Skip to main content
  • Original basic research
  • Open access
  • Published:

Dabigatran versus warfarin under standard or pharmacogenetic-guided management for the prevention of stroke and systemic thromboembolism in patients with atrial fibrillation: a cost/utility analysis using an analytic decision model

Abstract

Background

Atrial fibrillation (AF) is the most common form of heart arrhythmia and a leading cause of stroke and systemic embolism. Chronic anticoagulation is recommended for preventing those complications. Our study aimed to compare the cost/utility (CU) of three main anticoagulation options: 1) standard warfarin dosing (SD-W) 2) warfarin dosage under the guidance of CYP2C9 and VKORC1 genotyping (GT-W) and 3) dabigatran 150 mg twice a day.

Methods

A Markov state transition model was built to simulate the expected C/U of dabigatran, SD-W and GT-W anticoagulation therapy for the prevention of stroke and systemic thromboembolism in patients with atrial fibrillation over a period of 5 years under the perspective of the public health care system. Model inputs were derived from extensive literature search and government’s data bases. Outcomes considered were the number of total major events (thromboembolic and hemorrhagic events), total costs in Canadian dollars (1CAD$ = 1$US), total quality-adjusted life years (QALYs), costs/QALYs and incremental costs/QALYs gained (ICUR).

Results

Raw base case results show that SD-W has the lowest C/U ratio. However, the dabigatran option might be considered as an alternative, as its cost per additional QALY gained compared to SD-W is CAD $ 4 765, i.e. less than 50 000, the ICUR threshold generally accepted to adopt an intervention. At the same threshold, GT-W doesn’t appear to be an alternative to SD-W. Our results were robust to one-way and multi-way sensitivity analyses.

Conclusion

SD-W has the lowest C/U ratio among the 3 options. However, dabigatran might be considered as an alternative. GT-W is not C/U and should not currently be recommended for the routine anticoagulotherapy management of AF patients.

Introduction

Atrial fibrillation (AF) is the most common form of heart arrhythmia. Its prevalence exceeds 2% in the general population of individuals who are 40 years old and more and increases with age from 0.1% in 50 years old patients to 10%-15% in those who are more than 80 years old[1]. The number of AF patients is expected to increase as the result of population ageing[1]. AF is the leading cause of stroke and is also associated with a high risk of systemic embolism[2, 3]. Thus, long-term anticoagulation therapy is recommended in all patients with AF. Vitamin K antagonists such as warfarin are currently the most prescribed oral anticoagulants with, in the USA only, around 31 million prescriptions filled each year[4].

By depleting active vitamin K, warfarin therapy reduces the risk of stroke by about 60 percent in patients with AF[2]. However, warfarin has a narrow therapeutic range and is not devoid of adverse reactions: hemorrhages or thromboses occur in 6 to 39% of patients annually and are mainly related to the effectiveness of anticoagulation[57].

Yet, there is a large inter-individual heterogeneity in the optimal warfarin dosage required to achieve a therapeutic effect (inter-patients variability varies by a factor of 20)[810]. This optimal dosage is defined as a 2 to 3 score in the international normalized ratio (INR) score of the prothrombin time, as a score below 2 increases the risk of thrombotic events and a score above 3 of hemorrhagic events. Maintaining patients in the therapeutic range is therefore of utmost importance. Close INR monitoring is needed to reduce the risk of bleeding and thromboembolism[810].

Inter-patient variability in plasma warfarin concentration is dependent on variables such as age, diet, drug interactions, and liver function but also on some genes that affect the metabolism of the drug[11]. The most important known genes in the pharmacokinetics of warfarin are CYP2C9 (the gene coding for cytochrome P450 2C9) and VKORC1 (a gene coding for the vitamin K epoxide reductase complex subunit). Genetic variation in these two genes account for approximately 30% to 50% of the variance in warfarin concentration between individuals[9, 10, 12]. Adjusting the first doses of warfarin under the guidance of CYP2C9 and VKORC1 alleles determination reduces the risk of bleeding and thromboembolic complications[13]. Indeed, the US Federal Drug Administration (FDA) suggested, in August 2007, to update the warfarin information sheet to include information on the possibility of CYP2C9 and VKORC1 pharmacogenetic testing[14].

Several new drugs have been developed recently that represent a new strategy in the fight against thromboembolism in FA patients. Dabigatran is the first of these new products and constitutes the main alternative to warfarin presently. Dabigatran exerts its anti-thrombotic effect by binding to thrombin, which prevents the conversion of fibrinogen to fibrin. It has been shown to be associated with a lower occurrence of systemic embolism and stroke than warfarin and does not require a close laboratory monitoring[15, 16]. However, because of its higher price, some health authorities still consider dabigatran as an exception drug that requires an authorization prior to its prescription for patients covered by public health insurances.

