Additional file 2: Table S4. of Diagnosis, misdiagnosis, lucky guess, hearsay, and more: an ontological analysis

Entities in Scenario 3: Incorrect diagnosis. Table S5. Additional temporal entities in Scenario 3: Incorrect diagnosis. Table S6. Relationships among particulars in Scenario 3: Incorrect diagnosis. (DOCX 88 kb

Supplementary methods.

Detailed description of data elements and methods. (DOCX)</p

Details on output categories for CKD algorithm.

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.</div

Administrative codes used for chronic kidney disease.

Administrative codes used for chronic kidney disease.</p

Logical Observation Identifier Names and Codes (LOINC) codes used for CKD A-staging.

Logical Observation Identifier Names and Codes (LOINC) codes used for CKD A-staging.</p

Formulas used for estimated creatinine and estimated glomerular filtration rate calculations by the three algorithms.

Formulas used for estimated creatinine and estimated glomerular filtration rate calculations by the three algorithms.</p

Reclassification of CKD status and CKD G-stages, using race agnostic algorithm 2, among African American patients after race-adjustment.

Reclassification of CKD status and CKD G-stages, using race agnostic algorithm 2, among African American patients after race-adjustment.</p

Reclassification of AKI status and AKI stages, using race agnostic algorithm 1, after race adjustment among African American patients.

Reclassification of AKI status and AKI stages, using race agnostic algorithm 1, after race adjustment among African American patients.</p

Reclassification of CKD status and CKD stages after race adjustment among African American patients who do not have CKD by medical history.

Reclassification of CKD status and CKD stages after race adjustment among African American patients who do not have CKD by medical history.</p

AKI characteristics using race-adjusted and race-agnostic algorithms.

AKI characteristics using race-adjusted and race-agnostic algorithms.</p