Using Machine Learning to Predict Postoperative Liver Dysfunction After Aortic Arch Surgery

Objectives The study compared machine-learning models with traditional logistic regression to predicting liver outcomes after aortic arch surgery. Design Retrospective review from January 2013 to May 2017. Setting Fuwai Hospital. Participants The study comprised 672 consecutive patients who had undergone aortic arch surgery. Measurements and Main Results Three machine-learning methods were compared with logistic regression with regard to the prediction of postoperative liver dysfunction (PLD) after aortic arch surgery. The perioperative characteristics, including the 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. Naïve Bayes had the best discriminative ability for the prediction of PLD (area under the receiver operating characteristic curve = 0.77) compared with random forest (0.76), support vector machine (0.73), and logistic regression (0.72). The primary endpoint of PLD was observed in 185 patients (27.5%). The cardiopulmonary bypass time, long surgery time, long aortic clamp time, high preoperative bilirubin value, and low rectal temperature were strongly associated with the development of PLD after aortic arch surgery. Conclusion The machine-learning method of naïve Bayes predicts PLD after aortic arch surgery significantly better than traditional logistic regression. The study compared machine-learning models with traditional logistic regression to predicting liver outcomes after aortic arch surgery. Retrospective review from January 2013 to May 2017. Fuwai Hospital. The study comprised 672 consecutive patients who had undergone aortic arch surgery. Three machine-learning methods were compared with logistic regression with regard to the prediction of postoperative liver dysfunction (PLD) after aortic arch surgery. The perioperative characteristics, including the 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. Naïve Bayes had the best discriminative ability for the prediction of PLD (area under the receiver operating characteristic curve = 0.77) compared with random forest (0.76), support vector machine (0.73), and logistic regression (0.72). The primary endpoint of PLD was observed in 185 patients (27.5%). The cardiopulmonary bypass time, long surgery time, long aortic clamp time, high preoperative bilirubin value, and low rectal temperature were strongly associated with the development of PLD after aortic arch surgery. The machine-learning method of naïve Bayes predicts PLD after aortic arch surgery significantly better than traditional logistic regression.