Impact of ventricular arrhythmias on outcomes in children with myocarditis

Othman, Hasan F.; Byrnes, Jonathan W.; Elsamny, Esraa; Hamzah, Mohammed

doi:10.1007/s00431-020-03687-4

Cited by 8 publications

(10 citation statements)

References 20 publications

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“…A multivariable model predicting survival was developed using Bayesian model averaging after multiple imputation [ 14 ]. Bayesian model averaging was performed on each of the imputed datasets, and the models were combined to form a final prediction model in which coefficients were averaged across the 20 models (Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

Clinical characteristics and mortality risk prediction model in children with acute myocarditis

et al. 2022

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Background Acute myocarditis (AMC) can cause poor outcomes or even death in children. We aimed to identify AMC risk factors and create a mortality prediction model for AMC in children at hospital admission. Methods This was a single-center retrospective cohort study of AMC children hospitalized between January 2016 and January 2020. The demographics, clinical examinations, types of AMC, and laboratory results were collected at hospital admission. In-hospital survival or death was documented. Clinical characteristics associated with death were evaluated. Results Among 67 children, 51 survived, and 16 died. The most common symptom was digestive disorder (67.2%). Based on the Bayesian model averaging and Hosmer–Lemeshow test, we created a final best mortality prediction model (acute myocarditis death risk score, AMCDRS) that included ten variables (male sex, fever, congestive heart failure, left-ventricular ejection fraction < 50%, pulmonary edema, ventricular tachycardia, lactic acid value > 4, fulminant myocarditis, abnormal creatine kinase-MB, and hypotension). Despite differences in the characteristics of the validation cohort, the model discrimination was only marginally lower, with an AUC of 0.781 (95% confidence interval = 0.675–0.852) compared with the derivation cohort. Model calibration likewise indicated acceptable fit (Hosmer‒Lemeshow goodness-of-fit, P¼ = 0.10). Conclusions Multiple factors were associated with increased mortality in children with AMC. The prediction model AMCDRS might be used at hospital admission to accurately identify AMC in children who are at an increased risk of death.

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Section: Methodsmentioning

confidence: 99%

Clinical characteristics and mortality risk prediction model in children with acute myocarditis

et al. 2022

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“…The most serious types of arrhythmias have been ventricular tachycardia and ventricular fibrillation. Ventricular arrhythmia has been associated with poor patient outcomes, including the use of mechanical circulatory support and death ( 19 , 20 ). This arrhythmia potentially manifests as cardiopulmonary arrest and sudden cardiac death.…”

Section: Discussionmentioning

confidence: 99%

Case report: Myocarditis with nonsustained ventricular tachycardia following COVID-19 mRNA vaccination in a female adolescent

Han

Lee²,

Choi³

et al. 2022

Front. Pediatr.

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Children with underlying medical conditions potentially develop severe illness from Coronavirus disease 2019 (COVID-19). The use of vaccines against COVID-19 is currently recommended for the pediatric population. The COVID-19 vaccine has a temporal association with the occurrence of myocarditis. Although most patients with COVID-19 vaccination-associated myocarditis (C-VAM) exhibit a mild clinical course and rapid recovery, C-VAM potentially causes electrical instability and sudden cardiac death. Herein, we report the case of a 17-year-old woman who presented with chest pain and syncope following the first dose of the messenger RNA COVID-19 vaccine. The patient's heart function was impaired, and nonsustained ventricular tachycardia was frequent. Cardiac magnetic resonance (CMR) imaging satisfied the criteria for myocarditis. Despite the administration of immunomodulatory drugs, the patient's heart function was not fully restored, and the concentration of cardiac enzymes remained above the normal range. Persistence of late gadolinium enhancement was observed on short-term follow-up CMR imaging. Although most patients with C-VAM exhibit mild symptoms, significant cardiac arrhythmias potentially occur. Furthermore, some patients with C-VAM demonstrate prolonged impaired heart function and sustained late gadolinium enhancement on follow-up CMR imaging. Therefore, monitoring of electrical and functional cardiac abnormalities in patients with C-VAM is crucial and the long-term outcomes and prognosis of patients with C-VAM require further investigation.

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“…Moreover, there was lack of external validation. Similarly, Othman et al reported a multivariable linear regression model for mortality association, but there was no data on model performance as well as validation (5).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Mortality Prediction in Pediatric Myocarditis

Chou

Ghimire

2021

Front. Pediatr.

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Background: Pediatric myocarditis is a rare disease. The etiologies are multiple. Mortality associated with the disease is 5–8%. Prognostic factors were identified with the use of national hospitalization databases. Applying these identified risk factors for mortality prediction has not been reported.Methods: We used the Kids' Inpatient Database for this project. We manually curated fourteen variables as predictors of mortality based on the current knowledge of the disease, and compared performance of mortality prediction between linear regression models and a machine learning (ML) model. For ML, the random forest algorithm was chosen because of the categorical nature of the variables. Based on variable importance scores, a reduced model was also developed for comparison.Results: We identified 4,144 patients from the database for randomization into the primary (for model development) and testing (for external validation) datasets. We found that the conventional logistic regression model had low sensitivity (~50%) despite high specificity (>95%) or overall accuracy. On the other hand, the ML model struck a good balance between sensitivity (89.9%) and specificity (85.8%). The reduced ML model with top five variables (mechanical ventilation, cardiac arrest, ECMO, acute kidney injury, ventricular fibrillation) were sufficient to approximate the prediction performance of the full model.Conclusions: The ML algorithm performs superiorly when compared to the linear regression model for mortality prediction in pediatric myocarditis in this retrospective dataset. Prospective studies are warranted to further validate the applicability of our model in clinical settings.

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Impact of ventricular arrhythmias on outcomes in children with myocarditis

Cited by 8 publications

References 20 publications

Clinical characteristics and mortality risk prediction model in children with acute myocarditis

Clinical characteristics and mortality risk prediction model in children with acute myocarditis

Case report: Myocarditis with nonsustained ventricular tachycardia following COVID-19 mRNA vaccination in a female adolescent

Machine Learning for Mortality Prediction in Pediatric Myocarditis

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