JACC. Cardiovascular interventions · 2023
Implications of a Race Term in GFR Estimates Used to Predict AKI After Coronary Intervention.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Uzendu Anezi, Kennedy Kevin, Chertow Glenn, Amin Amit P, Giri Jay S, Rymer Jennifer A, Bangalore Sripal, Lavin Kimberly, Anderson Cornelia, Spertus John A
Аннотация
BACKGROUND: The prediction of mortality, bleeding, and acute kidney injury (AKI) after percutaneous coronary intervention (PCI) traditionally relied on race-based estimates of the glomerular filtration rate (GFR). Recently, race agnostic equations were developed to advance equity.
OBJECTIVES: The authors aimed to compare the accuracy and implications of various GFR equations when used to predict AKI after PCI.
METHODS: Using the National Cardiovascular Data Registry (NCDR) CathPCI data set, we identified patients undergoing PCI in 2020 and calculated their AKI risk using the 2014 NCDR AKI risk model. We created 4 AKI models per patient for each estimate of baseline renal function: the traditional GFR equation with a race term, 2 GFR equations without a race term, and serum creatinine alone. We then compared each model's performance predicting AKI.
RESULTS: Among 455,806 PCI encounters, the median age was 67 years, 32.2% were women, and 8.5% were Black. In Black patients, risk models without a race term were better calibrated than models incorporating an equation with a race term (intercept: -0.01 vs 0.15). Race-agnostic models reclassified 6% of Black patients into higher-risk categories, potentially prompting appropriate mitigation efforts. However, even with a race-agnostic model, AKI occurred in Black patients 18% more often than expected, which was not explained by captured patient or procedural characteristics.
CONCLUSIONS: Incorporating a GFR estimate without a Black race term into the NCDR AKI risk prediction model yielded more accurate prediction of AKI risk for Black patients, which has important implications for reducing disparities and benchmarking.
OBJECTIVES: The authors aimed to compare the accuracy and implications of various GFR equations when used to predict AKI after PCI.
METHODS: Using the National Cardiovascular Data Registry (NCDR) CathPCI data set, we identified patients undergoing PCI in 2020 and calculated their AKI risk using the 2014 NCDR AKI risk model. We created 4 AKI models per patient for each estimate of baseline renal function: the traditional GFR equation with a race term, 2 GFR equations without a race term, and serum creatinine alone. We then compared each model's performance predicting AKI.
RESULTS: Among 455,806 PCI encounters, the median age was 67 years, 32.2% were women, and 8.5% were Black. In Black patients, risk models without a race term were better calibrated than models incorporating an equation with a race term (intercept: -0.01 vs 0.15). Race-agnostic models reclassified 6% of Black patients into higher-risk categories, potentially prompting appropriate mitigation efforts. However, even with a race-agnostic model, AKI occurred in Black patients 18% more often than expected, which was not explained by captured patient or procedural characteristics.
CONCLUSIONS: Incorporating a GFR estimate without a Black race term into the NCDR AKI risk prediction model yielded more accurate prediction of AKI risk for Black patients, which has important implications for reducing disparities and benchmarking.