Economic studies have shown that dabigatran is expected to be a C/U option under an acceptability threshold of 50 000$/QALY[1722], while results on the comparison between warfarin standard dosing (SD-W) and warfarin genetic-guided dosing (GT-W) remain inconclusive[2326]. Only one study has compared the 3 strategies (dabigatran 150 mg, SD-W and GT-W). It concluded that dabigatran 150 mg is a C/U option, under the perspective of the acceptability threshold of 50 000$/QALY. However, this study did not consider all major events related to anticoagulation and used a time horizon poorly supported by data[21]. Our study was conducted to evaluate the expected C/U of these anticoagulation options under the perspective of the public health care system in order to provide elements in support to the decision making process by health authorities.

Methodology

Modeling and event probabilities

A Markov state transition model was built to simulate the C/U of anticoagulation therapy for a virtual population of 10 000 individuals with a new diagnosis of AF under the perspective of the public health care system (Figure 1). The virtual population consisted of new AF patients with a mean age of 64 years (sd. = 8), who never had a previous stroke and who didn’t have a contraindication to anticoagulation therapy.

Figure 1
figure 1

Markov state transition model.

Outcomes considered were major hemorrhagic and thromboembolic events per 100 person-years, direct medical costs and quality-adjusted life-years (QALY). The model consists of daily cycles starting at the first day of anticoagulation treatment and ending 5 years later or at the death of an individual. The 5 years time horizon was chosen in order to take into account the chronicity of anticoagulation therapy in AF while avoiding to speculate about the long term effects of dabigatran.

Input parameters were retrieved from an extensive literature search of guidelines on anticoagulation in FA patients, and peer-reviewed published studies prioritized according to the following order: Quebec, other provinces of Canada, United States of America (USA), Europe and Australia.

The model begins by presenting the following three options: 1) SD-W; 2) GT-W; and 3) dabigatran 150 mg twice per day. Dabigatran was chosen among the new oral anticoagulants because it is the first one that has been approved in Canada and the first one that has been included in drug lists of the Canadian provincial public insurance schemes.

In the warfarin options (SD-W and GT-W), treatment monitoring is performed by measuring the international normalized ratio (INR). INR may be below (<2), within (between 2 and 3), or above (> 3) the therapeutic range. In the SD-W base case scenario, the time spent in each category (below, within and above) was based on the results of the RE-LY clinical trial comparing dabigatran and warfarin[16], while in the GT-W option, it was calculated using data from Anderson et al. 2007[13]. We assumed that after one year 100% of individuals, whatever the warfarin group they belong to, had reached a stable maintenance dose[27]. The proportion of patient time spent in each INR category (below, within and above) was considered to be similar for both groups[27]. In the dabigatran option, it was assumed that no laboratory monitoring was required.

According to event probabilities[16, 17, 28, 29], patients move into the Markov model through the following health states: no major event, major hemorrhagic event, major thromboembolic event, and death (Figure 2).

Figure 2
figure 2

Diagram of Markov health states transition.

Major thromboembolic and hemorrhagic events were considered according to the definition of Fihn et al.[30]. Hemorrhages were classified into two categories: 1) intracranial (intracerebral and subdural hematoma) and 2) extracranial. We assumed that all extracranial hemorrhages were gastrointestinal as they constitute the majority of extracranial hemorrhages related to anticoagulant therapy regardless of the type of treatment[28, 31]. Major thromboembolic events considered were strokes, myocardial infarctions (MI), deep vein thromboses (DVT) and pulmonary embolisms (PE). Following the occurrence of a major event, the model assumed that an individual visits the emergency room and that he has a defined probability of being hospitalized for a short or a long period of time. In addition, the model considers the probability of survival and of death after each event as well as the probability to have mild or severe post-event sequelae (see Table 1). It also considers a complete cessation of anticoagulant therapy in case of intracranial hemorrhage and a one month cessation in case of gastrointestinal bleeding[26].

Table 1 Model input parameters

Costs

Costs included in the study are those of the public health care system. Only direct costs were estimated (Table 2).

Health services consumed along the course of an anticoagulation therapy relate primarily to services associated with prescription, warfarine monitoring and management of thromboembolic or hemorrhagic complications (deep vein thromboses, pulmonary embolisms, strokes, myocardial infarctions, intracranial, sub-dural and extracranial hemorrhages)[42, 4955] as well as the cost of follow-up in case of sequelae from a stroke or an intracranial hemorrhage.

The quantification of services consumed was based on the literature. In all cases, we considered for baseline values, recommendations by the Canadian guidelines on the management of thrombosis and anticoagulation[42, 4955]. For items not found in the guidelines, we used relevant economic studies published in peer-reviewed journals. Unit prices for services consumed were calculated from the administrative data of the Quebec public health care system. The lowest prices from the list of drugs covered by Quebec public healthcare insurance (Régie d’assurance maladie du Québec (RAMQ)) were used to estimate the cost of outpatient medication, to which were added 6% for wholesalers as well as the pharmacist fees paid by RAMQ. Diagnosis related-groups (DRG) data were used to calculate the average cost of hospitalization to which were added the physician fees paid by RAMQ. The ministry of health SIFO data bank was used to calculate activity center unit prices for ambulatory care. These prices were increased to reflect the contribution of support activity centers to clinical services, using the direct method[56]. The rate base used for these sources was the values of the 2010-2011 fiscal year.

