Estimating One-Year Risk of Incident Chronic Kidney Disease: Retrospective Development and Validation Study Using Electronic Medical Record Data From the State of Maine

Shiying Hao; Tianyun Fu; Qian Wu; Bo Jin; Chunqing Zhu; Zhongkai Hu; Yanting Guo; Yan Zhang; Yunxian Yu; Terry Fouts; Phillip Ng; Devore S Culver; Shaun T Alfreds; Frank Stearns; Karl G Sylvester; Eric Widen; Doff B McElhinney; Xuefeng B Ling

doi:10.2196/medinform.7954

Estimating One-Year Risk of Incident Chronic Kidney Disease: Retrospective Development and Validation Study Using Electronic Medical Record Data From the State of Maine

JMIR Med Inform. 2017 Jul 26;5(3):e21. doi: 10.2196/medinform.7954.

Authors

Shiying Hao^{1

2

3}, Tianyun Fu⁴, Qian Wu^{5

6}, Bo Jin⁴, Chunqing Zhu⁴, Zhongkai Hu^{2

3}, Yanting Guo^{5

7}, Yan Zhang^{5

8}, Yunxian Yu¹, Terry Fouts⁹, Phillip Ng¹⁰, Devore S Culver¹¹, Shaun T Alfreds¹¹, Frank Stearns⁴, Karl G Sylvester⁵, Eric Widen⁴, Doff B McElhinney^{2

3}, Xuefeng B Ling^{1

3

5}

Affiliations

¹ Department of Epidemiology and Health Statistics, School of Public Health, School of Medicine, Zhejiang University, Hangzhou, China.
² Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States.
³ Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States.
⁴ HBI Solutions Inc, Palo Alto, CA, United States.
⁵ Department of Surgery, Stanford University, Stanford, CA, United States.
⁶ China Electric Power Research Institute, Beijing, China.
⁷ School of Management, Zhejiang University, Hangzhou, China.
⁸ Department of Oncology, The First Hospital of Shijiazhuang, Shijiazhuang, China.
⁹ Empactful Capital, San Francisco, CA, United States.
¹⁰ Sequoia Hospital, Redwood City, CA, United States.
¹¹ HealthInfoNet, Portland, ME, United States.

Abstract

Background: Chronic kidney disease (CKD) is a major public health concern in the United States with high prevalence, growing incidence, and serious adverse outcomes.

Objective: We aimed to develop and validate a model to identify patients at risk of receiving a new diagnosis of CKD (incident CKD) during the next 1 year in a general population.

Methods: The study population consisted of patients who had visited any care facility in the Maine Health Information Exchange network any time between January 1, 2013, and December 31, 2015, and had no history of CKD diagnosis. Two retrospective cohorts of electronic medical records (EMRs) were constructed for model derivation (N=1,310,363) and validation (N=1,430,772). The model was derived using a gradient tree-based boost algorithm to assign a score to each individual that measured the probability of receiving a new diagnosis of CKD from January 1, 2014, to December 31, 2014, based on the preceding 1-year clinical profile. A feature selection process was conducted to reduce the dimension of the data from 14,680 EMR features to 146 as predictors in the final model. Relative risk was calculated by the model to gauge the risk ratio of the individual to population mean of receiving a CKD diagnosis in next 1 year. The model was tested on the validation cohort to predict risk of CKD diagnosis in the period from January 1, 2015, to December 31, 2015, using the preceding 1-year clinical profile.

Results: The final model had a c-statistic of 0.871 in the validation cohort. It stratified patients into low-risk (score 0-0.005), intermediate-risk (score 0.005-0.05), and high-risk (score ≥ 0.05) levels. The incidence of CKD in the high-risk patient group was 7.94%, 13.7 times higher than the incidence in the overall cohort (0.58%). Survival analysis showed that patients in the 3 risk categories had significantly different CKD outcomes as a function of time (P<.001), indicating an effective classification of patients by the model.

Conclusions: We developed and validated a model that is able to identify patients at high risk of having CKD in the next 1 year by statistically learning from the EMR-based clinical history in the preceding 1 year. Identification of these patients indicates care opportunities such as monitoring and adopting intervention plans that may benefit the quality of care and outcomes in the long term.

Keywords: chronic kidney disease; electronic medical record; retrospective study; risk model.

©Shiying Hao, Tianyun Fu, Qian Wu, Bo Jin, Chunqing Zhu, Zhongkai Hu, Yanting Guo, Yan Zhang, Yunxian Yu, Terry Fouts, Phillip Ng, Devore S Culver, Shaun T Alfreds, Frank Stearns, Karl G Sylvester, Eric Widen, Doff B McElhinney, Xuefeng B Ling. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 26.07.2017.