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PO-AKID-teller: An interpretable machine learning tool for predicting acute kidney injury requiring dialysis after acute type A aortic dissection surgery
Qiuying Chen, Biao Fu, Jue Yang, Zhe Jin, Lu Zhang, Ruixin Fan, Bin Zhang, Shuixing Zhang
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PO-AKID-teller: An interpretable machine learning tool for predicting acute kidney injury requiring dialysis after acute type A aortic dissection surgery
Postoperative acute kidney injury requiring dialysis (PO-AKID) is a serious adverse event that not only affects acute morbidity and mortality, but also long-term prognosis. Here, we developed a practical and explainable web-based calculator (PO-AKID-teller) to detect patients who might experience PO-AKID after acute type A aortic dissection (ATAAD) surgery. This retrospective study reviewed 549 patients undergoing ATAAD surgery from October 2016 to June 2021. PO-AKID frequency was 19.7% (108 of 549 patients). The initial dataset was split into an 80% training cohort (n = 439) and a 20% test cohort (n = 110). There were seven predictors that could indicate PO-AKID, including prior cardiovascular surgery, platelet, serum creatinine, the terminal site of dissection involvement, right coronary artery involvement, estimated blood loss, and urine output. Among six machine learning classifiers, the random forest model exhibited the best predictive performance, with an area under the curve of 0.863 in the training cohort and 0.763 in the test cohort. This model was translated into a web-based risk calculator PO-AKID-teller to estimate an individual’s probability of PO-AKID. The PO-AKID-teller can accurately estimate an individual’s risk for PO-AKID in an interpretable manner, which may aid in informed decision-making, patient counseling, perioperative optimization, and longer-term care provision.
acute kidney injury / acute type A aortic dissection / dialysis / interpretability / machine learning
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