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

Frontiers in Medicine

article

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

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

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

DOI  :  10.3389/fmed.2020.592007

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY