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Do VisiTag ablation parameter indices predict gaps in mature atrial scar assessed by CMR imaging in patients undergoing first-time RF ablation?
Do VisiTag ablation parameter indices predict gaps in mature atrial scar assessed by CMR imaging in patients undergoing first-time RF ablation?
Gaps in VisiTag markers predict gaps in CMR-assessed mature atrial scar with high specificity at default settings, though sensitivity can be improved with more stringent thresholds at the cost of positive predictive value.
VisiTag thresholds may aid gap prediction in AF ablation; leaves open prospective validation before clinical adoption.
Introduction The VisiTag module (CARTO3) provides an objective assessment of radiofrequency (RF) ablation parameters. This study aimed to determine the predictive value and optimal VisiTag threshold settings for prediction of gaps in mature atrial scar, as assessed non‐invasively using cardiac magnetic resonance (CMR) imaging. Methods Twenty‐four subjects (11 paroxysmal atrial fibrillation) underwent first‐time RF ablation with operators blinded to VisiTag data. Three‐dimensional late gadolinium enhancement (LGE) CMR scans were performed at 3 months (1.3 × 1.3 × 4 mm 3 ). A survey of UK operators defined the standard VisiTag settings (“Force,” 8 g; “Time,” 10 seconds; “Percentage Time,” 50%; “Range,” 3 mm; “Impedance” and “Temperature” “off”). Each ablation procedure was exported 27 times, varying single VisiTag parameters from default values. The presence of gaps in VisiTag markers (18 sectors) was assessed for each export and compared with gaps in CMR enhancement. Results At default settings, VisiTag gaps were specific (97.5%) but less sensitive (50.4%) for CMR gaps. Sensitivity improved at higher thresholds (89.2% at 20 g, 85.6% at 30 seconds, 88.5% impedance 10 Ω, 92.8% temperature 42°C), but with a lower positive predictive value (PPV) (42.3%, 42.7%, 41.1%, and 37.7%, respectively, vs 90.9% at baseline). “Force” thresholds demonstrated stable PPV from 2 to 8 g ( P = 0.24), but a rapid fall at forces more than 10 g. The binomial logistic regression model explained 41.7% of gaps; χ 2 (4), 148; P < 0.0001, correctly classifying 82% of cases (specificity 94.9%, sensitivity 56.8%). Conclusion Gaps in VisiTags predict gaps in CMR LGE enhancement with high specificity at default settings. Sensitivity may be improved using more stringent thresholds but at the potential cost of unnecessary ablation, particularly when a force more than 10 g is stipulated.
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Chubb et al. (2018) studied this question.
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