Differences in atrial substrate localization using late gadolinium enhancement-magnetic resonance imaging, electrogram voltage, and conduction velocity: a cohort study using a consistent anatomical reference frame in patients with persistent atrial fibrillation.

AIMS: Electro-anatomical voltage, conduction velocity (CV) mapping, and late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) have been correlated with atrial cardiomyopathy (ACM). However, the comparability between these modalities remains unclear. This study aims to (i) compare pathological substrate extent and location between current modalities, (ii) establish spatial histograms in a cohort, (iii) develop a new estimated optimized image intensity threshold (EOIIT) for LGE-MRI identifying patients with ACM, (iv) predict rhythm outcome after pulmonary vein isolation (PVI) for persistent atrial fibrillation (AF). METHODS AND RESULTS: Thirty-six ablation-naive persistent AF patients underwent LGE-MRI and high-definition electro-anatomical mapping in sinus rhythm. Late gadolinium enhancement areas were classified using the UTAH, image intensity ratio (IIR >1.20), and new EOIIT method for comparison to low-voltage substrate (LVS) and slow conduction areas <0.2 m/s. Receiver operating characteristic analysis was used to determine LGE thresholds optimally matching LVS. Atrial cardiomyopathy was defined as LVS extent ≥5% of the left atrium (LA) surface at <0.5 mV. The degree and distribution of detected pathological substrate (percentage of individual LA surface are) varied significantly (P < 0.001) across the mapping modalities: 10% (interquartile range 0-14%) of the LA displayed LVS <0.5 mV vs. 7% (0-12%) slow conduction areas <0.2 m/s vs. 15% (8-23%) LGE with the UTAH method vs. 13% (2-23%) using IIR >1.20, with most discrepancies on the posterior LA. Optimized image intensity thresholds and each patient's mean blood pool intensity correlated linearly (R2 = 0.89, P < 0.001). Concordance between LGE-MRI-based and LVS-based ACM diagnosis improved with the novel EOIIT applied at the anterior LA [83% sensitivity, 79% specificity, area under the curve (AUC): 0.89] in comparison to the UTAH method (67% sensitivity, 75% specificity, AUC: 0.81) and IIR >1.20 (75% sensitivity, 62% specificity, AUC: 0.67). CONCLUSION: Discordances in detected pathological substrate exist between LVS, CV, and LGE-MRI in the LA, irrespective of the LGE detection method. The new EOIIT method improves concordance of LGE-MRI-based ACM diagnosis with LVS in ablation-naive AF patients but discrepancy remains particularly on the posterior wall. All methods may enable the prediction of rhythm outcomes after PVI in patients with persistent AF.

Reproducibility analysis of the computerized tomography angiography-derived left atrial wall thickness maps.

BACKGROUND: Adapting the ablation index (AI) to the left atrial wall thickness (LAWT) derived from computed tomography angiography (CTA) allows for a personalized approach that showed to improve PVI safety and outcomes. METHODS: Three observers with different degrees of experience performed complete LAWT analysis of CTA for 30 patients and repeated the analysis for 10 of these patients. Intra- and inter-observer reproducibility of these segmentations was assessed. RESULTS: Geometric congruence of repeated reconstruction of LA endocardial surface showed that 99.4% of points in the 3D reconstructed mesh were within < 1 mm distance for the intra-observer variability and 95.1% for the inter-observer. For the LA epicardial surface, an 82.4% of points were within < 1 mm for intra-observer and a 77.7% for inter-observer. A 1.99% of points were further than 2 mm for the intra-observer and a 4.1% for the inter-observer. Colour agreement between LAWT maps showed that a 95.5% and a 92.9% intra- and inter-observer respectively presented the same colour or a change to the colour immediately above or below. The ablation index (AI), which was adapted to this LAWT colour maps to perform a personalized pulmonary vein isolation (PVI), showed an average difference in the derived AI lower than 25 units in all cases. For all analyses, the concordance increased with user-experience. CONCLUSION: Geometric congruence of LA shape was high, for both endocardial and epicardial segmentations. LAWT measurements were reproducible, increasing with user experience. This translated into a negligible impact in the target AI.