This study aims to answer multiple unsolved questions in the field of arrhythmic myocarditis.

• Improving the diagnostic work-up. While endomyocardial biopsy (EMB) and cardiac magnetic resonance (CMR) constitute the gold standard diagnostic techniques for myocarditis, the role of genetic testing is still unclear. Identifying the subset of patients with CGVs, will contribute to justifying the application of genetic testing in myocarditis.
• Generating models for risk prediction. Outcomes and arrhythmic risk stratification remain uncertain for myocarditis. Based on an advanced multimodal work-up, multiparametric risk scores may be created and subsequently validated, in order to predict the arrhythmic risk of specific myocarditis, especially in the case of CGVs.
• Identifying disease-specific and genotype-specific signatures. Genotype-phenotype associations are expected to benefit from a multimodal and multiparametric approach, in order to allow etiology-specific features in arrhythmic myocarditis. Most of the current signatures are limited to combined EMB-CMR studies. Signatures would likely benefit from implementing additional parameters, including arrhythmia features and myocardial inflammatory status.
• Tailoring treatment strategies. Transcriptional analysis will identify overexpressed genes associated with myocarditis and arrhythmias, representing a possible therapeutic target. A multimodal and multidisciplinary model will integrate phenotype, genotype, and transcriptional profile for a personalized treatment.

Arrhythmic myocarditis is responsible for a significant proportion of out-of-hospital cardiac arrest and death in the young population. Although considered an uncommon feature, arrhythmias may present in myocarditis in both acute and chronic phase, leading to sudden cardiac death (SCD), especially in young males. Overall, the prevalence of undiagnosed myocarditis in post-mortem series ranges from 9% to 44%, involving 2% of infants, 5% of children, and 4-8% of athletes <40-year-old. Ventricular arrhythmias (VAs) are reported secondary to lymphocytic myocarditis. However, they are more commonly associated with giant cell myocarditis (GCM) and cardiac sarcoidosis (CS), with prevalence of 29% and 55%, respectively In addition, genetically-determined susceptibility might underlie arrhythmic myocarditis.

First, recent reports suggest that pathogenic variants in genes associated with nonischemic cardiomyopathies (NICM), hereby defined as cardiomyopathic gene variants (CGVs) are frequently found in patients with myocarditis proven by CMR and/or EMB, complicated by ventricular arrhythmias (VA). NICM constitute a heterogeneous group of diseases characterized by distinct structural and functional myocardial abnormalities in the absence of obstructive epicardial coronary artery disease. The main NICM overlapping with myocarditis are dilated (DCM) and arrhythmogenic cardiomyopathy (ACM).

Second, myocardial inflammation (M-Infl) has also been recently described in NICM complicated by arrhythmias. Preclinical data support a relevant role of M-Infl in the pathophysiology of AINICM, and its association with adverse outcomes.

Furthermore, there is a growing interest in transcriptomics in the setting of inflammatory and genetic cardiomyopathies. Cardiac transcriptome revealed specific subgroups of patients with early and overt DCM. In animal models of experimental autoimmune myocarditis, transcriptomics, transcriptomics allowed to depict the single-cell landscape of the cardiac immune cells in different phases of the disease. The addition of the cardiac transcriptome to the genotype and phenotype of patients increases the possibility for individualized medicine.

Inclusion Criteria:

Patients with ventricular arrhythmias (VA+) and without (VA-)

• Diagnosis of myocarditis proven by EMB (ESC criteria) and/or CMR (updated Lake Louise criteria)
• Age ≥ 10 years
• Baseline ECG telemonitoring
• Written informed consent Healthy controls
• Provided a negative known history of myocarditis
• The sample must have been biobanked as part of the study IMMUNORADAR

Exclusion Criteria:

Patients with ventricular arrhythmias (VA+) and without (VA-)

• Obstructive coronary artery disease, or lack of coronary angiography/computed tomography (CT) scan in patients >40 years.
• Absent informed consent.

Statistical analyses will be performed by certified programs (i.e. SPSS, R). For risk stratification, conventional statistical methods, including Kaplan-Meier survival analysis, univariable and multivariable logistic regression, Cox regression model, and mixed models will be used. In addition, machine learning and artificial intelligence will be applied for the setting-up of cost-effective prediction models. The best indexes for descriptive statistics, as well as optimal tests for comparing quantitative and qualitative variables, will be chosen depending on the distribution of data, as appropriate. Confidence intervals will be set at 95%. Statistical significance threshold will be set at p <0.05 (two-sided).