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Developing Chinese race-specific warfarin dose prediction algorithms

  • Research Article
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Abstract

Background

Numerous genotype-guided warfarin dosing algorithms have been developed to individualize warfarin doses, but they can only explain 47–52% of the variability.

Aim

This study aimed to develop new warfarin algorithms suitable to predict the stable warfarin dose for the Chinese population and to compare their prediction performance with those of the most commonly used algorithms.

Method

Multiple linear regression analysis with the warfarin optimal dose (WOD), logarithm (log) WOD, 1/WOD, and \(\sqrt {\text {WOD}}\), respectively, as the dependent variables were performed to deduce a new warfarin algorithm (NEW-Warfarin). WOD was the stable dose that maintained the international normalized ratio (INR) within the target range (2.0–3.0). Three major genotype-guided warfarin dosing algorithms were selected and compared against NEW-Warfarin predictive performance using the mean absolute error (MAE). Furthermore, patients were divided into five groups according to warfarin indications [atrial fibrillation (AF), pulmonary embolism (PE), cardiac-related disease (CRD), deep vein thrombosis (DVT), and other diseases (OD)]. Multiple linear regression analyses were also performed for each group.

Results

The regression equation with \(\sqrt {\text {WOD}}\) as the dependent variable had the highest coefficient of determination (R2 = 0.489). The NEW-Warfarin had the best predictive accuracy compared to the three algorithms selected. Group analysis, according to indications, showed that the R2 of the five groups were PE (0.902) > DVT (0.608) > CRD (0.569) > OD (0.436) > AF (0.424).

Conclusion

Dosing algorithms based on warfarin indications are more suitable for predicting warfarin doses. Our research provides a novel strategy to develop indication-specific warfarin dosing algorithms to improve the efficacy and safety of warfarin prescribing.

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References

  1. Xie C, Xue L, Zhang Y, et al. Comparison of the prediction performance of different warfarin dosing algorithms based on Chinese patients. Pharmacogenomics. 2020;21:23–32.

    Article  CAS  PubMed  Google Scholar 

  2. Shaw K, Amstutz U, Kim RB, et al. Clinical practice recommendations on genetic testing of CYP2C9 and VKORC1 variants in warfarin therapy. Ther Drug Monit. 2015;37:428–36.

    Article  CAS  PubMed  Google Scholar 

  3. Johnson JA, Caudle KE, Gong L, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for pharmacogenetics-guided warfarin dosing: 2017 update. Clin Pharmacol Ther. 2017;102:397–404.

    Article  CAS  PubMed  Google Scholar 

  4. Gustafsson D, Bylund R, Antonsson T, et al. A new oral anticoagulant: the 50-year challenge. Nat Rev Drug Discov. 2004;3:649–59.

    Article  CAS  PubMed  Google Scholar 

  5. Miao L, Yang J, Huang C, et al. Contribution of age, body weight, and CYP2C9 and VKORC1 genotype to the anticoagulant response to warfarin: proposal for a new dosing regimen in Chinese patients. Eur J Clin Pharmacol. 2007;63:1135–41.

    Article  CAS  PubMed  Google Scholar 

  6. Wei M, Ye F, Xie D, et al. A new algorithm to predict warfarin dose from polymorphisms of CYP4F2, CYP2C9 and VKORC1 and clinical variables: derivation in Han Chinese patients with non valvular atrial fibrillation. Thromb Haemost. 2012;107:1083–91.

    Article  CAS  PubMed  Google Scholar 

  7. Zhong SL, Yu XY, Liu Y, et al. Integrating interacting drugs and genetic variations to improve the predictability of warfarin maintenance dose in Chinese patients. Pharmacogenet Genomics. 2012;22:176–82.

    Article  CAS  PubMed  Google Scholar 

  8. Bader L, Mahfouz A. The effect of genetic and nongenetic factors on warfarin dose variability in Qatari population. Pharmacogenomics J. 2020;20:277–84.

    Article  CAS  PubMed  Google Scholar 

  9. Wu AH. Use of genetic and nongenetic factors in warfarin dosing algorithms. Pharmacogenomics. 2007;8:851–61.

    Article  CAS  PubMed  Google Scholar 

  10. Huang SW, Chen HS, Wang XQ, et al. Validation of VKORC1 and CYP2C9 genotypes on interindividual warfarin maintenance dose: a prospective study in Chinese patients. Pharmacogenet Genomics. 2009;19:226–34.

    Article  CAS  PubMed  Google Scholar 

  11. Li X, Liu R, Luo ZY, et al. Comparison of the predictive abilities of pharmacogenetics-based warfarin dosing algorithms using seven mathematical models in Chinese patients. Pharmacogenomics. 2015;16:583–90.

    Article  CAS  PubMed  Google Scholar 

  12. Wadelius M, Chen LY, Lindh JD, et al. The largest prospective warfarin-treated cohort supports genetic forecasting. Blood. 2009;113:784–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yang M, Choi R, Kim JS, et al. Evaluation of 16 genotype-guided warfarin dosing algorithms in 310 Korean patients receiving warfarin treatment: poor prediction performance in VKORC1 1173C carriers. Clin Ther. 2016;38:2666–74.

    Article  CAS  PubMed  Google Scholar 

  14. Zhu YB, Hong XH, Wei M, et al. Development of a novel individualized warfarin dose algorithm based on a population pharmacokinetic model with improved prediction accuracy for Chinese patients after heart valve replacement. Acta Pharmacol Sin. 2017;38:434–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gao W, Li Z, Chen W, et al. Performance evaluation of warfarin dose prediction algorithms and effects of clinical factors on warfarin dose in Chinese patients. Ther Drug Monit. 2021;43(4):527–35.

