Evidence map›Paper›PMID 42390389›Full record

ReviewJACC. Asia2026

Race and Ethnicity in Cardiovascular Disease Risk Prediction for Multiethnic Populations: Insights From Global Guidelines.

Ann Hui Ching, Hazirah Mohamad, Satveer Kaur-Gill, Reuben Ng, Nathan D Wong, Eugene Yang, Howard Bauchner, Mayank Dalakoti

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ann Hui ChingUniversity of Oxford School of Anthropology and Museum Ethnography, Oxford, United Kingdom.
Hazirah MohamadDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Satveer Kaur-GillDepartment of Communication Studies, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
Reuben NgLee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore.
Nathan D WongHeart Disease Prevention Program, Mary and Steve Wen Cardiovascular Division, University of California, Irvine, School of Medicine, Irvine, California, USA.
Eugene YangDivision of Cardiology, University of Washington School of Medicine, Seattle, Washington, USA.
Howard BauchnerChobanian & Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA.
Mayank DalakotiBritish Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Cardiovascular Metabolic Translational Research Program, National University of Singapore, Singapore, Singapore; Department of Cardiology, National University Heart Centre Singapore, Singapore, Singapore. Electronic address: mayankd.89@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

clinical practice guidelinespreventive cardiologyrace/ethnicityrisk prediction modelssocial determinants of health

Identifiers

PMID42390389
PMCPMC13491071

What OpenQuestion holds

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Registered trials

None linked

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.