使用机器学习预测胸段和复杂血管腔内主动脉瘤修复后的结果。
Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.
作者信息
Li Ben, Eisenberg Naomi, Beaton Derek, Lee Douglas S, Aljabri Badr, Al-Omran Leen, Wijeysundera Duminda N, Rotstein Ori D, Lindsay Thomas F, de Mestral Charles, Mamdani Muhammad, Roche-Nagle Graham, Al-Omran Mohammed
机构信息
Department of Surgery University of Toronto Toronto Canada.
Division of Vascular Surgery St. Michael's Hospital, Unity Health Toronto Toronto Canada.
出版信息
J Am Heart Assoc. 2025 Mar 4;14(5):e039221. doi: 10.1161/JAHA.124.039221. Epub 2025 Mar 3.
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.
背景
胸主动脉腔内修复术（TEVAR）和复杂腹主动脉瘤腔内修复术（EVAR）是复杂的手术，具有显著的并发症风险。虽然风险预测工具有助于临床决策，但它们仍然存在局限性。我们开发了机器学习算法来预测TEVAR和复杂EVAR后的结果。
方法
使用血管质量倡议数据库识别2012年至2023年间因非肾下腹主动脉瘤接受择期TEVAR和复杂EVAR的患者。我们从首次住院中提取了172个特征，包括93个术前（人口统计学/临床）、46个术中（手术）和33个术后（住院过程/并发症）变量。主要结局是1年胸主动脉瘤改变生活的事件，定义为新的永久性透析、新的永久性瘫痪、中风或死亡。数据被分为训练集（70%）和测试集（30%）。我们使用术前特征和10倍交叉验证训练了6个机器学习模型。使用校准图和Brier评分评估模型的稳健性。
结果
总体而言，10738例患者接受了TEVAR或复杂EVAR，其中1485例（13.8%）发生了1年胸主动脉瘤改变生活的事件。极端梯度提升是最佳的术前预测模型，受试者操作特征曲线下面积为0.96（95%CI，0.95-0.97），而逻辑回归为0.70（95%CI，0.68-0.72）。极端梯度提升模型在术中及术后阶段保持了优异的性能，受试者操作特征曲线下面积分别为0.97（95%CI，0.96-0.98）和0.98（95%CI，0.97-0.99）。校准图表明预测/观察到的事件概率之间具有良好的一致性，Brier评分为0.
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