Cost-effectiveness analysis of pharmacogenetic-guided warfarin dosing in Thailand

Thrombosis Research 134 (2014) 1278–1284

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Regular Article

Cost-effectiveness analysis of pharmacogenetic-guided warfarin dosing in Thailand Huey Yi Chong a, Surasak Saokaew b,c, Kuntika Dumrongprat d, Unchalee Permsuwan e, David Bin-Chia Wu a, Piyamitr Sritara f, Nathorn Chaiyakunapruk a,c,g,h,⁎ a

School of Pharmacy, Monash University Malaysia, Selangor, Malaysia Center of Health Outcomes Research and Therapeutic Safety (COHORTS), School of Pharmaceutical Sciences, University of Phayao, Phayao, Thailand Center of Pharmaceutical Outcomes Research (CPOR), Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Naresuan University, Phitsanulok, Thailand d Welsh School of Pharmacy, Cardiff University, King Edward VII Avenue Cardiff CFJ 0 3XF Wales UK e Department of Pharmaceutical Care, Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand f Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand g School of Pharmacy, University of Wisconsin, Madison, USA h School of Population Health, University of Queensland, Brisbane, Australia b c

a r t i c l e

i n f o

Article history: Received 23 June 2014 Received in revised form 22 September 2014 Accepted 7 October 2014 Available online 14 October 2014 Keywords: Pharmacogenetic Warfarin Cost-effectiveness Cost-utility Bleeding

a b s t r a c t Introduction: Pharmacogenetic (PGx) test is a useful tool for guiding physician on an initiation of an optimal warfarin dose. To implement of such strategy, the evidence on the economic value is needed. This study aimed to determine the cost-effectiveness of PGx-guided warfarin dosing compared with usual care (UC). Methods: A decision analytic model was used to compare projected lifetime costs and quality-adjusted life years (QALYs) accrued to warfarin users through PGx or UC for a hypothetical cohort of 1,000 patients. The model was populated with relevant information from systematic review, and electronic hospital-database. Incremental cost-effectiveness ratios (ICERs) were calculated based on healthcare system and societal perspectives. All costs were presented at year 2013. A series of sensitivity analyses were performed to determine the robustness of the findings. Results: From healthcare system perspective, PGx increases QALY by 0.002 and cost by 2,959 THB (99 USD) compared with UC. Thus, the ICER is 1,477,042 THB (49,234 USD) per QALY gained. From societal perspective, PGx results in 0.002 QALY gained, and increases costs by 2,953 THB (98 USD) compared with UC (ICER 1,473,852 THB [49,128 USD] per QALY gained). Results are sensitive to the risk ratio (RR) of major bleeding in VKORC1 variant, the efficacy of PGx-guided dosing, and the cost of PGx test. Conclusion: Our finding suggests that PGx-guided warfarin dosing is unlikely to be a cost-effective intervention in Thailand. This evidence assists policy makers and clinicians in efficiently allocating scarce resources. © 2014 Elsevier Ltd. All rights reserved.

Introduction In recent years, as genetic and molecular markers have been quickly discovered to be associated with treatment responses and disease development, personalized medicine has been increasingly used to determine treatment choices in various disease areas. For instance, a genetic marker, HLA-B*1502, has been found to be strongly associated with StevensJohnson syndrome induced by carbamazepine, a drug commonly prescribed for the treatment of seizures in Han Chinese [1]. In Taiwan, testing for the KRAS mutation and human epidermal growth factor receptor type 2 (HER2) has been adopted to determine who is more likely to be responsive to cetuximab and trastuzumab, respectively [2,3]. ⁎ Corresponding author at: School of Pharmacy, Monash University Malaysia, Jalan Lagoon Selatan, 46150 Bandar Sunway, Selangor, Malaysia. E-mail address: [email protected] (N. Chaiyakunapruk).

http://dx.doi.org/10.1016/j.thromres.2014.10.006 0049-3848/© 2014 Elsevier Ltd. All rights reserved.

