Multistage ECG analysis with heart rate variability features improved detection of mental stress-induced myocardial ischemia in women versus single-stage ECG models.
Does multistage ECG analysis with machine learning improve the detection of mental stress-induced myocardial ischemia compared to single-stage analysis in women with nonobstructive coronary artery disease?
Multistage ECG analysis incorporating machine learning and heart rate variability significantly improves the noninvasive detection of mental stress-induced myocardial ischemia in women with nonobstructive CAD compared to single-stage ECG.
Absolute Event Rate: 0% vs 0%
BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, while the reference standard, positron emission tomography/computed tomography, is costly and less accessible. We aimed to characterize mental stress-induced myocardial ischemia using multistage ECG analysis and machine learning to improve early, noninvasive detection. METHODS: We enrolled 2 female cohorts (18–75 years) from a single-center tertiary hospital in Guangzhou, China: a study cohort (2020–2021) and an independent test cohort (2022–2023). Participants included women with angina, nonobstructive coronary artery disease, and age-matched healthy controls. Multistage ECGs were recorded during 3 mental stress tasks and segmented into Rest, Stress, and Recovery phases. A total of 88 interpretable ECG variables, including morphological indices and heart rate variability, were extracted from each stage, and interstage differences were then calculated. Mental stress-induced myocardial ischemia labeling was obtained from positron emission tomography/computed tomography. Machine learning classifiers (K-nearest neighbors, logistic regression, random forest, support vector machine, extreme gradient boosting) were trained on the study cohort and evaluated in the test cohort using single-stage, multistage, and interstage features. RESULTS: The study cohort (n=119, mean age 53±8 years) showed significant heart rate variability, heart rate asymmetry, and prognostic-related changes, with 103 of 264 intrastage features differing across Rest, Stress, and Recovery ( P <0.05). Interstage differences provided complementary information, with 73 of 264 interstage features showing significant group differences. In the test cohort (n=53, mean age 53±7 years), models using multistage and interstage features all outperformed single-stage models. CONCLUSIONS: Multistage ECG analysis, particularly heart rate variability, captures electrophysiological signatures of mental stress-induced myocardial ischemia and enhances noninvasive detection. These findings support a paradigm shift from single-phase, ischemia-focused ECG interpretation to dynamic, multivariable assessment, providing a foundation for early identification and personalized risk stratification in women with nonobstructive coronary disease.
Peng et al. (Mon,) reported a other. Multistage ECG analysis with heart rate variability features improved detection of mental stress-induced myocardial ischemia in women versus single-stage ECG models.