期刊
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
卷 22, 期 4, 页码 872-880出版社
OXFORD UNIV PRESS
DOI: 10.1093/jamia/ocv024
关键词
risk prediction; electronic health records; topic modeling; survival analysis
类别
资金
- National Science Foundation [IIS-1344668, IIS-0745520, IIS-1247664, IIS-1009542]
- Office of Naval Research [N00014-11-1-0651]
- Alfred P. Sloan Foundation
- Defense Advanced Research Projects Agency [FA8750-14-2-0009]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1344668] Funding Source: National Science Foundation
Background As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. Objective The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. Methods The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. Results A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P<.031) and a model based on estimated glomerular filtration rate (concordance 0.779, P<.001). Conclusions A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据