Machine learning and decision making in aortic arch repair.

Abstract

Background: Decision making during aortic arch surgery regarding cannulation strategy and nadir temperature are important in reducing risk, and there is a need to determine the best individualized strategy in a data-driven fashion. Using machine learning (ML), we modeled the risk of death or stroke in elective aortic arch surgery based on patient characteristics and intraoperative decisions.

Methods: The study cohort comprised 1323 patients from 9 institutions who underwent an elective aortic arch procedure between 2002 and 2021. A total of 69 variables were used in developing a logistic regression and XGBoost ML model trained for binary classification of mortality and stroke. Shapely additive explanations (SHAP) values were studied to determine the importance of intraoperative decisions.

Results: During the study period, 3.9% of patients died and 5.4% experienced stroke. XGBoost (area under the curve [AUC], 0.77 for death, 0.87 for stroke) demonstrated better discrimination than logistic regression (AUC, 0.65 for death, 0.75 for stroke). From SHAP analysis, intraoperative decisions are 3 of the top 20 predictors of death and 6 of the top 20 predictors of stroke. Predictor weights are patient-specific and reflect the patient's preoperative characteristics and other intraoperative decisions. Patient-level simulation also demonstrates the variable contribution of each decision in the context of the other choices that are made.

Conclusions: Using ML, we can more accurately identify patients at risk of death and stroke, as well as the strategy that better reduces the risk of adverse events compared to traditional prediction models. Operative decisions made may be tailored based on a patient's specific characteristics, allowing for maximized, personalized benefit.