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Prediction of Postoperative Mortality After Fontan Procedure: A Clinical Prediction Model Study Using Deep Learning

Jacek Kolcz1, Anna Budzynska1, Justyna Stefaniak2

  • 1Department of Pediatric Cardiac Surgery, Collegium Medicum, Jagiellonian University, Wielicka 265 St., 31-007 Krakow, Poland.

Journal of Cardiovascular Development and Disease
|November 26, 2025
PubMed
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A deep learning model accurately predicts postoperative mortality after Fontan surgery, improving risk stratification for single-ventricle congenital heart disease patients. Key factors like pulmonary artery pressure are identified, enhancing personalized care.

Area of Science:

  • Cardiology
  • Medical Artificial Intelligence
  • Computational Biology

Background:

  • The Fontan procedure is a critical surgery for single-ventricle congenital heart disease (CHD).
  • Postoperative and long-term risks associated with the Fontan procedure necessitate improved risk stratification methods.
  • Current risk models for Fontan surgery have limitations in providing accurate, individualized predictions.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for predicting postoperative mortality after Fontan procedure.
  • To identify key factors influencing mortality risk in Fontan surgery patients.
  • To create a user-friendly tool for personalized risk assessment.

Main Methods:

  • Retrospective analysis of 230 patients undergoing Fontan procedure (2010-2024).
Keywords:
Fontan procedureartificial intelligencedeep learningmortality

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  • Development of a Deep Neural Network (DNN) model using comprehensive clinical, biochemical, and hemodynamic data.
  • Utilized five-fold cross-validation, SMOTE for class imbalance, and SHAP for interpretability, with a Streamlit interface for clinical application.

    • Main Results:

    • The DNN model achieved high predictive performance: 91.5% accuracy, 83.3% precision, 90.9% recall, and 0.94 AUC-ROC.
    • SHAP analysis identified pulmonary artery pressure, ventricular end-diastolic pressure, BNP levels, and AV valve regurgitation severity as key mortality predictors.
    • A Streamlit application was developed for accessible, personalized risk evaluation.

    Conclusions:

    • A DL model utilizing detailed clinical data can accurately predict postoperative mortality in Fontan surgery.
    • AI-driven risk assessment, enhanced by interpretability, offers a valuable tool for personalized patient care.
    • This approach has the potential to improve preoperative counseling, perioperative management, and patient outcomes, pending external validation.