Development and validation of a race-agnostic computable phenotype for kidney health in adult hospitalized patients

• DOI: 10.1371/journal.pone.0299332
• Publication Date: 2024-04-23T17:31:48Z
• Authors: Tezcan Ozrazgat-B...; Yuanfang Ren; Esra Adiyeke; Rubab Islam; Haleh Hashemighou...; Matthew Ruppert; Shunshun Miao; Tyler Loftus; Crystal Johnson-Mann; R. W. M. A. Madus...; Elizabeth A. Shen...; William Hogan; Mark S. Segal; Gloria Lipori; Azra Bihorac; Charles Hobson
• Keywords: Male; Adult; Science; Q; R; Middle Aged; Acute Kidney Injury; Kidney; Hospitalization; Black or African American; Phenotype; Creatinine; Medicine; Humans; Female; Renal Insufficiency, Chronic; Algorithms; Research Article; Glomerular Filtration Rate; Aged

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.