Tree-based survival analysis improves mortality prediction in cardiac surgery
Metadatos
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Frontiers
Materia
Machine learning Tree-based machine learning Cardiac surgery Mortality Survival analaysis
Fecha
2023-07-10Referencia bibliográfica
Penny-Dimri JC, Bergmeir C, Reid CM, Williams-Spence J, Perry LA and Smith JA (2023) Tree-based survival analysis improves mortality prediction in cardiac surgery. Front. Cardiovasc. Med. 10:1211600. [doi: 10.3389/fcvm.2023.1211600]
Patrocinador
The ANZSCTS National Cardiac Surgery Database Program is funded by the Department of Health (Victoria); the Clinical Excellence Commission (NSW); Queensland Health (QLD); Cardiac surgical units participating in the registry. ANZSCTS Database Research activities are supported through a National Health and Medical Research Council Principal Research Fellowship (APP 1136372); Program Grant (APP 1092642)Resumen
Objectives: Machine learning (ML) classification tools are known to accurately
predict many cardiac surgical outcomes. A novel approach, ML-based survival
analysis, remains unstudied for predicting mortality after cardiac surgery. We
aimed to benchmark performance, as measured by the concordance index
(C-index), of tree-based survival models against Cox proportional hazards (CPH)
modeling and explore risk factors using the best-performing model.
Methods: 144,536 patients with 147,301 surgery events from the Australian and New
Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) national database were
used to train and validate models. Univariate analysis was performed using Student’s
T-test for continuous variables, Chi-squared test for categorical variables, and
stratified Kaplan-Meier estimation of the survival function. Three ML models were
tested, a decision tree (DT), random forest (RF), and gradient boosting machine
(GBM). Hyperparameter tuning was performed using a Bayesian search strategy.
Performance was assessed using 2-fold cross-validation repeated 5 times.
Results: The highest performing model was the GBM with a C-index of 0.803
(0.002), followed by RF with 0.791 (0.003), DT with 0.729 (0.014), and finally CPH
with 0.596 (0.042). The 5 most predictive features were age, type of procedure,
length of hospital stay, drain output in the first 4 h (ml), and inotrope use greater
than 4 h postoperatively.
Conclusion: Tree-based learning for survival analysis is a non-parametric and
performant alternative to CPH modeling. GBMs offer interpretable modeling of
non-linear relationships, promising to expose the most relevant risk factors and
uncover new questions to guide future research.