ArticleJournal of the American Heart Association2025
Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.
Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Impact of Single Antiplatelet, Dual Antiplatelet, and Anticoagulation Plus Antiplatelet Therapy on Branch Vessel Patency and Bleeding Risk Following Branched Endovascular Aortic Repair.Cardiovascular drugs and therapy · 2026Article
- Neurological Complications After Thoracic Endovascular Repair (TEVAR): A Narrative Review of the Incidence, Mechanisms and Strategies for Prevention and Management.Journal of personalized medicine · 2026Review
- Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.Journal of personalized medicine · 2026Review
- Does Artificial Intelligence Bring New Insights in Diagnosing Phlebological Diseases?-A Systematic Review.Biomedicines · 2025Review
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThoracic 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.
methodsThe 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.
resultsOverall, 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).
conclusionsMachine learning models can accurately predict 1-year outcomes following TEVAR and complex EVAR, performing better than logistic regression.
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