A cardiovascular risk assessment tool developed by the American Heart Association (AHA) may be effective for use worldwide, according to a new study.
Accurately identifying individuals at high risk of cardiovascular disease (CVD) allows clinicians to target preventive measures more effectively, including cholesterol-lowering therapies, stricter blood pressure control, and lifestyle interventions such as smoking cessation, healthier diets, and increased physical activity.
The study evaluated AHA’s risk-prediction model, Predicting Risk of Cardiovascular Disease EVENTs (PREVENT), developed in collaboration with investigators from the NYU Grossman School of Medicine. Introduced in 2023, PREVENT estimates a person’s 10- and 30-year likelihood of developing cardiovascular disease. Unlike earlier models, it includes the risk of heart failure alongside heart attack and stroke, reflecting the availability of therapies that can help prevent all three conditions.
PREVENT has already been incorporated into treatment recommendations issued by several U.S. medical societies for guiding therapies in patients with hypertension and elevated cholesterol, based on research involving more than six million Americans. However, researchers noted that broader international adoption required strong evidence demonstrating the model’s effectiveness across diverse populations and healthcare settings.
Investigators report that PREVENT accurately predicted cardiovascular risk in more than 6.4 million people across North America, Europe, Asia, and other regions. The model performed especially well in predicting heart failure and in identifying low-to-moderate risk individuals, a group in which early intervention may help prevent progression to severe disease. The inclusion of kidney health indicators further improved the tool’s predictive accuracy.
Physician uncertainty about whether PREVENT could be applied reliably across different geographical populations had been a major obstacle to its wider global use.
Researchers analysed data from 6.8 million individuals without cardiovascular disease at baseline across 62 studies, including 44 cohort studies from North America, Europe, and Asia, and 18 multinational clinical trials involving more than 53,000 participants. Predictions generated by PREVENT at the start of the studies were compared against approximately 300,000 cardiovascular events recorded during an average follow-up of 5.5 years.
The team assessed the model using two key measures of predictive performance: discrimination and calibration. Discrimination refers to how effectively a model distinguishes between people who will develop disease and those who will not. PREVENT demonstrated particularly strong discrimination in lower-risk populations, supporting its potential use in primary care settings where treatment decisions often depend on accurately distinguishing between low and moderate risk.
The model’s accuracy improved further when researchers incorporated measures of kidney health, particularly the presence of albuminuria, a condition characterised by excess protein in the urine caused by kidney damage, often related to hypertension or diabetes.
Calibration, which measures how closely predicted outcomes match actual outcomes over time, also favoured PREVENT over the older Pooled Cohort Equation (PCE) model. The PCE significantly underestimated cardiovascular risk, predicting roughly half the number of events that ultimately occurred.
The large-scale validation effort was essential because PREVENT-based guidelines may influence national healthcare policies and treatment recommendations worldwide. Findings confirm PREVENT as a dependable tool for assessing cardiovascular risk across diverse global populations.
The research was conducted by the Chronic Kidney Disease (CKD) Prognosis Consortium, funded in part by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases, part of the National Institutes of Health. The U.S. National Kidney Foundation also provided support.
Source: NYU Langone Health/NYU Grossman School of Medicine
Image Credit: iStock
