Validation of a Prediction Model for Acute Kidney Injury after Cardiac Surgery in a Retrospective Asian Cohort

Abstract

Background: The incidence of postoperative acute kidney injury (AKI) is relatively high in some Asian regions. The objective of this study was to examine the performance of an AKI prediction model developed based on data from a White-dominant population in a retrospective Asian cohort of patients undergoing cardiovascular surgery. Methods: We retrospectively identified 549 patients who underwent elective major cardiovascular surgery (coronary artery bypass graft, valve surgery, and aorta surgery), and excluded those who underwent a percutaneous cardiovascular procedure. Patients with a baseline estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 were also excluded. AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) definition. Performance of the prediction model for AKI was expressed as area under the receiver operating characteristic curve (AUC). Results: The prediction model had a good predictive accuracy for postoperative AKI (all AUC > 0.92). The AUC of the prediction model in subgroups of age (<65 years and ≥65 years), sex (male and female), hypertension, and diabetes were all >0.85 (all p values < 0.001). Conclusions: The model could be used to predict postoperative AKI in Asian patients undergoing cardiovascular surgery with a baseline eGFR ≥ 60 mL/min/1.73 m2.