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Predicting outcomes following endovascular aortoiliac revascularization using machine learning.

Ben Li1,2,3,4, Badr Aljabri5, Derek Beaton6

  • 1Department of Surgery, University of Toronto, Toronto, ON, Canada.

NPJ Digital Medicine
|July 24, 2025
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Summary

Machine learning models predict 30-day outcomes for endovascular aortoiliac revascularization. The XGBoost model demonstrated superior accuracy in forecasting major adverse limb events or death post-procedure.

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Area of Science:

  • Vascular Surgery
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Endovascular aortoiliac revascularization is a prevalent treatment for peripheral artery disease.
  • This procedure involves significant risks, necessitating improved outcome prediction.
  • Current outcome prediction tools for this intervention are limited.

Purpose of the Study:

  • To develop and evaluate machine learning algorithms for predicting 30-day post-procedural outcomes.
  • To identify pre-operative factors influencing major adverse limb events (MALE) or death.
  • To compare the performance of machine learning models against traditional logistic regression.

Main Methods:

  • Utilized the National Surgical Quality Improvement Program targeted vascular database (2011-2021).
  • Included 6,601 patients undergoing endovascular aortoiliac revascularization.
  • Trained 6 machine learning models, including XGBoost and logistic regression, using 37 pre-operative variables and 10-fold cross-validation.

Main Results:

  • The XGBoost model achieved an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.94 (95% CI: 0.93-0.95).
  • Logistic regression yielded a significantly lower AUROC of 0.74 (95% CI: 0.73-0.76).
  • The study identified 7.1% of patients experienced 30-day MALE or death.

Conclusions:

  • Machine learning, particularly XGBoost, offers a highly accurate method for predicting 30-day MALE or death after endovascular aortoiliac revascularization.
  • These advanced predictive models can enhance clinical decision-making and patient risk stratification.
  • The developed XGBoost model significantly outperforms logistic regression in predicting adverse outcomes.