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Preferences for Oral Anticoagulants in Atrial Fibrillation: a Best–Best Discrete Choice Experiment

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Abstract

Background

Atrial fibrillation (AF) is recognised as a growing clinical and public health problem in many countries, owing to disability and death from stroke associated with the condition, high hospitalisation costs and an increasing prevalence with ageing populations. Under-treatment with oral anticoagulants has been a significant challenge of treatment, historically related to patient concerns over the safety and convenience of warfarin, which until recently was the only oral anticoagulant available.

Objectives

The aim of this study is to examine: (1) patient preferences for attributes of warfarin and the new oral anticoagulants (dabigatran, rivaroxaban, apixaban) in AF; (2) which attributes are most important; and (3) whether current under-treatment is likely to improve with the new oral anticoagulants.

Methods

This study was conducted in Melbourne, Australia, with members of the general public with or without AF aged ≥40 years, where those without AF proxy for newly-diagnosed patients. Participants completed a computerised best–best discrete choice experiment (and follow-up interview) as if they had AF with a moderate-to-high risk of stroke. Choice data were modelled using mixed rank-ordered logit. Relative value was explored via estimation of marginal rates of substitution with predicted probability analysis used to simulate potential uptake of oral anticoagulants.

Results

Seventy-six participants were recruited and completed the study. Efficacy (stroke risk) was more important than safety (bleed risk, antidote), which were both considerably more important than convenience factors (blood tests, dose frequency, drug or food interactions). Cost was also important. Predicted use of the new oral anticoagulants (and under-treatment of AF) using simulation, given moderate-to-high risk of stroke, is 25 % (52 %), 54 % (29 %) and 70 % (21 %) assuming a market price of AUD$120/month, AUD$30/month (subsidised price) and AUD$30/month with an antidote, respectively.

Conclusions

Based on the study sample and the modelled attributes, the overall profiles of the new oral anticoagulants were preferred to warfarin as their cost decreased. Public subsidisation and the development of antidotes (such as vitamin K for warfarin) for the new oral anticoagulants may have a positive effect on the under-treatment of AF.

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Notes

  1. Altered blood flow through the heart can result in blood clot formation, which can block blood flow to other areas of the body, including the brain causing an ischaemic stroke.

  2. Under-treatment includes patients going untreated or using aspirin in lieu of oral anticoagulants where oral anticoagulants are recommended.

  3. The CHADS2 risk index estimates stroke risk in AF patients based on the following risk factors: Congestive heart failure, Hypertension, Age ≥75 years, Diabetes, and prior Stroke, transient ischaemic attach or thromboembolism.

  4. The HAS-BLED risk index estimates bleed risk in AF patients based on the following risk factors: Hypertension, Abnormal renal or liver function, prior Stroke, prior Bleeding, Labile INRs, Age ≥65 years (“Elderly”), current use of Drugs with elevated bleed risk (including alcohol).

  5. Results available on request.

Reference

  1. Wong CX, Brooks AG, Lau DH, Leong DP, Sun MT, Sullivan T, et al. Factors associated with the epidemic of hospitalizations due to atrial fibrillation. Am J Cardiol. 2012;110(10):1496–9.

    Article  PubMed  Google Scholar 

  2. Wattigney WA, Mensah GA, Croft JB. Increasing trends in hospitalization for atrial fibrillation in the United States, 1985 through 1999: implications for primary prevention. Circulation. 2003;108(6):711–6.

    Article  PubMed  Google Scholar 

  3. Stewart S, Murphy NF, Walker A, McGuire A, McMurray JJV. Cost of an emerging epidemic: an economic analysis of atrial fibrillation in the UK. [Erratum appears in Heart. 2007 Nov; 93(11):1472 Note: Murphy, N [corrected to Murphy, N F]]. Heart. 2004;90(3):286–92.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  4. Riley AB, Manning WJ. Atrial fibrillation: an epidemic in the elderly. Expert Rev Cardiovasc Ther. 2011;9(8):1081–90.

    Article  PubMed  Google Scholar 

  5. Lip GY, Gibbs CR. Does heart failure confer a hypercoagulable state? Virchow’s triad revisited. J Am Coll Cardiol. 1999;33(5):1424–6.

    Article  PubMed  CAS  Google Scholar 

  6. Myat A, Ahmad Y, Haldar S, Tantry US, Redwood SR, Gurbel PA, et al. Is bleeding a necessary evil? The inherent risk of antithrombotic pharmacotherapy used for stroke prevention in atrial fibrillation. Expert Rev Cardiovasc Ther. 2013;11(8):1029–49.

