Abstract 4369177: From Voltage to Velocity: Functional Insights via Integrated Unipolar–Bipolar Mapping

Background: Accurate delineation of the atrial substrate is critical to tailoring therapy for atrial fibrillation (AF). Although bipolar voltage (BV) mapping is widely used, its functional meaning and correlation with local conduction velocity (CV) remain unclear. Unipolar voltage (UV) mapping, which captures deeper tissue, may complement bipolar data by better reflecting transmural structural changes. Objective: To determine whether combining UV and BV thresholds can classify atrial tissue into conduction-based phenotypes. Methods: High-density sinus-rhythm electro-anatomic maps from 63 patients with AF were retrospectively analysed. Local activation times in sinus rhythm were used to compute CV via a triangulation algorithm. Point-by-point CV values were plotted against corresponding unipolar and bipolar voltages. Using iterative threshold optimisation, regions exhibiting similar CV were clustered to define voltage-based tissue classes. Results: Plotting CV against UV–BV pairs revealed that CV rose steadily as both voltages increased (Figure 1). Six UV–BV ranges best separated conduction characteristics as shown in Figure 2: Cat 1: BV < 0.10 mV&UV < 0.50 mV—designated deep scar ; hence, no CV was assigned to this category. Cat 2: 0.10 ≤ BV < 0.50 mV&0.50 ≤ UV < 1.00 mV—median CV 0.53 m/s (IQR 0.27–1.02). Cat 3: 0.10 ≤ BV < 0.50 mV&1.00 ≤ UV < 2.50 mV—0.57 m/s (0.28–1.03). Cat 4: 0.50 ≤ BV < 1.70 mV&0.50 ≤ UV < 1.00 mV—0.62 m/s (0.33–1.13). Cat 5: 0.50 ≤ BV < 1.70 mV&1.00 ≤ UV < 2.50 mV—0.74 m/s (0.73–1.27). Cat 6: BV ≥ 1.70 mV&UV ≥ 2.50 mV—0.88 m/s (0.52-1.45). Pairwise comparisons between adjacent classes (Cat 6 vs 5, 5 vs 4, 4 vs 3, and 3 vs 2) showed progressively faster conduction with higher voltages (t-tests; all p < 0.001, Figure 3). The empirically derived thresholds align closely with histology-guided voltage ranges reported previously. Conclusion: The integration of unipolar and bipolar voltage thresholds effectively enables a conduction-based classification of atrial substrate, offering a promising approach for enhanced substrate characterization in AF.