Contemporary Methods for Predicting Acute Kidney Injury After Coronary Intervention.

BACKGROUND: Acute kidney injury (AKI) is the most common complication after percutaneous coronary intervention (PCI). Accurately estimating patients' risks not only creates a means of benchmarking performance but can also be used prospectively to inform practice. OBJECTIVES: The authors sought to update the 2014 National Cardiovascular Data Registry (NCDR) AKI risk model to provide contemporary estimates of AKI risk after PCI to further improve care. METHODS: Using the NCDR CathPCI Registry, we identified all 2020 PCIs, excluding those on dialysis or lacking postprocedural creatinine. The cohort was randomly split into a 70% derivation cohort and a 30% validation cohort, and logistic regression models were built to predict AKI (an absolute increase of 0.3 mg/dL in creatinine or a 50% increase from preprocedure baseline) and AKI requiring dialysis. Bedside risk scores were created to facilitate prospective use in clinical care, along with threshold contrast doses to reduce AKI. We tested model calibration and discrimination in the validation cohort. RESULTS: Among 455,806 PCI procedures, the median age was 67 years (IQR: 58.0-75.0 years), 68.8% were men, and 86.8% were White. The incidence of AKI and new dialysis was 7.2% and 0.7%, respectively. Baseline renal function and variables associated with clinical instability were the strongest predictors of AKI. The final AKI model included 13 variables, with a C-statistic of 0.798 and excellent calibration (intercept = -0.03 and slope = 0.97) in the validation cohort. CONCLUSIONS: The updated NCDR AKI risk model further refines AKI prediction after PCI, facilitating enhanced clinical care, benchmarking, and quality improvement.

Implications of a Race Term in GFR Estimates Used to Predict AKI After Coronary Intervention.

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.

Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention.

BACKGROUND: Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. OBJECTIVES: This study sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables. METHODS: Data from 706,263 PCIs performed between July 2018 and June 2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development cohort (70%, n = 495,005) and a validation cohort (30%, n = 211,258). The authors created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance. RESULTS: In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (C-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7% to 2.1%). CONCLUSIONS: The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts.