Prospective evaluation of a pharmacogenetics-guided warfarin loading and maintenance dose regimen for initiation of therapy
Bibliographic record
Abstract
Single-nucleotide polymorphisms in genes that affect warfarin metabolism (cytochrome P450 2C9 gene, CYP2C9) and response (vitamin K epoxide reductase complex 1 gene, VKORC1) have an important influence on warfarin therapy, particularly during initiation; however, there is a lack of consensus regarding the optimal pharmacogenetics-based initiation strategy. We conducted a prospective cohort study in which patients requiring warfarin therapy for atrial fibrillation or venous thromboembolism were initiated with a novel pharmacogenetics-initiation protocol (WRAPID, Warfarin Regimen using A Pharmacogenetics-guided Initiation Dosing) that incorporated loading and maintenance doses based on genetics, clinical variables, and response (n = 167, followed up for 90 days), to assess the influence of genetic variations on anticoagulation responses. Application of the WRAPID algorithm resulted in a negligible influence of genetic variation in VKORC1 or CYP2C9 on time to achievement of first therapeutic response (P = .52, P = .28) and risk of overanticoagulation (P = .64, P = .96). After adjustment for covariates, time to stable anticoagulation was not influenced by VKORC1 or CYP2C9 genotype. Importantly, time spent within or above the therapeutic range did not differ among VKORC1 and CYP2C9 genotype groups. Moreover, the overall time course of the anticoagulation response among the genotype groups was similar and predictable. We demonstrate the clinical utility of genetics-guided warfarin initiation with the WRAPID protocol to provide safe and optimal anticoagulation therapy for patients with atrial fibrillation or venous thromboembolism.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".