Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair - Ben Li; Naomi Eisenberg; Derek Beaton; Douglas S. Lee; Badr Aljabri; Leen Al‐Omran; Duminda N. Wijeysundera; Ori D. Rotstein; Thomas F. Lindsay; Charles de Mestral; Muhammad Mamdani; Graham Roche‐Nagle; et al. (Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease)
Background Thoracic endovascular aortic repair (TEVAR) and complex endovascular aneurysm repair (EVAR) are complex procedures that carry a significant risk of complications. While risk prediction tools can aid in clinical decision making, they remain limited. We developed machine learning algorithms to predict outcomes following TEVAR and complex EVAR. Methods The Vascular Quality Initiative database was used to identify patients who underwent elective TEVAR and complex EVAR for noninfrarenal aortic aneurysms between 2012 and 2023. We extracted 172 features from the index hospitalization, including 93 preoperative (demographic/clinical), 46 intraoperative (procedural), and 33 postoperative (in‐hospital course/complications) variables. The primary outcome was 1‐year thoracoabdominal aortic aneurysm life‐altering event, defined as new permanent dialysis, new permanent paralysis, stroke, or death. The data were split into training (70%) and test (30%) sets. We trained 6 machine learning models using preoperative features with 10‐fold cross‐validation. Model robustness was evaluated using calibration plots and Brier scores. Results Overall, 10 738 patients underwent TEVAR or complex EVAR, with 1485 (13.8%) experiencing 1‐year thoracoabdominal aortic aneurysm life‐altering event. Extreme Gradient Boosting was the best preoperative prediction model, achieving an area under the receiver operating characteristic curve of 0.96 (95% CI, 0.95–0.97), compared with 0.70 (95% CI, 0.68–0.72) for logistic regression. The Extreme Gradient Boosting model maintained excellent performance at the intra‐ and postoperative stages, with areas under the receiver operating characteristic curves of 0.97 (95% CI, 0.96–0.98) and 0.98 (95% CI, 0.97–0.99), respectively. Calibration plots indicated good agreement between predicted/observed event probabilities, with Brier scores of 0.09 (preoperative), 0.08 (intraoperative), and 0.05 (postoperative). Conclusions Machine learning models can accurately predict 1‐year outcomes following TEVAR and complex EVAR, performing better than logistic regression.