Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction.
Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction.
复制标题
机器学习预测急性心肌梗死患者对比剂引起的急性肾损伤
DOI：
10.3389/fmed.2020.592007
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发表时间：
2020
影响因子：
3.9
通讯作者：
Zhang F
中科院分区：
文献类型：
--
作者：
Sun L;Zhu W;Chen X;Jiang J;Ji Y;Liu N;Xu Y;Zhuang Y;Sun Z;Wang Q;Zhang F
目的：建立有创治疗的急性心肌梗死（AMI）患者中造影剂诱导的急性肾损伤（CI-AKI）的预测模型。研究方法：入选接受血管造影治疗的AMI患者，并随机分为培训队列（75%）和验证队列（25%）。使用机器学习算法来构建CI-AKI的预测模型。在验证队列中测试预测模型。结果：共纳入1,495例AMI患者。在所有患者中，226例（15.1%）发生了CI-AKI。在验证队列中，具有前15个变量的随机森林（RF）模型的曲线下面积（AUC）为0.82（95% CI：0.76-0.87），而最佳逻辑模型的AUC为0.69（95% CI：0.62-0.76）。ACEF（年龄、肌酐和射血分数）模型的AUC为0.62（95% CI：0.53-0.71）。在验证组中，前15个变量的RF模型达到了71.9%的高召回率和73.5%的准确率。随机森林模型在每次比较中均显著优于逻辑回归。结论：机器学习算法特别是随机森林算法提高了AMI患者风险分层的准确性，应用于准确识别AMI患者的CI-AKI风险。
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.
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影响因子：
20.1
作者：
Ambale-Venkatesh B;Yang X;Wu CO;Liu K;Hundley WG;McClelland R;Gomes AS;Folsom AR;Shea S;Guallar E;Bluemke DA;Lima JAC
通讯作者：
Lima JAC
影响因子：
16.2
作者：
Bertsimas, Dimitris;Kallus, Nathan;Zhuo, Ying Daisy
通讯作者：
Zhuo, Ying Daisy
影响因子：
1.3
作者：
Lin, Chunjin;Lin, Kaiyang;Zhu, Pengli
通讯作者：
Zhu, Pengli
影响因子：
5.8
作者：
Kursa, Miron B.;Rudnicki, Witold R.
通讯作者：
Rudnicki, Witold R.
影响因子：
3
作者：
Shah JS;Rai SN;DeFilippis AP;Hill BG;Bhatnagar A;Brock GN
通讯作者：
Brock GN