    Article  PubMed  CAS  Google Scholar 

  7. Singer DE, Chang Y, Fang MC, Borowsky LH, Pomernacki NK, Udaltsova N, et al. The net clinical benefit of warfarin anticoagulation in atrial fibrillation. Ann Intern Med. 2009;151(5):297–305.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Fang MC, Stafford RS, Ruskin JN, Singer DE. National trends in antiarrhythmic and antithrombotic medication use in atrial fibrillation. Arch Intern Med. 2004;164(1):55–60.

    Article  PubMed  Google Scholar 

  9. Gage BF, Boechler M, Doggette AL, Fortune G, Flaker GC, Rich MW, et al. Adverse outcomes and predictors of underuse of antithrombotic therapy in medicare beneficiaries with chronic atrial fibrillation. Stroke. 2000;31(4):822–7.

    Article  PubMed  CAS  Google Scholar 

  10. Walker AM, Bennett D. Epidemiology and outcomes in patients with atrial fibrillation in the United States. Heart Rhythm. 2008;5(10):1365–72.

    Article  PubMed  Google Scholar 

  11. Ogilvie IM, Newton N, Welner SA, Cowell W, Lip GYH. Underuse of oral anticoagulants in atrial fibrillation: a systematic review. Am J Med. 2010;123(7):638.e4–645.e4.

    Article  Google Scholar 

  12. Connolly SJ, Ezekowitz MD, Yusuf S, Eikelboom J, Oldgren J, Parekh A, et al. Dabigatran versus warfarin in patients with atrial fibrillation. [Erratum appears in N Engl J Med. 2010 Nov 4;363(19):1877]. N Engl J Med. 2009;361(12):1139–51.

    Article  PubMed  CAS  Google Scholar 

  13. Patel MR, Mahaffey KW, Garg J, Pan G, Singer DE, Hacke W, et al. Rivaroxaban versus warfarin in nonvalvular atrial fibrillation. N Engl J Med. 2011;365(10):883–91.

    Article  PubMed  CAS  Google Scholar 

  14. Granger CB, Alexander JH, McMurray JJV, Lopes RD, Hylek EM, Hanna M, et al. Apixaban versus warfarin in patients with atrial fibrillation. N Engl J Med. 2011;365(11):981–92.

    Article  PubMed  CAS  Google Scholar 

  15. Pengo V, Crippa L, Falanga A, Finazzi G, Marongiu F, Palareti G, et al. Questions and answers on the use of dabigatran and perspectives on the use of other new oral anticoagulants in patients with atrial fibrillation: a consensus document of the Italian Federation of Thrombosis Centers (FCSA). Thromb Haemost. 2011;106(5):868–76.

    Article  PubMed  CAS  Google Scholar 

  16. Lip GYH, Halperin JL, Tse H-F. The 2010 European Society of Cardiology Guidelines on the management of atrial fibrillation: an evolution or revolution? Chest. 2011;139(4):738–41.

    Article  PubMed  Google Scholar 

  17. Mark TL, Swait J. Using stated preference modeling to forecast the effect of medication attributes on prescriptions of alcoholism medications. Value Health. 2003;6(4):474–82.

    Article  PubMed  Google Scholar 

  18. Gage BF, Cardinalli AB, Albers GW, Owens DK. Cost-effectiveness of warfarin and aspirin for prophylaxis of stroke in patients with nonvalvular atrial fibrillation. JAMA. 1995;274(23):1839–45.

    Article  PubMed  CAS  Google Scholar 

  19. Gage BF, Cardinalli AB, Owens DK. Cost-effectiveness of preference-based antithrombotic therapy for patients with nonvalvular atrial fibrillation. Stroke. 1998;29(6):1083–91.

    Article  PubMed  CAS  Google Scholar 

  20. Protheroe J, Fahey T, Montgomery AA, Peters TJ. The impact of patients’ preferences on the treatment of atrial fibrillation: observational study of patient based decision analysis. BMJ. 2000;320(7246):1380–4.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  21. Naglie IG, Detsky AS. Treatment of chronic nonvalvular atrial fibrillation in the elderly: a decision analysis. Med Decis Making. 1992;12(4):239–49.

    Article  PubMed  CAS  Google Scholar 

  22. Disch DL, Greenberg ML, Holzberger PT, Malenka DJ, Birkmeyer JD. Managing chronic atrial fibrillation: a Markov decision analysis comparing warfarin, quinidine, and low-dose amiodarone. Ann Intern Med. 1994;120(6):449–57.

