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Predicting inferior vena cava filter complications using machine learning.

Ben Li1, Naomi Eisenberg2, Derek Beaton3

  • 1Department of Surgery, University of Toronto, Toronto, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Canada.

Journal of Vascular Surgery. Venous and Lymphatic Disorders
|July 31, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict 1-year inferior vena cava (IVC) filter complications, outperforming logistic regression. These algorithms can improve patient selection and management to reduce filter-related risks.

Keywords:
ComplicationsInferior vena cava filterMachine learningPrediction

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Area of Science:

  • Vascular surgery
  • Medical informatics
  • Machine learning in healthcare

Background:

  • Inferior vena cava (IVC) filter placement carries significant long-term complication risks.
  • Existing predictive models for IVC filter complications are limited.
  • Developing robust predictive tools is crucial for informed clinical decision-making.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) algorithms for predicting 1-year IVC filter-related complications.
  • To identify key preoperative predictors of filter complications.
  • To compare the performance of ML models against traditional logistic regression.

Main Methods:

  • Utilized the Vascular Quality Initiative database (2013-2024) for patient data.
  • Extracted 77 preoperative features and defined 1-year filter-related complications as the primary outcome.
  • Trained six ML models (including Extreme Gradient Boosting) using a 70/30 train/test split with 10-fold cross-validation.

Main Results:

  • The Extreme Gradient Boosting model achieved the highest predictive accuracy with an AUROC of 0.93, significantly outperforming logistic regression (AUROC 0.63).
  • Key predictors included thrombophilia, prior venous thromboembolism (VTE), antiphospholipid antibodies, and planned temporary filter duration.
  • Model performance was robust across diverse patient subgroups.

Conclusions:

  • Developed accurate ML models capable of predicting 1-year IVC filter complications.
  • These algorithms demonstrate potential to enhance patient selection, counseling, and management strategies.
  • Improved outcomes and reduced filter-related complications are anticipated with the application of these predictive tools.