Prognostic significance of global electrical heterogeneity (GEH) parameters between coronary microvascular dysfunction (CMD) and obstructive coronary artery disease (OCAD): a retrospective cohort study

• Coronary microvascular dysfunction (CMD) & obstructive coronary artery disease (OCAD). • 22 global electrical heterogeneity (GEH) features from CMD and OCAD patients. • Non-invasively differentiating CMD & OCAD: AUC 0.717 ([95 % CI 0.636–0.798], p < 0.0001). • Two GEH parameters are effective indicators for differentiating CMD and OCAD. • On validation set: sensitivity, 0.606; specificity, 0.611; accuracy, 0.608. Both coronary microvascular dysfunction (CMD) and obstructive coronary artery disease (OCAD) cause myocardial ischemia, making accurate diagnosis of CMD an unmet challenge. We investigate whether electrocardiogram (ECG)-constructed global electrical heterogeneity (GEH) can differentiate CMD and OCAD. This retrospective study comprised 74 OCAD and 77 CMD patients with their clinical characteristics and ECGs. QRS onset and offset, R-peak, and T-wave offset were marked on vector-magnitude ( VM) derived from ECG-constructed time-coherent global XYZ median beats. 22 GEH parameters (i.e., QT Interval, area and peak QRS-T angles, the area under curve of QT interval on VM (AUC_QT_VM), the direction (azimuth and elevation) and magnitudes of the spatial peak and area vectors for QRS, T, and spatial ventricular gradient (SVG) were calculated and tested using logistic regression analyses to identify significant predictors. Six GEH parameters (i.e., QT Interval, Peak QRS Azimuth, Area QRS Azimuth, Area T Elevation, Area T, and Area SVG Elevation) were identified as significant predictors. After multivariable adjustment, AUC_QT_VM (odds ratio (OR) = 0.979, [95 % confidence interval (CI) 0.964–0.995], P = 0.009), Area SVG Elevation (OR = 0.978, [95 % CI 0.959–0.998], P = 0.031), and QT Interval (OR = 1.014, [95 % CI 1.000–1.024], P = 0.010) were selected, with the area under curve (AUC) of 0.717 ([95 % CI 0.636–0.798], P < 0.0001) and accuracy of 0.645 on the training dataset and accuracy of 0.608 on the validation dataset. Area SVG Elevation and QT Interval could differentiate CMD and OCAD, indicating that our proposed GEH-based method may offer a new possibility and pathway for non-invasive detection of CMD.