    Article  PubMed  CAS  Google Scholar 

  23. Eckman MH, Falk RH, Pauker SG. Cost-effectiveness of therapies for patients with nonvalvular atrial fibrillation. Arch Intern Med. 1998;158(15):1669–77.

    Article  PubMed  CAS  Google Scholar 

  24. Man-Son-Hing M, Nichol G, Lau A, Laupacis A. Choosing antithrombotic therapy for elderly patients with atrial fibrillation who are at risk for falls. Arch Intern Med. 1999;159(7):677–85.

    Article  PubMed  CAS  Google Scholar 

  25. Thomson R, Parkin D, Eccles M, Sudlow M, Robinson A. Decision analysis and guidelines for anticoagulant therapy to prevent stroke in patients with atrial fibrillation. [Erratum appears in Lancet 2000 Apr 22;355(9213):1466]. Lancet. 2000;355(9208):956–62.

    Article  PubMed  CAS  Google Scholar 

  26. Eckman MH. Patient-centered decision making: a view of the past and a look toward the future. Med Decis Mak. 2001;21(3):241–7.

    Article  CAS  Google Scholar 

  27. Eckman MH, Levine HJ, Salem DN, Pauker SG. Making decisions about antithrombotic therapy in heart disease: decision analytic and cost-effectiveness issues. Chest. 1998;114(5 Suppl):699S–714S.

    Article  PubMed  CAS  Google Scholar 

  28. Johnston JA, Eckman MH. Use of regression modeling to simulate patient-specific decision analysis for patients with nonvalvular atrial fibrillation. Med Decis Mak. 2003;23(5):361–8.

    Article  Google Scholar 

  29. Wess ML, Schauer DP, Johnston JA, Moomaw CJ, Brewer DE, Cook EF, et al. Application of a decision support tool for anticoagulation in patients with non-valvular atrial fibrillation. J Gen Intern Med. 2008;23(4):411–7.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Man-Son-Hing M, Laupacis A, O’Connor A, Wells G, Lemelin J, Wood W, et al. Warfarin for atrial fibrillation: the patient’s perspective. Arch Intern Med. 1996;156(16):1841–8.

    Article  PubMed  CAS  Google Scholar 

  31. Howitt A, Armstrong D. Implementing evidence based medicine in general practice: audit and qualitative study of antithrombotic treatment for atrial fibrillation. BMJ. 1999;318(7194):1324–7.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  32. Devereaux PJ, Anderson DR, Gardner MJ, Putnam W, Flowerdew GJ, Brownell BF, et al. Differences between perspectives of physicians and patients on anticoagulation in patients with atrial fibrillation: observational study. BMJ. 2001;323(7323):1218–22.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  33. Alonso-Coello P, Montori VM, Sola I, Schunemann HJ, Devereaux P, Charles C, et al. Values and preferences in oral anticoagulation in patients with atrial fibrillation, physicians’ and patients’ perspectives: protocol for a two-phase study. BMC Health Serv Res. 2008;8:221.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Lip GYH, Andreotti F, Fauchier L, Huber K, Hylek E, Knight E, et al. Bleeding risk assessment and management in atrial fibrillation patients: a position document from the European Heart Rhythm Association, endorsed by the European Society of Cardiology Working Group on Thrombosis. Europace. 2011;13(5):723–46.

    Article  PubMed  Google Scholar 

  35. Lancsar E, Wildman J, Donaldson C, Ryan M, Baker R. Deriving distributional weights for QALYs through discrete choice experiments. J Health Econ. 2011;30(2):466–78.

    Article  PubMed  Google Scholar 

  36. Lancsar E, Louviere J, Donaldson C, Currie G, Burgess L. Best worst discrete choice experiments in health: methods and an application. Soc Sci Med. 2013;76(1):74–82.

    Article  PubMed  Google Scholar 

  37. Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user’s guide. Pharmacoeconomics. 2008;26(8):661–77.

    Article  PubMed  Google Scholar 

  38. de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012;21(2):145–72.

    Article  PubMed  Google Scholar 

  39. Sculpher M, Bryan S, Fry P, de Winter P, Payne H, Emberton M. Patients’ preferences for the management of non-metastatic prostate cancer: discrete choice experiment. BMJ. 2004;328(7436):382.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Bridges JFP, Hauber AB, Marshall D, Lloyd A, Prosser LA, Regier DA, et al. Conjoint analysis applications in health: a checklist: a report of the ISPOR good research practices for conjoint analysis task force. Value Health. 2011;14(4):403–13.

