Argatroban Anticoagulation in Patients With Heparin-Induced Thrombocytopenia
Bibliographic record
Abstract
BACKGROUND: Heparin-induced thrombocytopenia (HIT) is an intensely prothrombotic syndrome managed by discontinuation of heparin therapy and substitution of an alternative inhibitor of thrombin. We describe our experience with argatroban, a direct thrombin inhibitor, in patients with HIT or HIT with thrombosis (HITTS). METHODS: In this multicenter, nonrandomized prospective study, 418 patients with HIT were administered intravenous argatroban, 2 micro g/kg per minute, adjusted to maintain the activated partial thromboplastin time at 1.5 to 3 times the baseline value for a mean of 5 to 7 days. Comparisons were made with a historical control cohort (n = 185). The prospectively defined, primary efficacy end point was a composite of all-cause death, all-cause amputation, or new thrombosis in 37 days. Other end points included the components of the composite, death due to thrombosis, increased platelet count, and bleeding. RESULTS: In the HIT arm, the composite end point was significantly reduced in argatroban-treated patients vs controls (28.0% vs 38.8%; P =.04). In the HITTS arm, the composite end point occurred in 41.5% of argatroban-treated patients vs 56.5% of controls (P =.07). By time-to-event analysis of the composite end point, argatroban therapy was significantly better than historical control therapy in HIT (P =.02) and HITTS (P =.008). Argatroban therapy also significantly reduced new thrombosis in HIT and HITTS and death due to thrombosis in HITTS. There were no significant between-group differences in all-cause death or amputation. Platelet counts recovered more rapidly in argatroban-treated patients than in controls. Bleeding rates were similar between groups. CONCLUSION: Argatroban therapy, compared with historical control, improves outcomes, particularly new thrombosis and death due to thrombosis, in patients with heparin-induced thrombocytopenia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".