Predicting outcomes following endovascular aortoiliac revascularization using machine learning.

Journal: NPJ digital medicine
Published Date:

Abstract

Endovascular aortoiliac revascularization is a common treatment option for peripheral artery disease that carries non-negligible risks. Outcome prediction tools may support clinical decision-making but remain limited. We developed machine learning algorithms that predict 30-day post-procedural outcomes. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent endovascular aortoiliac revascularization between 2011-2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day post-procedural major adverse limb event (MALE) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using pre-operative features. Overall, 6601 patients were included, and 30-day MALE/death occurred in 470 (7.1%) individuals. The best-performing model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.74 (0.73-0.76). The XGBoost model accurately predicted 30-day post-procedural outcomes, performing better than logistic regression.

Authors

  • Ben Li
    School of Public Health, Shanxi Medical University, Taiyuan 030000, China. Electronic address: LBen@sxmu.edu.cn.
  • Badr Aljabri
    Department of Surgery King Saud University Riyadh Saudi Arabia.
  • Derek Beaton
    Data Science & Advanced Analytics, Unity Health Toronto University of Toronto Toronto Canada.
  • Leen Al-Omran
    School of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
  • Mohamad A Hussain
    Division of Vascular and Endovascular Surgery, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • 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.
  • Duminda N Wijeysundera
    Institute of Health Policy, Management and Evaluation, University of Toronto Toronto Canada.
  • 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.
  • Mohammed Al-Omran
    Department of Surgery University of Toronto Canada.

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