Key result
Novel LPM algorithm improves overall annotation accuracy to ~95% over the previous wavefront algorithm.
Why the study?
Automated systems for substrate mapping in VT ablation may annotate far-field signals, making maps hard to interpret, and quantitative assessment of local conduction velocity remains an unmet clinical need.
Does a novel late potential map (LPM) algorithm and local conduction velocity (LCV) analysis improve the detection of abnormal potentials and scar channels in patients undergoing scar-related VT ablation?
Observational (n=16)
Does a novel late potential map (LPM) algorithm and local conduction velocity (LCV) analysis improve the detection of abnormal potentials and scar channels in patients undergoing scar-related VT ablation?
Absolute Event Rate: 94.5% vs 83.7%
The novel LPM algorithm and LCV analysis significantly improve the automated detection of abnormal ventricular potentials and VT isthmuses compared to previous methods, potentially enhancing substrate characterization during ablation.
May enhance automated LAVA detection in scar VT ablation; leaves open whether it improves outcomes pending prospective validation.
BACKGROUND: Automated systems for substrate mapping in the context of ventricular tachycardia (VT) ablation may annotate far-field rather than near-field signals, rendering the resulting maps hard to interpret. Additionally, quantitative assessment of local conduction velocity (LCV) remains an unmet need in clinical practice. We evaluate whether a new late potential map (LPM) algorithm can provide an automatic and reliable annotation and localized bipolar voltage measurement of ventricular electrograms (EGMs) and if LCV analysis allows recognizing intrascar conduction corridors acting as VT isthmuses. METHODS: In 16 patients referred for scar-related VT ablation, 8 VT activation maps and 29 high-resolution substrate maps from different activation wavefronts were obtained. In offline analysis, the LPM algorithm was compared to manually annotated substrate maps. Locations of the VT isthmuses were compared with the corresponding substrate maps in regard to LCV. RESULTS: The LPM algorithm had an overall/local abnormal ventricular activity (LAVA) annotation accuracy of 94.5%/81.1%, which compares to 83.7%/23.9% for the previous wavefront algorithm. The resultant maps presented a spatial concordance of 88.1% in delineating regions displaying LAVA. LAVA median localized bipolar voltage was 0.22 mV, but voltage amplitude assessment had modest accuracy in distinguishing LAVA from other abnormal EGMs (area under the curve: 0.676; p < .001). LCV analysis in high-density substrate maps identified a median of two intrascar conduction corridors per patient (interquartile range: 2-3), including the one acting as VT isthmus in all cases. CONCLUSION: The new LPM algorithm and LCV analysis may enhance substrate characterization in scar-related VT.
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Cortez‐Dias et al. (2022) conducted an observational in Scar-related ventricular tachycardia (n=16). Late potential map (LPM) algorithm vs. Previous wavefront algorithm and manual annotation was evaluated on Overall annotation accuracy. The novel late potential map (LPM) algorithm achieved an overall annotation accuracy of 94.5% (LAVA accuracy 81.1%), compared to 83.7% (23.9%) for the previous wavefront algorithm.
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