Introduction. While 12-lead ECG features such as QRS duration and R-wave slope or peak are commonly used to assess dyssynchrony, their relationship to the underlying conduction substrate remains poorly defined. As a result, clinicians lack mechanistically grounded endpoints to determine what constitutes effective resynchronization during conduction system pacing (CSP). This study uses simultaneous optical mapping and pseudo-surface electrograms in ex vivo human hearts to characterize conduction abnormalities and quantify their expression in known ECG features. The aim is to define physiologically achievable activation patterns and electrical signatures to inform CSP optimization.
Methods. Human donor hearts unsuitable for transplant, along with explanted hearts from transplant recipients with end-stage heart failure, were reperfused using a Langendorff system and stained with the voltage-sensitive dye JPW-6003 for optical mapping. Epicardial surfaces were imaged at 500 frames per second using synchronized CMOS cameras. Simultaneously, unipolar electrograms were recorded from 192 electrodes embedded in a torso-shaped tank at 4 kHz. Pacing was applied from the right ventricular apex, basal septum, and left bundle branch region.
Results. Optical mapping revealed distinct activation patterns consistent with Purkinje conduction failure, interventricular conduction delays, and a localized conduction block contributing to intraventricular delay. Each pattern produced characteristic features in pseudo-surface electrograms, including prolonged QRS duration, reduced R-wave slope, and interelectrode peak delays. Apical pacing resulted in the most dispersed and delayed activation, while septal and LBB pacing improved synchrony and reduced activation time.
Conclusions. This study establishes a physiologically validated framework linking pseudo-surface ECG features to specific conduction abnormalities. By characterizing the conduction substrate and identifying achievable activation targets, these findings support the development of mechanism-based endpoints to guide and optimize CSP in heart failure patients.