Patient-specific modeling of ventricular activation pattern using surface ECG-derived vectorcardiogram in bundle...

Area of Science:

• Computational modeling in cardiovascular science
• Cardiac electrophysiology research
• Medical imaging and simulation

Background:

• Patient-specific computational models offer potential for enhanced cardiac disease diagnosis and treatment planning.
• Accurate simulation of cardiac electrical activity is crucial for understanding conditions like left-bundle branch block (LBBB) and right ventricular (RV) pacing.

Purpose of the Study:

• To develop and validate a novel method for simulating 3D cardiac activation patterns in patients with LBBB and RV pacing.
• To utilize non-invasive clinical measurements for creating patient-specific electrophysiological models.

Main Methods:

• Developed a method to estimate cardiac activation patterns using vectorcardiograms (VCG) derived from standard 12-lead electrocardiograms (ECG).
• Optimized parameters of a biventricular monodomain electrophysiology model to match measured VCG data.
• Validated simulated activation patterns against electroanatomic maps of local activation times from the left and right ventricles.

Main Results:

• The optimized computational models accurately predicted VCG-derived dipole orientation (within 6.7° ± 0.6°).
• Simulated local activation times closely matched measured electroanatomic maps (within 11.5 ms ± 0.8 ms), consistent with measurement accuracy.
• The method successfully simulated 3D activation patterns for LBBB and RV-paced conditions.

Conclusions:

• The described method enables accurate, patient-specific simulation of cardiac activation patterns from routine ECGs.
• This approach holds significant promise for improving the diagnosis and therapy planning of cardiac conditions affecting ventricular activation.
• The validated models provide a powerful tool for non-invasive assessment of cardiac electrophysiology.