Vaccine-induced immune thrombotic thrombocytopenia after COVID-19 vaccination: Description of a series of 39 cases in Brazil
Bibliographic record
Abstract
OBJECTIVE: Describe a case series of vaccine-induced immune thrombotic thrombocytopenia (VITT) after COVID-19 vaccination in Brazil that included ChAdOx1 nCoV-19, Ad26.COV2.S and BNT162b2 vaccines, describing their clinical and laboratory characteristics. METHODOLOGY: Descriptive case series study using Bio-Manguinhos/Fiocruz/AstraZeneca Brazil and National Immunization Program/Ministry of Health (NIP/MoH) data on COVID-19 AEFI surveillance. We obtained patient-level data from pharmacovigilance for AEFI surveillance and used both the NIP/MoH and Bio-Manguinhos/Fiocruz pharmacovigilance databases to create the study database. Thirty-nine cases of suspect VITT were included, 36 after ChAdOx1 nCoV-19, one after BNT162b2 and two after Ad26.COV2.S vaccine. All cases were based on meeting the Brighton Collaboration criteria for VITT. The primary outcomes were clinical and laboratory features, site of thrombosis, and anti-PF4 ELISA, when available. RESULTS: Thirty-nine cases met the criteria, 38 of which were classified as level 1 and one as level 3 according to Brighton Collaboration. Most cases had the central nervous system (CNS) as the main site of thrombosis (21/39) and happened after the vaccine first dose (34/39). The median age of the cases was 41 years old (23 to 86 yo). Most of the cases (61.5%) occurred in women. The median interval between vaccination and onset of symptoms was 8 days (0-37 days). The platelet count and D-dimer count had median values of 34,000/µL and 19,235 µg FEU/L, respectively. The ELISA anti-PF4 antibody was positive in 18 samples. The overall mortality rate was 51% and was higher in cases of CNS thrombosis with intracerebral bleeding. CONCLUSION: Our case series shows that Brazilian VITT cases have similar clinical and laboratory profiles as demonstrated in the literature. Brazil has administered more than 300 million doses of COVID-19 vaccines (more than 110 million from ChAdOx1 nCoV-19). VITT seems to be a very rare but serious adverse event following COVID-19 immunization, especially adenoviral vector immunization.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".