High-dose intravenous immunoglobulin for the treatment and prevention of heparin-induced thrombocytopenia: a review
Bibliographic record
Abstract
Introduction: Heparin-induced thrombocytopenia (HIT) is known for its strong association with thrombosis and distinct pathogenesis involving anti-PF4/polyanion antibodies that activate platelets strongly through clustering of platelet FcγIIa receptors. Autoimmune HIT (aHIT) refers to a subgroup of patients whose HIT antibodies have both heparin-dependent and heparin-independent platelet-activating properties. aHIT patients have atypical clinical presentations including delayed-onset HIT, persisting (refractory) HIT, heparin ‘flush’ HIT, fondaparinux-associated HIT, severe thrombocytopenia (platelet count <20 × 109/L) with overt disseminated intravascular coagulation, and spontaneous HIT syndrome.Areas covered: This article reviews all available literature describing the use of high-dose intravenous immunoglobulin (IVIG) as an adjunct treatment to anticoagulation in HIT patients. IVIG is usually effective in interrupting platelet activation by aHIT antibodies, manifesting as a rapid platelet count increase after starting IVIG (usual dose, 1g/kg × 2 days). Experience to date suggests IVIG de-escalates HIT and likely reduces thrombotic risk. A new case of aHIT successfully treated with IVIG is presented. Use of IVIG to prevent acute HIT with planned heparin reexposure in antibody-positive patients is also discussed.Expert opinion: High-dose IVIG appears to rapidly inhibit HIT antibody-induced platelet activation and has the potential to become an important treatment adjunct for HIT, particularly in patients with aHIT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".