As personalized medicine is gaining momentum in the real-world clinical practice, it is especially true in the role of pharmacogenomics in warfarin dosing. This is marked by the US-FDA issued a warfarin package insert change, advising physician to consider the use of pharmacogenetic (PGx) test for warfarin initiation in 2007 [4]. Over the last decade, substantial evidence base is generated on the association of warfarin dose with genetic variations in two genes; cytochrome P450 2C9 (CYP2C9) as warfarin metabolizing enzyme, and vitamin K epoxide reductase complex subunit 1 (VKORC1) acts as warfarin receptor. While non-genetic factors, e.g. body size and age were found to be poor predictors of determining dosage, the genetic variations explained 40-60% of warfarin dosage requirement [5–9]. However, variations exist between different ethnic groups in Asia. Indeed, the gene prevalence in Thai is more similar to Chinese and South-East Asia ethnics, e.g. Singaporean and Malay but differ from Korean and Indian [10–12]. CYP2C9*1/*1 (wild-type) is predominant among Thais (92%-95%).

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The rest is *1/*3 (poor metabolizer) [13–15]. In contrast, VKORC1 BB (wild-type) represents minority of Thais (2%) [13–15]. Considering the presentation of genetic variability in the CYP2C9 and VKORC1 genes in Thai population, relatively lower dose of warfarin compared to Caucasian is warranted. Furthermore, one of the main barriers of warfarin management in Thailand is the less intensive international normalized ratio (INR) monitoring and medical visits. These are likely due to difficulty of patients coming for clinic visits [13]. Consequently, the out-of-range INR imposes higher risk of developing warfarinrelated complications such as bleeding and thromboembolism (TE). CYP2C9 and VKORC1 PGx test service is available in Thailand since 2008. The analytical validity of this PGx test is demonstrated in Langley et al. where the accuracy was found to be 100% (100% for both sensitivity and specificity) [16]. Thus, the clinical value of this test is undoubtedly substantial at warfarin initiation phase to avoid the unpredictable side effects. Owing to the widespread of warfarin prescribing and its serious adverse effects, a warfarin PGx dosing algorithm may serve as a promising guidance for physicians to initiate warfarin more accurately. On the contrary, this PGx test contributes additional costs to medical care. Although evidence suggested a number of clinical benefits of this PGx test, its economic implication is still highly uncertain even in the US. Moreover, owing to genetic variations of CYP2C9 and VKORC1 in Asians and differences of healthcare system and associated medical care costs, the current evidences on economic studies of the PGx test in Caucasians cannot be easily transferred to Asians. In addition, despite the availability of direct thrombin inhibitors, several issues related to cost, indication, antidote and physician experience favoring a continuous use of warfarin [17] as the mainstay anticoagulant in Thailand. Thus, it is important to understand if this genetic test is economically feasible prior to administering warfarin. Therefore, this study aims to evaluate the projected cost-effectiveness of the PGx-guided warfarin dosing compared to the usual care (UC), i.e. standard warfarin dosing, so that an informed decision can be made to ensure efficient allocation of scarce medical resources in Thailand. Methods Overview A two-part construct model with a decision tree and a Markov model was adapted from a previous study [18] to evaluate the cost and outcomes for two strategies of warfarin initiation: PGx-guided and UC. In the base-case analysis, a hypothetical cohort of 1,000 patients newly initiated warfarin therapy for all indications aged 45 years old was entered into the model. We performed the analysis from both societal and health care system perspectives. All costs and outcomes were discounted at a rate of 3% [19] and adjusted to 2013 values in line with recommendations of Thai Health Technology Assessment (HTA) guideline [20]. The analyses were performed using Microsoft Excel® (Microsoft Corp., Redmond, WA). To model the result of patients’ genotype, the model begins with a decision tree. A cohort population might receive either PGx-guided dosing or UC (Fig. 1A). Patients were then stratified by genotype, by CYP2C9 and VKORC1 status. Patients with at least one of the following genotypes: (i) CYP2C9*2, (ii) CYP2C9*3, (iii) VKORC1 A, were classified as variant genotypes. Therefore, four types of two gene combination were presented for each strategy at the end of tree: (i) CYP2C9*1/*1 and VKORC1 BB, (ii) CYP2C9*1/*1 and VKORC1 variant, (iii) CYP2C9 variant and VKORC1 BB, and (iv) CYP2C9 variant and VKORC1 variant. Except for the first combination as wild-type group, the remaining three combinations were classified as variant groups. Subsequently, all patients entered the health state of no TE/no bleeding in the Markov model where six health states were specified to illustrate the consequence of major bleeding and TE events (Fig. 1B). Patients moved through these health states in 3-monthly cycles with life time horizon.

