Paper Info
Title
Predicting Adverse Outcomes in Heart Failure Patients Using Different Frailty Status Measures.
Abstract
Frailty is an important outcome predictor in older patients. We randomly sampled 12000 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, MIL 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.
Year
DOI
Venue
2017
10.3233/978-1-61499-830-3-327
Studies in Health Technology and Informatics
Keywords
Field
DocType
Medical Informatics,Frail Elderly
Heart failure,Knowledge management,Intensive care medicine,Health informatics,Medicine
Conference
Volume
ISSN
Citations 
245
0926-9630
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Yan Cheng154.17
Yijun Shao201.69
Charlene R. Weir316838.85
Rashmee Shah402.03
Bruce E. Bray55711.09
Jennifer H. Garvin63411.65
Qing T. Zeng7276.89