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A multimodal dataset for coronary microvascular disease biomarker discovery
Dantong Li1,2,3, Xiaoting Peng1,2,3, Lianting Hu1,2,3
1Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
Insights
Coronary microvascular disease (CMD) screening is crucial, especially for women. This study introduces a new dataset and uses electrocardiogram (ECG) data with machine learning to effectively identify CMD.
Area of Science:
- Cardiology
- Medical Imaging
- Biomedical Engineering
Background:
- Coronary microvascular disease (CMD) disproportionately affects women, leading to significant morbidity and mortality.
- Limited screening-level data impedes biomarker discovery for CMD.
- Effective clinical screening is vital for managing CMD.
Purpose of the Study:
- To create a novel dataset for CMD research.
- To develop machine learning models for CMD classification using ECG data.
- To validate the utility of ECG in differentiating CMD.
Main Methods:
- Prospective enrollment of 80 female angina patients and 40 controls.
- Adenosine stress electrocardiogram (ECG) monitoring across Rest, Stress, and Recovery stages.
- Machine learning models developed using ECG variables for CMD classification.
Main Results:
- A new dataset was curated to address data limitations in CMD research.
- Machine learning models demonstrated effectiveness in identifying CMD using multi-stage ECG.
- ECG showed potential for differential diagnosis of CMD, validated with mental stress data.
Conclusions:
- Multi-stage ECG analysis is valuable for CMD screening.
- The newly curated dataset is expected to advance CMD research.
- ECG-based machine learning models offer a promising approach for CMD identification.
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 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.
