Heart Failure Risk Prediction and Prevention

Heart failure remains a leading cause of morbidity and mortality worldwide, characterised by the heart’s inability to pump sufficient blood to meet metabolic demands. Early identification of individuals at elevated risk enables targeted prevention strategies, potentially delaying or averting progression to symptomatic disease. Traditional risk prediction models have relied on demographic factors, comorbidities such as hypertension, diabetes and obesity, and routine laboratory measures. In recent years, advances in biomarker discovery, imaging modalities and computational modelling have enriched our capacity to stratify risk across diverse populations. Biomarker-based screening—particularly measurement of natriuretic peptides—has been incorporated into clinical guidelines to detect subclinical cardiac dysfunction. Machine learning approaches applied to large electronic health record datasets and multi-centre cohorts have demonstrated improved predictive performance over conventional statistical models, capturing complex non-linear interactions among risk determinants. Preventive interventions encompass optimisation of blood pressure and glycaemic control, lifestyle modification, pharmacotherapy for at-risk individuals and personalised monitoring. Such measures are essential to reduce the global burden of heart failure, improve patient outcomes and contain healthcare costs.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Heart Failure Risk Prediction and Prevention publication trend

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

Technical terms

Biomarker: A measurable molecule indicating biological or pathogenic processes, used for risk assessment or diagnosis.

Electronic health record (EHR): A digital repository of patient health information used for clinical care and research.

Machine learning: Computational techniques that enable predictive models to learn complex patterns from data without explicit programming.

Risk stratification: The process of categorising individuals by likelihood of developing a disease to guide preventive measures.

Ejection fraction: The percentage of blood ejected from the left ventricle with each heartbeat, indicating cardiac pump function.

References

1. Risk factors for incident heart failure in age‐ and sex‐specific strata: a population‐based cohort using linked electronic health records. European Journal of Heart Failure (2019).
2. Biomarkers for Heart Failure Prediction and Prevention. Journal of Cardiovascular Development and Disease (2023).
3. Improving predictive performance in incident heart failure using machine learning and multi-center data. Frontiers in Cardiovascular Medicine (2022).