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Record W3193883735 · doi:10.1101/2021.08.12.21261955

Cardiac Inflammation after COVID-19 mRNA Vaccines: A Global Pharmacovigilance Analysis

2021· preprint· en· W3193883735 on OpenAlexafffund
Laurent Chouchana, Alice Blet, Mohammad Al‐Khalaf, Tahir S Kafil, Girish M. Nair, James A. Robblee, Milou‐Daniel Drici, Marie‐Blanche Valnet‐Rabier, Joëlle Micallef, Francesco Salvo, Jean‐Marc Tréluyer, Peter P. Liu

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of Ottawa
FundersGenome CanadaCanadian Institutes of Health ResearchUniversity of OttawaWorld Health Organization
KeywordsPharmacovigilanceMedicineMyocarditisVaccinationPericarditisConfidence intervalOdds ratioInternal medicineAdverse effectQT intervalHeart failureObservational studyAdverse Event Reporting SystemPediatricsImmunology

Abstract

fetched live from OpenAlex

ABSTRACT Background To counter the COVID-19 pandemic, mRNA vaccines, namely tozinameran and elasomeran, have been authorized in several countries. These next generation vaccines have shown high efficacy against COVID-19 and demonstrated a favorable safety profile. As widespread vaccinations efforts are taking place, incidents of myocarditis and pericarditis cases following vaccination have been reported. This safety signal has been recently confirmed by the European Medicine Agency and the U.S. Food and Drug Administration. This study aimed to investigate and analyze this safety signal using a dual pharmacovigilance database analysis. Methods This is as an observational study of reports of inflammatory heart reactions associated with mRNA COVID-19 vaccines reported in the World Health Organization’s global individual case safety report database (up to June 30 th 2021), and in the U.S. Vaccine Adverse Event Reporting System (VAERS, up to May 21 st 2021). Cases were described, and disproportionality analyses using reporting odds-ratios (ROR) and their 95% confidence interval (95%CI) were performed to assess relative risk of reporting according to patient sex and age. Results At a global scale, the inflammatory heart reactions most frequently reported were myocarditis (1241, 55%) and pericarditis (851, 37%), the majority requiring hospitalization (n=796 (64%)). Overall, patients were young (median age 33 [21-54] years). The main age group was 18-29 years old (704, 31%), and mostly males (1555, 68%). Pericarditis onset was delayed compared to 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 in males (ROR, 9.4 [8.3-10.6]) as well as in adolescents (ROR, 22.3 [19.2-25.9]) and 18-29 years old (ROR, 6.6 [5.9-7.5]) compared to older patients were observed. Conclusions The inflammatory heart reactions, namely myocarditis and pericarditis, have been reported world-wide shortly following COVID-19 mRNA vaccination. An important disproportionate reporting among adolescents and young adults, particularly in males, was observed especially for myocarditis. Guidelines must take this specific risk into account and to optimize vaccination protocols according to sex and age. While the substantial benefits of COVID-19 vaccination still prevail over risks, clinicians and the public should be aware of these reactions and seek appropriate medical attention.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.036
GPT teacher head0.379
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes2
Has abstractyes

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