Top Things to Know: Criteria to Assess the Predictive and Clinical Utility of Novel Models, Biomarkers, & Tools for Risk of Cardiovascular Disease
Published: February 11, 2026
Prepared by Sally S. Wong, PhD, RD, CDN, FAHA, National Senior Director, Science and Medicine, American Heart Association
- Risk prediction has been utilized in the primary prevention of cardiovascular disease (CVD) for greater than three decades.
- Contemporary cardiovascular (CV) risk assessment relies on multivariable models, which integrate established CV risk factors and have evolved from the Framingham Risk Model to the Pooled Cohort Equations to the Predicting Risk of CVD EVENTs (PREVENT) equations.
- This Scientific Statement provides an updated framework for the critical appraisal of the predictive and clinical utility of novel biomarkers, models, and tools, and builds upon the previously published AHA Scientific Statement on the “Criteria for Evaluation of Novel Markers of Cardiovascular Risk.”
- Recent scientific (i.e., genomics, proteomics, metabolomics) and methodological (i.e., artificial intelligence) advances have led to a proliferation of novel biomarkers and tools for potential use in risk prediction. In parallel, the growing armamentarium of preventive therapies, some with considerable cost, underscores the need for more accurate and precise risk assessment to prioritize those at highest risk who will derive the greatest absolute benefit.
- Accompanying the considerable enthusiasm for the potential of newer approaches to improve risk prediction is the need for rigorous evaluation and assessment of their performance (i.e., accuracy, precision, incremental performance when added to contemporary multivariable risk models or established risk factors) and clinical utility (i.e., actionability, scalability, generalizability) before adoption in clinical practice.
- Additional considerations in risk tool evaluation include reproducibility, cost-value considerations (including impact on downstream healthcare costs), and implications for health equity.
- Optimal communication of CVD risk supports shared decision-making or the partnership between the clinician and patient to share responsibility for the medical decision.
- Even if a novel biomarker is identified to improve predictive performance, the economic cost and value of a novel blood-based or imaging-based marker should be considered in addition to the implications on downstream healthcare costs.
- Evaluating the growing body of models, biomarkers, and tools that are available and emerging to quantify risk for incident CVD requires a standardized framework to rigorously assess predictive and clinical utility overall and in subgroups to ensure generalizable and equitable performance.
- This statement is intended to support clinicians, researchers, and policymakers in how best to evaluate current and emerging risk prediction tools and ultimately improve the prevention of cardiovascular disease in diverse populations.
Citation
Khan SS, Greenland P, Hayman LL, Khera R, Navar AM, Pencina MJ, Reza N, Shah SH, Shanbhag S, Weber B, Wong S, Khera A; on behalf of the American Heart Association Prevention Science Committee of the Council on Epidemiol¬ogy and Prevention and Council on Cardiovascular and Stroke Nursing; Council on Cardiovascular Radiology and Intervention; Council on Genomic and Precision Medicine; Council on Lifestyle and Cardiometabolic Health; and Council on Pe¬ripheral Vascular Disease. Criteria to assess the predictive and clinical utility of novel models, biomarkers, and tools for risk of cardiovascular disease: a scientific statement from the American Heart Association. Circulation. Published online February 11, 2026. doi: 10.1161/CIR.0000000000001401