Clinical Practice Recommendations on Genetic Testing of CYP2C9 and VKORC1 Variants in Warfarin Therapy
Bibliographic record
Abstract
OBJECTIVE: To systematically review evidence on genetic variants influencing outcomes during warfarin therapy and provide practice recommendations addressing the key questions: (1) Should genetic testing be performed in patients with an indication for warfarin therapy to improve achievement of stable anticoagulation and reduce adverse effects? (2) Are there subgroups of patients who may benefit more from genetic testing compared with others? (3) How should patients with an indication for warfarin therapy be managed based on their genetic test results? METHODS: A systematic literature search was performed for VKORC1 and CYP2C9 and their association with warfarin therapy. Evidence was critically appraised, and clinical practice recommendations were developed based on expert group consensus. RESULTS: Testing of VKORC1 (-1639G>A), CYP2C9*2, and CYP2C9*3 should be considered for all patients, including pediatric patients, within the first 2 weeks of therapy or after a bleeding event. Testing for CYP2C9*5, *6, *8, or *11 and CYP4F2 (V433M) is currently not recommended. Testing should also be considered for all patients who are at increased risk of bleeding complications, who consistently show out-of-range international normalized ratios, or suffer adverse events while receiving warfarin. Genotyping results should be interpreted using a pharmacogenetic dosing algorithm to estimate the required dose. SIGNIFICANCE: This review provides the latest update on genetic markers for warfarin therapy, clinical practice recommendations as a basis for informed decision making regarding the use of genotype-guided dosing in patients with an indication for warfarin therapy, and identifies knowledge gaps to guide future research.
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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 | 0.037 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".