A Review of Racial Differences and Disparities in ECG

Jianwei Zheng (), Chizobam Ani, Islam Abudayyeh, Yunfan Zheng, Cyril Rakovski, Ehsan Yaghmaei and Omolola Ogunyemi
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Jianwei Zheng: Department of Preventive and Social Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA
Chizobam Ani: Internal Medicine Department, Charles R Drew University of Medicine and Science, Los Angeles, CA 90059, USA
Islam Abudayyeh: Internal Medicine Department, Charles R Drew University of Medicine and Science, Los Angeles, CA 90059, USA
Yunfan Zheng: Department of Mathematics, University of California Los Angeles, Los Angeles, CA 90024, USA
Cyril Rakovski: Department of Mathematics, Schmid College of Science and Technology, Chapman University, Orange, CA 92886, USA
Ehsan Yaghmaei: Department of Mathematics, Schmid College of Science and Technology, Chapman University, Orange, CA 92886, USA
Omolola Ogunyemi: Department of Preventive and Social Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA

IJERPH, 2025, vol. 22, issue 3, 1-12

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.

Keywords: racial disparities; ECG; healthcare inequities; social determinants of health; machine learning; cardiovascular disease; artificial intelligence (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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