Clinical Characteristics and Outcomes of Patients With Biventricular and Left-Dominant Arrhythmogenic Cardiomyopathy With Ring-like Late Gadolinium Enhancement Pattern

Background: The ring-like pattern of late gadolinium enhancement (RL-LGE) on cardiac magnetic resonance (CMR) has been proposed as a distinctive imaging marker of arrhythmogenic cardiomyopathy (ACM) with left ventricular (LV) involvement. However, the clinical characteristics and prognostic significance remain to be further clarified. Objectives: This study sought to assess the clinical profile, genetic background, and prognostic significance of RL-LGE in ACM with LV involvement. Methods: In this observational cohort study, we included consecutive patients with a diagnosis of biventricular or left-dominant ACM (BIV-ACM or LD-ACM). RL-LGE was defined as subepicardial or midmyocardial LGE involving ≥3 contiguous LV segments on the same short-axis slice. The primary endpoint was a composite of sudden cardiac arrest, sustained ventricular tachycardia, or implantable cardioverter-defibrillator (ICD) interventions. Results: Among 149 patients (mean age 36 ± 12 years, 66% male), RL-LGE was identified in 73 (49%), most frequently in association with DSP variants, with a higher prevalence in BIV-ACM (70%) than in LD-ACM (30%). Over a median follow-up of 31 months, 24 patients experienced the primary endpoint, 67% of whom had RL-LGE. In multivariable Cox regression, RL-LGE emerged as an independent predictor of the primary endpoint (HR: 2.47; 95% CI: 1.10-6.02; P = 0.042), along with nonsustained ventricular tachycardia (HR: 2.62; 95% CI: 1.17-6.41; P = 0.033), whereas genetic status did not provide additional prognostic information. Incorporating RL-LGE into the arrhythmogenic right ventricular cardiomyopathy risk model significantly improved its predictive performance (likelihood ratio test: P = 0.006). Conclusions: RL-LGE independently predicts arrhythmic events in ACM, regardless of genotype status, and refines the prognostic performance of traditional risk models. Its detection may aid in early recognition of high-risk patients and inform primary-prevention ICD therapy.