Development and validation of Machine Learning Predictive Model for Contrast-associated Acute Kidney Injury in Patients with Acute Coronary Syndrom

Abstract

Abstract Background Patients with acute coronary syndrome (ACS) often need coronary angiography (CAG). After CAG, patients with ACS may develop contrast-associated acute kidney injury (CA-AKI). However, effective preventive measures are lacking in treatment for CA-AKI. Therefore, an efficient interpretable predictive model of CA-AKI is crucial. Methods We enrolled 1013 ACS patients who received percutaneous coronary intervention or coronary angiography in Sir Run Run Hospital, Nanjing Medical University, and Nanjing First Hospital from September 2020 to December 2021. To screen features, the sliding windows sequential forward feature selection technique (SWSFS) was used. The model was built using five machine learning (ML) algorithms: logical regression (LR), random forest (RF), support vector machines (SVM), extreme gradient boosting (XGBT), and ensemble model (ENS). We evaluated predictive performance by comparing the model with Mehran score. The model features were explained through shapley additive explanations (SHAP) and a web-based calculator was built. Results CA-AKI occurred in 215 patients (21.27%). In the training set, SWSFS identified 15 variables. The top 5 variables included diuretics, creatine phosphokinase MB isoenzyme, unstable angina, lactate dehydrogenase, and Triglycerides × Total Cholesterol × Body Weight Index (TCBI). Overall, ML models outperformed Mehran score. In the internal and external validation sets, the ENS model obtained the highest AUC of 0.828 (95%CI: 0.779 ~ 0.876) and 0.811 (95%CI: 0.750 ~ 0.867). SHAP explained the 15 selected features' importance and contribution. We also built a web-based calculator for clinical use. Conclusions Based on machine learning, 15 clinically accessible features were screened. The established model and the web-based calculator had the potential for real-time risk assessment of CA-AKI in clinical practice.