Direct oral anticoagulants for treatment of HIT: update of Hamilton experience and literature review
Bibliographic record
Abstract
Direct oral anticoagulants (DOACs) are attractive options for treatment of heparin-induced thrombocytopenia (HIT). We report our continuing experience in Hamilton, ON, Canada, since January 1, 2015 (when we completed our prospective study of rivaroxaban for HIT), using rivaroxaban for serologically confirmed HIT (4Ts score ≥4 points; positive platelet factor 4 [PF4]/heparin immunoassay, positive serotonin-release assay). We also performed a literature review of HIT treatment using DOACs (rivaroxaban, apixaban, dabigatran, edoxaban). We focused on patients who received DOAC therapy for acute HIT as either primary therapy (group A) or secondary therapy (group B; initial treatment using a non-DOAC/non-heparin anticoagulant with transition to a DOAC during HIT-associated thrombocytopenia). Our primary end point was occurrence of objectively documented thrombosis during DOAC therapy for acute HIT. We found that recovery without new, progressive, or recurrent thrombosis occurred in all 10 Hamilton patients with acute HIT treated with rivaroxaban. Data from the literature review plus these new data identified a thrombosis rate of 1 of 46 patients (2.2%; 95% CI, 0.4%-11.3%) in patients treated with rivaroxaban during acute HIT (group A, n = 25; group B, n = 21); major hemorrhage was seen in 0 of 46 patients. Similar outcomes in smaller numbers of patients were observed with apixaban (n = 12) and dabigatran (n = 11). DOACs offer simplified management of selected patients, as illustrated by a case of persisting (autoimmune) HIT (>2-month platelet recovery with inversely parallel waning of serum-induced heparin-independent serotonin release) with successful outpatient rivaroxaban management of HIT-associated thrombosis. Evidence supporting efficacy and safety of DOACs for acute HIT is increasing, with the most experience reported for rivaroxaban.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".