Utilities

Utility scores used for each health states are presented in Table 1. They were retrieved from published studies[21, 47, 48]. Utility scores were then used to weight the time spent in each health state to produce QALYs.

Simulation process and analysis

Simulations were carried out on the SCHNAPS platform simulator[57, 58] that runs on the CLUMEQ network super computers. In the “individual sampling model (ISM)” simulation that was performed, each virtual individual is generated and has his own path in the simulation process. Simulations were repeated 1000 times with different virtual populations of 10 000 individuals. However, each of the 1000 different virtual population generated was used for the three options compared. The principal outcome measured was the incremental cost-utility (ICUR) ratio calculated using the difference in average individual cost over the 1000 simulations divided by the difference in average QALYs. We considered the literature that proposes that an option can be accepted as cost-effective if its ICUR is CAD$ 50 000/QALY or less[59]. An option was considered dominated if it was more costly and less effective (less QALY) in comparison to its alternative (strict dominance) or if it’s incremental cost-effectiveness ratio was greater than that of the next, more effective, and more expensive alternative (extended dominance). All costs and QALY were discounted at a baseline annual rate of 3%.

Sensitivity analyses

Univariate and multiway probabilistic sensitivity analyses were performed using the parameters that were foreseen as having a possible impact on the outcomes (Table 1). One way sensitivity analyses were performed to evaluate the eventual impact of each single parameter on the results. We tested the minimum and the maximum values (from the 95% confidence intervals) for each of these variables. For the GT-W option, we tested several patient percentage times in therapeutic range (TTR) values in order to find the value for which GT-W would be cost-effective compared to SD-W. Subsequently, multi-way probabilistic sensitivity analyses using Monte Carlo simulations were performed using a virtual population of 5000 individuals. We assumed that event probabilities and utility scores followed a beta distribution, that costs followed a gamma distribution while relative risks were assumed to have a log-normal distribution[60]. A C/U acceptability curve[61] was then produced from 1000 Monte Carlo iterations in order to better define the joint uncertainty of the parameters on C/U ratios.

Validation

The Markov state decision model and the parameterization were validated by two experts (MA, PM) in genetics and anticoagulotherapy.

The simulation process was validated at each step to ensure that the data generated matched expected data. This consisted in verifying that the number of events (major bleedings and major thromboembolisms) corresponded to their predefined expected occurrence according to the literature[4954].

Ethical approval

This project was approved by the Research Ethics Committee of Laval University.

Results

Base-case C/U results are presented in Table 2. SD-W has the lowest raw C/U ratio. However, compared to SD-W, dabigatran increases QALYs by 0.2529 and increases the cost/individual by CAD$ 1205 for an ICUR of 4765/QALY gained. It is therefore under the commonly proposed threshold of 50 000$/QALY. GT-W is not C/U. Compared to SD-W, it increases QALYs only by 0.0085, and increases the cost/individual ratio by CAD$ 460 for an ICER of 54 118/QALY gained. It is also dominated by dabigatran (extended dominance).

Table 2 Base case results

Sensitivity analyses show that the results were robust either in one-way or in probabilistic multiway sensitivity analyses. In one-way sensitivity analyses, dabigatran remains an alternative to the SD-W option if the threshold of 50 000$/QALY is socially considered as acceptable[59]. Compared to SD-W, GT-W is not C/U except when this strategy allows patients to have an average of 76.8% of patient TTR, i.e. 20% more than in the SD-W option in the first year (Table 3). This value was found as the threshold for GT-W to be cost-effective and the ICUR is then 3250/QALY gained which is less than the acceptability threshold of 50 000 CAD $[59].

Table 3 One-way Sensitivity analysis results

In probabilistic multiway sensitivity analyses, dabigatran 150 mg remains the most C/U option. Compared to SD-W, it is dominant in 32% of iterations and cost-effective in 99.75% of iterations if the ceiling ratio threshold is fixed at CAD$ 50 000/QALY gained (Figure 3).

Figure 3
figure 3

Cost-utility acceptability curves. Blue: The curve represents the probability of dabigatran to be cost-effective compared to warfarin standard dosing at various ceiling ratios thresholds. Red: The curve represents the probability of warfarin pharmacogenetic guided dosing to be cost-effective compared to warfarin standard dosing at various ceiling ratios thresholds.

