Key result
Automated Local Activation Time Velocity Vector analysis achieved an AUC of 0.81 and reduced classification errors for SMVT inducibility to 15.9%, compared to 31.8% with manual measurements.
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Why the study?
The optimal conduction velocity threshold and best measurement approach for slowly conducting anatomical isthmuses in TOF patients remain to be determined.
Does automated Local Activation Time Velocity Vector (LVV) analysis improve the identification of slowly conducting anatomical isthmuses associated with SMVT inducibility compared to manual measurements in patients with tetralogy of Fallot?
Observational (n=44)
No
Does automated Local Activation Time Velocity Vector (LVV) analysis improve the identification of slowly conducting anatomical isthmuses associated with SMVT inducibility compared to manual measurements in patients with tetralogy of Fallot?
Absolute Event Rate: 15.9% vs 31.8%
Automated velocity vector analysis offers superior discriminative performance and reduces classification errors compared to manual measurements for identifying proarrhythmic anatomical isthmuses in tetralogy of Fallot.
BACKGROUND: Slowly conducting anatomical isthmuses (SCAI) represent the main substrate for ventricular arrhythmias (VAs) in tetralogy of Fallot (TOF). However, the optimal conduction velocity (CV) threshold and the best approach for measurement remain to be determined. OBJECTIVE: This study aimed at evaluating a new method for CV analysis. METHODS: This single-center retrospective study included consecutive patients with TOF undergoing right ventricular electroanatomic activation mapping. CV across anatomical isthmus 3 was assessed using both automated Local Activation Time Velocity Vector (LVV) analysis and conventional manual measurements. The primary outcome was the inducibility of sustained monomorphic ventricular tachycardia (VT; SMVT). RESULTS: A total of 44 patients were included (mean age 44.6±13.8 years, 56.8% males). Overall, a SMVT was inducible in 23 (52.3%) patients, including 12/14 (85.7%) patients referred for catheter ablation of VT (history of clinical VT) and 11/30 (36.7%) patients referred for EPS without spontaenous VT. LVV-CV showed a strong correlation with manual measurements (r = 0.81). The optimal cutoff value of LVV-CV based on the Youden index was 0.5 m/s. AUC, sensitivity, and specificity were 0.81, 0.96, and 0.71 using LVV-CV vs 0.75, 0.57, and 0.81 using manual CV measurements, respectively. LVV analysis reduced the number of classification errors from 14 (31.8%) cases with manual CV measurement to 7 (15.9%) cases when LVV-CV was used. CONCLUSION: This novel, reproducible, and easy-to-use LVV-based approach to CV measurement for substrate evaluation in patients with TOF suggests that LVV-CV provides superior discriminative performance compared with manual CV measurements for identifying SCAIs associated with SMVT inducibility. Further studies are warranted to evaluate this approach in larger populations.
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Souëf et al. (2026) conducted an observational in Tetralogy of Fallot (n=44). Automated Local Activation Time Velocity Vector (LVV) analysis vs. Conventional manual measurements was evaluated on Inducibility of sustained monomorphic ventricular tachycardia (SMVT). Automated Local Activation Time Velocity Vector analysis achieved an AUC of 0.81 and reduced classification errors for SMVT inducibility to 15.9%, compared to 31.8% with manual measurements.
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