Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.
Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.
复制标题
使用异质电子健康记录数据和时间序列分析的慢性肾脏疾病进展的风险预测。
DOI：
10.1093/jamia/ocv024
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发表时间：
2015-07
期刊：
影响因子：
--
通讯作者：
Elhadad N
中科院分区：
文献类型：
--
作者：
Perotte A;Ranganath R;Hirsch JS;Blei D;Elhadad N
随着电子健康记录的采用持续增加，有机会将临床文件以及实验室值和人口统计学纳入风险预测模型。 目的建立一个慢性肾脏病（CKD）从III期进展到IV期的风险预测模型，该模型包括纵向数据和从临床文献中提取的特征。 方法研究队列包括2908名在2013年1月1日之前至少接受过3次访视的初级保健诊所患者，这些患者在记录病史期间患有CKD III期。从该队列中随机选择开发和验证队列，研究数据集包括这些人群的纵向住院和门诊数据。时间序列分析（卡尔曼滤波）和生存分析（考克斯比例风险）相结合，以产生一系列的风险模型。这些模型进行了评估，使用一致性，歧视性统计。 结果风险模型结合了临床记录和实验室检查结果的纵向数据（一致性0.849）与无实验室检查结果的类似模型相比，更准确地预测从III期CKD进展至IV期CKD（一致性0.733，P<.001），该模型仅考虑最近的实验室检查结果（一致性0.819，P < .031）和基于估计的肾小球滤过率的模型（一致性0.779，P < .001）。 结论：将纵向实验室检查结果和临床记录考虑在内的风险预测模型可以比不考虑所有这些变量的三种模型更准确地预测CKD从III期进展到IV期。
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.
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影响因子：
2.3
作者：
Greer RC;Powe NR;Jaar BG;Troll MU;Boulware LE
通讯作者：
Boulware LE
影响因子：
3.7
作者：
Cohen R;Aviram I;Elhadad M;Elhadad N
通讯作者：
Elhadad N
影响因子：
13.2
作者：
Agrawal, Varun;Ghosh, Amit K.;McCullough, Peter A.
通讯作者：
McCullough, Peter A.
DOI：
10.2215/cjn.06210909
发表时间：
2010-01-01
影响因子：
9.8
作者：
Chapin, Erica;Zhan, Min;Fink, Jeffrey C.
通讯作者：
Fink, Jeffrey C.
影响因子：
8
作者：
Drion, I.;Joosten, H.;Bilo, H. J. G.
通讯作者：
Bilo, H. J. G.