Discussion

In this study, we evaluated through a simulation model the expected cost-utility of three anticoagulation options namely SD-W, GT-W and dabigatran 150 mg twice daily, over a 5 years’ time horizon and under a public health care perspective. Our results show that the dabigatran option is the most C/U option if the public health care system accepts to invest CAD $4 800 per additional QALY gained. This amount is lower than the 50 000 per QALY gained commonly proposed as the threshold to adopt an innovation in North-America[59]. Although our study has used a more complete model than several other C/E analyses, its results are consistent with the literature[1720, 22]. Nevertheless, we don’t know if the results could be generalized to all other NOACs as there is still a lack of data concerning the direct comparison between the different products on the market. Currently, only indirect comparisons exist on the efficacy and safety of the NOACs that show that they are effective compared to warfarin. Yet, when they are compared to each other, there is no difference in efficacy, although some differences in safety might exist[62, 63].

The GT-W option was not cost/effective (if not dominated) compared to the two other options. This is in line with other studies that have compared warfarin treatments with or without genetic testing[23, 25, 26]. However, GT-W could be the most C/U option compared to SD-W and dabigatran150 mg, if the average patient time in the therapeutic range moves from 66.6% to 76.8% i.e. if it is 20% higher than in the SD-W option in the first year. However, this is a very ambitious objective to reach even in clinical trial conditions[13]. This result is close to that of You et al.[21] who showed that the GT-W option could become the most cost-effective option if the patient time in the therapeutic range was > 77%.

This study has some limitations. First, the key input parameters (events probabilities, INR control) comparing dabigatran vs. SD-W or SD-W vs. GT-W were taken from one single randomized controlled clinical trial. This issue could have decreased the ICUR since the effectiveness of dabigatran or GT-W may be overestimated by controlled clinical trials compared to the situation in real life. Nevertheless, we have done extensive sensitivity analyses in order to handle this problem.

The second limitation is the complexity of mapping the reality. Some simplifications and assumptions were inevitable in the modeling approach. For example, we did not consider minor events (bleedings and thromboembolism) that occur with anticoagulation therapy. This issue could have increased the ICURs.

Thirdly, our model did not consider patients’ adherence with medication in order to make a fairly comparison of the dabigatran option with the two others. Indeed, while longtime adherence for warfarin is available, there is still a lack of data on longtime adherence for dabigatran[64]. Taking into account the medication adherence would have led us to make assumptions about the adherence for dabigatran.

Finally, our model is limited by the consideration of only direct costs and one single perspective, i.e. the public healthcare perspective. The addition of the patients’ perspective could increase the ICUR especially in the case of SD-W that might require time and travel expenses for INR control.

Despite these limitations, the results of this study suggest that dabigatran 150 mg twice per day is a C/U alternative to SD-W. Its additional cost per QALY gained is considered as socially acceptable. GT-W is not C/U and should not now be recommended in routine management of warfarin anticoagulotheray in FA patients. However, our results produced in the Quebec/Canadian context (a quasi-exclusive public health care system) remain to be confirmed for other health care jurisdictions especially where the public system is not dominant.

References

  1. Healey JS, Parkash R, Pollak T, Tsang T, Dorian P, CCS Atrial Fibrillation Guidelines Committee: Canadian cardiovascular society atrial fibrillation guidelines 2010: etiology and initial investigations. Can J Cardiol 2011, 27(1):31-37. 10.1016/j.cjca.2010.11.015

    Article  PubMed  Google Scholar 

  2. Hart RG, Pearce LA, Aguilar MI: Meta-analysis: anti-thrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med 2007, 146(12):857-867. 10.7326/0003-4819-146-12-200706190-00007

    Article  PubMed  Google Scholar 

  3. Poli D, Antonucci E, Grifoni E, et al.: Stroke risk in atrial fibrillation patients on warfarin: predictive ability of risk stratification schemes for primary and secondary prevention. Thrombo Haemost 2009, 101(2):367-372.

    CAS  Google Scholar 

  4. Wysowski DK, Nourjah P, Swartz L: Bleeding complications with warfarin use: a prevalent adverse effect resulting in regulatory action. Arch Intern Med 2007, 167(13):1414-1419. 10.1001/archinte.167.13.1414

    Article  PubMed  Google Scholar 

  5. Krynetskiy E, McDonell P: Building individualized medicine: prevention of adverse reactions to warfarin therapy. J Pharmacol Exp Ther 2007, 322(2):427-434. 10.1124/jpet.106.117952

    Article  CAS  PubMed  Google Scholar 

  6. Lip GYH, Wrigley BJ, Pisters R: Anticoagulation versus placebo for heart failure in sinus rhythm. Cochrane Database Syst Rev 2012, (Issue 6):Art. No.: CD003336. 10.1002/14651858.CD003336.pub2

  7. Palareti G, Legnani C, Guazzaloca G, Lelia V, Cosmi B, Lunghi B, Marchetti G, Poli D, Pengo V, ad hoc Study Group of the Italian Federation of Anticoagulation Clinics*: Risks factors for highly unstable response to oral anticoagulation: a case–control study. Br J Haematol 2005, 129(1):72-78. 10.1111/j.1365-2141.2005.05417.x

    Article  CAS  PubMed  Google Scholar 

  8. Adcock DM, Koftan C, Crisan D, Kiechle FL: Effect of polymorphisms in the cytochrome P450 CYP2C9 gene on warfarin anticoagulation. Arch Pathol Lab Med 2004, 128(12):1360-1363.

