Using machine learning to predict outcomes following transcarotid artery revascularization
Abstract 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 pr...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81625-2 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Ben Li |
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| description | Abstract 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. |
| format | Article |
| id | doaj-art-353aa34871ee43fc9d20114010ab5720 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-353aa34871ee43fc9d20114010ab57202025-02-02T12:16:36ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-024-81625-2Using machine learning to predict outcomes following transcarotid artery revascularizationBen Li0Naomi Eisenberg1Derek Beaton2Douglas S. Lee3Leen Al-Omran4Duminda N. Wijeysundera5Mohamad A. Hussain6Ori D. Rotstein7Charles de Mestral8Muhammad Mamdani9Graham Roche-Nagle10Mohammed Al-Omran11Department of Surgery, University of TorontoDivision of Vascular Surgery, Peter Munk Cardiac Centre, University Health NetworkData Science & Advanced Analytics, Unity Health Toronto, University of TorontoDivision of Cardiology, Peter Munk Cardiac Centre, University Health NetworkSchool of Medicine, Alfaisal UniversityInstitute of Health Policy, Management and Evaluation, University of TorontoDivision of Vascular and Endovascular Surgery and the Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical SchoolDepartment of Surgery, University of TorontoDepartment of Surgery, University of TorontoInstitute of Medical Science, University of TorontoDepartment of Surgery, University of TorontoDepartment of Surgery, University of TorontoAbstract 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.https://doi.org/10.1038/s41598-024-81625-2Machine learningPredictionOutcomeStrokeDeathTranscarotid artery revascularization (TCAR) |
| spellingShingle | 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 Using machine learning to predict outcomes following transcarotid artery revascularization Scientific Reports Machine learning Prediction Outcome Stroke Death Transcarotid artery revascularization (TCAR) |
| title | Using machine learning to predict outcomes following transcarotid artery revascularization |
| title_full | Using machine learning to predict outcomes following transcarotid artery revascularization |
| title_fullStr | Using machine learning to predict outcomes following transcarotid artery revascularization |
| title_full_unstemmed | Using machine learning to predict outcomes following transcarotid artery revascularization |
| title_short | Using machine learning to predict outcomes following transcarotid artery revascularization |
| title_sort | using machine learning to predict outcomes following transcarotid artery revascularization |
| topic | Machine learning Prediction Outcome Stroke Death Transcarotid artery revascularization (TCAR) |
| url | https://doi.org/10.1038/s41598-024-81625-2 |
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