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<p>Transcarotid artery revascularization (TCAR) is a relatively new and technically challenging procedure that carries a non-negligible risk of complications. Risk prediction tools may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year outcomes following TCAR. The Vascular Quality Initiative (VQI) database was used to identify patients who underwent TCAR between 2016 and 2023. We identified 115 features from the index hospitalization (82 pre-operative [demographic/clinical], 14 intra-operative [procedural], and 19 post-operative [in-hospital course/complications]). The primary outcome was 1-year post-procedural stroke or death. The data was divided into training (70%) and test (30%) sets. Six ML models were trained using pre-operative features with tenfold cross-validation. Overall, 38,325 patients were included (mean age 73.1 [SD 9.0] years, 14,248 [37.2%] female) and 2,672 (7.0%) developed 1-year stroke or death. The best pre-operative prediction model was XGBoost, achieving an AUROC of 0.91 (95% CI 0.90–0.92). In comparison, logistic regression had an AUROC of 0.68 (95% CI 0.66–0.70). The XGBoost model maintained excellent performance at the intra- and post-operative stages, with AUROC’s (95% CI’s) of 0.92 (0.91–0.93) and 0.94 (0.93–0.95), respectively. Our ML algorithm has potential for important utility in guiding peri-operative risk-mitigation strategies to prevent adverse outcomes following TCAR.</p>

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Using machine learning to predict outcomes following transcarotid artery revascularization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月31日

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  • 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

摘要

Transcarotid artery revascularization (TCAR) is a relatively new and technically challenging procedure that carries a non-negligible risk of complications. Risk prediction tools may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year outcomes following TCAR. The Vascular Quality Initiative (VQI) database was used to identify patients who underwent TCAR between 2016 and 2023. We identified 115 features from the index hospitalization (82 pre-operative [demographic/clinical], 14 intra-operative [procedural], and 19 post-operative [in-hospital course/complications]). The primary outcome was 1-year post-procedural stroke or death. The data was divided into training (70%) and test (30%) sets. Six ML models were trained using pre-operative features with tenfold cross-validation. Overall, 38,325 patients were included (mean age 73.1 [SD 9.0] years, 14,248 [37.2%] female) and 2,672 (7.0%) developed 1-year stroke or death. The best pre-operative prediction model was XGBoost, achieving an AUROC of 0.91 (95% CI 0.90–0.92). In comparison, logistic regression had an AUROC of 0.68 (95% CI 0.66–0.70). The XGBoost model maintained excellent performance at the intra- and post-operative stages, with AUROC’s (95% CI’s) of 0.92 (0.91–0.93) and 0.94 (0.93–0.95), respectively. Our ML algorithm has potential for important utility in guiding peri-operative risk-mitigation strategies to prevent adverse outcomes following TCAR.