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Likelihood of events The probabilities of CYP2C9 and VKORC1 allele frequency and clinical events used in the decision-analytic Markov model are shown in Table 1. To reflect Thai population, the probabilities used in this study were derived from Thai literature or Thai electronic hospital database if possible. We used Buddhachinaraj Regional Hospital (BCRH) database due to its richness and comprehensiveness of data. In addition, this database is widely accepted and used in a number of literatures [21–24]. To date, four studies reported on the frequency of CYP2C9 alleles and five on VKORC1 alleles. The probabilities of CYP2C9 and VKORC1 allele frequency in Thai population were derived based on the findings of our meta-analysis [13–15,25,26] (Appendix S1, Table 1 and 2). It is found that the probability of CYP2C9*1/*1 to be 0.94 (95% CI 0.92-0.96) while VKORC1 BB to be 0.05 (95% CI 0.02-0.07). For Markov model, the probabilities of developing TE and major bleeding in the general population per 3-month were adapted from previous study [27]. Assuming that PGx-guided warfarin dosing only decreased the risk of major bleeding, the risk of developing major bleeding was calculated based on the genotype of the patients, while the risk of developing TE remained unchanged [28]. The probability of major bleeding in wild-type group was calculated based on the probability of major bleeding in general Thai population [27] and the relative frequency of the variants (Appendix S2). The result was 0.97% per 3-month. For other three variant groups, the probability of developing major bleeding were determined by multiplying the risk of major bleeding in wild-type group with the summary risk ratio (RR) from previous meta-analysis [17]. The meta-analysis revealed that CYP2C9 variant genotypes were associated with a 119% increase in major bleeding (RR 2.19; 95%CI 1.33-3.60) and VKORC1 variant genotypes were associated with an 8% increase in major bleeding (RR 1.08; 95%CI 0.55-2.10). Since there is no data on the risk of major bleeding in patients with CYP2C9 variant and VKORC1 variant genotypes, we assume that the RR of major bleeding is similar to that of CYP2C9 variant in the base-case analysis. This assumption was then tested in the sensitivity analysis with the multiplicative effect of the low and high values of both RR of major bleeding in CYP2C9 variant and VKORC1 variant genotypes. For the PGx-guided dosing arm, the probability of major bleeding was calculated by multiplying probability of major bleeding in the UC group by the summary RR of major bleeding from an updated metaanalysis [14,29–33] (Appendix S1, Table 3). The meta-analysis reported a 39% reduction in major bleeding of PGx-guided dosing compared to UC (RR 0.61; 95%CI 0.16-2.27). We assumed that the benefit in reducing in risk of major bleeding of PGx-guided warfarin occur only the first 3 months of warfarin initiation. This is because the maintenance dose is reached in most patients within the induction phase [3]. The probability of death from major bleeding within 3 months (5.61%) was derived from BCRH database. Major bleeding was categorized as either intracranial hemorrhage (ICH) or extracranial hemorrhage (ECH). ECHs were assumed to be gastrointestinal (GI) bleeding, since almost anticoagulation-related ECH is located in GI tract [29] and patients with GI bleeding recovered within a month [18] and have no deficits [30]. Thus, in our model, we used the probability from ICH to represent the probability of transitioning from major bleeding to sequelae. The ICH patients were assigned a 3.70% chance with sequelae [31]. Minor bleeding events were not included as no treatment attention was sought. We assumed that the stroke and bleeding survivors with no sequelae returned to the no-events state. Ischemic stroke was chosen to represent TE since all TE patients diagnosed in our previous study were ischemic strokes [32]. Patients with ischemic stroke had a 22.43% chance of having long-term sequelae [31] and a 7.19% chance of dying within 3 months of the event [33]. The transition probability from no-events to death was based on the age-specific mortality rate (ASMR) for Thai population [34]. A study in mixed patients population (e.g. ischemic stroke, cerebral hemorrhage, AF, valvular defect) showed that patients receiving

