Viral myocarditis in pediatrics: A review of current diagnostic methods and future directions

Abstract

Viral myocarditis is the inflammation of heart myocytes resulting from viral infection. Incidence in the pediatric population could reach 2 per 100,000 per year, and COVID-19 infection is a significant risk factor, which increases the possibility of having an infection by 40 times. Early detection results in catching the disease early and consequently improves outcomes. Clinical presentation of viral myocarditis in children could vary from mild prodromal symptoms to severe heart failure. Clinical examination, electrocardiogram, and chest X-ray may give clues for physiological and structural signs usually associated with the disease. However, they are inconclusive as they lack both accuracy and specificity. Biomarkers used to track the disease usually lack sensitivity and specificity. Cardiac magnetic resonance (CMR) is the imaging of choice to diagnose viral myocarditis by showing edema and late gadolinium enhancement. Point-of-care ultrasound has been approved as a good imaging method for early detection. It can be used as an effective screening tool for high-risk patients. Positron emission tomography scan is very sensitive in detecting disease early in its acute phase, especially if combined with CMR. All imaging studies are prone to interpretation bias, leading to a misdiagnosis. Endomyocardial biopsy is the gold standard method for diagnosis. However, it is time-consuming and ineffective as an early detection tool. Artificial intelligence (AI) helps with interpretation, decreasing bias, improving accuracy, and saving time and manpower. With more research and evidence, adopting AI-based methods to diagnose myocarditis in pediatrics could offer early detection, reduce costs, and save time for early intervention. Genetics helps identify inflammatory pathways involved in vulnerable patients, and genetic therapy may suppress disease progression by mitigating these pathways. Research focused on children is highly encouraged, and collaboration between healthcare institutions to develop telemedicine-based programs is influential.

Keywords: Acquired heart disease in pediatrics; artificial intelligence; cardiac magnetic resonance; early diagnosis.