    CAS  PubMed  Google Scholar 

  9. Wadelius M, Pirmohamed M: Pharmacogenetics of warfarin: current status and future challenges. Pharmacogenomics J 2007, 7(2):99-111. 10.1038/sj.tpj.6500417

    Article  CAS  PubMed  Google Scholar 

  10. Wadelius M, Chen LY, Eriksson N, Bumpstead S, Ghori J, Wadelius C, Bentley D, McGinnis R, Deloukas P: Association of warfarin dose with genes involved in its action and metabolism. Hum Genet 2007, 121(1):23-34. 10.1007/s00439-006-0260-8

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Thomas FJ, Mcleod HL, Watters JW: Pharmacogenomics: the influence of genomic variation on drug response. Curr Top Med Chem 2004, 4(13):1399-1409.

    Article  CAS  PubMed  Google Scholar 

  12. Tan G-M, Wu E, Lam YY, Yan BP: Role of warfarin pharmacogenetic testing in clinical practice. Pharmacogenomics J 2010, 11(3):439-448. 10.2217/pgs.10.8

    Article  CAS  Google Scholar 

  13. Anderson JL, Horne BD, Stevens SM, Grove AS, Barton S, Nicholas ZP, Kahn SF, May HT, Samuelson KM, Muhlestein JB, Carlquist JF, Couma-Gen Investigators: Randomized trial of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation. Circulation 2007, 116(22):2563-2570. 10.1161/CIRCULATIONAHA.107.737312

    Article  CAS  PubMed  Google Scholar 

  14. Gage BF, Lesko LJ: Pharmacogenetics of warfarin: regulatory, scientific, and clinical issues. J Thromb Thrombolysis 2007, 25(1):45-51.

    Article  PubMed  Google Scholar 

  15. Reddy P, Atay JK, Selbovitz LG, Connors JM, Piazza G, Block CC, Arpino PA, Berliner N, Hutter AM Jr, Fischer MA, Kuter D, Weitzman J, Sherwood GK, Almozlino A, Giugliano RP: Dabigatran: a review of clinical and pharmacoeconomic evidence. Crit Pathw Cardiol 2011, 10(3):117-127. 10.1097/HPC.0b013e3182315c03

    Article  PubMed  Google Scholar 

  16. Connolly SJ, Ezekowitz MD, Yusuf S, Eikelboom J, Oldgren J, Parekh A, Pogue J, Reilly PA, Themeles E, Varrone J, Wang S, Alings M, Xavier D, Zhu J, Diaz R, Lewis BS, Darius H, Diener HC, Joyner CD, Wallentin L, RE-LY Steering Committee and Investigators: Dabigatran versus warfarin in patients with atrial fibrillation. N Engl J Med 2009, 361(12):1139-1151. 10.1056/NEJMoa0905561

    Article  CAS  PubMed  Google Scholar 

  17. Freeman JV, Zhu RP, Owens DK, Garber AM, Hutton DW, Go AS, Wang PJ, Turakhia MP: Cost-effectiveness of dabigatran compared with warfarin for stroke prevention in atrial fibrillation. Ann Intern Med 2011, 154(1):1-11. 10.7326/0003-4819-154-1-201101040-00289

    Article  PubMed  Google Scholar 

  18. Kansal AR, Sorensen SV, Gani R, Robinson P, Pan F, Plumb JM, Cowie MR: Cost-effectiveness of dabigatran etexilate for the prevention of stroke and systemic embolism in UK patients with atrial fibrillation. Heart 2012, 98(7):573-578. 10.1136/heartjnl-2011-300646

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Langkilde LK, Bergholdt AM, Overgaard M: Cost-effectiveness of dabigatran etexilate for stroke prevention in non-valvular atrial fibrillation. Applying RE-LY to clinical practice in Denmark. J Med Econ 2012, 15(4):695-703. 10.3111/13696998.2012.673525

    Article  PubMed  Google Scholar 

  20. Sorensen SV, Kansal AR, Connolly S, Peng S, Linnehan J, Bradley-Kennedy C, Plumb JM: Cost-effectiveness of dabigatran etexilate for the prevention of stroke and systemic embolism in atrial fibrillation: a Canadian payer perspective. Thromb Haemost 2011, 105(5):908-919. 10.1160/TH11-02-0089

    Article  CAS  PubMed  Google Scholar 

  21. You JHS, Tsui KKN, Wong RSM, Cheng G: Cost-effectiveness of dabigatran versus genotype-guided management of warfarin therapy for stroke prevention in patients with atrial fibrillation. PLoS ONE 2012, 7(6):e39640. 10.1371/journal.pone.0039640

