Predicting outcomes following open abdominal aortic aneurysm repair using machine learning

Figures

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  Fig. 1. Receiver operating characteristic curve for predicting 30-day major adverse cardiovascular events following open abdominal aortic aneurysm repair using Extreme Gradient Boosting (XGBoost) model. AUROC (area under the receiver operating characteristic curve), CI (confidence interval).

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  Fig. 2. Calibration plot with Brier score for predicting 30-day major adverse cardiovascular events following open abdominal aortic aneurysm repair using Extreme Gradient Boosting (XGBoost) model.

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  Fig. 3. Variable importance scores (gain) for the top 10 predictors of 30-day major adverse cardiovascular events (MACE) following open abdominal aortic aneurysm repair in the Extreme Gradient Boosting (XGBoost) model. Abbreviations: EVAR (endovascular aneurysm repair), CHF (congestive heart failure), AAA (abdominal aortic aneurysm), COPD (chronic obstructive pulmonary disease). Explanation of figure: patients who underwent a prior EVAR, required concomitant renal/visceral revascularization, had a more proximal AAA extent, or were transferred from another hospital likely underwent a more anatomically/technically challenging repair, and therefore, were more likely to suffer 30-day MACE. Furthermore, patients with older age or more comorbidities, including CHF, COPD, or chronic kidney disease as measured by pre-operative creatinine, thereby contributing to a higher ASA class, were more medically complex and therefore more likely to suffer 30-day MACE.