title: Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.
abstract: 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.
authors.affiliation: Biomedical Informatics Department, Columbia University, New York, NY, USA ajp2120@columbia.edu.
authors.forename: Adler
authors.initials: A
authors.lastname: Perotte
authors.affiliation: Computer Science Department, Princeton University, Princeton, NJ, USA.
authors.forename: Rajesh
authors.initials: R
authors.lastname: Ranganath
authors.affiliation: Biomedical Informatics Department, Columbia University, New York, NY, USA Division of Nephrology, Columbia University, New York, NY, USA.
authors.forename: Jamie S
authors.initials: JS
authors.lastname: Hirsch
authors.affiliation: Statistics Department, Columbia University, New York, NY, USA.
authors.forename: David
authors.initials: D
authors.lastname: Blei
authors.affiliation: Biomedical Informatics Department, Columbia University, New York, NY, USA.
authors.forename: Noémie
authors.initials: N
authors.lastname: Elhadad
mesh_terms: D000368:Aged; D015331:Cohort Studies; D018450:Disease Progression; D057286:Electronic Health Records; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011320:Primary Health Care; D016016:Proportional Hazards Models; D051436:Renal Insufficiency, Chronic / Q000503:physiopathology*; D018570:Risk Assessment; D016019:Survival Analysis; D013995:Time
keywords: electronic health records; risk prediction; survival analysis; topic modeling