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  22. Wouters H, Thijs V, Annemans L: Cost-effectiveness of dabigatran etexilate in the prevention of stroke and systemic embolism in patients with atrial fibrillation in Belgium. J Med Econ 2013, 16(3):407-414. 10.3111/13696998.2013.766200

    Article  PubMed  Google Scholar 

  23. Eckman MH, Rosan J, Greenberg SM, Gage BF: Cost-effectiveness of using pharmacogenetic information in warfarin dosing for patients with nonvalvular atrial fibrillation. Ann Intern Med 2009, 150(2):73-83. 10.7326/0003-4819-150-2-200901200-00005

    Article  PubMed  Google Scholar 

  24. Meckley LM, Gudgeon JM, Anderson JL, Williams MS, Veenstra DL: A policy model to evaluate the benefits, risks and costs of warfarin pharmacogenomic testing. PharmacoEconomics 2010, 28(1):61-74. 10.2165/11318240-000000000-00000

    Article  PubMed  Google Scholar 

  25. Patrick AR, Avorn J, Choudhry NK: Cost-effectiveness of genotype-guided warfarin dosing for patients with atrial fibrillation. Circ Cardiovasc Qual Outcomes 2009, 2(5):429-436. 10.1161/CIRCOUTCOMES.108.808592

    Article  PubMed  Google Scholar 

  26. You JH, Tsui KK, Wong RS, Cheng G: Potential clinical and economic outcomes of CYP2C9 and VKORC1 genotype-guided dosing in patients starting warfarin therapy. Clin Pharmacol Ther 2009, 86(5):540-547. 10.1038/clpt.2009.104

    Article  CAS  PubMed  Google Scholar 

  27. Meckley LM, Wittkowsky AK, Rieder MJ, Rettie AE, Veenstra DL: An analysis of the relative effects of VKORC1 and CYP2C9 variants on anticoagulation related outcomes in warfarin-treated patients. Thromb Haemost 2008, 100(2):229-239.

    CAS  PubMed  Google Scholar 

  28. Eikelboom JW, Wallantin L, Connolly SJ, Ezekowitz M, Healey JS, Oldgren J, Yang S, Alings M, Kaatz S, Hohnloser SH, Diener HC, Franzosi MG, Huber K, Reilly P, Varrone J, Yusuf S: Risk of bleeding with 2 doses of dabigatran compared with warfarin in older and younger patients with atrial fibrillation: an analysis of the randomized evaluation of long-term anticoagulant therapy (RE-LY) trial. Circulation 2011, 123(21):2363-2372. 10.1161/CIRCULATIONAHA.110.004747

    Article  CAS  PubMed  Google Scholar 

  29. Schulman S, Kearon C, Kakkar AK, Mismetti P, Schellong S, Eriksson H, Baanstra D, Schnee J, Goldhaber SZ, RE-COVER Study Group: Dabigatran versus warfarin in the treatment of acute venous thromboembolism. N Engl J Med 2009, 361(24):2342-2352. 10.1056/NEJMoa0906598

    Article  CAS  PubMed  Google Scholar 

  30. Fihn SD, MacDonell M, Martin D, Henikoff J, Vermes D, Kent D, White RH: Risk factors for complications of chronic anticoagulation. A multicenter study. Warfarin Optimized Outpatient Follow-up Study Group. Ann Intern Med 1993, 118(7):511-520. 10.7326/0003-4819-118-7-199304010-00005

    Article  CAS  PubMed  Google Scholar 

  31. Fang MC, Chen J, Rich MW: Atrial fibrillation in the elderly. Am J Med 2007, 120(6):481-487. 10.1016/j.amjmed.2007.01.026

    Article  PubMed  Google Scholar 

  32. Oake N, Jennings A, Forster AJ, Fergusson D, Doucette S, van Walraven C: Anticoagulation intensity and outcomes among patients prescribed oral anticoagulant therapy: a systematic review and meta-analysis. CMAJ 2008, 179(3):235-244.

    Article  PubMed Central  PubMed  Google Scholar 

  33. Reynolds MW, Fahrback K, Hauch O, Wygant G, Estok R, Cella C, Nalysnyk L: Warfarin anticoagulation and outcomes in patients with atrial fibrillation: a systematic review and metaanalysis. Chest 2004, 126(6):1938-1945. 10.1378/chest.126.6.1938

    Article  PubMed  Google Scholar 

  34. Lafata JE, Martin SA, Kaatz S, Ward RE: The cost-effectiveness of different management strategies for patients on chronic warfarin therapy. J Gen Intern Med 2000, 15(1):31-37. 10.1046/j.1525-1497.2000.01239.x

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Connock M, Stevens C, Fry-Smith A, Jowett S, Fitzmaurice D, Moore D, Song F: Clinical effectiveness and cost-effectiveness of different models of managing long-term oral anticoagulation therapy: a systematic review and economic modelling. Health Technol Assess 2007, 11(38):iii-iv. ix-66

