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.
Study Type
OBSERVATIONAL
Enrollment
262
Scientific Institute San Raffaele
Milan, Italy/Milan, Italy
RECRUITINGTo provide baseline characterization of VA in the study cohort.
Prevalence of baseline VA
Time frame: Baseline
To provide baseline characterization of VA in the study cohort.
Type of VA
Time frame: baseline
To provide baseline characterization of VA in the study cohort.
Morphology of VA
Time frame: baseline
To provide follow-up characterization of VA in study cohort.
Occurrence of VA by 12 months
Time frame: 12 months
To provide follow-up characterization of VA in study cohort.
Burden of VA (number of VA events per month)
Time frame: 12 months
To screen for CGVs in study groups
Prevalence of CGVs in myocarditis with VA (VA+) vs. without VA (VA-)
Time frame: 6 months
To screen for CGVs in study groups vs healthy controls
Prevalence of CGVs in myocarditis with VA (VA+) and without VA (VA-) vs. healthy controls
Time frame: 6 months
To investigate the pathophysiological basis of arrhythmic myocarditis in study group (exploratory endpoint)
Comparison of gene expression profile on EMB in inflammatory group vs no inflammatory group
Time frame: during the procedure
To provide baseline characterization of VA according to sex
Prevalence of baseline VA in male vs female.
Time frame: Baseline
To provide baseline characterization of VA according to sex
Type of VA in male vs female.
Time frame: baseline
To provide baseline characterization of VA according to sex
Morphology of VA in male vs female
Time frame: baseline
To provide follow-up characterization of VA according to sex
Occurrence of VA by 12 months in male vs female.
Time frame: 12 months
To provide follow-up characterization of VA according to sex
Burden of VA (number of VA events per month) in male vs female.
Time frame: 12 months
To screen for CGVs in study groups
Prevalence of CGVs in myocarditis with VA (VA+) vs without VA (VA-)
Time frame: 6 months
To screen for CGVs in study groups vs healthy controls
Prevalence of CGVs in myocarditis with VA (VA+) and without VA (VA-) vs healthy controls
Time frame: 6 months
To investigate the pathophysiological basis of arrhythmic myocarditis according to sex (exploratory endpoint)
Comparison of gene expression profile on EMB in inflammatory group vs no inflammatory group, in male vs female
Time frame: during the procedure
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