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.
The ARIC Study Design and Population
To explore the intersection of clinical risk scores and preclinical markers, researchers performed a prospective analysis of 2,714 participants from ARIC Visit 5. The cohort was specifically filtered to include participants under 80 years of age who did not have a baseline history of cardiovascular disease. The mean age of the group was 74 years, reflecting a population at significant risk for HF development. Notably, the study included a diverse demographic: 63% were women and 22% were Black adults.
Researchers defined preclinical heart failure (pCHF) using two primary modalities:
1. Cardiac Biomarkers
This included N-terminal pro-b-type natriuretic peptide (NT-proBNP) levels ≥125 pg/mL or high-sensitivity cardiac troponin T (hs-cTnT) levels exceeding sex-specific thresholds (≥22 ng/L for men and ≥14 ng/L for women).
2. Echocardiographic Findings
Structural or functional abnormalities identified via echocardiography, such as left ventricular hypertrophy, left atrial enlargement, or impaired systolic/diastolic function.
Participants were categorized into five PREVENT-HF 10-year risk tiers, ranging from low (<7.5%) to high (≥20%), to determine how pCHF prevalence and subsequent HF incidence varied within these clinical categories.
Key Findings: Preclinical Measures and Absolute Risk
The study yielded several transformative insights for the field of preventive cardiology. First, it established a strong correlation between the PREVENT-HF score and the presence of preclinical heart failure. As the clinical risk score increased, so did the prevalence of pCHF. In the highest risk category (PREVENT-HF ≥20%), the prevalence of combined elevated biomarkers and abnormal echocardiograms reached 37%.
However, the most striking results emerged from the longitudinal follow-up. Over a median period of 9.9 years, 262 incident heart failure events occurred. The researchers found that within every PREVENT-HF risk category, the presence of preclinical markers radically altered the absolute risk of developing HF.
For instance, among patients in the highest clinical risk tier (≥20%):
Those without preclinical markers had an incident rate of 9.5 per 1,000 person-years.
Those with both elevated biomarkers and abnormal echocardiography had an incident rate of 51.5 per 1,000 person-years.
This more than five-fold difference in incidence within the same clinical risk category highlights the limitations of relying solely on traditional clinical variables. It suggests that many patients categorized as ‘high risk’ by clinical scores may actually be at relatively low risk if their biomarkers and imaging are normal, while others are at extreme risk if they exhibit preclinical damage.
Improving Predictive Utility: The Power of Biomarkers
A primary objective of the study was to determine if adding pCHF measures to the PREVENT-HF score improved its predictive accuracy. The results were statistically significant and clinically relevant.
The addition of cardiac biomarkers (NT-proBNP and hs-cTnT) to the PREVENT-HF model improved the C-statistic—a measure of discrimination—from 0.69 to 0.75 (P < 0.001). Furthermore, the categorical Net Reclassification Index (NRI) was 0.17 (95% CI: 0.09-0.26), indicating that the biomarkers allowed for a more accurate classification of patients into appropriate risk categories. While adding echocardiographic data provided a modest further improvement, the biomarkers were the most potent drivers of enhanced prediction.
Expert Commentary and Clinical Implications
The ARIC study findings underscore a shift toward a more nuanced, ‘biologically informed’ approach to heart failure prevention. For clinicians, the message is clear: clinical risk scores like PREVENT-HF are excellent starting points, but they do not tell the whole story.
Cardiac biomarkers, particularly NT-proBNP and high-sensitivity troponins, serve as ‘canaries in the coal mine.’ They reflect ongoing myocardial stress and subclinical injury that clinical history and physical examination cannot detect. The high NRI suggests that using these biomarkers could prevent the over-treatment of lower-risk individuals while ensuring that those at the highest risk receive aggressive preventive care, such as optimized blood pressure control and the use of SGLT2 inhibitors or other cardioprotective therapies.
From a health policy perspective, these findings support the routine measurement of biomarkers in older adults who fall into intermediate or high-risk categories based on clinical scores. While echocardiography is valuable, its lower incremental predictive value compared to the significantly less expensive and more accessible biomarker tests suggests that blood-based screening should be the priority in resource-limited settings.
Study Limitations and Future Directions
Despite the robust nature of the ARIC study, certain limitations must be acknowledged. The study population was older (mean age 74), which may limit the generalizability of the findings to younger cohorts. Additionally, the definition of preclinical heart failure is subject to the specific thresholds used for biomarkers; different cut-off points might yield different risk estimates.
Future research should focus on whether ‘biomarker-guided’ interventions actually improve clinical outcomes compared to standard care. While we are better at predicting who will develop heart failure, the clinical community still needs definitive evidence that intervening based on preclinical markers significantly reduces the incidence of symptomatic HF.
Conclusion
The integration of preclinical heart failure measures—specifically cardiac biomarkers—into the PREVENT-HF framework offers a substantial leap forward in risk stratification. By identifying the specific individuals within clinical risk categories who are at the highest absolute risk for incident heart failure, healthcare providers can move toward a more personalized and effective prevention strategy. As the population ages and the prevalence of HF continues to rise, these refined predictive tools will be essential in the effort to reduce the global burden of cardiovascular disease.
References
Grant JK, Zhang S, Khan SS, et al. Predicted Risk, Preclinical Heart Failure Measures, and Incident Heart Failure: The ARIC Study. JACC Heart Fail. 2025 Nov;13(11):102659. doi: 10.1016/j.jchf.2025.102659. Epub 2025 Oct 3. PMID: 41045906.
