Li Dantong, Peng Xiaoting, Hu Lianting, Chen Jintai, Long Xinyang, Zhang Xueli, Ye Siting, Bai Xiaohe, Wu Chao, Yang Huan, Huang Shuai, Kong Lingcong, Liu Entao, Wang Shuxia, Ma Huan, Geng Qingshan, Liang Huiying
Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
Guangdong Provincial Cardiovascular Institute, Guangzhou, Guangdong Province, 510080, China.
Sci Data. 2025 Jun 12;12(1):990. doi: 10.1038/s41597-025-05022-8.
Coronary microvascular disease (CMD), particularly prevalent among women, is associated with increased morbidity and mortality, making clinical screening vital for effective management. However, limited publicly available screening-level data hinders disease-specific biomarker discovery. To address this gap, 80 female angina patients without obstructive coronary artery disease and 40 age-matched female controls were prospectively enrolled to curate a new dataset. All participants underwent adenosine stress with electrocardiogram (ECG) monitoring across Rest, Stress, and Recovery stages. CMD diagnosis was confirmed with the standard clinical criterion, i.e., coronary flow reserve (CFR) < 2.0 via PET/CT. Using ECG variables from different stages, we developed machine learning models to classify CMD, thus validating dataset's effectiveness in CMD identification. We also validated the potential of ECG for differential diagnosis through joint analysis with the published mental stress-induced myocardial ischemia (MSIMI) dataset, which is based on the same cohort under different stress conditions. Disease-specific ECG variable sets were identified. Our findings highlight the value of multi-stage ECG in CMD screening. We expect this dataset to significantly advance CMD research.
冠状动脉微血管疾病（CMD）在女性中尤为普遍，与发病率和死亡率的增加相关，因此临床筛查对于有效管理至关重要。然而，公开可用的筛查水平数据有限，阻碍了疾病特异性生物标志物的发现。为了填补这一空白，前瞻性招募了80名无阻塞性冠状动脉疾病的女性心绞痛患者和40名年龄匹配的女性对照，以创建一个新的数据集。所有参与者在静息、应激和恢复阶段均接受腺苷负荷试验并进行心电图（ECG）监测。CMD诊断通过标准临床标准确认，即通过PET/CT检测冠状动脉血流储备（CFR）<2.0。利用不同阶段的心电图变量，我们开发了机器学习模型来对CMD进行分类，从而验证数据集在CMD识别中的有效性。我们还通过与已发表的基于同一队列在不同应激条件下的精神应激诱导心肌缺血（MSIMI）数据集进行联合分析，验证了心电图在鉴别诊断中的潜力。确定了疾病特异性心电图变量集。我们的研究结果突出了多阶段心电图在CMD筛查中的价值。我们期望这个数据集能显著推动CMD的研究。
