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Abstract: FR-PO061

Race-Agnostic Computable Phenotype for Kidney Health Assessment in Adult Hospitalized Patients

Session Information

Category: Acute Kidney Injury

  • 101 AKI: Epidemiology‚ Risk Factors‚ and Prevention

Authors

  • Ozrazgat-Baslanti, Tezcan, University of Florida, Gainesville, Florida, United States
  • Ren, Yuanfang, University of Florida, Gainesville, Florida, United States
  • Adiyeke, Esra, University of Florida, Gainesville, Florida, United States
  • Loftus, Tyler J., University of Florida, Gainesville, Florida, United States
  • Segal, Mark S., University of Florida, Gainesville, Florida, United States
  • Bihorac, Azra, University of Florida, Gainesville, Florida, United States
Background

Acute kidney injury (AKI) and chronic kidney disease (CKD) are clinically used categorizations of kidney health. Standard race adjustments for estimating glomerular filtration rate (GFR) and reference creatinine can yield a lower AKI and CKD prevalence among African Americans (AA) to ones from non-adjusted estimates.

Methods

We developed race-agnostic computable phenotypes that assess kidney health among 139,152 subjects admitted to the University of Florida Health between 1/2012-8/2019. We removed the race modifier from the formula used by the race-adjusted algorithm to calculate the estimated GFR and creatinine (race-agnostic algorithm 1). In the second race-agnostic algorithm (race-agnostic algorithm 2), these calculations rely on 2021 CKD-EPI refit without race formula as endorsed by the National Kidney Foundation. We validated computable phenotypes developed for preadmission CKD and AKI presence on 300 selected cases using clinical adjudication.

Results

Race-agnostic algorithms identified CKD and AKI in 23% and 15% of encounters, respectively. Among 86,379 AA admissions, 26,908 (31%) and 26,003 (30%) had CKD based on race-agnostic algorithm 1 and 2, respectively. 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, while it 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 (15%) AKI encounters based on race-agnostic algorithm 1, the race adjustment reclassified 591 (4.7%) to no AKI and 305 (2.4%) to a less severe AKI stage. Of 12,251 (14%) AKI encounters based on race-agnostic algorithm 2, the race adjustment reclassified 382 (3.1%) to no AKI and 196 (1.6%) to a less severe AKI stage. Phenotyping algorithm based on refit without race formula performed well in identifying patients with CKD and AKI with a sensitivity of 100% (95% CI 97%-100%), 99% (95% CI 97%-100%), and a specificity of 88% (95% CI 82%-93%) and 98% (95% CI 93%-100%), respectively.

Conclusion

Race-agnostic algorithms identified substantial proportions of additional patients with CKD and AKI compared to race-adjusted algorithms in AA patients. The phenotyping algorithm is promising in identifying patients with kidney disease, assessing quality of care, and improving clinical decision-making.

Funding

  • NIDDK Support