    Article  PubMed  Google Scholar 

  41. Reed Johnson F, Lancsar E, Marshall D, Kilambi V, Mühlbacher A, Regier DA, et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR conjoint analysis experimental design good research practices task force. Value Health. 2013;16(1):3–13.

    Article  PubMed  CAS  Google Scholar 

  42. Train K. Discrete choice methods with simulation. Cambridge: Cambridge University Press; 2009.

    Book  Google Scholar 

  43. Louviere JJ, Street D, Burgess L, Wasi N, Islam T, Marley AAJ. Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information. J Choice Model. 2008;1(1):128–64.

    Article  Google Scholar 

  44. Wolf PA, D’Agostino RB, Belanger AJ, Kannel WB. Probability of stroke: a risk profile from the Framingham Study. Stroke. 1991;22(3):312–8.

    Article  PubMed  CAS  Google Scholar 

  45. Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA. 2001;285(18):2370–5.

    Article  PubMed  CAS  Google Scholar 

  46. Fuller R, Dudley N, Blacktop J. Avoidance hierarchies and preferences for anticoagulation: semi-qualitative analysis of older patients’ views about stroke prevention and the use of warfarin. Age Ageing. 2004;33(6):608–11.

    Article  PubMed  Google Scholar 

  47. Man-Son-Hing M, O’Connor AM, Drake E, Biggs J, Hum V, Laupacis A. The effect of qualitative vs. quantitative presentation of probability estimates on patient decision-making: a randomized trial. Health Expect. 2002;5(3):246–55.

    Article  PubMed  Google Scholar 

  48. Holbrook A, Labiris R, Goldsmith CH, Ota K, Harb S, Sebaldt RJ. Influence of decision aids on patient preferences for anticoagulant therapy: a randomized trial. CMAJ. 2007;176(11):1583–7.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lane DA, Ponsford J, Shelley A, Sirpal A, Lip GYH. Patient knowledge and perceptions of atrial fibrillation and anticoagulant therapy: effects of an educational intervention programme. The West Birmingham Atrial Fibrillation Project. Int J Cardiol. 2006;110(3):354–8.

    Article  PubMed  Google Scholar 

  50. Lip G, Agnelli G, Thach A, Knight E, Rost D, Tangelder M. Oral anticoagulation in atrial fibrillation: a pan-European patient survey. Eur J Intern Med. 2007;18(3):202–8.

    Article  PubMed  CAS  Google Scholar 

  51. Dantas GC, Thompson BV, Manson JA, Tracy CS, Upshur REG. Patients’ perspectives on taking warfarin: qualitative study in family practice. BMC Family Pract. 2004;5:15.

    Article  Google Scholar 

  52. Abstract presentations from the AABB Annual Meeting and TXPO. Transfusion. 2009;49.

  53. Abstract Presentations from the AABB Annual Meeting and CTTXPO. Transfusion. 2010;50.

  54. Aspirin. MIMS. 2012. http://www.mims.com.au. Accessed 15 March 2012.

  55. Warfarin. MIMS. 2012. http://www.mims.com.au. Accessed 15 March 2012.

  56. Rivaroxaban. MIMS. 2012. http://www.mims.com.au. Accessed 15 March 2012.

  57. Gage BF, Waterman AD, Shannon W, Boechler M, Rich MW, Radford MJ. Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. JAMA. 2001;285(22):2864–70.

    Article  PubMed  CAS  Google Scholar 

  58. Pisters R, Lane DA, Nieuwlaat R, de Vos CB, Crijns HJGM, Lip GYH. A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey. Chest. 2010;138(5):1093–100.

    Article  PubMed  Google Scholar 

  59. Moulds RTGLCEG. Therapeutic guidelines: cardiovascular. West Melbourne: Therapeutic Guidelines; 2012.

  60. Rietbrock S, Heeley E, Plumb J, van Staa T. Chronic atrial fibrillation: incidence, prevalence, and prediction of stroke using the Congestive heart failure, Hypertension, Age > 75, Diabetes mellitus, and prior Stroke or transient ischemic attack (CHADS2) risk stratification scheme. Am Heart J. 2008;156(1):57–64.

    Article  PubMed  Google Scholar 

  61. Lip GYH, Edwards SJ. Stroke prevention with aspirin, warfarin and ximelagatran in patients with non-valvular atrial fibrillation: a systematic review and meta-analysis. Thromb Res. 2006;118(3):321–33.

