Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction

Objective To develop predictive models for acute kidney injury (CI-AKI) among acute myocardial infarction (AMI) patients treated invasively. Methods Patients with AMI underwent intervention therapy were enrolled and randomly divided into training cohort (75%) and validation cohort (25%). Machine learning algorithms were used to construct predictive models for CI-AKI. The predictive models were tested in validation cohort. Discrimination was assessed using receiver operating characteristic curve (ROC) analysis. Results A total of 1,495 patients with AMI were included. The average age was 66.55±13.86 years with 71.2% men. Of all the patients, 225 (15.1%) cases developed CI-AKI. In the validation cohort, random Forest (RF) model with top 15 variables reached an AUC of 0.82 (95%CI: 0.76-0.87), while the best logistic model had an AUC of 0.69 (95%CI: 0.62-0.76). ACEF model reached an AUC of 0.59, (95%CI: 0.53-0.71). RF model with top 15 variables achieved a high recall rate of 71.93% and an accuracy of 73.46% in the validation group. Random forest model significantly outperformed logistic regression in every comparison. Conclusions Machine learning algorithms especially random forest algorithm improved the accuracy of risk stratifying patients with AMI and can be used to accurately identify patients at high risk of developing CI-AKI.