Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies.

ABSTRACT: Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate ("A-by-G" grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demons

SUBMITTER: Shang N 

PROVIDER: S-EPMC8044136 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature