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Predicting Major Adverse Cardiovascular Events After Cardiac Surgery Using Combined Clinical, Laboratory, and

Mladjan Golubovic^1,2, Velimir Peric^1,2, Marija Stosic^1,2

¹Clinic of Cardiovascular Surgery, University Clinical Center Nis, 18000 Nis, Serbia.

Medicina (Kaunas, Lithuania)

|August 28, 2025

Summary

This study developed a machine learning model to predict major adverse cardiovascular events (MACE) after heart surgery, integrating novel echocardiographic and biochemical markers for improved risk stratification.

Keywords:

aortic valve replacement coronary artery bypass machine learning major adverse cardiovascular events predictive modeling

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Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty

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Published on: January 28, 2020

Area of Science:

Cardiology
Medical Informatics
Biomedical Engineering

Background:

Major adverse cardiovascular events (MACE) are a leading cause of mortality post-cardiac surgery.
Current risk models often lack comprehensive assessment of atrial mechanics and biomarkers.
Improved preoperative risk stratification is crucial for patient outcomes.

Purpose of the Study:

To develop an interpretable machine learning model for predicting perioperative MACE.
To integrate clinical, biochemical, and novel echocardiographic features into the predictive model.
To identify key physiological markers influencing cardiovascular risk.

Main Methods:

Retrospective analysis of 131 patients undergoing major cardiac surgery.
Development of an Extreme Gradient Boosting (XGBoost) classifier.
Utilized SHapley Additive exPlanations (SHAPs) for model interpretability.

Main Results:

The machine learning model demonstrated robust performance with an average AUC of 0.846 ± 0.092 and F1-score of 0.807 ± 0.096.
Key predictors included total atrial conduction time, mitral/tricuspid annular orifice areas, and HDL cholesterol.
The model provided clinically intuitive insights into individual risk profiles.

Conclusions:

Combining echocardiography, biochemistry, and machine learning offers potential for individualized cardiovascular risk prediction.
Novel parameters significantly enhance predictive performance, even in patients with preserved left ventricular function.
Prospective validation in larger cohorts is needed for clinical integration.