Evolution of Coagulation and Platelet Activation Markers After Transcatheter Edge-to-Edge Mitral Valve Repair
Bibliographic record
Abstract
Background/Objectives: The recommendations for antithrombotic therapy after transcatheter edge-to-edge mitral valve repair (TEER) are empirical, and the benefit of antiplatelet (APT) or anticoagulation therapy (ACT) remains undetermined. The study sought to investigate the degree and the timing of coagulation and platelet marker activation after TEER. Methods: This was a prospective study including 46 patients undergoing TEER. The markers of coagulation activation, namely prothrombin fragment 1 + 2 (F1 + 2) and thrombin-antithrombin III (TAT), and the markers of platelet activation, namely soluble P-Selectin and soluble CD-40 ligand (sCD40L), were measured at baseline, 24 h, 1 month, and 1 year after TEER. Results: At discharge, 20 (43%) patients received APT (single: 16, dual: 4), 24 (52%) received ACT, and 2 (4%) had both single APT and ACT. Levels of F1 + 2 and TAT significantly increased at 24 h post TEER (both p < 0.001), rapidly returning to baseline levels at 1 month. However, levels of F1 + 2 and TAT remained higher at 1 month in patients without ACT compared to patients with ACT (respectively, 303.1 vs. 148.1 pmol/L; p < 0.001 and 4.6 vs. 3.0 µg/L; p = 0.020), with a similar trend at 1 year. Levels of soluble P-selectin and sCD40L remained stable at all times after TEER (respectively, p = 0.071 and p = 0.056), regardless of the APT. Conclusions: TEER is associated with an acute activation of the coagulation system, with no increase in platelet activation markers. Hence, the use of dual APT is questionable in this population. Our results raise the hypothesis that the optimal antithrombotic therapy after TEER could be short-term ACT over APT. Further larger studies are warranted.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".