ReviewJACC. Asia2026
Race and Ethnicity in Cardiovascular Disease Risk Prediction for Multiethnic Populations: Insights From Global Guidelines.
Review in JACC. Asia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Advancing Cardiovascular Disease Prevention in Asian Populations.JACC. Asia · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular disease is the leading cause of death worldwide, with certain racial/ethnic groups facing higher risks. Global clinical guidelines for the prevention of cardiovascular disease vary in their approach to addressing racial/ethnic differences among patients. The authors compare the American Heart Association's 2024 PREVENT (Predicting Risk of Cardiovascular Disease Events) equations, the European Society of Cardiology's 2021 Systematic Coronary Risk Evaluation 2 model, and the Singapore-modified Framingham risk score, with a focus on their differing approaches to race and ethnicity. The PREVENT model removes race and ethnicity as a factor, instead incorporating the Social Deprivation Index to address social determinants of health. SCORE2 introduces multiplier factors for different ethnicities, while the Singapore-modified Framingham risk score retains ethnicity as a variable. Race-neutral models such as PREVENT aim to avoid reinforcing race as a biological construct while still accounting for social determinants of health that are highly correlated with race. In Asia, the path toward race-neutral risk prediction begins with strengthening data infrastructure and increasing participation in clinical trials to ensure adequate representation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.