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Related Experiment Video

Updated: Jan 13, 2026

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Machine learning-based cardiovascular risk calculator for non-cardiac surgery.

Nour Al Khatib1, Ali Chehab1, Hani Tamim2,3

  • 1Department of Electrical and Computer Engineering, American University of Beirut Maroun Semaan Faculty of Engineering and Architecture, Beirut, Lebanon.

Open Heart
|January 7, 2026
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Summary
This summary is machine-generated.

A machine learning model using LightGBM accurately predicts cardiovascular risk in patients over 50 undergoing non-cardiac surgery. This tool aids in identifying high-risk individuals for better surgical outcomes.

Keywords:
BiostatisticsMyocardial InfarctionRISK FACTORSSTROKE

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Area of Science:

  • Cardiovascular medicine
  • Machine learning applications in healthcare
  • Surgical risk assessment

Background:

  • 4% of the global population undergoes non-cardiac surgery annually.
  • 30% of these patients have cardiovascular risk factors, with 0.5%-2% 30-day mortality.
  • Need for accurate cardiovascular risk prediction in this population.

Purpose of the Study:

  • Develop an interpretable machine learning model for cardiovascular risk scoring.
  • Predict risk for patients >50 years old undergoing non-cardiac surgery.
  • Assess risk from surgery date to 30 days post-surgery.

Main Methods:

  • Utilized the NSQIP 2022 dataset (4,970,011 patients).
  • Defined primary endpoint as 30-day death, myocardial infarction, cardiac arrest, or stroke.
  • Trained and evaluated multiple machine learning algorithms (Logistic Regression, Naive Bayes, Random Forest, boosting trees) using AUROC.

Main Results:

  • LightGBM achieved the highest AUROC of 0.9009 (95% CI: 0.8889-0.9126).
  • The best model identified six key predictors: surgery type, ASA classification, BUN, sepsis, emergent surgery, and mechanical ventilation.
  • The model demonstrated strong predictive accuracy and generalization.

Conclusions:

  • LightGBM classifier is optimal for cardiovascular risk scoring in this context.
  • The model balances prediction accuracy and generalization effectively.
  • Identified key factors for cardiovascular risk assessment in non-cardiac surgery patients.