zeng-Treitler Qing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2017: Precision Healthcare through Informatics"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T15:55:33Z","timestamp":1740153333000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-829-7&spage=327&doi=10.3233\/978-1-61499-830-3-327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-830-3-327","relation":{},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2017]]}}}