Development and internal validation of a prediction model for acute kidney injury following cardiac valve replacement surgery. (1st January 2023)

• Record Type: Journal Article
• Authors: Pan, Ling; Deng, Yang; Dai, Shichen; Feng, Xu; Feng, Li; Yang, Zhenhua; Liao, Yunhua; Zheng, Baoshi
• Is Part Of: International journal of cardiology. Volume 370(2023)
• Page Start: 345
• Page End: 350
• Publication Date: 2023-01-01
• Subjects: Acute kidney injury (AKI) -- Cardiac valve replacement surgery -- Prediction model -- Internal validation; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2022.10.147
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: Background: Acute kidney injury (AKI) is a common complication after cardiac surgery. This study aims to develop and validate a risk model for predicting AKI after cardiac valve replacement surgery. Methods: Data from patients undergoing surgical valve replacement between January 2015 and December 2018 in our hospital were retrospectively analyzed. The subjects were randomly divided into a derivation cohort and a validation cohort at a ratio of 7:3. The primary outcome was defined as AKI within 7 days after surgery. Logistic regression analysis was conducted to select risk predictors for developing the prediction model. Receiver operator characteristic curve (ROC), calibration plot and clinical decision curve analysis (DCA) will be used to evaluate the discrimination, precision and clinical benefit of the prediction model. Results: A total of 1159 patients were involved in this study. The prevalence of AKI following surgery was 37.0% (429/1159). Logistic regression analysis showed that age, hemoglobin, fibrinogen, serum uric acid, cystatin C, bicarbonate, and cardiopulmonary bypass time were independent risk factors associated with AKI after surgical valve replacement (all P < 0.05). The areas under the ROC curves (AUCs) in the derivation cohort and the validation cohort were 0.777 (95% CI 0.744–0.810) and 0.760 (95% CI 0.706–0.813), respectively. The calibration plots indicated excellent consistency between the prediction probability and actual probability. DCA demonstrated great clinical benefit of the prediction model. Conclusions: We developed a prediction model for predicting AKI after cardiac valve replacement surgery that was internally validated to have good discrimination, calibration, and clinical practicability. Highlights: Few studies have focused on the prediction of AKI following surgical valve replacement. This study aimed to develop and validate a risk model for the early prediction of AKI after cardiac valve replacement surgery. Age, hemoglobin, fibrinogen, serum uric acid, cystatin C, bicarbonate, and cardiopulmonary bypass time are independent risk factors associated with AKI after surgical valve replacement. We developed a prediction model for predicting AKI after cardiac valve replacement surgery based on 7 risk factors that were internally validated to have good discrimination, calibration, and clinical practicability.