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a

b Fig. 1. a. Decision tree model. Patients who newly-initiated with warfarin are classified according to the warfarin dosing method and genotype. b. Markov model. Patients entered the Markov model in the state A from the decision tree model. They transition to different health state in 3-monthly cycles.

warfarin therapy was associated with improved survival [35]. However, this study has shown that patients receiving warfarin therapy remain at higher risk of death compared to general population, thus ASMR of Thai population was not applicable. We calculated transition probability for no-events to death by multiplying ASMR of Thai population by 1.3 [35] to represent ASMR for Thai patients receiving warfarin therapy. Costs The costs included (i) direct medical care costs, (ii) direct nonmedical care costs (Table 1). Indirect cost was excluded to avoid double-counting. Because QALY has already captured healthrelated quality of life of patients with morbid conditions, measuring the productivity loss associated with morbidity would result in double counting [36]. All costs were estimated from Thai literature or Thai database. The costs were then converted to 2013 value using the Medical Care consumer price index (CPI) [37]. From the societal perspective, all costs were considered, while only direct medical

care costs were considered for the health care system’s perspective. Cost of TE management was obtained from BCRH database using ICD-10 to identify the patients and calculated using cost-to-charge ratio method (Table 1). The cost of INR testing was derived from the standard cost lists for HTA in Thailand [38]. We did not include warfarin drug costs because the costs would have a relatively small effect on total cost due to its very low cost in Thailand (~ 1-3 THB) [39]. We did not include cost of physician since it was assumed to be similar between both groups. Cost of sequelae management included cost of rehabilitation and cost of care. Cost was derived from the existing study conducted in 320 stroke survivors from 9 centers in Thailand [40]. The cost of sequelae management was 8,170 THB per 3 months (Table 1). Direct non-medical costs included cost of transportation, and additional food cost. These costs were based on the standard cost lists for Thai HTA [38]. The costs were counted for patient and one relative since a previous study showed that a stroke survivor on average came to a hospital with one caretaker [41].

H.Y. Chong et al. / Thrombosis Research 134 (2014) 1278–1284 Table 1 Input parameters, values and data sources used in the model. Parameters Allele frequency CYP2C9 *1/*1 VKORC1 BB Probabilities (3-month) TE Major bleeding CYP2C9 *1/*1 and VKORC1 BB

Base case

Range

Source(s)

0.941 0.048

0.924 - 0.955 0.021 - 0.071

[13–15, 25] [13–15,25,26]

0.008

0.007 - 0.009

[32]

0.010

0.010 - 0.013

[32], Calculated~ Calculated# Calculated# Calculated#

CYP2C9 variant 0.021 0.013 - 0.035 VKORC1 variant 0.010 0.005 - 0.020 CYP2C9 variant and VKORC1 0.021 0.013 - 0.035 variant Sequelae from TE 0.224 0.191 - 0.258 [31] Death with TE 0.072 0.061 - 0.083 [33] Sequelae from major bleeding 0.037 0.032 - 0.043 [31] Death with major bleeding 0.056 0.034 - 0.077 BCRH ⁎ Death with sequelae of TE 0.057 0.048 - 0.065 BCRH ⁎ Death with sequelae of major 0.045 0.038 - 0.051 BCRH ⁎ bleeding Major bleeding in variants versus wild-type alleles during initiation phase (risk ratios, RR) CYP2C9 variant 2.190 1.330 - 3.600 [17] VKORC1 variant 1.080 0.550 - 2.100 [17] CYP2C9 variant and VKORC1 2.190 1.330 - 3.600 Assumed variant Efficacy of PGx (RR) On major bleeding 0.690 0.160 - 2.910 [61] Costs (THB, year of costing: 2013) Direct medical care costs PGx test 3,000.0 2,000.0 [62] 5,000.0 TE (per episode) 49,295.1 41,796.9 BCRH ⁎ 54,934.1 Major bleeding (per episode) 64,622.6 33,279.9 BCRH ⁎ Sequelae (3-month) INR test (per test) Direct non-medical care costs Transportation (per visit) Additional food cost (per visit) Indirect costs (daily productivity loss by age) Age 15-29 Age 30-39 Age 40-59 Age 60-69 Age 70-79 Utilities (EQ-5D) Warfarin use (no event) TE Major bleeding Sequelae of TE Sequelae of major bleeding

