Predicting lack of clinical improvement following varicose vein ablation using machine learning.

Journal: Journal of vascular surgery. Venous and lymphatic disorders
PMID: 39732288

Abstract

OBJECTIVE: Varicose vein ablation is generally indicated in patients with active/healed venous ulcers. However, patient selection for intervention in individuals without venous ulcers is less clear. Tools that predict lack of clinical improvement (LCI) after vein ablation may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year LCI after varicose vein ablation.

Authors

  • Ben Li
    School of Public Health, Shanxi Medical University, Taiyuan 030000, China. Electronic address: LBen@sxmu.edu.cn.
  • Naomi Eisenberg
    Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, Canada.
  • Derek Beaton
    Data Science & Advanced Analytics, Unity Health Toronto University of Toronto Toronto Canada.
  • Douglas S Lee
    Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada; Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Institute for Health Policy, Management and Evaluation, Toronto, Ontario, Canada; Toronto General Hospital Research Institute, Toronto, Ontario, Canada; University of Toronto, Toronto, Ontario, Canada. Electronic address: dlee@ices.on.ca.
  • Leen Al-Omran
    School of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
  • Duminda N Wijeysundera
    Institute of Health Policy, Management and Evaluation, University of Toronto Toronto Canada.
  • Mohamad A Hussain
    Division of Vascular and Endovascular Surgery, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Ori D Rotstein
    Department of Surgery, University of Toronto, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Division of General Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
  • Charles de Mestral
    Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada.
  • Muhammad Mamdani
    Unity Health Toronto (Verma, Murray, Straus, Pou-Prom, Mamdani); Li Ka Shing Knowledge Institute of St. Michael's Hospital (Verma, Straus, Pou-Prom, Mamdani); Department of Medicine (Verma, Shojania, Straus, Mamdani) and Institute of Health Policy, Management, and Evaluation (Verma, Mamdani) and Department of Statistics (Murray), University of Toronto, Toronto, Ont.; University of Alberta (Greiner); Alberta Machine Intelligence Institute (Greiner), Edmonton, Alta.; Montreal Institute for Learning Algorithms (Cohen), Montréal, Que.; Centre for Quality Improvement and Patient Safety (Shojania), University of Toronto; Sunnybrook Health Sciences Centre (Shojania); Vector Institute (Ghassemi, Mamdani) and Department of Computer Science (Ghassemi); Leslie Dan Faculty of Pharmacy (Mamdani), University of Toronto, Toronto, Ont.; Department of Radiology, Stanford University (Cohen), Stanford, Calif. muhammad.mamdani@unityhealth.to amol.verma@mail.utoronto.ca.
  • Graham Roche-Nagle
    Department of Surgery, University of Toronto, Toronto, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, Canada; Division of Vascular and Interventional Radiology, University Health Network, Toronto, Canada.
  • Mohammed Al-Omran
    Department of Surgery University of Toronto Canada.

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