Predicting Outcomes Following Carotid Artery Stenting Using Machine Learning
Our ML models accurately predict 30-day outcomes following transfemoral CAS using preoperative data. They have the potential for important utility in guiding risk-mitigation strategies for patients being considered for CAS to improve outcomes.Clinical ImpactTransfemoral carotid artery stenting (CAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. Using data from the National Surgical Quality Improvement Program (NSQIP) targeted vascular database, we developed machine learning (ML) models that accurately predict 30-day outcomes following transfemoral CAS using preoperative data, outperforming logistic regression and existing tools in the literature. The models were well-calibrated and remained robust across demographic and clinical subpopulations. These ML algorithms have the potential for important utility in guiding risk-mitigation strategies for patients being considered for transfemoral CAS to improve outcomes.
Authors
- Mohamad A. Hussain (ORCID: https://orcid.org/0000-0003-2471-8167)
- Ori D. Rotstein (ORCID: https://orcid.org/0009-0004-4388-5352)
- Graham Roche‐Nagle (ORCID: https://orcid.org/0000-0002-3560-6788)
- Charles de Mestral (ORCID: https://orcid.org/0000-0001-7177-7480)
- Badr Aljabri (ORCID: https://orcid.org/0000-0002-6543-522X)
- Duminda N. Wijeysundera (ORCID: https://orcid.org/0000-0002-5897-8605)
- Derek Beaton (ORCID: https://orcid.org/0000-0001-6118-4366)
- Douglas S. Lee (ORCID: https://orcid.org/0000-0001-7078-745X)
- Muhammad Mamdani (ORCID: https://orcid.org/0000-0001-5199-6344)
- Ben Li (ORCID: https://orcid.org/0000-0002-7191-1034)
- Mohammed Al‐Omran (ORCID: https://orcid.org/0000-0003-3325-0420)
Institutions
- Brigham and Women's Hospital (US)
- St. Michael's Hospital (CA)
- University Health Network (CA)
- Harvard University (US)
- University of Toronto (CA)
- King Faisal Specialist Hospital & Research Centre (SA)
- King Saud University (SA)
- Unity Health Toronto
Publication Details
- Journal
- Journal of Endovascular Therapy
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1177/15266028251333670
- Citations
- 1
- Primary Topic
- Cerebrovascular and Carotid Artery Diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- Ontario Ministry of Health and Long-Term Care
- Physicians' Services Incorporated Foundation
- Canadian Institutes of Health Research