Effects of LBBAP Pacing-Site Position under a Modelled Proximal LBBB: A 24-Model Electrophysiological Simulation Study.

Left bundle branch area pacing (LBBAP) may recruit the distal left-sided conduction &#xD;system, but the electrical effect of pacing-site position along the left bundle branch (LBB) &#xD;remains incompletely defined. We evaluated this effect in 24 four-chamber virtual heart &#xD;anatomies using a Reaction-Eikonal finite-element model with rule-based myocardial &#xD;fibres and algorithmically generated His-Purkinje networks. A modelled proximal His&#xD;to-LBB disconnection preserved the distal LBB-Purkinje network while interrupting its &#xD;proximal connection. Six conditions were simulated in each anatomy: a sinoatrial-node &#xD;(SAN)-initiated left bundle branch block (LBBB) control, right ventricular apical pacing, &#xD;right ventricular outflow-tract pacing and a modelled non-selective LBB capture &#xD;configuration at proximal, nominal and distal positions. The primary outcome was &#xD;BiV_TAT95, the 95th percentile of activation times across left-ventricular, right&#xD;ventricular and interventricular-septal nodes. The control-versus-paced analysis used a &#xD;common simulation-clock origin and was interpreted as contextual benchmarking, &#xD;whereas the positional analysis used matched direct ventricular stimulation. In the &#xD;matched positional analysis, median BiV_TAT95 was 104.5, 93.5 and 88.5 ms for &#xD;proximal, nominal and distal LBBAP, respectively (Friedman chi-square = 48.000, P < &#xD;0.001, Kendall's W = 1.000); every anatomy followed distal < nominal < proximal. In &#xD;three selected anatomies, a 20% decrease or increase in the prescribed myocardial &#xD;conduction speeds altered absolute activation times but preserved this ordering in all &#xD;nine anatomy-by-speed combinations. Under the tested homogeneous substrate and &#xD;modelled non-selective capture configuration, the distal tested position produced the &#xD;lowest simulated BiV_TAT95; this does not imply superiority for every activation metric. &#xD;This conditional prediction requires validation against patient-specific electrical data.