Fixed Doses of Anticoagulants Cause Wide Variation of Thrombin Generation
Bibliographic record
Abstract
Abstract Current practice in the prevention of venous thromboembolism (VTE) is either controlled dosage of vitamin K antagonists (VKA) or standard dosage of a low molecular weight heparin (LMWH) or an orally administrated direct inhibitor of thrombin or factor Xa (DOACs). This is justified by clinical trials showing the non-inferiority of standard dosage to VKA treatment - disregarding that the latter could be improved upon. Whether standard dosage is optimal for the individual patient depends upon how well (s)he is represented by the "average" patient that meets the inclusion criteria of the trial. Here we show that in a set of normal plasmas the individual thrombin generating power (the endogenous thrombin potential: ETP), after spiking with a fixed concentration (~ IC50) of different anticoagulants show such a wide variation that, even when plasma levels would be identical, a considerable percentage of patients could be over- or under-anticoagulated. Consequently adapted doses must be considered if better results than those with VKA are our aim. We recall that the ETP is close to constant in the individual person but varies in the population with a broad log-normal distribution (CV 16%). The ETP is highly correlated to the risk of thrombosis: The relative risk of VTE in the upper quartile being 5 - 7 times higher than that in the lower one (Winckers K., et al., poster PO617, ISTH 2015, Toronto). Reducing the ETP is the common feature of all antithrombotic therapy. The desired range of reduction is not exactly known. In congenital bleeding disorders bleeding risks increase sharply at ETP below 33%. In monitoring VKA treatment an INR below 2, which corresponds to an ETP > 66% of normal, is generally considered inadequate. We therefore arbitrarily choose the range 33 - 66% of normal, as a target range (TR). Experimental: Individual plasmas from 60 normal healthy volunteers were spiked with a fixed amount of either unfractionated heparin (UFH), low molecular weight heparin (LMWH), antithrombin binding pentasaccharide (penta), DOAC acting on thrombin or DOAC acting on factor Xa. We found the residual ETP to be highly variable (table column 3), obviously because the variation in susceptibility to the inhibitor superimposes upon the natural variation. Using these CVs we calculated what % falls either above or below the target range. The total variance of the ETP under treatment is the combined effect of natural variance, the pharmacokinetic- and the pharmacodynamic -variance. Here we determined pharmacodynamic effects only and calculated the % of ETP values that would fall outside the target range A: if there would be no pharmacokinetic variance (table column 4 & 5) and B: If the pharmacokinetic variance would equal the pharmacodynamic variance (table column 6 & 7). It is seen that in any case over 15% of the population will be outside the target range and that in the more likely case that pharmacokinetic variation counts as much as pharmacodynamic variation does around half of all patients will be outside the safe range. This could be avoided by measuring the effect of a standard dose of anticoagulant on the ETP once and increasing the dose in those with an ETP > 66% and decreasing it in those with ETP < 33%. The present work is meant to provide the rationale for starting clinical studies on the actual variation of the ETP attained under standard dosage of different anticoagulants and the effects thereon of personalised dose adjustment. Table Table. Disclosures Hemker: Diagnostica Stago: Consultancy.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".