Using Machine Learning to Predict Acute Kidney Injury After Aortic Arch Surgery
| Autor: | Guiyu Lei, Congya Zhang, Yimeng Chen, Xiying Yang, Guyan Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
China
Aorta Thoracic 030204 cardiovascular system & hematology Logistic regression Machine learning computer.software_genre Risk Assessment Machine Learning Multiclass classification 03 medical and health sciences Postoperative Complications 0302 clinical medicine Risk Factors 030202 anesthesiology medicine Clinical endpoint Humans Stage (cooking) Retrospective Studies Receiver operating characteristic business.industry Acute kidney injury Perioperative Acute Kidney Injury medicine.disease Anesthesiology and Pain Medicine Artificial intelligence Cardiology and Cardiovascular Medicine business computer Kidney disease |
| Zdroj: | Journal of Cardiothoracic and Vascular Anesthesia. 34:3321-3328 |
| ISSN: | 1053-0770 |
| DOI: | 10.1053/j.jvca.2020.06.007 |
| Popis: | Objectives Machine learning models were compared with traditional logistic regression with regard to predicting kidney outcomes after aortic arch surgery. Design Retrospective review. Setting Single quaternary care center, Fuwai Hospital, Beijing, China. Participants The study comprised 897 consecutive patients who underwent aortic arch surgery from January 2013 to May 2017. Three machine learning methods were compared with logistic regression with regard to the prediction of acute kidney injury (AKI) after aortic arch surgery. Perioperative characteristics, including patients’ baseline medical condition and intraoperative data, were analyzed. The performance of the models was assessed using the area under the receiver operating characteristic curve. Measurements and Main Results The primary endpoint, postoperative AKI, was defined using the Kidney Disease: Improving Global Outcomes criteria. During the first 7 postoperative days, AKI was observed in 652 patients (72.6%), and stage 2 or 3 AKI developed in 283 patients (31.5%). Gradient boosting had the best discriminative ability for the prediction of all stages of AKI in both the binary classification and the multiclass classification (area under the receiver operating characteristic curve 0.8 and 0.71, respectively) compared with logistic regression, support vector machine, and random forest methods. Conclusion Machine learning methods were found to predict AKI after aortic arch surgery significantly better than traditional logistic regression. |
| Databáze: | OpenAIRE |
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