Predicting lack of clinical improvement following varicose vein ablation using machine learning
We developed ML models that can accurately predict outcomes after endovenous and surgical varicose vein treatment for CEAP C2 to C4 disease, performing better than logistic regression. These algorithms have potential for important utility in guiding patient counseling and perioperative risk mitigation strategies to prevent LCI after varicose vein ablation.
Authors
- Mohamad A. Hussain (ORCID: https://orcid.org/0000-0003-2471-8167)
- Ori D. Rotstein (ORCID: https://orcid.org/0009-0004-4388-5352)
- Charles de Mestral (ORCID: https://orcid.org/0000-0001-7177-7480)
- Naomi Eisenberg (ORCID: https://orcid.org/0000-0002-1237-9244)
- 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)
- Leen Al‐Omran
- Ben Li (ORCID: https://orcid.org/0000-0002-7191-1034)
- Graham Roche‐Nagle
- Mohammed Al-Omran
Institutions
- Brigham and Women's Hospital (US)
- St. Michael's Hospital (CA)
- University Health Network (CA)
- Alfaisal University (SA)
- Harvard University (US)
- University of Toronto (CA)
- Toronto Rehabilitation Institute (CA)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- Journal of Vascular Surgery Venous and Lymphatic Disorders
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1016/j.jvsv.2024.102162
- Citations
- 5
- Primary Topic
- Diagnosis and Treatment of Venous Diseases
- Type
- article
- Field-Weighted Citation Impact
- 2.86
Funders
- Physicians' Services Incorporated Foundation
- University of Toronto
- Canadian Institutes of Health Research