Key result
AI and biomarkers enhance HF risk prediction but lack proven impact on clinical outcomes.
Why the study?
Multiple risk prediction models and strategies have been developed to identify individuals at high risk of developing heart failure and to implement preventive therapies effectively, but recent advancements and their clinical impact require synthesis.
Advancements in heart failure risk prediction tools offer promising opportunities to improve preventive strategies, though clinical implementation and outcome benefits require further investigation.
Heart failure (HF) is a major cause of mortality and morbidity in the United States that carries substantial healthcare costs. Multiple risk prediction models and strategies have been developed over the past 30 years with the aim of identifying those at high risk of developing HF and of implementing preventive therapies effectively. This review highlights recent developments in HF risk prediction tools including emerging risk factors, innovative risk prediction models, and novel screening strategies from artificial intelligence to biomarkers. These developments allow more accurate prediction, but their impact on clinical outcomes remains to be investigated. Implementation of these risk models in clinical practice is a considerable challenge, but HF risk prediction tools offer a promising opportunity to improve outcomes while maintaining value.
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Matasic et al. (2024) conducted a review in Heart failure. Heart failure risk prediction and screening tools was evaluated. Recent developments in heart failure risk prediction tools, including AI and biomarkers, allow more accurate prediction, though their impact on clinical outcomes remains to be investigated.
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