Key result
Neural network models achieved the highest overall accuracy for predicting mortality in heart failure patients with a pooled AUC of 0.808, while support vector machines performed best for predicting readmission.
Why the study?
Heart failure creates substantial healthcare burdens, and machine learning models offer potential to identify patients at higher risk of adverse outcomes to improve clinical decision-making.
Do machine learning models accurately predict mortality and readmission in heart failure patients?
Meta-Analysis (n=943,941)
Do machine learning models accurately predict mortality and readmission in heart failure patients?
Answers are grounded in this paper and cite their sources.
Machine learning models, particularly neural networks and support vector machines, show strong potential in accurately predicting mortality and readmission in heart failure patients.
BACKGROUND: Heart failure (HF) impacts nearly 6 million individuals in the U.S., with a projected 46% increase by 2030, is creating significant healthcare burdens. Predictive models, particularly machine learning (ML)-based models, offer promising solutions to identify patients at greater risk of adverse outcomes, such as mortality and hospital readmission. This review aims to assess the effectiveness of ML models in predicting HF-related outcomes, with a focus on their potential to improve patient care and clinical decision-making. We aim to assess how effectively machine learning models predict mortality and readmission in heart failure patients to improve clinical outcomes. METHOD: The study followed PRISMA 2020 guidelines and was registered in the PROSPERO database (CRD42023481167). We conducted a systematic search in PubMed, Scopus, and Web of Science databases using specific keywords related to heart failure, machine learning, mortality and readmission. Extracted data focused on study characteristics, machine learning details, and outcomes, with AUC or c-index used as the primary outcomes for pooling analysis. The PROBAST tool was used to assess bias risk, evaluating models based on participants, predictors, outcomes, and statistical analysis. The meta-analysis pooled AUCs for different machine learning models predicting mortality and readmission. Prediction accuracy data was categorized by timeframes, with high heterogeneity determined by an I² value above 50%, leading to a random-effects model when applicable. Publication bias was assessed using Egger's and Begg's tests, with a p-value below 0.05 considered significant RESULT: A total of 4,505 studies were identified, and after screening, 64 were included in the final analysis, covering 943,941 patients. Of these, 40 studies focused on mortality, 17 on readmission, and 7 on both outcomes. In total, 346 machine learning models were evaluated, with the most common algorithms being random forest, logistic regression, and gradient boosting. The neural network model achieved the highest overall AUC for mortality prediction (0.808), while the support vector machine performed best for readmission prediction (AUC 0.733). The analysis revealed a significant risk of bias, primarily due to reliance on retrospective data and inadequate sample size justification. CONCLUSION: In conclusion, this review emphasizes the strong potential of ML models in predicting HF readmission and mortality. ML algorithms show promise in improving prognostic accuracy and enabling personalized patient care. However, challenges like model interpretability, generalizability, and clinical integration persist. Overcoming these requires refined ML techniques and a robust regulatory framework to enhance HF outcomes.
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Hajishah et al. (2025) conducted a meta-analysis in Heart failure (n=943,941). Machine learning models vs. Other machine learning models or traditional statistical models was evaluated on Overall mortality prediction accuracy (AUC) (AUC 0.808, 95% CI 0.754-0.863). Neural network models achieved the highest overall accuracy for predicting mortality in heart failure patients with a pooled AUC of 0.808, while support vector machines performed best for predicting readmission.
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