Official websites use .gov
A
.gov
website belongs to an official government organization in the
United States.
Secure .gov websites use HTTPS
A
lock
(
) or
https://
means you’ve safely connected to the .gov website. Share sensitive
information only on official, secure websites.
PubMed ID:
33850243
Public Release Type:
Journal
Publication Year: 2021
DOI:
https://doi.org/10.1038/s41746-021-00428-1
Authors:
Shang Ning, Khan Atlas, Polubriaginof Fernanda, Zanoni Francesca, Mehl Karla, Fasel David, Drawz Paul E., Carrol Robert J., Denny Joshua C., Hathcock Matthew A., Arruda-Olson Adelaide M., Peissig Peggy L., Dart Richard A., Brilliant Murray H., Larson Eric B., Carrell David S., Pendergrass Sarah, Verma Shefali Setia, Ritchie Marylyn D., Benoit Barbara, Gainer Vivian S., Karlson Elizabeth W., Gordon Adam S., Jarvik Gail P., Stanaway Ian B., Crosslin David R., Mohan Sumit, Ionita-Laza Iuliana, Tatonetti Nicholas P., Gharavi Ali G., Hripcsak George, Weng Chunhua, Kiryluk Krzysztof
Request IDs:
22384
Studies:
African American Study of Kidney Disease and Hypertension Cohort Study
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 demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.
