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

Abstract

Objective: To develop predictive models for contrast induced acute kidney injury (CI-AKI) among acute myocardial infarction (AMI) patients treated invasively. Methods: Patients with AMI who underwent angiography 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 a validation cohort. Results: A total of 1,495 patients with AMI were included. Of all the patients, 226 (15.1%) cases developed CI-AKI. In the validation cohort, Random Forest (RF) model with top 15 variables reached an area under the curve (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 (age, creatinine, and ejection fraction) model reached an AUC of 0.62 (95% CI: 0.53–0.71). RF model with top 15 variables achieved a high recall rate of 71.9% and an accuracy of 73.5% in the validation group. Random Forest model significantly outperformed logistic regression in every comparison. Conclusions: Machine learning algorithms especially Random Forest algorithm improves the accuracy of risk stratifying patients with AMI and should be used to accurately identify the risk of CI-AKI in AMI patients.