Interpretable Machine Learning Analysis of Preoperative NT-proBNP and Creatinine for Predicting Acute Kidney Injury After Cardiac Valve Surgery

Background: Acute kidney injury (AKI) is a common and serious complication of cardiac valve surgery, and is associated with high mortality and healthcare costs. Existing prediction models for AKI are often incapable of capturing complex biomarker interactions. This study aimed to build an interpretable machine learning (ML) model that incorpates preoperative N-terminal pro-B-type natriuretic peptide (NT-proBNP) and serum creatinine (SCr) levels to predict of AKI risk in valve surgery patients. Methods: Consecutive adults who underwent isolated valve surgery with cardiopulmonary bypass (CPB) in the first affiliated hospital of Nanchang University from October 2016 to October 2021 were included in this retrospective cohort study. Patients who had preoperative dialysis or were having an emergency surgery were excluded as well as those with missing NT-proBNP/SCr data. The main outcome was any stage AKI within 7 days after surgery (Kidney Disease: Improving Global Outcomes, KDIGO criteria). Utilizing preoperative variables, five ML models Logistic regression, support vector machine (SVM), Random Forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbors (KNN) were developed after handling class imbalance synthetic minority oversampling technique (SMOTE). Key predictors were identified through feature selection techniques. Evaluation of model performance was done at area under the curve (AUC), sensitivity, specificity and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) values provided interpretability. Results: Among 333 patients eligible for inclusion, 106 experienced AKI (31.8%). Seven predictors were consistently selected: age, NT-proBNP, SCr, CPB duration, aortic cross-clamp (ACC) duration, hemoglobin and albumin. Overall, the RandomForest model outperformed the other models, with AUC of 0.872, accuracy of 0.835, sensitivity of 0.718, specificity of 0.923 and F1-score of 0.789 in the testing cohort (n = 91). DCA demonstrated excellent calibration and the highest net benefit with this model. SHAP analysis identified NT-proBNP, SCr, and duration of ACC as the three leading risk factors with clear, personalized risk evaluation. Conclusions: This novel, interpretable ML model leverages preoperative NT-proBNP and SCr to accurately predict AKI after cardiac valve surgery. It demonstrated promising predictive performance in internal validation, with the potential to surpass traditional models and have future potential for clinical application. Prospective trials are needed to assess whether model-guided interventions can truly reduce AKI incidence.