    Article  PubMed  CAS  Google Scholar 

  62. Bucher HC, Guyatt GH, Griffith LE, Walter SD. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol. 1997;50(6):683–91.

    Article  PubMed  CAS  Google Scholar 

  63. Edwards A, Elwyn G, Mulley A. Explaining risks: turning numerical data into meaningful pictures. BMJ. 2002;324(7341):827–30.

    Article  PubMed  PubMed Central  Google Scholar 

  64. McFadden D. In: Zarembka P (ed). Frontiers of econometrics.: Academic Press, 1974.

  65. Beggs S, Cardell S, Hausman J. Assessing the potential demand for electric cars. J Econom. 1981;17(1):1–19.

    Article  Google Scholar 

  66. Revelt D, Train K. Mixed logit with repeated choices: households’ choices of appliance efficiency level. Rev Econ Stat. 1998;80(4):647–57.

    Article  Google Scholar 

  67. Schiele F, van Ryn J, Canada K, Newsome C, Sepulveda E, Park J et al. A specific antidote for dabigatran: functional and structural characterization. Blood. 121(18):3554–62.

  68. Jackson SL, Peterson GM, Vial JH, Daud R, Ang SY. Outcomes in the management of atrial fibrillation: clinical trial results can apply in practice. Intern Med J. 2001;31(6):329–36.

    Article  PubMed  CAS  Google Scholar 

  69. Inglis S, McLennan S, Dawson A, Birchmore L, Horowitz JD, Wilkinson D, et al. A new solution for an old problem? Effects of a nurse-led, multidisciplinary, home-based intervention on readmission and mortality in patients with chronic atrial fibrillation. J Cardiovasc Nurs. 2004;19(2):118–27.

    Article  PubMed  Google Scholar 

  70. Kelly AM, Kerr D, Hew R. Prevention of stroke in chronic and recurrent atrial fibrillation: role of the emergency department in identification of “at-risk” patients. Aust Health Rev. 2001;24(3):61–5.

    Article  PubMed  CAS  Google Scholar 

  71. Fang MC, Go AS, Chang Y, Borowsky LH, Pomernacki NK, Udaltsova N, et al. Warfarin discontinuation after starting warfarin for atrial fibrillation. Circ Cardiovasc Qual Outcomes. 2010;3(6):624–31.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Man-Son-Hing M, Laupacis A, O’Connor AM, Biggs J, Drake E, Yetisir E, et al. A patient decision aid regarding antithrombotic therapy for stroke prevention in atrial fibrillation: a randomized controlled trial. JAMA. 1999;282(8):737–43.

    Article  PubMed  CAS  Google Scholar 

  73. Thomson RG, Eccles MP, Steen IN, Greenaway J, Stobbart L, Murtagh MJ, et al. A patient decision aid to support shared decision-making on anti-thrombotic treatment of patients with atrial fibrillation: randomised controlled trial. Qual Safe Health Care. 2007;16(3):216–23.

    Article  Google Scholar 

  74. Falloon G. Using avatars and virtual environments in learning: what do they have to offer? Br J Educat Technol. 2010;41(1):108–22.

    Article  Google Scholar 

  75. Ryan M, Skåtun D. Modelling non-demanders in choice experiments. Health Econ. 2004;13(4):397–402.

    Article  PubMed  Google Scholar 

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Conflict of Interest

All authors declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work. All authors had full access to all of the data (including statistical reports and tables) in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis.

Contributors

PG and EL initiated the research and designed the study with SZ. PG and EL undertook the data collection, follow-up interviews and data analyses. PG and EL drafted the manuscript with input from SZ. PG and EL will act as guarantors for the paper. They accept full responsibility for the conduct of the study and controlled the decision to publish.

Ethical approval

Approval was obtained from the Monash University Human Ethics Committee (MUHREC). Project Number: CF12/0783–2012000337. All participants gave informed consent before taking part in the study. Participants in the study are not identifiable. Consent was obtained for the study with the provision that participant responses would remain anonymous.

Funding

This study was funded by the Centre for Health Economics, Monash University. No other specific funding was received.

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Ghijben, P., Lancsar, E. & Zavarsek, S. Preferences for Oral Anticoagulants in Atrial Fibrillation: a Best–Best Discrete Choice Experiment. PharmacoEconomics 32, 1115–1127 (2014). https://doi.org/10.1007/s40273-014-0188-0

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