Persistence of anti-platelet factor 4 antibodies in vaccine-induced immune thrombocytopenia and thrombosis for 3 years
Bibliographic record
Abstract
BACKGROUND: Vaccine-induced immune thrombocytopenia and thrombosis (VITT) is a rare and potentially life-threatening complication of adenoviral vector-based vaccines against SARS-CoV-2. In VITT, antibodies against platelet factor 4 (PF4; ie, CXCL4) cause platelet activation, which then leads to thrombocytopenia and thrombosis. VITT resembles the immune-mediated drug reaction of heparin-induced thrombocytopenia; however, unlike heparin-induced thrombocytopenia antibodies, which have been shown to be transient, VITT antibodies appear to persist for much longer. OBJECTIVE(S): To identify the clinical outcomes and laboratory testing features of VITT patients beyond the acute phase of disease. METHODS: In this study, we followed a Canadian cohort of VITT patients (N = 30) for nearly 3 years from their initial presentation and report serial testing for anti-PF4 antibody levels by enzyme immunoassay, as well as the ability to activate platelets in the PF4-serotonin release assay. The median latest follow-up time was 715 days (range, 126-1065) post-vaccination. RESULTS: Using Kaplan-Meier analysis, we found that 65.2% of VITT patients continued to test positive for anti-PF4 antibodies, and 34.1% of patients continued to test positive for platelet-activating anti-PF4 antibodies. There were no cases of recurrent thrombosis; however, 7/8 (87.5%) VITT patients with persistent platelet-activating anti-PF4 antibodies remained on anticoagulant or antiplatelet therapy. CONCLUSION: Our findings demonstrate that VITT antibodies can persist for nearly 3 years in some patients, and a proportion of those maintain the ability to activate platelets in vitro. The complete duration of VITT antibody persistence remains unknown, and whether this has clinical implications requires ongoing surveillance with further evaluation.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".