Faculty Collaboration Database

Development and validation of a race-agnostic computable phenotype for kidney health in adult hospitalized patients. PLoS One 2024;19(4):e0299332

Date

04/23/2024

Pubmed ID

38652731

Pubmed Central ID

PMC11037544

DOI

10.1371/journal.pone.0299332

Scopus ID

2-s2.0-85191405922 (requires institutional sign-in at Scopus site)

Abstract

Standard race adjustments for estimating glomerular filtration rate (GFR) and reference creatinine can yield a lower acute kidney injury (AKI) and chronic kidney disease (CKD) prevalence among African American patients than non-race adjusted estimates. We developed two race-agnostic computable phenotypes that assess kidney health among 139,152 subjects admitted to the University of Florida Health between 1/2012-8/2019 by removing the race modifier from the estimated GFR and estimated creatinine formula used by the race-adjusted algorithm (race-agnostic algorithm 1) and by utilizing 2021 CKD-EPI refit without race formula (race-agnostic algorithm 2) for calculations of the estimated GFR and estimated creatinine. We compared results using these algorithms to the race-adjusted algorithm in African American patients. Using clinical adjudication, we validated race-agnostic computable phenotypes developed for preadmission CKD and AKI presence on 300 cases. Race adjustment reclassified 2,113 (8%) to no CKD and 7,901 (29%) to a less severe CKD stage compared to race-agnostic algorithm 1 and reclassified 1,208 (5%) to no CKD and 4,606 (18%) to a less severe CKD stage compared to race-agnostic algorithm 2. Of 12,451 AKI encounters based on race-agnostic algorithm 1, race adjustment reclassified 591 to No AKI and 305 to a less severe AKI stage. Of 12,251 AKI encounters based on race-agnostic algorithm 2, race adjustment reclassified 382 to No AKI and 196 (1.6%) to a less severe AKI stage. The phenotyping algorithm based on refit without race formula performed well in identifying patients with CKD and AKI with a sensitivity of 100% (95% confidence interval [CI] 97%-100%) and 99% (95% CI 97%-100%) and a specificity of 88% (95% CI 82%-93%) and 98% (95% CI 93%-100%), respectively. Race-agnostic algorithms identified substantial proportions of additional patients with CKD and AKI compared to race-adjusted algorithm in African American patients. The phenotyping algorithm is promising in identifying patients with kidney disease and improving clinical decision-making.

Author List

Ozrazgat-Baslanti T, Ren Y, Adiyeke E, Islam R, Hashemighouchani H, Ruppert M, Miao S, Loftus T, Johnson-Mann C, Madushani RWMA, Shenkman EA, Hogan W, Segal MS, Lipori G, Bihorac A, Hobson C

Author

William R. Hogan MD Director, Professor in the Data Science Institute department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Acute Kidney Injury
Adult
Aged
Algorithms
Creatinine
Female
Glomerular Filtration Rate
Hospitalization
Humans
Kidney
Male
Middle Aged
Phenotype
Renal Insufficiency, Chronic