Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting - Ben Li; Naomi Eisenberg; Derek Beaton; Douglas S. Lee; Leen Al‐Omran; Duminda N. Wijeysundera; Mohamad A. Hussain; Ori D. Rotstein; Charles de Mestral; Muhammad Mamdani; Graham Roche‐Nagle; Mohammed Al‐Omran (Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 2024)
Background Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision‐making but remain limited. We developed machine learning algorithms that predict 1‐year stroke or death following TFCAS. Methods and Results The VQI (Vascular Quality Initiative) database was used to identify patients who underwent TFCAS for carotid artery stenosis between 2005 and 2024. We identified 112 features from the index hospitalization (82 preoperative [demographic/clinical], 13 intraoperative [procedural], and 17 postoperative [in‐hospital course/complications]). The primary outcome was 1‐year postprocedural stroke or death. The data were divided into training (70%) and test (30%) sets. Six machine learning models were trained using preoperative features with 10‐fold cross‐validation. The primary model evaluation metric was area under the receiver operating characteristic curve. The algorithm with the best performance was further trained using intra‐ and postoperative features. Model robustness was assessed using calibration plots and Brier scores. Overall, 35 214 patients underwent TFCAS during the study period and 3257 (9.2%) developed 1‐year stroke or death. The best preoperative prediction model was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.93–0.95). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63–0.67). The extreme gradient boosting model maintained excellent performance at the intra‐ and postoperative stages, with area under the receiver operating characteristic curve values of 0.94 (95% CI, 0.93–0.95) and 0.98 (95% CI, 0.97–0.99), respectively. Calibration plots showed good agreement between predicted/observed event probabilities with Brier scores of 0.11 (preoperative), 0.11 (intraoperative), and 0.09 (postoperative). Conclusions Machine learning can accurately predict 1‐year stroke or death following TFCAS, performing better than logistic regression.