Functional Assessment of Ventricular Tachycardia Circuits and Their Underlying Substrate Using Automated Conduction Velocity Mapping.

OBJECTIVES: This study sought to describe the utility of automated conduction velocity mapping (ACVM) in ventricular tachycardia (VT) ablation. BACKGROUND: Identification of areas of slowed conduction velocity (CV) is critical to our understanding of VT circuits and their underlying substrate. Recently, an ACVM called Coherent Mapping (Biosense Webster Inc) has been developed for atrial mapping. However, its utility in VT mapping has not been described. METHODS: Patients with paired high-density VT activation and substrate maps were included. ACVM was applied to paired VT activation and substrate maps to assess regional CV and activation patterns. A combination of ACVM, traditional local activation time maps, electrogram analysis, and off-line calculated CV using triangulation were used to characterize zones of slowed conduction during VT and in substrate mapping. RESULTS: Fifteen patients were included in the study. In all cases, ACVM identified slow CV within the putative VT isthmus, which colocalized to the VT isthmus identified with entrainment. The dimensions of the VT isthmus with local activation time mapping were 37.8 ± 13.7 mm long and 8.7 ± 4.2 mm wide. In comparison, ACVM produced an isthmus that was shorter (length: 25.1 ± 10.6 mm; mean difference: 12.8; 95% CI: 7.5-18.0; P < 0.01) and wider (width: 18.8 ± 8.1 mm; mean difference: 10.1; 95% CI: 6.1-14.2; P < 0.01). In VT, the CV using triangulation at the entrance (8.0 ± 3.6 cm/s) and midisthmus (8.1 ± 4.3 cm/s) was not significantly different (P = 0.92) but was significantly faster at the exit (16.2 ± 9.7 cm/s; P < 0.01). In the paired substrate analysis, traditional local activation time isochronal mapping identified 6.3 ± 2.0 deceleration zones. In contrast, ACVM identified a median of 0 deceleration zones (IQR: 0-1; P < 0.01). CONCLUSIONS: ACVM is a novel complementary tool that can be used to accurately resolve complex VT circuits and identify slow conduction zones in VT but has limited accuracy in identifying slowed conduction during substrate-based mapping.

Accuracy of wrist-worn heart rate monitors for rate control assessment in atrial fibrillation.

BACKGROUND: Wrist-worn heart rate (HR) monitors are increasingly popular. A paucity of data exists on their accuracy in atrial fibrillation (AF) in ambulatory patients. We sought to assess the HR accuracy of two commercially available smart watches [SW] (Fitbit Charge HR [FB] and Apple Watch Series 3 [AW]) compared with Holter monitoring in an ambulant patient cohort. METHODS: Thirty-two participants ≥18 years referred for 24-hour Holter monitoring were prospectively recruited. Each participant was randomly allocated to wear either a FB or AW along with their Holter monitor. RESULTS: Across all devices, 53,288 heart rate values were analysed from 32 participants. Twenty wore the AW (17 had persistent AF and 3 had sinus rhythm [SR]) while 12 participants wore the FB (9 in persistent AF and 3 in SR). Participants in SR demonstrated strong agreement compared to Holter monitoring (bias <1 beat, limits of agreement [LoA] -11 to 11 beats). In AF, both devices underestimated HR measurements (bias -9 beats, LoA -41 to 23). The degree of underestimation was more pronounced when HR > 100 bpm (bias of -28 beats for HR range 100-120 bpm, -48 for 120-140 bpm, and -69 for >140 bpm) compared to a slower HR (bias of -6 for HR range 80-100 bpm, <1 for 60-80 bpm, and -1 for <60 bpm). CONCLUSION: In ambulatory patients, smartwatches underestimated HR in AF particularly at HR ranges >100 bpm. Further improvements in device technology are needed before integrating them into the clinical management of rate control in AF.