Mechanism of catheter thrombosis: comparison of the antithrombotic activities of fondaparinux, enoxaparin, and heparin in vitro and in vivo
Bibliographic record
Abstract
In patients undergoing percutaneous coronary intervention, catheter thrombosis is more frequent with fondaparinux than heparin. This study was undertaken to identify the responsible mechanism and to develop strategies for its prevention. Percutaneous coronary intervention catheter segments shortened plasma clotting times from 971 ± 92 to 352 ± 22 seconds. This activity is factor XII (fXII) dependent because it was attenuated with corn trypsin inhibitor and was abolished in fXII-deficient plasma. Heparin and enoxaparin blocked catheter-induced clotting at 0.5 and 2 anti-Xa U/mL, respectively, whereas fondaparinux had no effect. Addition of fondaparinux to bivalirudin or low-dose heparin attenuated catheter-induced clotting more than either agent alone. In a rabbit model of catheter thrombosis, a 70 anti-Xa U/kg intravenous bolus of heparin or enoxaparin prolonged the time to catheter occlusion by 4.6- and 2.5-fold, respectively, compared with saline, whereas the same dose of fondaparinux had no effect. Although 15 anti-Xa U/kg heparin had no effect on its own, when given in conjunction with 70 anti-Xa U/kg fondaparinux, the time to catheter occlusion was prolonged 2.9-fold. These findings indicate that (1) catheters are prothrombotic because they trigger fXII activation, and (2) fondaparinux does not prevent catheter-induced clotting unless supplemented with low-dose heparin or bivalirudin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".