<i>VKORC1</i> and <i>CYP2C9</i> genotypes are predictors of warfarin‐related outcomes in children
Bibliographic record
Abstract
BACKGROUND: Despite substantial evidence supporting a pharmacogenetic approach to warfarin therapy in adults, evidence on the importance of genetics in warfarin therapy in children is limited, particularly for clinical outcomes. We assessed the contribution of CYP2C9/VKORC1/CYP4F2 genotypes and variation in other genes involved in vitamin K and coagulation pathways to warfarin dose and related clinical outcomes in children. PROCEDURE: Clinical and genetic data for 93 children (age ≤ 18 years) who received warfarin therapy were obtained. DNA was genotyped for 93 selected single nucleotide polymorphisms using a custom assay. RESULTS: With a median age of 4.8 years, our cohort included more young children than most previous studies. Overall, 76.3% of dose variability was explained by weight, indication, VKORC1-1639G/A and CYP2C9 *2/*3, with genotypes accounting for 21.1% of variability. There was a strong correlation (R(2) = 0.68; P < 0.001) between actual and predicted warfarin dose using a pediatric genotype-based dosing model. VKORC1 genotype had a significant impact on time to therapeutic international normalized ratio (INR) (P = 0.047) and time to over-anticoagulation (INR > 4; P = 0.024) during the initiation of therapy. CYP2C9*3 carriers were also at increased risk of major bleeding while receiving warfarin (adjusted OR = 11.28). An additional variant in CYP2C9 (rs7089580) was significantly associated with warfarin dose (P = 0.020) in a multivariate clinical and genetic model. CONCLUSIONS: This study confirms the importance of VKORC1/CYP2C9 genotypes for warfarin dosing in a young pediatric cohort and demonstrates an impact of genetic factors on clinical outcomes in children. Furthermore, we identified an additional variant in CYP2C9 of potential relevance for warfarin dosing in children.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".