Predicting Adverse Outcomes in Heart Failure Patients Using Different Frailty Status Measures
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.
Medical Informatics Frail Elderly
Yan Cheng Yan Cheng Charlene R.Weir Rashmee U.Shah Bruce E.Bray Jennifer H.Garvin Qing Zeng-Treitler
Biomedical Informatics Center,George Washington University,Washington,DC,USA Department of Biomedical Informatics,University of Utah,Salt Lake City,UT,USA;Veterans Affairs Medic Division of Cardiovascular Medicine,University of Utah,Salt Lake City,UT,USA Department of Biomedical Informatics,University of Utah,Salt Lake City,UT,USA Biomedical Informatics Center,George Washington University,Washington,DC,USA;Washington DC VA Medica
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
327-331
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）