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Related Experiment Video

Updated: Jul 21, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Tree-based survival analysis improves mortality prediction in cardiac surgery.

Jahan C Penny-Dimri1, Christoph Bergmeir2,3, Christopher M Reid1,4

  • 1Department of Surgery, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia.

Frontiers in Cardiovascular Medicine
|July 26, 2023
PubMed
Summary

Machine learning survival analysis accurately predicts cardiac surgery mortality. Gradient boosting machines outperformed Cox models, identifying key risk factors like age and procedure type for improved patient outcomes.

Keywords:
cardiac surgerymachine learningmortalitysurvival analaysistree-based machine learning

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Area of Science:

  • Cardiovascular Surgery
  • Medical Informatics
  • Machine Learning

Background:

  • Machine learning classification tools are established for predicting cardiac surgical outcomes.
  • ML-based survival analysis is an underexplored method for predicting post-cardiac surgery mortality.

Purpose of the Study:

  • To benchmark the performance of tree-based survival models against Cox proportional hazards (CPH) modeling for predicting cardiac surgery mortality.
  • To identify key risk factors for mortality using the best-performing ML model.

Main Methods:

  • Utilized a national database of 144,536 patients (147,301 surgery events).
  • Compared three ML models: decision tree (DT), random forest (RF), and gradient boosting machine (GBM) using 2-fold cross-validation.
  • Assessed performance using the concordance index (C-index).

Main Results:

  • Gradient Boosting Machine (GBM) achieved the highest C-index (0.803), outperforming Random Forest (0.791), Decision Tree (0.729), and Cox Proportional Hazards (0.596).
  • The top predictors of mortality included age, procedure type, length of hospital stay, early postoperative drain output, and duration of inotrope use.

Conclusions:

  • Tree-based survival analysis presents a non-parametric and high-performing alternative to traditional CPH modeling.
  • GBMs provide interpretable insights into non-linear relationships, highlighting critical risk factors and guiding future cardiac surgery research.