About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
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
Disqualifiers
Obstructive coronary artery disease, or lack of coronary angiography/computed tomography (CT) scan in patients >40 years.
Absent informed consent.
Trial population
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).
Trial design
Case-control
Other
Treatments tested in this trial
Not listed
Trial groups
Trial outcomes
Primary outcomes
To provide baseline characterization of VA in the study cohort.
Prevalence of baseline VA
To provide baseline characterization of VA in the study cohort.
Type of VA
To provide baseline characterization of VA in the study cohort.
Morphology of VA
To provide follow-up characterization of VA in study cohort.
Occurrence of VA by 12 months
Secondary outcomes
To provide baseline characterization of VA according to sex
Prevalence of baseline VA in male vs female.
To provide baseline characterization of VA according to sex
Type of VA in male vs female.
To provide baseline characterization of VA according to sex
Morphology of VA in male vs female
To provide follow-up characterization of VA according to sex
Occurrence of VA by 12 months in male vs female.
Sponsors and contacts
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