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

• Published in: PloS one Vol. 19; no. 4; p. e0299332
• Main Authors: Ozrazgat-Baslanti, Tezcan, Ren, Yuanfang, Adiyeke, Esra, Islam, Rubab, Hashemighouchani, Haleh, Ruppert, Matthew, Miao, Shunshun, Loftus, Tyler, Johnson-Mann, Crystal, Madushani, R. W. M. A., Shenkman, Elizabeth A., Hogan, William, Segal, Mark S., Lipori, Gloria, Bihorac, Azra, Hobson, Charles
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.04.2024; Public Library of Science (PLoS)
• Subjects: Acute Kidney Injury - diagnosis; Acute Kidney Injury - epidemiology; Adult; African Americans; Aged; Algorithms; Biology and Life Sciences; Black or African American; Chronic kidney failure; Comparative analysis; Computer and Information Sciences; Creatinine - blood; Decision-making; Diagnosis; Evaluation; Female; Genetic aspects; Glomerular Filtration Rate; Health aspects; Hospital patients; Hospitalization; Humans; Kidney - physiopathology; Kidney diseases; Male; Medical research; Medicine and Health Sciences; Medicine, Experimental; Middle Aged; People and places; Phenotype; Physical Sciences; Renal Insufficiency, Chronic - diagnosis; Renal Insufficiency, Chronic - epidemiology; Renal Insufficiency, Chronic - physiopathology; Research and Analysis Methods; Social aspects; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0299332

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.