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Warfarin dosing algorithms: A systematic review.

Innocent G Asiimwe1, Eunice J Zhang1, Rostam Osanlou1

  • 1The Wolfson Centre for Personalized Medicine, MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom.

British Journal of Clinical Pharmacology
|October 20, 2020
PubMed
Summary

Numerous warfarin dosing algorithms exist, but most lack external validation and rigorous bias assessment. Further development is needed to ensure reliability and applicability, especially for under-served populations.

Keywords:
clinical factorsdemographic factorsdosing algorithmsgenetic factorswarfarin

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Area of Science:

  • Pharmacogenomics and Precision Medicine
  • Clinical Pharmacology
  • Biostatistics and Health Informatics

Background:

  • Warfarin dosing algorithms aim to optimize patient outcomes.
  • Existing algorithms vary in development, validation, and applicability across populations.

Purpose of the Study:

  • To review existing warfarin dosing algorithms.
  • To assess their covariates, performance, and risk of bias.

Main Methods:

  • Systematic literature search of MEDLINE up to May 2020.
  • Inclusion of studies on warfarin dosing algorithm development, external validation, or clinical utility.
  • Data extraction and quality assessment by two independent investigators.

Main Results:

  • 266 articles reviewed, detailing 433 developed algorithms, 481 external validations, and 52 clinical utility assessments.
  • Most algorithms were for dose initiation, used linear regression, and were developed for Asian or White populations.
  • Common covariates included age, medications, weight, and genetic variants (CYP2C9, VKORC1).
  • Low rates of external validation (26%) and clinical utility assessment (7%); <2% had low risk of bias.

Conclusions:

  • Most warfarin dosing algorithms are not sufficiently validated or assessed for clinical utility.
  • Algorithms developed primarily in Asian and White populations may not apply to under-served groups.
  • Prioritizing algorithm development and assessment in under-represented populations is crucial for reliable clinical use.