JACC. Cardiovascular interventions · 2023
Contemporary Methods for Predicting Acute Kidney Injury After Coronary Intervention.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Uzendu Anezi, Kennedy Kevin, Chertow Glenn, Amin Amit P, Giri Jay S, Rymer Jennifer A, Bangalore Sripal, Lavin Kimberly, Anderson Cornelia, Wang Tracy Y, Curtis Jeptha P, Spertus John A
Аннотация
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.
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.