Predicting inferior vena cava filter complications using machine learning.

Journal: Journal of vascular surgery. Venous and lymphatic disorders
PMID: 39084408

Abstract

OBJECTIVE: Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year IVC filter complications using preoperative data.

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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