83.3

75,190.9 2,292.1 17,348.8 -

146.6 54.0

123.2 - 170.0 43.2 - 64.8

198.0 412.0 576.0 248.0 99.0

-

0.987 0.713 0.790 0.320 0.620

0.967 - 0.998 0.271 - 1.000 0.672 - 0.909 0.000 - 0.700 0.370 - 0.870

8,169.6

[40] [38] § [38] ¶ [38] ¶ [48] ~~

[46,63] [41] [28] [43,44] [45]

~ Calculated from the probability of major bleeding in general population. # Calculated by multiplying the probability of major bleeding in CYP2C9*1/*1 or VKORC1 BB and RR of bleeding on the basis of variant genotype, respectively. * Calculated from Buddhachinaraj Regional Hospital (BCRH) database using ICD-10 for identification of patients. § Mean INR test is every 3 months [32]. ¶ Used for patients’ cost and one relative for each visit [41]. ~ ~ Used for sensitivity analysis with incorporated productivity loss. TE, thromboembolism; RR, risk ratio; THB, Thai Baht; INR, international normalized ratio; EQ-5D, European Quality of Life-5 Dimensions.

Utility We conducted a literature search for the utility of each health state for the QALY estimation. The utility value for TE was derived from the quality of life in Thai stroke patients measured by SF-36

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[41]. The values from SF-36 were converted into EQ-5D using the equation of Ara et al. [42]. Based on such equation, the utility of TE in our study was calculated as 0.713 (Table 1). The utility value for major bleeding was derived for ICH; consistent with the probability from major bleeding to sequelae was based on ICH. Since we did not find any literature about utility of major bleeding in Thailand, we adopted the utilities of ICH from a previous study conducted in Western population [28] where 0.79 was used (Table 1). For the utility of sequelae of TE, we adopted the utility from previous study which was 0.32 [43]. In addition, to reflect Thai population, the utility of patients after stroke (EQ-5D ~ 0) in Thai setting was used for sensitivity analysis [44]. For the utility of sequelae of bleeding, we also adopted the utility from previous study which was 0.62 [45]. For patients taking warfarin without complications, the utility was estimated at 0.987 [46]. Base-case analyses Using the societal perspective, we calculated the expected costs and outcomes in warfarin users aged 45 years old. This age was chosen to represent Thai population based on the average age of warfarin users in a recent report [32]. The results were presented as an incremental cost per QALYs gained (ICER) for PGx versus UC. According to the Thai HTA recent recommendation, the threshold approach is derived based on the country’s gross national income (GNI). A health technology is considered ‘cost effective’ if ICER is found to be less than 1.2 times the per-capita GNI (160,000 THB [5,333 USD]) [47]. Sensitivity analyses To determine the robustness of the estimates from base-case analysis, we also performed cost-effectiveness analysis (CEA) and incorporated the cost of productivity loss into the model [36] by multiplied daily productivity loss cost with the number of day loss for illness. The average daily income was varied by age based on socioeconomic survey [48]. We used 1 day loss for regular followup, while 12 and 8 days loss (estimated length of hospital stay from BCRH database) for TE and bleeding event, respectively. Since life expectancy depends on patient’s age, we also vary patient age group at initial treatment to figure out the ICER for each age group. In addition, a series of one-way sensitivity analyses were performed to investigate the effects of altering parameters within the plausible ranges including epidemiologic data, risk of major bleeding in variant genotypes, efficacy of PGx, costs, and utilities (Table 1). Discount rate on cost and outcome of 0% - 6% [19] was also included in one-way sensitivity analysis. A threshold analysis on the RR of major bleeding in patients with VKORC1 variant genotype was also conducted to determine the value that drives the ICER to fall below 160,000 THB per QALY. Furthermore, probabilistic sensitivity analysis (PSA) was undertaken to address uncertainty in the assumptions underlying the model by allowing all of input parameters values to vary simultaneously over their respective feasible ranges within the model. This analysis requires thousand iterations. When specific ranges or confidence intervals were not available, it was assumed that the range varies by ± 15% to present the uncertainty for each estimate. All input parameters were assigned a probability distribution to reflect the feasible range of values the each parameter could attain. The rationale for distributional assumption selection for each variable has been given detail elsewhere [49,50]. Cost-effectiveness acceptability curve was provided to illustrate the relationship between the values of ceiling ratio and the probability of favoring each strategy [51].

