Evidence map›Paper›PMID 23329393›Full record

ArticleClinical pharmacokinetics2013

A Bayesian dose-individualization method for warfarin.

Daniel F B Wright, Stephen B Duffull

Abstract read
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Article in Clinical pharmacokinetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
5.6field-weighted citation impact, top 5% 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

21 citing papers in PubMed, 47 citations in OpenAlex.

  1. Review
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  6. Drug metabolism and disposition: the biological fate of chemicals · 2022
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  15. Review
  16. Effects of VKORC1 Genetic Polymorphisms on Warfarin Maintenance Dose Requirement in a Chinese Han Population.Medical science monitor : international medical journal of experimental and clinical research · 2015
    Article
  17. How to manage warfarin therapy.Australian prescriber · 2015
    Review
  18. Quantification of the Forgiveness of Drugs to Imperfect Adherence.CPT: pharmacometrics & systems pharmacology · 2015
    Article
  19. Article
  20. 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

2 authors at 1 institution in 1 country.

Daniel F B WrightSchool of Pharmacy, University of Otago, PO Box 56, Dunedin, 9054, New Zealand. dan.wright@otago.ac.nz
Stephen B Duffull
University of Otago · NZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWarfarin is a difficult drug to dose accurately and safely due to large inter-individual variability in dose requirements. Current dosing strategies appear to be sub-optimal, with reports indicating that patients achieve international normalized ratios (INRs) within the therapeutic range only 40-65 % of the time. The consequences of poor INR control are potentially severe with INRs below 2 carrying an increased risk of clotting while INRs >4 increase the risk of major bleeding events. Bayesian forecasting methods have the potential to improve INR control.

aimsThe aims of this study were to (1) prospectively assess the predictive performance of a Bayesian dosing method for warfarin implemented in TCIWorks; and (2) determine the expected time in the therapeutic range (TTR) of INRs predicted using TCIWorks.

methodsPatients who were initiating warfarin therapy were prospectively recruited from Dunedin Hospital, Dunedin, New Zealand. Warfarin doses were entered into TCIWorks from the first day of therapy until a stable steady-state INR (INR(ss)) was achieved. The predicted INR(ss) values were determined using the first zero to six serially collected INR observations. Observed and predicted INR(ss) values were compared using measures of bias (mean prediction error [MPE]) and imprecision (root mean square error [RMSE]). The TTR was determined by calculating the percentage of predicted INR(ss) values between 2 and 3 when zero to six serially collected INR observations were available.

resultsA total of 55 patients were recruited between March and November 2011. When no observed INR values were available the resulting INR(ss) predictions were positively biased (MPE 0.52 [95 % CI 0.30, 0.73]); however, this disappeared once observed INR values were entered into TCIWorks. The precision of the predicted INR(ss) values improved dramatically once three or more observed INR values were available (RMSE <0.53) compared with no INRs (RMSE 0.96). These results suggest that TCIWorks will be effective at maintaining the INR within the therapeutic INR range (2-3) 65 % of the time when three INR measurements are available and 80 % of the time when six INR measurements are available.

conclusionThe TCIWorks warfarin dosing method produced accurate and precise INR(ss) predictions. We predict that the method will provide an INR value within the therapeutic range 65-80 % of the time once three or more INR observations are available, making this a useful tool for clinicians and warfarin clinics. Further research to assess the impact of this method on long-term INR control is warranted.

Indexed as

Bayes TheoremAdultAgedAged, 80 and overAnticoagulantsDose-Response Relationship, DrugFemaleHemorrhageHumansInternational Normalized RatioMaleMetabolic Clearance RateMiddle AgedModels, BiologicalProspective StudiesSoftwareAnticoagulantsWarfarin

Identifiers

PMID23329393
OpenAlexW2056370385

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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.