title: Multi-Trajectory Models of Chronic Kidney Disease Progression.
abstract: An ever increasing number of people are affected by chronic kidney disease (CKD). A better understanding of the progression ofCKD and its complications is needed to address what is becoming a major burden for health-care systems worldwide. Utilizing a rich data set consisting of the Electronic Health Records (EHRs) of more than33,000patients from a leading community nephrology practice in Western Pennsylvania, we applied group-based trajectory modeling (GBTM) in order to detect patient risk groups and uncover typical progressions of CKD and related comorbidities and complications. We have found distinct risk groups with differing trajectories and are able to classify new patients into these groups with high accuracy (up to ≈ 90%). Our results suggest that multitrajectory modeling via GBTM can shed light on the developmental course ofCKD and the interactions between related complications.
authors.affiliation: Carnegie Mellon University, Pittsburgh, PA.
authors.forename: Philipp
authors.initials: P
authors.lastname: Burckhardt
authors.affiliation: Carnegie Mellon University, Pittsburgh, PA.
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Nagin
authors.affiliation: Carnegie Mellon University, Pittsburgh, PA.
authors.forename: Rema
authors.initials: R
authors.lastname: Padman
mesh_terms: D066264:Datasets as Topic; D018450:Disease Progression; D057286:Electronic Health Records; D005919:Glomerular Filtration Rate; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D010414:Pennsylvania; D051436:Renal Insufficiency, Chronic / Q000503:physiopathology