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Using Machine Learning and Feature Importance to Identify Risk Factors for Mortality in Pediatric Heart Surgery.

Lorenz A Kapsner1,2, Manuel Feißt3, Ariawan Purbojo4

  • 1Medial Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany.

Diagnostics (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning identified key mortality risk factors after pediatric congenital heart defect (CHD) surgery. High serum creatinine levels within 72 hours post-operation were a significant predictor of mortality in children with CHDs.

Keywords:
congenital heart defects (CHDs)eXtreme Gradient Boosting (XGB)feature importancemachine learning (ML)mortalityrandom survival forest (RSF)risk factors

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning

Background:

  • Congenital heart defects (CHDs) are common congenital malformations and a leading cause of mortality from birth defects.
  • Pediatric cardiac surgery for CHDs carries significant mortality risks.
  • Identifying predictive factors for mortality is crucial for improving patient outcomes.

Purpose of the Study:

  • To identify risk factors for mortality following surgery for congenital heart defects (CHDs) in pediatric patients.
  • To apply machine learning (ML) models for predicting mortality after CHD surgery.
  • To utilize model explainability tools to understand ML predictions.

Main Methods:

  • Retrospective monocentric study of 1302 pediatric patients (<18 years) undergoing CHD-related cardiac surgery (2011-2020).
  • Application of Random Survival Forest (RSF) and eXtreme Gradient Boosting (XGB) algorithms to model mortality.
  • Validation of models on an independent holdout test dataset (40% of cohort).
  • Feature importance assessment using SHapley Additive exPlanations (SHAP) and SurvSHAP(t).

Main Results:

  • Both RSF and XGB models demonstrated strong predictive performance (C-indices of 0.85 and 0.79, respectively).
  • Machine learning models identified maximum serum creatinine levels within 72 hours post-surgery as a critical predictor of mortality.
  • Model explainability tools (SHAP, SurvSHAP(t)) confirmed the importance of early postoperative serum creatinine.

Conclusions:

  • Machine learning methods effectively identify mortality risk factors in pediatric CHD surgery patients.
  • Early postoperative serum creatinine is a significant modifiable risk factor for mortality.
  • The analytical workflow provides a foundation for federated learning in medical research.