Key result
Machine learning models demonstrated moderate accuracy in predicting post-heart transplantation mortality with a pooled AUC of 0.65, with CatBoost achieving the highest accuracy at an AUC of 0.80.
Why the study?
Machine learning models have increasingly been used to predict post-heart transplantation mortality, but their overall predictive performance and the factors influencing model accuracy needed evaluation.
How accurately do machine learning models predict post-heart transplantation mortality?
Meta-Analysis
How accurately do machine learning models predict post-heart transplantation mortality?
Machine learning models show moderate accuracy in predicting post-heart transplant mortality, with CatBoost performing best, though significant biases and heterogeneity exist in current literature.
Answers are grounded in this paper and cite their sources.
ML models show moderate accuracy for post-HT mortality; leaves open the need for standardized external validation before clinical adoption.
Introduction Machine learning (ML) models have been increasingly applied to predict post-heart transplantation (HT) mortality, aiming to improve decision-making and optimize outcomes. This systematic review and meta-analysis evaluates the performance of ML algorithms in predicting mortality and explores factors contributing to model accuracy. Method A systematic search of PubMed, Scopus, Web of Science, and Embase identified relevant studies, with 17 studies included in the review and 12 in the meta-analysis. The algorithms assessed included random forests, CatBoost, neural networks, and others. Model performance was evaluated using pooled area under the curve (AUC) values, with subgroup analyses for algorithm type, validation methods, and prediction timeframes. The risk of bias was assessed using the QUADAS-2 tool. Results The pooled AUC of all ML algorithms was 0.65 (95% CI: 0.64, 0.67), with no significant difference between machine learning and deep learning models ( p = 0.67). Among the algorithms, CatBoost demonstrated the highest accuracy (AUC 0.80, 95% CI: 0.74, 0.86), while K-nearest neighbor had the lowest accuracy (AUC 0.53, 95% CI: 0.50, 0.55). A meta-regression indicated improved model performance with longer post-transplant periods ( p = 0.008). When pooling only the best-performing models, the AUC improved to 0.73 (95% CI: 0.68, 0.78). The risk of bias was high in eight studies, with the flow and timing domains most commonly contributing to bias. Conclusion ML models demonstrate moderate accuracy in predicting post-HT mortality, with CatBoost achieving the best performance. While ML shows potential for improving predictive precision, significant heterogeneity and biases highlight the need for standardized methods and further external validations to enhance clinical applicability. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD42024509630 , CRD42024509630
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Mohammadi et al. (2025) conducted a meta-analysis in Post-heart transplantation mortality. Machine learning models vs. Standard clinical care practices or non-AI predictive models was evaluated on Area under the receiver operating characteristic curve (AUC) for mortality prediction (AUC 0.65, 95% CI 0.64, 0.67). Machine learning models demonstrated moderate accuracy in predicting post-heart transplantation mortality with a pooled AUC of 0.65, with CatBoost achieving the highest accuracy at an AUC of 0.80.
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