Multicenter Retrospective Observational Study to Investigate the Characteristics and Prognosis of Patients With Acute Myocarditis in Korea
Conditions: Myocarditis Acute
Sponsor: Samsung Medical Center

 
Study Title: 
Multicenter Retrospective Observational Study of 
Acute Myocarditis in Korea 
 
Protocol ID: 2022-01-003-003 
NCT number: didn’t approved yet 
Document Date : July 3, 2023 
 
 

Study protocol 
Background  
Acute myocarditis is divided into fulminant or non-fulminant myocarditis depending on the 
presence or absence of hemodynamic stability. It is known that the prognosis of fulminant 
myocarditis is poorer than that of non-fulminant. In addition, additional research is needed on the 
risk factors contributing to the deterioration of the clinical course. In particular, studies on acute 
myocarditis published in renowned international journals have only been conducted with a small 
number of patients, ranging from dozens to hundreds of patients, so it is more necessary to 
conduct studies that include a large number of patients with public confidence on the overall 
clinical course and prognosis of myocarditis. 
Objective  
In this study, we investigated the clinical characteristics of patients with acute myocarditis in Korea 
and sought to find out the treatment results and prognosis of fulminant myocarditis requiring 
vasopressors or mechanical assist devices. 
Design  
Patients diagnosed with acute myocarditis from January 2004 to December 2021 at 6 domestic 
institutions were investigated for baseline characteristics and clinical course. 
Method  
1) Analysis method  
- Review medical records and reanalyze previously performed blood tests, electrocardiograms,  
echo-cardiographic tests, imaging tests, and biopsies. 
- Clinical results (in hospital mortality, death from any cause, heart transplantation, permanent left 
ventricular assist device insertion, hospitalization) and changes in echocardiographic findings of 
patients treated for myocarditis should be confirmed prior to the clinical trial approval date. 
2) Observational data and Clinical test   
(1) Observational data  

① General characteristics: patient identification code, time of visit (year/month), age, sex, height, 
weight, vital signs upon admission, symptoms 
② Underlying diseases: diabetes, hypertension, chronic kidney disease, coronary artery disease, 
peripheral vascular disease 
③ Causes of myocarditis: Myocarditis caused by viral, giant cell, immune checkpoint inhibitors 
④ Conservative treatment of myocarditis and treatment with immunosuppressants and 
vasopressors 
⑤ ECMO treatment: date of insertion of ECMO, date of ECMO removal, whether or not venting is 
performed during ECMO maintenance, date of venting 
⑥ Clinical results: in-hospital mortality, all-cause mortality, heart transplant, left ventricular assist 
device insertion, re-hospitalization 
(2) Clinical test   
① Laboratory test: cardiac enzyme, NT-proBNP, hemoglobin, platelet count, Aspartate 
Aminotransferase(AST), alanine aminotransferase (ALT), Blood Urea Nitrogen (BUN), Creatinine 
② Cardiac echocardiography: LVEF, TAPSE, TV s', LV dimension, IVS thickness, LV posterior wall 
thickness, E flow, e velocity, RVSP, LAVI 
③ Heart magnetic resonance imaging (MRI): presence of late gadolinium enhancement, edema 
 
Statistical Analysis Plan (SAP) 
All data for statistical test are analyzed using SPSS and R. All measurements for continuous 
variables are expressed as mean±standard deviation, and measurements for discrete variables are 
expressed as percentages. Student's t-test is used for comparative analysis of continuous variables, 
and x2-test is used for comparison between two groups for discrete variables. After treatment for 
myocarditis, recovery of myocardial function and correlation with clinical results were analyzed using 
paired t test and Kaplan-Meier survival analysis. Predictors of death and factors requiring ECMO 
treatment were identified through binary logistic regression analysis. Binary logistic regression 

analysis requires the occurrence of 10-15 events per independent variable, but in this study, about 
30%, or about 270 people, are expected to die, so enough risk factors can be studied. The goodness 
of fit of the risk prediction model will be tested by C-statistic and Hosmer-Lemeshow tests. In 
addition, death or re-hospitalization during follow-up will be estimated by the Cox survival analysis 
method. After checking the fitness of the model by checking Schoenfeld's residual, the hazard of 
each risk factor is identified. A case of P<0.05 is considered statistically significant.