Differences in Atrial Substrate Localization using LGE-MRI, Electrogram Voltage and Conduction Velocity - A Cohort Study Using a Consistent Anatomical Reference Frame in Patients with Persistent Atrial Fibrillation.

BACKGROUND AND AIMS: Electro-anatomical voltage, conduction velocity (CV) mapping and late gadolinium enhancement magnetic resonance imaging (LGEMRI) have been correlated with atrial cardiomyopathy (ACM). However, the comparability between these modalities remains unclear. Aims: (1) Compare pathological substrate extent and location between current modalities. (2) Establish spatial histograms in a cohort. (3) Develop a new estimated optimised image intensity threshold (EOIIT) for LGE-MRI identifying patients with ACM. (4) Predict rhythm outcome after pulmonary vein isolation (PVI) for persistent atrial fibrillation.

METHODS: 36 ablation-naive persistent AF patients underwent LGE-MRI and high-definition electro-anatomical mapping in SR. LGE areas were classified using the UTAH, image intensity ratio (IIR > 1.20) and new EOIIT method for comparison to LVS and slow conduction areas <0.2 m/s. ROC analysis was used to determine LGE thresholds optimally matching LVS. ACM was defined as low voltage substrate (LVS) extent ≥ 5% of the left atrium (LA) surface at <0.5 mV.

RESULTS: The degree and distribution of detected pathological substrate varied significantly (p < 0.001) across the mapping modalities: 3% (IQR 0-12%) of the LA displayed LVS < 0.5 mV vs. 14% (3-25%) slow conduction areas < 0.2 m/s vs. 16% (6-32%) LGE with the UTAH method vs. 17% (11-24%) using IIR > 1.20, with most discrepancies on the posterior LA. Optimised 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, 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 prediction of rhythm outcome after PVI in patients with persistent AF.