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

Ling Sun; Wenwu Zhu; Xin Chen; Jianguang Jiang; Yuan Ji; Nan Liu; Yajing Xu; Yi Zhuang; Zhiqin Sun; Qingjie Wang; Fengxiang Zhang

doi:10.3389/fmed.2020.592007

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

Front Med (Lausanne). 2020 Nov 13:7:592007. doi: 10.3389/fmed.2020.592007. eCollection 2020.

Authors

Ling Sun¹, Wenwu Zhu², Xin Chen¹, Jianguang Jiang¹, Yuan Ji¹, Nan Liu³, Yajing Xu¹, Yi Zhuang¹, Zhiqin Sun⁴, Qingjie Wang¹, Fengxiang Zhang²

Affiliations

¹ Department of Cardiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China.
² Section of Pacing and Electrophysiology, Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
³ Department of DSA, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China.
⁴ School of Clinical Medicine, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China.

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.

Keywords: Random Forest algorithm; acute myocardial infarction; contrast induced acute kidney injury; logistic regression; machine learning; predictive models.