    Article  CAS  PubMed  Google Scholar 

  36. Matchar DB, Jacobson A, Dolor R, Edson R, Uyeda L, Phibbs CS, Vertrees JE, Shih MC, Holodniy M, Lavori P, THINRS Executive Committee and Site Investigators: Effect of home testing of international normalized ratio on clinical events. N Engl J Med 2010, 363(17):1608-1620. 10.1056/NEJMoa1002617

    Article  CAS  PubMed  Google Scholar 

  37. Hylek EM, Evans-Molina C, Shea C, Henault LE, Regan S: Major hemorrhage and tolerability of warfarin in the first year of therapy among elderly patients with atrial fibrillation. Circulation 2007, 115(21):2689-2696. 10.1161/CIRCULATIONAHA.106.653048

    Article  CAS  PubMed  Google Scholar 

  38. White R: The epidemiology of venous thromboembolism. Circulation 2003, 107(23 Suppl 1):14-18.

    Google Scholar 

  39. Goldhaber SZ, Visani L, De Rosa M: Acute pulmonary embolism: clinical outcomes in the International Cooperative Pulmonary Embolism Registry (ICOPER). Lancet 1999, 353(9162):1386-1389. 10.1016/S0140-6736(98)07534-5

    Article  CAS  PubMed  Google Scholar 

  40. Cushman M, Psaty BM, Meilahn EN, Dobs AS, Kuller LH: Post-menopausal hormone therapy and concentrations of protein C and antithrombin in elderly women. Br J Haematol 2001, 114(1):162-168. 10.1046/j.1365-2141.2001.02911.x

    Article  CAS  PubMed  Google Scholar 

  41. Régie d’assurance maladie du Québec Direction du service à la clientèle professionnelle. Québec. In Manuel des pharmaciens. Québec: Gouvernement du Québec; 2011.

    Google Scholar 

  42. Parent M, Chrétien D, Plante S: Coût de revient du suivi de la warfarine en clinique d’anticoagulothérapie. Pharmactuel 2010, 43(4):232-236.

    Google Scholar 

  43. Régie d’assurance maladie du Québec Direction du service à la clientèle professionnelle. Québec. In Manuel des médecins spécialistes. Québec: RAMQ; 2011.

    Google Scholar 

  44. Ministère de la santé et des services sociaux Direction de l’information. In SIFO Système d’Information Financière et Opérationnelle:méthodologie. Québec: Gouvernement du Québec; 2011.

    Google Scholar 

  45. Ministère de la santé et des services sociaux: Banque de données APR-DRG (All Patient Refined Diagnosis Related Groups). Québec. Québec: Gouvernement du Québec; 2011.

    Google Scholar 

  46. Ministère de la santé et des services sociaux Direction de la documentation. Québec. In Diem-Hébergement en soins de longue durée. Québec: Gouvernement du Québec; 2010.

    Google Scholar 

  47. Gage BF, Cardinalli AB, Owens DK: The effect of stroke and stroke prophylaxis with aspirin or warfarin on quality of life. Arch Intern Med 1996, 156: 1829-1836. 10.1001/archinte.1996.00440150083009

    Article  CAS  PubMed  Google Scholar 

  48. Sullivan PW, Arrant TW, Ellis SL, et al.: The cost effectiveness of anticoagulation management services for patients with atrial fibrillation and at high risk of stroke in the US. PharmacoEconomics 2006, 24: 1021-1033. 10.2165/00019053-200624100-00009

    Article  PubMed  Google Scholar 

  49. Snow V, Weiss KB, LeFevre M, McNamara R, Bass E, Green LA, Michl K, Owens DK, Susman J, Allen DI, Mottur-Pilson C, AAFP Panel on Atrial Fibrillation; ACP Panel on Atrial Fibrillation: Management of newly detected atrial fibrillation: a clinical practice guideline from the American Academy of Family Physicians and the American College of Physicians. Ann Intern Med 2003, 139(12):1009-1017. 10.7326/0003-4819-139-12-200312160-00011

    Article  PubMed  Google Scholar 

  50. Torbicki A, Perrier A, Konstantinides S, Agnelli G, Galiè N, Pruszczyk P, Bengel F, Brady AJ, Ferreira D, Janssens U, Klepetko W, Mayer E, Remy-Jardin M, Bassand JP, ESC Committee for Practice Guidelines (CPG): Guidelines on the diagnosis and management of acute pulmonary embolism: the Task Force for the Diagnosis and Management of Acute Pulmonary Embolism of the European Society of Cardiology (ESC). Eur Heart J 2008, 29(18):2276-2315.

    Article  CAS  PubMed  Google Scholar 

  51. Lindsay P, Baylay M, McDonald A, Graham ID, Warner G, Phillips S: Toward a more effective approach to stroke: Canadian best practice recommendations for stroke care. CMAJ 2008, 178(11):1418-1425.

