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Record W2594345661 · doi:10.1182/blood.v112.11.988.988

Use of Pharmacogenetic Factors to Predict Warfarin Dose in Children - a Retrospective Cohort Study

2008· article· en· W2594345661 on OpenAlexaff
Emma Jones, Mary Bauman, M. Patricia Massicotte, Brian F. Gage, Ebony Courtney, Lisa Bomgaars

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsStollery Children's Hospital
Fundersnot available
KeywordsWarfarinVKORC1MedicineCYP2C9Vitamin K epoxide reductasePharmacogeneticsRetrospective cohort studyGenotypingPopulationInternal medicineDosingSurgeryPediatricsAtrial fibrillationGenotypeBiology

Abstract

fetched live from OpenAlex

Abstract Background: Warfarin is the most commonly used oral anticoagulant, but its use is limited by the narrow therapeutic index. Single nucleotide polymorphisms in cytochrome P450 2C9 (CYP2C9) and vitamin K epoxide reductase (VKORC1) have been shown to impact warfarin dose requirements. Use of such pharmacogenetic data to improve warfarin dosing has been widely studied in adult populations and is being integrated into clinical practice. However, there remains a paucity of data on the appropriate use of this genetic information in the pediatric population. We developed a retrospective review of pediatric patients on warfarin therapy to evaluate associations between CYP2C9 and VKORC1 polymorphisms and warfarin dose requirements in children. Methods: Patients were recruited from outpatient clinics at Texas Children’s Hospital and Stollery Children’s Hospital. Any patient less than 18 years of age that had taken warfarin and achieved maintenance dose was eligible. Blood sample or buccal swab was collected for genotyping and clinical data was collected from the medical record. Genotyping was performed by melting curve analysis to detect CYP2C9*2, CYP2C9*3, or VKORC1 −1639 mutations. Warfarin genotyping reagents were obtained from Idaho Technology and assays were run using the Roche LightCyler instrument. Results: This study was initiated in March 2008 and is ongoing. To date 47 children (23M: 24F) have been enrolled. Indications for warfarin therapy include: arrhythmia (n=2), artificial heart valve (n=10), pulmonary hypertension (n=10), deep vein thrombosis (n=9), pulmonary embolism (n=2), Fontan (n=10), stroke (n=2), other (n=2). The mean age is 9.3 years (range 1–18); median target International Normalized Ratio (INR) range is 2–3 (with similar target INRs across genotypes). Twenty-nine patients have fully evaluable data at the time of this report, of whom 21 (72%) are wild type CYP2C9, 6 (21%) carry at least 1 mutant allele, and 2 (7%) are homozygous or compound heterozygote. As compared with wild type patients (mean dose 0.14mg/ kg/day), patients with heterozygous (0.09mg/kg/day; p=0.04) or homozygous (0.05mg/ kg/day; p=0.001) genotype required significantly lower maintenance daily warfarin dose. For VKORC1 −1639 genotype 6 (21%) are AA, 11 (38%) are GA, and 12 (41%) are GG. As compared with GG patients (mean dose 0.16mg/kg/day), patients with 1 (0.11mg/kg/ day; p=0.12) or 2 (0.09mg/kg/day; p=0.02) A allele required lower doses. Discussion: We present the first pediatric series to evaluate the influence of CYP2C9 and VKORC1 genotype on warfarin dosing. These data demonstrate genotype frequency similar to adults, and suggests that genotype influences the maintenance warfarin dose in children. Enrollment to this study is ongoing, and will allow a more precise evaluation of the impact of genotype and additional demographic data on pediatric dosing. These data will provide for development of a pediatric dosing algorithm to predict maintenance warfarin dose a priori.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.104
GPT teacher head0.385
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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