Beyond Traditional Risk Scores: How Preclinical Measures Refine Heart Failure Prediction in the ARIC Study

Introduction: The Challenge of Early Heart Failure Detection

Heart failure (HF) remains a significant global health burden, characterized by high morbidity, frequent hospitalizations, and substantial mortality rates. Despite advancements in therapeutic interventions, the primary challenge in clinical practice is the transition from identifying at-risk individuals to implementing effective preventive strategies. Traditional risk prediction models have historically relied on clinical variables such as age, blood pressure, and diabetes status. However, these models often fail to capture the underlying structural and functional changes that precede symptomatic heart failure.

The Predicting Risk of Cardiovascular Events-Heart Failure (PREVENT-HF) tool was developed to estimate the 10-year risk of incident heart failure. While it represents a leap forward in clinical risk assessment, its relationship with preclinical heart failure (pCHF)—defined by objective cardiac abnormalities in asymptomatic patients—has remained largely undefined. A recent analysis of the Atherosclerosis Risk in Communities (ARIC) study, published in JACC: Heart Failure, provides critical evidence on how biomarkers and imaging can refine our understanding of HF risk.