    Article  PubMed Central  PubMed  Google Scholar 

  52. Barkun A, Bardou M, Marshall JK, Nonvariceal Upper GI Bleeding Consensus Conference Group: Consensus recommendations for managing patients with nonvariceal upper gastrointestinal bleeding. Ann Intern Med 2003, 139(10):843-857. 10.7326/0003-4819-139-10-200311180-00012

    Article  PubMed  Google Scholar 

  53. Barkun A, Fallone CA, Chiba N, Fishman M, Flook N, Martin J, Rostom A, Taylor A, Nonvariceal Upper GI Bleeding Consensus Conference Group: A Canadian clinical practice algorithm for the management of patients with nonvariceal upper gastrointestinal bleeding. Can J Gastroenterol 2004, 18(10):605-609.

    PubMed  Google Scholar 

  54. Som R, Gossage JA, Crane A, Rowe PH: Surgical workload, risk factors and complications in patients on warfarin with gastrointestinal bleeding. Int J Surg 2010, 8(1):52-55. 10.1016/j.ijsu.2009.10.010

    Article  PubMed  Google Scholar 

  55. Collège des Médecins du Québec et Ordre des pharmaciens du Québec: Lignes directrices-Anticoagulothérapie en milieu ambulatoire. Montréal (Québec): Collège des médecins du Québec et Ordre des pharmaciens du Québec; 2005.

    Google Scholar 

  56. Drummond MF, Sculpher MJ, Torrance GW, O’Brien BJ, Stoddart GL: Methods for the economic evaluation of health care programmes. London: Oxford, Oxford University Press; 2005.

    Google Scholar 

  57. Durand A, Gagné C, Nshimyumukiza L, Gagnon M, Rousseau F, Giguère Y, Reinharz D: Population-based simulation for public health: generic software infrastructure and its application to osteoporosis. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 2012, 42(no 6):1396-1409.

    Article  Google Scholar 

  58. Durand A, Gagné C, Gardner M-A, Rousseau F, Giguére Y: SCHNAPS: a generic population-based simulator for public health purposes. In Proceedings of the 2010 Summer Simulation Multiconference July 12-14. Ottawa, ON; Canada: Summer Computer simulation Conference (SCSC; 2010:182-189.

    Google Scholar 

  59. Menon D, Stafinski T: Health technology assessment in Canada: 20 years strong? Value Health 2009, 12(Suppl 2):S14-S19.

    Article  PubMed  Google Scholar 

  60. Briggs A, Claxton K, Sculpher M: Decision modelling for health economic evaluation. New York: Oxford University Press; 2006.

    Google Scholar 

  61. Sendi PP, Briggs AH: Affordability and cost-effectiveness: decision-making on the cost-effectiveness plane. Health Econ 2001, 10(7):675-680. 10.1002/hec.639

    Article  CAS  PubMed  Google Scholar 

  62. Mantha S, A.nsell J: An indirect comparison of dabigatran, rivaroxaban and apixaban for atrial fibrillation. Thromb Haemost 2012, 108(3):476-484. 10.1160/TH12-02-0093

    Article  CAS  PubMed  Google Scholar 

  63. Harenberg J, Marx S, Diener HC, Lip GY, Marder VJ, Wehling M, Weiss C: Comparison of efficacy and safety of dabigatran, rivaroxaban and apixaban in patients with atrial fibrillation using network meta-analysis. Int Angiol 2012, 31(4):330-339.

    CAS  PubMed  Google Scholar 

  64. Kopesky S: New anticoagulants for stroke prophylaxis in atrial fibrillation: assessing the impact on medication adherence. Am J Cardiovasc Drugs 2012, 12(5):287-294.

    Article  Google Scholar 

Download references

Funding

This work having led to this chapter was supported by a grant from the Canadian Institutes of Health Research (CIHR), and also in part by the APOGÉE-Net/CanGèneTest Research and Knowledge Network in Genetic health Services, funded by the Canadian Institutes for Health Research (http://www.cangenetest.org), (grant number ETG-92250), as well as by the “Chaire de recherche FRSQ/MSSS/CHUQ en évaluation des technologies et des pratiques de pointe en medicine de laboratoire” held by FR.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daniel Reinharz.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

All authors: Conception, design, acquisition and validation of data. MG, XD, DF: computer simulations. NL, DR, YG, FR, CG, AM, MP: analysis and interpretation of results. NL, DR, JD: Drafting the article. NL, DR, FR, AM, YG, MP: Critically revising of the article. All authors approved the final version of article.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Authors’ original file for figure 3

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Nshimyumukiza, L., Duplantie, J., Gagnon, M. et al. Dabigatran versus warfarin under standard or pharmacogenetic-guided management for the prevention of stroke and systemic thromboembolism in patients with atrial fibrillation: a cost/utility analysis using an analytic decision model. Thrombosis J 11, 14 (2013). https://doi.org/10.1186/1477-9560-11-14

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/1477-9560-11-14

Keywords