Dabigatran is Less Effective Than Warfarin at Attenuating Mechanical Heart Valve‐Induced Thrombin Generation
Bibliographic record
Abstract
BACKGROUND: Patients with mechanical heart valves (MHV) require warfarin to prevent thromboembolism. Although dabigatran was as effective as warfarin for stroke prevention in atrial fibrillation when compared with warfarin in patients with MHV, the study was stopped early because of more strokes and bleeding with dabigatran. To determine why dabigatran was less effective than warfarin, we compared their effects on thrombin generation induced by MHV. METHODS AND RESULTS: Thrombin generation in the absence or presence of valve leaflets or sewing ring segments (SRS) was quantified. Studies were done in control plasma, plasma depleted of factors (F) XII, XI, or VII, plasma containing varying concentrations of dabigatran, or plasma from patients on dabigatran or warfarin with varying dabigatran concentrations or international normalized ratio (INR) values. Mean endogenous thrombin potential (ETP) increased 1.2-, 1.5-, and 1.8-fold in the presence of leaflets, Teflon SRS, and Dacron SRS, respectively. Whereas ETP in FVII-depleted and control plasma was similar, ETP was reduced to background levels in FXII-depleted plasma and abrogated in FXI-depleted plasma. Dabigatran had little effect on ETP at concentrations below 400 ng/mL, whereas in plasma from warfarin-treated patients, ETP was suppressed with INR values over 1.5. CONCLUSIONS: MHV induce thrombin generation via the intrinsic pathway and generate sufficient thrombin to overwhelm clinically relevant dabigatran concentrations. In contrast, warfarin is more effective than dabigatran at suppressing MHV-induced thrombin generation. These data explain why dabigatran failed in MHV patients and suggest that strategies targeting FXII or FXI may suppress the root cause of thrombosis in such patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".