Evidence map›Paper›PMID 22116191›Full record

Trial reportThrombosis and haemostasis2012

The Creating an Optimal Warfarin Nomogram (CROWN) Study.

Todd S Perlstein, Samuel Z Goldhaber, Kerrie Nelson, Victoria Joshi, T Vance Morgan, Lawrence J Lesko, Joo-Yeon Lee, Jogarao Gobburu, David Schoenfeld, Raju Kucherlapati and 2 more

Open access · bronzeAbstract readClinical TrialMulticenter Study
In one paragraph

Trial report in Thrombosis and haemostasis, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
4.1field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 15 citations in OpenAlex.

  1. Observational
  2. Review
  3. Review
  4. Genetics-based pediatric warfarin dosage regimen derived using pharmacometric bridging.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2013
    Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 1 country.

Todd S PerlsteinDivision of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
Samuel Z Goldhaber
Kerrie Nelson
Victoria Joshi
T Vance Morgan
Lawrence J Lesko
Joo-Yeon Lee
Jogarao Gobburu
David Schoenfeld
Raju Kucherlapati
Mason W Freeman
Mark A Creager
Harvard University · USBrigham and Women's Hospital · USMassachusetts General Hospital · USMass General Brigham · US

Funding

Clinical Research Training Program for Academic VascularK12HL083786 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CREAGER, MARK A · 2006 to 2011
$4.0M
NHLBI NIH HHS K12 HL083786NHLBI NIH HHS K12-HL083786
6 · The paper itself

Abstract

A significant proportion of warfarin dose variability is explained by variation in the genotypes of the cytochrome P450 CYP2C9 and the vitamin K epoxide reductase complex, VKORC1, enzymes that influence warfarin metabolism and sensitivity, respectively. We sought to develop an optimal pharmacogenetic warfarin dosing algorithm that incorporated clinical and genetic information. We enroled patients initiating warfarin therapy. Genotyping was performed of the VKORC1, -1639G>A, the CYP2C9*2, 430C>T, and the CYP2C9*3, 1075C>A genotypes. The initial warfarin dosing algorithm (Algorithm A) was based upon established clinical practice and published warfarin pharmacogenetic information. Subsequent dosing algorithms (Algorithms B and Algorithm C) were derived from pharmacokinetic / pharmacodynamic (PK/PD) modelling of warfarin dose, international normalised ratio (INR), clinical and genetic factors from patients treated by the preceding algorithm(s). The primary outcome was the time in the therapeutic range, considered an INR of 1.8 to 3.2. A total of 344 subjects are included in the study analyses. The mean percentage time within the therapeutic range for each subject increased progressively from Algorithm A to Algorithm C from 58.9 (22.0), to 59.7 (23.0), to 65.8 (16.9) percent (p = 0.04). Improvement also occurred in most secondary endpoints, which included the per-patient percentage of INRs outside of the therapeutic range (p = 0.004), the time to the first therapeutic INR (p = 0.07), and the time to achieve stable therapeutic anticoagulation (p < 0.001). In conclusion, warfarin pharmacogenetic dosing can be optimised in real time utilising observed PK/PD information in an adaptive fashion.

Indexed as

AdultAgedAlgorithmsAnticoagulantsAryl Hydrocarbon HydroxylasesCytochrome P-450 CYP2C9FemaleGenotypeHumansInternational Normalized RatioMaleMiddle AgedMixed Function OxygenasesModels, GeneticNomogramsPharmacogeneticsAnticoagulantsAryl Hydrocarbon HydroxylasesCYP2C9 protein, humanCytochrome P-450 CYP2C9Mixed Function OxygenasesVitamin K Epoxide ReductasesVKORC1 protein, humanWarfarin

Identifiers

PMID22116191
PMCPMC4133142
OpenAlexW2127265305

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.