In Vitro Reversal of the Anti-Aggregant Effect of Ticagrelor Using Untreated Platelets
Bibliographic record
Abstract
BACKGROUND: Ticagrelor is an anti-platelet agent that is indicated for prevention of thrombosis after acute coronary syndrome or intra-coronary artery stent implantation, but it increases the risk of bleeding. Platelet transfusion has the potential to treat or prevent bleeding in patients taking ticagrelor, but the optimal quantity of platelets and timing of administration have not been fully defined. METHODS AND RESULTS: Ten healthy subjects took ticagrelor in combination with acetylsalicylic acid for 5 days, and had blood collected prior to treatment and at 2, 10, 24, 48, 72 and 96 hours after the last doses. The potential of platelet transfusion to prevent or reverse bleeding was evaluated by mixing subject and donor platelet-rich plasma in vitro in nine different proportions, and measuring adenosine diphosphate-mediated aggregation by light transmission aggregometry. Spontaneous offset of the anti-aggregant effect of ticagrelor occurred gradually and was complete at 72 hours after the last dose. The addition of donor platelets enhanced the recovery. The addition of the equivalent of six apheresis platelet units produced a 50% relative reversal at 10 hours, and > 90% reversal at 24 hours. CONCLUSION: Donor platelets enhance reversal of the anti-aggregant effect of ticagrelor in vitro. Donor platelets given in clinically relevant amounts partially reversed ticagrelor at 10 hours after the last dose, and almost fully reversed ticagrelor at 24 hours. The results inform on the potential to reverse ticagrelor in patients who develop bleeding or require emergency surgery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".