Two leading heart risk tools pass a major global test

By Pooja Toshniwal PahariaReviewed by Susha Cheriyedath, M.Sc.May 11 2026

A massive multinational analysis suggests two leading cardiovascular risk tools can help clinicians identify high-risk patients across regions, but local calibration remains key to making prevention more precise.

Study: Multinational validation of the PREVENT and SCORE2 cardiovascular risk equations across 6.4 million individuals. Image Credit: Dragon Claws / Shutterstock

Cardiovascular disease (CVD) risk calculators guide treatment decisions for millions of people worldwide every day. Most of them, however, have been developed for specific populations and regions. A large multinational analysis accepted for publication in the journal Nature Medicine supports the broader application of two major prediction tools. These include the American Heart Association (AHA)’s Predicting Risk of cardiovascular disease EVENT (PREVENT) equations and Europe’s Systematic COronary Risk Evaluation 2 (SCORE2) algorithm. Their generally good performance across study populations suggests that clinicians can use them to identify and stratify high-risk individuals across various clinical settings.

PREVENT and SCORE2 Risk Prediction Background

Cardiovascular diseases continue to cause widespread morbidity and mortality across the globe. In preventive care settings, CVD risk prediction tools could help healthcare providers identify individuals most likely to benefit early from cholesterol- and blood pressure-reducing interventions. The tools could therefore enable more prompt treatment and improve resource allocation, thereby reducing CVD burden on individuals and healthcare systems.

PREVENT and SCORE2 are among the most widely used models today. Researchers have developed these tools using large regional datasets, and major clinical guidelines now recommend their use in routine care. However, they have been validated primarily in populations of origin, leaving uncertainty about their accuracy in more diverse populations.

Multinational CVD Validation Study Design

In the present study, researchers evaluated the performance of the AHA’s PREVENT and Europe’s SCORE2 cardiovascular risk equations using data from 18 randomized controlled trials (RCTs) and 44 observational study cohorts within the CKD Prognosis Consortium. The study included more than 6.4 million individuals for PREVENT and 5.4 million for SCORE2. None of the participants had a prior CVD diagnosis. The study included individuals from Europe, North America, and Asia-Pacific and other regions, with RCTs enrolling participants from nearly 50 countries.

The team assessed discrimination, followed by calibration. Discrimination refers to the ability of the models to distinguish between people who developed CVD and those who did not. They then investigated whether the predicted risks matched observed outcomes (calibration). The researchers used Harrell’s C-statistics and calibration slope analyses within each study and stratified results by region. They also estimated short-term CVD risk over 1 to 9 years using scaling factors derived from the PREVENT algorithms.

The analysis included general population cohorts, CKD-specific cohorts, electronic health record-based cohorts, and multinational randomized trials. The randomized trials evaluated modern cardiovascular, kidney, and metabolic therapies. These included glucagon-like peptide-1 receptor agonists (GLP-1RA), sodium-glucose cotransporter-2 inhibitors (SGLT2i), renin-angiotensin system (RAS) blockers, and non-steroidal mineralocorticoid receptor antagonists (nsMRAs).

Furthermore, the team investigated whether incorporating metabolic and renal markers such as glycated hemoglobin (HbA1c) and albuminuria could improve prediction accuracy. As part of the sensitivity analyses, they compared PREVENT’s atherosclerotic CVD (ASCVD) equation with commonly used pooled cohort equations (PCEs).

PREVENT and SCORE2 Performance Findings

Researchers tracked participants for a mean of about five years. During this follow-up period, they documented 293,737 observed CVD events using the PREVENT definition and 258,086 using SCORE2 among more than six million participants worldwide. Events captured by PREVENT included fatal and nonfatal ASCVD and heart failure, while SCORE2 focused on myocardial infarction, stroke, and cardiovascular death. Despite these differences, both equations demonstrated similarly reliable performance across observational cohorts and multinational randomized trials.

The PREVENT equations showed moderate-to-strong discrimination, with a median C-statistic of 0.702 for CVD prediction. SCORE2 achieved a comparable C-statistic of 0.683. PREVENT also performed well when predicting ASCVD alone (0.695) and showed particularly strong discrimination for heart failure events (0.78). These models showed generally consistent results across regions and multinational trials. Discrimination, however, declined in higher-risk populations, likely due to differences in patient risk profiles and case-mix heterogeneity instead of limitations of the models themselves.

Both tools modestly overestimated overall CVD risk, particularly SCORE2. Signals of overprediction emerged in Asian and other underrepresented populations, although limited data restricted conclusions. Nevertheless, calibration remained robust in multinational trials with rigorously validated CVD outcomes. Adding albuminuria improved PREVENT’s predictive performance, especially in high-risk populations with diabetes or CKD, while HbA1c produced smaller improvements. Compared with the older PCE estimates used in the United States (US), PREVENT also demonstrated consistently better calibration.

Global CVD Risk Tool Implications

The findings strengthen evidence for using PREVENT and SCORE2 to reliably stratify individuals at high risk of CVD across diverse geographical and clinical settings, while emphasizing the need for local calibration and further validation in underrepresented populations. These tools may be especially valuable in primary care, where preventive decisions can change lives the most. The findings also suggest that PREVENT may outperform older US risk prediction tools due to its better calibration and broader incorporation of cardiovascular, kidney, and metabolic risk factors. Evaluating additional biomarkers such as albuminuria could help clinicians more accurately identify people at higher CVD risk, particularly those with diabetes or CKD.

These tools need to be continually refined to improve regional adaptations, particularly for populations across Asia, Africa, and the Middle East. Future efforts should focus on population-specific calibration, further validation in underrepresented regions, and clearer treatment thresholds for emerging cardiometabolic treatments. Integrating available biomarkers could improve the precision and accessibility of CVD risk prediction worldwide.

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