Risk of myocarditis and pericarditis in mRNA COVID-19-vaccinated and unvaccinated populations: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To summarise the available evidence on the risk of myocarditis and/or pericarditis following mRNA COVID-19 vaccination, compared with the risk among unvaccinated individuals in the absence of COVID-19 infection. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Electronic databases (Medline, Embase, Web of Science and WHO Global Literature on Coronavirus Disease), preprint repositories (medRxiv and bioRxiv), reference lists and grey literature were searched from 1 December 2020 until 31 October 2022. STUDY SELECTION: Epidemiological studies of individuals of any age who received at least one dose of an mRNA COVID-19 vaccine, reported a risk of myo/pericarditis and compared the risk of myo/pericarditis to individuals who did not receive any dose of an mRNA COVID-19 vaccine. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently conducted screening and data extraction. The rate of myo/pericarditis among vaccinated and unvaccinated groups was recorded, and the rate ratios were calculated. Additionally, the total number of individuals, case ascertainment criteria, percentage of males and history of SARS-CoV-2 infection were extracted for each study. Meta-analysis was done using a random-effects model. RESULTS: Seven studies met the inclusion criteria, of which six were included in the quantitative synthesis. Our meta-analysis indicates that within 30-day follow-up period, vaccinated individuals were twice as likely to develop myo/pericarditis in the absence of SARS-CoV-2 infection compared to unvaccinated individuals, with a rate ratio of 2.05 (95% CI 1.49-2.82). CONCLUSION: Although the absolute number of observed myo/pericarditis cases remains quite low, a higher risk was detected in those who received mRNA COVID-19 vaccinations compared with unvaccinated individuals in the absence of SARS-CoV-2 infection. Given the effectiveness of mRNA COVID-19 vaccines in preventing severe illnesses, hospitalisations and deaths, future research should focus on accurately determining the rates of myo/pericarditis linked to mRNA COVID-19 vaccines, understanding the biological mechanisms behind these rare cardiac events and identifying those most at risk.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".