Key result
Novel EOIIT method improves LGE-MRI diagnosis of atrial cardiomyopathy, achieving a 0.94 AUC.
Why the study?
Discordances remain in the location and extent of atrial cardiomyopathy detected across electro-anatomical voltage, conduction velocity mapping, and LGE-MRI.
Does a new estimated optimised image-intensity-thresholds (EOIIT) method for LGE-MRI improve the diagnosis of atrial cardiomyopathy compared to standard methods in ablation-naive persistent AF patients?
Observational (n=36)
Single-blind
No
Does a new estimated optimised image-intensity-thresholds (EOIIT) method for LGE-MRI improve the diagnosis of atrial cardiomyopathy compared to standard methods in ablation-naive persistent AF patients?
Absolute Event Rate: 0.94% vs 0.76%
A novel estimated optimized image-intensity-threshold method for LGE-MRI significantly improves the diagnostic accuracy for atrial cardiomyopathy in patients with persistent AF compared to traditional methods.
May support refined LGE-MRI diagnosis of atrial cardiomyopathy; leaves open prospective validation before clinical use.
Background Electro-anatomical voltage, conduction velocity (CV) mapping and late-gadolinium-enhancement-magnetic-resonance-imaging (LGE-MRI) are different diagnostic modalities for atrial cardiomyopathy (ACM). However, discordances remain in the location and extent of detected ACM. Objectives (1) Comparison of ACM extent and location between current modalities. (2) Development of new estimated optimised image-intensity-thresholds (EOIIT) for LGE-MRI identifying patients with ACM. Methods Thirty-six ablation-naive persistent AF patients underwent LGE-MRI and high-definition electro-anatomical mapping in sinus rhythm. Significant ACM was defined as low-voltage-substrate (LVS) extent≥5% of the left atrium (LA) surface at <0.5 mV. LGE areas were classified using the Utah, image-intensity-ratio (IIR>1.20) and new EOIIT method for comparison to LVS and slow-conductionareas <0.2 m/s. ROC-analysis determined the LGE-extent enabling accurate diagnosis of ACM. 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 Utah method vs. 17%(11-24%) using IIR>1.20, with enhanced discrepancies on posterior LA. A linear correlation was found between the OIIT and each patient’s mean blood pool intensity (R 2 =0.89, p<0.001). LGE-MRI-based ACM-diagnosis improved with the novel EOIIT (83% sensitivity, 88% specificity, AUC:0.94) in comparison to the Utah method (60% sensitivity, 75% specificity, AUC:0.76), and IIR>1.20 (58% sensitivity, 75% specificity, AUC:0.71) Conclusion Important discordances in distribution of pathological substrate exist between LA-LVS, CV and LGE-MRI, irrespective of the LGE-detection protocol that is used. However, the new EOIIT method improves LGE-MRI-based ACM-diagnosis in ablation-naive AF-patients.
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Nairn et al. (2022) conducted an observational in Persistent Atrial Fibrillation (n=36). Estimated optimised image-intensity-thresholds (EOIIT) for LGE-MRI vs. Utah method and IIR>1.20 was evaluated on Diagnosis of atrial cardiomyopathy (LVS extent ≥5% of LA surface at <0.5 mV). The novel estimated optimised image-intensity-thresholds (EOIIT) method improved LGE-MRI-based diagnosis of atrial cardiomyopathy, achieving 83% sensitivity, 88% specificity, and an AUC of 0.94.
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