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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.

SUBMITTER: Sun L 

PROVIDER: S-EPMC7691423 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Sun Ling L   Zhu Wenwu W   Chen Xin X   Jiang Jianguang J   Ji Yuan Y   Liu Nan N   Xu Yajing Y   Zhuang Yi Y   Sun Zhiqin Z   Wang Qingjie Q   Zhang Fengxiang F  

Frontiers in medicine 20201113


<b>Objective:</b> To develop predictive models for contrast induced acute kidney injury (CI-AKI) among acute myocardial infarction (AMI) patients treated invasively. <b>Methods:</b> 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. <b>Results:</b> A total of 1,495 pat  ...[more]

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