Refining left bundle branch block criteria for improved left bundle branch area pacing response in heart failure

Background Left bundle branch block (LBBB) on the surface electrocardiogram (ECG) is a key criterion for selecting patients suitable for cardiac resynchronization therapy (CRT). However, the diagnostic criteria for LBBB vary across guidelines, and their impact on the efficacy of left bundle branch area pacing (LBBAP) remains unclear. Objective This study aims to investigate the correlation between different LBBB-related ECG criteria, as well as ECG characteristics, on the efficacy of LBBAP in heart failure (HF). Methods Consecutive systolic HF patients with wide QRS complexes who successfully underwent LBBAP were enrolled. Preoperative standard 12-lead ECG was assessed. This included LBBB-related parameters, including time to notch/slur in lateral leads. The associations between different LBBB criteria (2009 AHA guidelines, 2013 ESC guidelines, 2021 ESC guidelines, MADIT-CRT, and Strauss criteria) were evaluated. The endpoints were post-LBBAP electrical synchronization, echocardiographic response and clinical outcomes. Results Among 149 patients included in the analyses (baseline LVEF: 32.6±7.0%, QRS duration: 170.8±20.3ms), substantial variability in LBBB classification was observed among different criteria. The Strauss criteria demonstrated the highest predictive accuracy for QRS narrowing (AUC: 0.710), and it also showed favorable performance for identifying echocardiographic responders (AUC: 0.631). Patients who meet the Strauss LBBB criteria had a significantly lower risk of composite clinical endpoint compared to those without LBBB (adjusted HR=0.216, 95% CI: 0.057-0.810, P=0.023). Combining the Strauss criteria with delayed time to notch/slur in lead V5/V6 (>60ms) further improved the predictive value of ECG-based assessment. Conclusion LBBAP provides the most clinical benefit among HF patients with Strauss-defined LBBB. The combination of Strauss criteria with delayed notch/slur in lead V5/V6 maximizes the predictive value of the ECG.