Novel insights into myocardial synchrony: A CMR-based approach for improving the detection of coronary artery disease at rest.
Authors
Affiliations (8)
Affiliations (8)
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China.
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
- Tianjin University of Traditional Chinese Medicine, Tianjin, China; Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China; Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China; Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
- Department of Cardiology, Chest Hospital, Tianjin University, Tianjin, China.
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China. Electronic address: [email protected].
Abstract
We aimed to develop a novel cardiac magnetic resonance (CMR)-based method for quantifying myocardial synchrony and evaluate its diagnostic value in detecting myocardial dysfunction of coronary artery disease (CAD). Consecutive participants with anatomically/angiographically obstructive CAD (n = 112) and healthy participants (n = 87) undergoing CMR imaging were prospectively enrolled. Myocardial strain was analyzed using feature-tracking, and myocardial synchrony was quantified via Pearson correlation coefficients of segmental strain time series across the cardiac cycle. Machine learning models (strain-only, synchrony-only, combined) were developed and validated in an independent external cohort. Healthy participants exhibited high left ventricular myocardial synchrony (radial: 0.91 [IQR: 0.88, 0.93]; circumferential: 0.90 ± 0.04; longitudinal: 0.97 ± 0.02), significantly reduced in participants with CAD (radial: 0.84 [IQR: 0.75, 0.89]; circumferential: 0.81 ± 0.12; longitudinal: 0.90 ± 0.08), including those with preserved left ventricular ejection fraction (LVEF ≥50%) (radial: 0.86 [IQR: 0.82, 0.90]; circumferential: 0.86 ± 0.07; longitudinal: 0.91 ± 0.07), all p < 0.001. In model analysis, the combined model significantly outperformed individual models (AUC: 0.94 [95% CI: 0.89-1.00] vs. 0.84 [0.75-0.94] for strain model, p = 0.037; vs. 0.79 [0.68-0.90] for synchrony model, p = 0.001). Superiority persisted in CAD with preserved LVEF (AUC: 0.91 [95% CI: 0.83-1.00]) and external validation (AUC: 0.93 [95% CI: 0.84-1.00]). This CMR-derived approach demonstrated the high degree of left ventricular synchrony in healthy populations and significant dyssynchrony in CAD, even in those with preserved LVEF. Integrating myocardial synchrony with strain significantly enhanced CAD myocardial dysfunction detection relative to strain alone, with robust diagnostic performance maintained in CAD with preserved LVEF.