Development and Validation of a Novel Nomogram for Predicting Perioperative Acute Kidney Injury Following Isolated Off-Pump Coronary Artery Bypass Grafting Surgery

Abstract

Background: The aim of this work was to create a novel model for predicting acute kidney injury (AKI) after off-pump coronary artery bypass graft (OPCABG). Methods: The individuals who underwent OPCABG were randomly separated into a derivation group and a validation group, at a 7:3 ratio. The primary outcome was AKI under the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. To optimize feature selection and construct a nomogram, both least absolute shrinkage and selection operator regression (LASSO) and logistic regression analysis were utilized. The nomogram was assessed in various ways: with the C-index, calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA). Results: The use of an intra-aortic balloon pump (IABP), systolic blood pressure, smoking and baseline serum creatinine were identified as independent impact factors. The C-index of the nomogram was 0.733 (95% confidence interval (CI) = 0.669–0.791) and 0.786 (95% CI = 0.693–0.878) in the training and validation groups, respectively. The area under the curve (AUC) of the internal validation was 0.715 using bootstrapping with 1000 replicates. The calibration plot revealed that the predicted outcomes aligned well with the observations. DCA and CICA suggested that the model had clinical benefit. Conclusion: The nomogram that relied on clinical characteristics proved to be a dependable instrument to predict AKI after OPCABG. This model is conveniently applicable in clinical settings and will be a valuable resource for assessing timely medical measures to mitigate risk.