Do Genetic Contributors to Warfarin Responsiveness or Common Thrombophilias Influence the Risk of Major Bleeding in Patients on Extended Duration Vitamin K Antagonist (VKA) for Venous Thromboembolic Disease?
Bibliographic record
Abstract
Abstract Background: Recent studies have indicated that genetic factors such as polymorphisms in the CYP2C9 and the VKORC1 genes play an important role in vitamin K antagonist (VKA) response and perhaps bleeding. There are also data to suggest, especially for factor V Leiden, that thrombophilia may confer a decreased risk of bleeding (which could explain its persistence and high prevalence in the population). The influence of major genetic factors on risk of bleeding have not previously been evaluated in large prospective cohort studies of patients with VTE on extended (treatment after the first 3 to 6 months) VKA therapy. Aims: We sought to determine the effect of genetic variants that influence warfarin metabolism and thrombophilias on the rates of major bleeding during extended VKA therapy in patients with VTE disease. Methods: The Bleeding Risk study is a multicentre, multinational prospective cohort study of patients on extended VKA for unprovoked VTE, or provoked VTE with prior VTE. Patients were enrolled after at least 3 months of VKA. All major bleeding events during long term VKA were captured and adjudicated. A blood sample was taken from each patient at their first study visit and analysed in a central lab to identify the following genetic variant types: CYP2C9*2 (C/T), CYP2C9*3 (T/G), 1639G-A (G/A), Factor V Leiden (G/A: FVL), CYP4F2 (G/A), and Prothrombin gene 20210 variant (PGV). Rates of major bleeding were then evaluated for patients with each genetic variant in isolation and in combination. Results: 2290 of the 2514 patients enrolled at 12 sites have contributed over 7000 years of observation and were included in this analysis. The mean patient age was 60.2±14.7 years, 64% were male, 92% Caucasian, average BMI was 31.3, and 9% of patients were on antiplatelet agents. Patients were followed for a mean of 2.8 years (range, 0.1 to 6.8 years. 121 patients (4.8%) experienced at least one episode of major bleeding. The annual rate of bleeding was 1.7 per 100 patient-years of observation. The CYP2C9*2 variant heterozygous/homozygous versus wildtype (476 patients vs 1584) was protective against bleeding (Incidence risk ratio 0.5, p =0.01), CYP2C9*3 variant heterozygous/homozygous versus wildtype (215 patients vs 1845) was associated with major bleeding (Incidence risk ratio 2.0, p =0.01), VKORC1 and CYP4F2 had no association with major bleeding, and CYP2C9*3 variant heterozygous/homozygous in combination with wildtype CYP2C9*2 (186 patients) was associated with major bleeding (Incidence risk ratio 3.24, p =0.02). Both FVL (19% of patients) and PGV (8% of patients) had no effect on major bleeding, with p values > 0.35 for comparison of the wildtype to heterozygous/homozygous. Conclusion: This is the largest study we are aware of to determine if warfarin metabolic genotype variants and common thrombophilias influence major bleeding risk in patients followed long-term with extended duration VKA therapy for VTE. The thrombophilias do not influence bleeding risk but those with CYP2C9*3 hetero/homozygous inheritance had double the risk and CYP2C9*2 hetero/homozygous inheritance half the risk. There appears to be an interaction between the CYP2C9*2 and *3 genotypes. Disclosures Wells: BMS/Pfizer: Research Funding; Bayer Healthcare: Other: Speaker Fees and Advisory Board; Janssen Pharmaceuticals: Consultancy; Itreas: Other: Served on a Writing Committee. Kovacs:LEO Pharma: Honoraria; Bayer: Honoraria, Research Funding; Daiichi Sankyo Pharma: Research Funding; Pfizer: Honoraria, Research Funding. Anderson:Bayer Healthcare: Research Funding. Rodger:Canadian Agency for Drugs and Technologies in Health: Consultancy; Boehringer Ingelheim: Research Funding.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".