Risk Prediction Models in Heart Failure Management

Risk prediction models in heart failure management have evolved from simple clinical scores to sophisticated algorithms that integrate demographic, clinical, laboratory and imaging data. Initial approaches relied on multivariable regression to create point‐based systems, such as those incorporating age, New York Heart Association class and left ventricular ejection fraction, often augmented by biomarkers like B‐type natriuretic peptide. More recently, machine learning techniques – including random forest, gradient boosting and penalised regression – have been applied to large registries and electronic health records to enhance individual risk stratification for outcomes such as mortality, rehospitalisation and progression to advanced therapies. Interpretability methods, for example SHAP (Shapley Additive Explanations) values and partial dependence plots, have been layered onto these models to clarify the contribution of each predictor. Despite advances, challenges remain in ensuring robust external validation, calibration across diverse populations and seamless integration into clinical workflows. The global rise in heart failure prevalence underscores the value of accurate, accessible risk calculators to guide personalised treatment, resource allocation and shared decision-making.

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Risk Prediction Models in Heart Failure Management publication trend

The graph below shows the total number of articles in risk prediction models in heart failure management across all publications each year (not limited to Nature Index journals).

Technical terms

C-statistic: A measure of a prediction model’s ability to distinguish between individuals who experience an event and those who do not, with values from 0.5 (no discrimination) to 1.0 (perfect discrimination).

SHAP value: A method for interpreting machine learning predictions by quantifying the contribution of each feature to an individual prediction.

Natriuretic peptides (e.g., NT-proBNP): Hormonal biomarkers released in response to myocardial wall stretch, widely used for prognosis and guiding therapy in heart failure.

External validation: The process of testing a prediction model’s performance in an independent dataset separate from the one used for model development to assess generalisability.

References

1. Interpretable prediction of 3-year all-cause mortality in patients with chronic heart failure based on machine learning. BMC Medical Informatics and Decision Making (2023).
2. Comparing Machine Learning Models and Statistical Models for Predicting Heart Failure Events: A Systematic Review and Meta-Analysis. Frontiers in Cardiovascular Medicine (2022).
3. A heart failure phenotype stratified model for predicting 1-year mortality in patients admitted with acute heart failure: results from an individual participant data meta-analysis of four prospective European cohorts. BMC Medicine (2021).