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Aneurysm management involves either conservative medical therapy or surgical intervention, depending on the size and symptoms of the aneurysm. Conservative management is generally reserved for smaller, asymptomatic aneurysms, while larger or symptomatic aneurysms often necessitate surgical repair.Conservative Medical TherapyFor small, asymptomatic aneurysms, particularly abdominal aortic aneurysms (AAA) less than 5.5 centimeters in diameter, conservative medical therapy is recommended. This...
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Vigilant monitoring for aneurysm rupture is essential for patients undergoing aortic surgery.Preoperative Nursing ManagementContinuously monitor the patient for manifestations of aneurysm rupture, such as pallor, weakness, tachycardia, hypotension, abdominal, back, groin, or periumbilical pain, changes in consciousness, and a pulsating abdominal mass. Regularly assess the patient's peripheral pulses.Instruct the patient to consume a clear liquid diet the day before surgery and administer...
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Predicting outcomes following open abdominal aortic aneurysm repair using machine learning.

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

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

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|April 24, 2025
PubMed
Summary

Machine learning accurately predicts 30-day major adverse cardiovascular events (MACE) after open abdominal aortic aneurysm (AAA) repair. This tool can guide risk assessment and improve patient outcomes in AAA surgery.

Keywords:
Machine learningOpen abdominal aortic aneurysm repairOutcomesPrediction

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Area of Science:

  • Cardiovascular Surgery
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Open surgical repair of abdominal aortic aneurysms (AAA) carries a significant risk of post-operative complications.
  • Currently, there is a lack of validated tools for predicting surgical risk in patients undergoing open AAA repair.

Purpose of the Study:

  • To develop and validate automated machine learning (ML) algorithms for predicting 30-day adverse outcomes following open AAA repair.
  • To identify pre-operative predictors of major adverse cardiovascular events (MACE) in this patient cohort.

Main Methods:

  • Utilized the National Surgical Quality Improvement Program vascular database (2011-2021) for patients undergoing elective, non-ruptured open AAA repair.
  • Trained six ML models using 35 pre-operative variables, with logistic regression as a baseline.
  • Evaluated model performance using 10-fold cross-validation, focusing on 30-day MACE (myocardial infarction, stroke, or death).

Main Results:

  • The XGBoost ML model demonstrated superior predictive performance, achieving an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.90 (95% CI: 0.89-0.91).
  • Logistic regression achieved a significantly lower AUROC of 0.66 (95% CI: 0.64-0.68).
  • The best ML model showed good calibration, with a Brier score of 0.03, indicating strong agreement between predicted and observed event probabilities.

Conclusions:

  • Automated ML algorithms can effectively predict 30-day MACE after open AAA repair.
  • These ML tools offer a promising approach to guide pre-operative risk stratification and inform clinical decision-making.
  • Implementation of such algorithms may facilitate targeted risk-mitigation strategies to improve patient outcomes.