Novel Method for Assessment of His Bundle Pacing Morphology Using Near Field and Far Field Device Electrograms.

Background: The 12-lead ECG is considered the gold standard to differentiate between selective (S), nonselective (NS) His bundle pacing (HBP), and right ventricular septal capture in routine clinical practice. We sought to assess the utility of device EGM recordings as a tool to identify the type of HBP morphology.

Methods: One hundred forty-eight consecutive patients underwent HBP with a 3830 Select Secure lead (Medtronic, Inc) at 3 centers between October 2016 and October 2017. The near field V-EGM morphology (NF EGM), near field V-EGM time to peak (NF), and far-field EGM QRS duration (QRSd) were recorded while pacing the His lead with simultaneous 12-lead ECG rhythm strips.

Results: Indications for HBP were sinus node dysfunction, atrioventricular conduction disease, and cardiac resynchronization therapy in 68 (46%), 56 (38%), and 24 (16%) patients, respectively. Baseline QRSd was 108±38 ms with QRSd >120 ms in 57 (39%) patients (27 right bundle branch block, 18 left bundle branch block, and 12 intraventricular conduction delay). S-HBP was noted in 54 (36%) patients. A positive NF and NF >40 ms were highly sensitive (94% and 93%, respectively) and specific (90% and 94%) for S-HBP irrespective of baseline QRSd. All 3 parameters (+NF, NF >40 ms, and far-field EGM QRSd <120 ms) had high negative predictive value (97%, 95%, and 92%). A novel device-based algorithm for S-HBP was proposed. EGM transitions correlated with ECG transitions during threshold testing and can help accurately differentiate between S-HBP, NS-HBP, and right ventricular septal pacing with a cumulative positive predictive value of 91% (positive predictive value =100% in patients with baseline QRSd <120 ms).

Conclusions: We propose a novel and simple criteria for accurate differentiation between S-HBP, NS-HBP, and right ventricular septal capture morphologies by careful analysis of device EGMs alone. This study paves the way for future studies to assess autocapture algorithms for devices with HBP.