A multimodal dataset for coronary microvascular disease biomarker discovery.

ABSTRACT: 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 class

SUBMITTER: Li D 

PROVIDER: S-EPMC12163074 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature