Features of Inflammatory Heart Reactions Following mRNA COVID‐19 Vaccination at a Global Level
Bibliographic record
Abstract
Myocarditis and pericarditis may constitute adverse reactions of mRNA coronavirus disease 2019 (COVID-19) vaccines. This study aimed to document these reactions and to assess the association with patient sex and age. This is as an observational retrospective study using a case-non-case design (also called disproportionality study) on inflammatory heart reactions reported with mRNA COVID-19 vaccines within the World Health Organization (WHO) global safety database (VigiBase), up to June 30, 2021. Results are expressed using reporting odds ratios (RORs) and their 95% confidence interval (95% CI). Of 716,576 reports related to mRNA COVID-19 vaccines, 2,277 were cases of inflammatory heart reactions, including 1241 (55%) myocarditis and 851 (37%) pericarditis. The main age group was 18-29 years (704, 31%), and mostly male patients (1,555, 68%). Pericarditis onset was delayed compared with myocarditis with a median time to onset of 8 (3-21) vs. 3 (2-6) days, respectively (P = 0.001). Regarding myocarditis, an important disproportionate reporting was observed in adolescents (ROR, 22.3, 95% CI 19.2-25.9) and in 18-29 years old (ROR, 6.6, 95% CI 5.9-7.5) compared with older patients, as well as in male patients (ROR, 9.4, 95% CI 8.3-10.6). Reporting rate of myocarditis was increased in young adults and adolescents. Inflammatory heart reactions may rarely occur shortly following mRNA COVID-19 vaccination. Although an important disproportionate reporting of myocarditis was observed among adolescents and young adults, particularly in male patients, reporting rates support a very rare risk, that does not seem to compromise the largely positive benefit-risk balance of these vaccines. Furthermore, this study confirmed the value of disproportionality analyses for estimation of relative risks among subgroups of patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".