ABSTRACT
Background: The advent of COVID-19 vaccines represents a major public health breakthrough towards reducing morbidity and mortality associated with the COVID-19 pandemic. As these immunizations become increasingly available to the pediatric population, there is increased wariness of adverse effects associated with their use. While vaccine adverse event monitoring has previously been restricted to federal registries, big data tools represent a viable avenue for providers and researchers alike to detect signals associated with vaccine usage at the population level. This study utilizes an aggregate health record to explore pediatric myocarditis as a potential vaccine adverse event. Methods: An analysis of de-identified aggregate health record data from Epic Cosmos. Age groups considered include 12-15, 16-18, 19-21, and 22-25. COVID-19 vaccination was defined as immunization record of Pfizer, Moderna, J&J, or unspecified SARS-CoV-2 vaccine. Myocarditis was defined as ICD10 codes I40, I51.4, B33.22, or I41. Myocarditis following vaccination was defined as an active diagnosis within six months after immunization. Active patients were defined as those with any record of encounter with their health system. Statistical analysis was calculated using chi-squared test for independence and relative risks with a 95% confidence interval. Results: As of June 24, 2021, there were 16,378,103 active patients on Epic Cosmos between the ages of 12-25. Of those patients, 1,483,211 received at least one dose of COVID-19 vaccine (Table 1). Of the 199 myocarditis cases in the vaccinated group, 154 were male. The highest prevalence of myocarditis following vaccination was in the 16-18-year-olds. However, this was not significantly different from the prevalence of myocarditis among unvaccinated 16-18-year-olds (p=0.059). The prevalence of myocarditis was significantly increased in the COVID-vaccinated group as compared to the COVID-unvaccinated group among ages 12-15 (p<0.001). The COVID-vaccinated group had a 2.07 (1.42, 3.00) increased risk of myocarditis compared to the COVID-unvaccinated group in this age range. By contrast, the prevalence of myocarditis was significantly decreased in the COVID-vaccinated group compared to the COVID-unvaccinated group for ages 19-25. At ages 19-21, the COVID-vaccinated group had a 0.50 (0.36, 0.69) decreased risk of myocarditis as compared to the age-matched COVID-unvaccinated group. For ages 22-25, the COVID-vaccinated group had a 0.63 (0.50, 0.81) decreased risk compared to age-matched controls. Conclusion: These results suggest that there is an increased risk, over twofold, of myocarditis following COVID-19 vaccination among 12- 15-year-olds. Meanwhile, the vaccine nearly halves the risk relative of myocarditis among 19-25-year-olds. This study underscores the utility of big data tools in surveillance of vaccine adverse events. The strength of big data in this area of research arises from its ability to detect signals in real time, as such these effects should be monitored alongside increasing rates of pediatric vaccination. The independent effect of SARS-CoV-2 on pediatric myocarditis prevalence warrants further research.
ABSTRACT
Despite significant scientific and medical discoveries, the genetics of novel infectious diseases like COVID-19 remains far from understanding. SARS-CoV-2 is a single-stranded RNA respiratory virus that causes COVID-19 by binding to the ACE2 receptor in the lung and other organs. Understanding its clinical presentation and metabolomic and genetic profile will lead to the discovery of diagnostic, prognostic and predictive biomarkers, which may lead to more effective medical therapy. It is important to investigate correlations and overlap between reported diagnoses of a patient with COVID-19 in clinical data with identified germline and somatic mutations, and highly expressed genes from genomics data analysis. Timely model clinical, genomics and metabolomics data to find statistical patterns across millions of features to identify underlying biological pathways, modifiable risk factors and actionable information that supports early detection and prevention of COVID-19, and development of new therapies for better patient care. Next, ensuring security reconcile noise, need to build and train machine learning prognostic models to find actionable information that supports early detection and prevention of COVID-19. Based on the myriad data, applying appropriate machine learning algorithms to stratify patients, understand scenarios, optimise decision-making, identify high-risk rare variants (including ACE2, TMPRSS2) and making medically relevant predictions. Innovative and intelligent solutions are required to improve the traditional symptom-driven practice, and allow earlier interventions using predictive diagnostics and tailor better personalised treatments, when confronted with the challenges of pandemic situations.