Zheng Jianwei, Ani Chizobam, Abudayyeh Islam, Zheng Yunfan, Rakovski Cyril, Yaghmaei Ehsan, Ogunyemi Omolola
Department of Preventive and Social Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA.
Internal Medicine Department, Charles R Drew University of Medicine and Science, Los Angeles, CA 90059, USA.
Int J Environ Res Public Health. 2025 Feb 25;22(3):337. doi: 10.3390/ijerph22030337.
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.
心电图（ECG）是一种广泛使用的非侵入性工具，用于诊断一系列心血管疾病，包括心律失常和与心脏病相关的结构变化。尽管其在临床护理中发挥着关键作用，但在研究中，心电图读数的种族和民族差异往往未得到充分探索或妥善解决。不同种族群体之间已注意到关键心电图参数的差异，如PR间期、QRS时限、QT间期和T波形态。然而，该领域有限的研究阻碍了考虑这些差异的诊断标准的发展，这可能会导致医疗保健差异，因为主要基于大多数人群数据开发的心电图解读算法可能会导致对少数群体的误诊或不适当治疗。本综述旨在帮助心脏研究人员和心血管专家更好地理解、探索和解决心电图读数中的种族和民族差异的影响。通过确定从遗传因素到环境影响等潜在原因，并探索由此产生的医疗保健结果差异，我们提出了一些策略，如制定针对特定种族的心电图规范、应用人工智能（AI）提高诊断准确性以及使心电图数据库多样化。通过这些努力，医学界可以朝着更个性化和公平的心血管护理迈进。
