Evidence map›Paper›PMID 40238300›Full record

ReviewInternational journal of environmental research and public health2025

A Review of Racial Differences and Disparities in ECG.

Jianwei Zheng, Chizobam Ani, Islam Abudayyeh, Yunfan Zheng, Cyril Rakovski, Ehsan Yaghmaei, Omolola Ogunyemi

Abstract readReview
In one paragraph

Review in International journal of environmental research and public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Jianwei ZhengDepartment of Preventive and Social Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA.ORCID 0000-0002-3930-228X
Chizobam AniInternal Medicine Department, Charles R Drew University of Medicine and Science, Los Angeles, CA 90059, USA.ORCID 0000-0002-6125-5475
Islam AbudayyehInternal Medicine Department, Charles R Drew University of Medicine and Science, Los Angeles, CA 90059, USA.
Yunfan ZhengDepartment of Mathematics, University of California Los Angeles, Los Angeles, CA 90024, USA.
Cyril RakovskiDepartment of Mathematics, Schmid College of Science and Technology, Chapman University, Orange, CA 92886, USA.ORCID 0000-0002-9051-1466
Ehsan YaghmaeiDepartment of Mathematics, Schmid College of Science and Technology, Chapman University, Orange, CA 92886, USA.
Omolola OgunyemiDepartment of Preventive and Social Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA.ORCID 0000-0002-1388-244X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The electrocardiogram (ECG) is a widely used, non-invasive tool for diagnosing a range of cardiovascular conditions, including arrhythmia and heart disease-related structural changes. Despite its critical role in clinical care, racial and ethnic differences in ECG readings are often underexplored or inadequately addressed in research. Variations in key ECG parameters, such as PR interval, QRS duration, QT interval, and T-wave morphology, have been noted across different racial groups. However, the limited research in this area has hindered the development of diagnostic criteria that account for these differences, potentially contributing to healthcare disparities, as ECG interpretation algorithms largely developed from major population data may lead to misdiagnoses or inappropriate treatments for minority groups. This review aims to help cardiac researchers and cardiovascular specialists better understand, explore, and address the impact of racial and ethnic differences in ECG readings. By identifying potential causes-ranging from genetic factors to environmental influences-and exploring the resulting disparities in healthcare outcomes, we propose strategies such as the development of race-specific ECG norms, the application of artificial intelligence (AI) to improve diagnostic accuracy, and the diversification of ECG databases. Through these efforts, the medical community can advance toward more personalized and equitable cardiovascular care.

Indexed as

Cardiovascular DiseasesElectrocardiographyHealthcare DisparitiesRacial GroupsEthnicityHumansartificial intelligencecardiovascular diseaseECGhealthcare inequitiesmachine learningracial disparitiessocial determinants of health

Identifiers

PMID40238300
PMCPMC11942291

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.