    Article  CAS  PubMed  Google Scholar 

  16. Klein TE, Altman RB, Eriksson N, et al. Estimation of the warfarin dose with clinical and pharmacogenetic data. N Engl J Med. 2009;360:753–64.

    Article  CAS  PubMed  Google Scholar 

  17. Ohno M, Yamamoto A, Ono A, et al. Influence of clinical and genetic factors on warfarin dose requirements among Japanese patients. Eur J Clin Pharmacol. 2009;65:1097–103.

    Article  CAS  PubMed  Google Scholar 

  18. Sasano M, Ohno M, Fukuda Y, et al. Verification of pharmacogenomics-based algorithms to predict warfarin maintenance dose using registered data of Japanese patients. Eur J Clin Pharmacol. 2019;75:901–11.

    Article  CAS  PubMed  Google Scholar 

  19. Kaye JB, Schultz LE, Steiner HE, et al. Warfarin pharmacogenomics in diverse populations. Pharmacotherapy. 2017;37:1150–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lin M, Yu L, Qiu H, et al. Verification of five pharmacogenomics-based warfarin administration models. Indian J Pharmacol. 2016;48:258–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Cho SM, Lee KY, Choi JR, et al. Development and comparison of warfarin dosing algorithms in stroke patients. Yonsei Med J. 2016;57:635–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lei X, Guo Y, Sun J, et al. Accuracy assessment of pharmacogenetic algorithms for warfarin dose prediction in Chinese patients. Am J Hematol. 2012;87:541–4.

    Article  CAS  PubMed  Google Scholar 

  23. Cho HJ, On YK, Bang OY, et al. Development and comparison of a warfarin-dosing algorithm for Korean patients with atrial fibrillation. Clin Ther. 2011;33:1371–80.

    Article  CAS  PubMed  Google Scholar 

  24. Li W, Zhao P, Chen L, et al. Impact of CYP2C9, VKORC1, ApoE and ABCB1 polymorphisms on stable warfarin dose requirements in elderly Chinese patients. Pharmacogenomics. 2020;21:101–10.

    Article  CAS  PubMed  Google Scholar 

  25. Kirchhof P, Benussi S, Kotecha D, et al. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur Heart J. 2016;37:2893–962.

    Article  PubMed  Google Scholar 

  26. Nishimura RA, Otto CM, Bonow RO, et al. 2017 AHA/ACC focused update of the 2014 AHA/ACC guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2017;135:e1159–95.

    Article  PubMed  Google Scholar 

  27. Kılıç S, Çelik A, Çekirdekçi E, et al. The prevalence and risks of inappropriate combination of aspirin and warfarin in clinical practice: results from WARFARIN-TR study. Balkan Med J. 2019;36:17–22.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Makar-Aušperger K, Krželj K, Lovrić Benčić M, et al. Warfarin dosing according to the genotype-guided algorithm is most beneficial in patients with atrial fibrillation: a randomized parallel group trial. Ther Drug Monit. 2018;40:362–8.

    Article  PubMed  Google Scholar 

  29. Zhang W, Zhang WJ, Zhu J, et al. Genetic polymorphisms are associated with variations in warfarin maintenance dose in Han Chinese patients with venous thromboembolism. Pharmacogenomics. 2012;13:309–21.

    Article  CAS  PubMed  Google Scholar 

  30. Maramba A, Ncube S, Mandisodza A, et al. An assessment of the effectiveness of warfarin therapy monitoring systems on thrombophilic patients in zimbabwe. TH Open. 2018;2:e325–8.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Dang MT, Hambleton J, Kayser SR. The influence of ethnicity on warfarin dosage requirement. Ann Pharmacother. 2005;39:1008–12.

    Article  CAS  PubMed  Google Scholar 

  32. Gaikwad T, Ghosh K, Shetty S. VKORC1 and CYP2C9 genotype distribution in Asian countries. Thromb Res. 2014;134:537–44.

    Article  CAS  PubMed  Google Scholar 

  33. Miura T, Nishinaka T, Terada T, et al. Relationship between aging and dosage of warfarin: the current status of warfarin anticoagulant therapy for Japanese outpatients in a department of cardiovascular medicine. J Cardiol. 2009;53:355–60.

    Article  PubMed  Google Scholar 

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Funding

This study was supported by the Fundamental Research Program of Shanxi Province (20210302124649) and WU JIEPING Medical Foundation (320.6750.2021-08-12).

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Authors and Affiliations

  1. Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, 99 Longcheng Street, Taiyuan, 030032, China

    Weiqi Gao, Weihong Chen & Zhihong Li

  2. Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China

    Weiqi Gao, Weihong Chen & Zhihong Li

  3. School of Pharmacy, Shanxi Medical University, Taiyuan, 030001, China

    Zhijiao Zhang & Zhaobo Guan

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  1. Weiqi Gao
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  2. Zhijiao Zhang
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  4. Weihong Chen
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  5. Zhihong Li
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Correspondence to Zhihong Li.

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Gao, W., Zhang, Z., Guan, Z. et al. Developing Chinese race-specific warfarin dose prediction algorithms. Int J Clin Pharm 45, 731–738 (2023). https://doi.org/10.1007/s11096-023-01565-1

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  • DOI: https://doi.org/10.1007/s11096-023-01565-1

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