This is a cross-sectional study of 29 published cases of acute myopericarditis following COVID-19 mRNA vaccination. The most common presentation was chest pain within 1–5 days after the second dose of mRNA COVID-19 vaccination. All patients had an elevated troponin. Cardiac magnetic resonance imaging revealed late gadolinium enhancement consistent with myocarditis in 69% of cases. All patients recovered clinically rapidly within 1–3 weeks. Most patients were treated with non-steroidal anti-inflammatory drugs for symptomatic relief, and 4 received intravenous immune globulin and corticosteroids. We speculate a possible causal relationship between vaccine administration and myocarditis. The data from our analysis confirms that all myocarditis and pericarditis cases are mild and resolve within a few days to few weeks. The bottom line is that the risk of cardiac complications among children and adults due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection far exceeds the minimal and rare risks of vaccination-related transient myocardial or pericardial inflammation.

Pediatric heart failure (HF) represents an important cause of morbidity and mortality in childhood. There is an overlapping relationship of HF, congenital heart disease, and cardiomyopathy. The goal of treatment of HF in children is to maintain stability, prevent progression, and provide a reasonable milieu to allow somatic growth and optimal development. Current management and therapy for HF in children are extrapolated from treatment approaches in adults. There are significant barriers in applying adult data to children because of developmental factors, age variation from birth to adolescence, and differences in the genetic expression profile and β-adrenergic signaling. At the same time, there are significant challenges in performing well-designed drug trials in children with HF because of heterogeneity of diagnoses identifying a clinically relevant outcome with a high event rate, and a difficulty in achieving sufficient enrollment. A judicious balance between extrapolation from adult HF guidelines and the development of child-specific data on treatment represent a wise approach to optimize pediatric HF management. This approach is helpful as reflected by the increasing role of ventricular assist devices in the management of advanced HF in children. This review discusses the causes, epidemiology, pathophysiology, clinical manifestations, conventional medical treatment, clinical trials, and the role of device therapy in pediatric HF.

Expert microscopic analysis of cells obtained from frequent heart biopsies is vital for early detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare condition is prone to low levels of expert agreement due to the difficulty of identifying subtle rejection signs within biopsy samples. The rarity of pediatric heart transplant rejection also means that very few gold-standard images are available for developing machine learning models. To solve this urgent clinical challenge, we developed a deep learning model to automatically quantify rejection risk within digital images of biopsied tissue using an explainable synthetic data augmentation approach. We developed this explainable AI framework to illustrate how our progressive and inspirational generative adversarial network models distinguish between normal tissue images and those containing cellular rejection signs. To quantify biopsy-level rejection risk, we first detect local rejection features using a binary image classifier trained with expert-annotated and synthetic examples. We converted these local predictions into a biopsy-wide rejection score via an interpretable histogram-based approach. Our model significantly improves upon prior works with the same dataset with an area under the receiver operating curve (AUROC) of 98.84% for the local rejection detection task and 95.56% for the biopsy-rejection prediction task. A biopsy-level sensitivity of 83.33% makes our approach suitable for early screening of biopsies to prioritize expert analysis. Our framework provides a solution to rare medical imaging challenges currently limited by small datasets.