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Results Base-case analyses In base-case analyses from societal perspective, PGx results in 0.002 QALY gained, and increases costs by 2,953.22 THB (98 USD) compared with UC (ICER 1,473,851.92 THB [49,128 USD] per QALY gained) (Table 2). The ICER result derived from health system perspective was also presented in Table 2. Sensitivity analyses A sensitivity analysis showed that PGx was unlikely to be costeffective at willingness-to-pay (WTP) 160,000 THB (5,333 USD; 1.2 GNI) per QALY. A series of one-way sensitivity analysis showed that the most influential parameter were RR of major bleeding in VKORC1 variant genotype. When the RR was varied from 1.08 to 0.55, it led to an increase in cost and poorer outcome with negative ICER. If RR is changed to 2.10, the ICER of PGx compared with usual care was shifted to 299,692 THB (9,990 USD) per QALY (Fig. 2). A critical point was found at 0.906 resulted in zero-QALY gained and the associated ICER was 1,531,825,727 THB (51,060,857 USD) per QALY gained. At any RR value below the critical point, the resulting QALYs gained were negative. In the sensitivity analysis with RR of major bleeding in CYP2C9 variant and VKORC1 variant genotypes using the multiplicative effect of low and high values of respective RR, the RR of major bleeding was ranged from 0.74 to 7.56. The ICERs were 2,414,135 THB (80,471 USD) and 1,046,294 THB (34,877 USD), corresponding to low and high RR values. We also conducted a threshold analysis on the RR of major bleeding in VKORC1 variant genotype to determine its threshold value that drives the ICER to less than 160,000 THB per QALY. The ICER did not fall to below the WTP threshold unless the RR of major bleeding in VKORC1 variant genotype was as high as 4.17 from health system perspective and 4.06 from societal perspective. In the PSA, at a 160,000 THB (5,333 USD) per QALY threshold, 41% of the simulations were cost effective from health system perspective, and 42% of the simulations were cost effective from societal perspective (Fig. 3). Discussion To the best of our knowledge, this is the first formal costeffectiveness study evaluating PGx-guided warfarin dosing in Thailand and probably the only study reflecting the Asia-Pacific context. Our findings show that PGx-guided warfarin dosing offers slight societal gain at high cost, demonstrated by the very high ICER exceeding the WTP threshold level in Thailand. Policy makers and clinicians may consider using this information as part of placing the position of PGx for guiding warfarin dosing in Thailand. At present, several economic studies of warfarin PGx test showed controversial results, ranging from being cost-effective to the contrary. Our findings of PGx test being not cost-effective are consistent with results in previous CEA studies [18,28,30,52]. You et al. [53] performed

a literature review of economic studies of PGx test in patients requiring oral anticoagulation therapy and found that in all four US-based economic analyses [18,28,30,52], the incremental cost was above 50,000 USD per QALY gained, implying that the PGx test was not costeffective in general patients requiring long-term warfarin therapy. This has led to You et al. to conclude that PGx test should not be adopted in warfarin management [53]. Nevertheless, it is noted that certain patient groups are more likely to benefit from the PGx test such as those with high underlying risk of bleeding [28,53] or at clinical sites with suboptimal management of INR control [53]. The PGx test might be economically attractive under certain condition: if it reduces the out-of range INR by more than 5-9 percentage points [30]. Conversely, in the most recent UK study using a novel application of a clinical trial simulation based on pharmacology model and discrete event simulation, the PGx-guided warfarin dose was found to be at £13,226/QALY comparing to clinical adjusted warfarin dose, indicating that it was cost-effective [54]. In a nutshell, using different health economics modeling approach for different settings might lead to differing costeffectiveness findings. In addition, findings of PGx test being not cost-effective are robust in a series of sensitivity analyses. In the one-way sensitivity analysis, varying these variables in our model did not alter the cost-effectiveness results. Furthermore, when accounting for simultaneous changes of all parameters, PSA showed that PGx test was only cost-effective with 41-42% of likelihood at a 160,000 THB (5,333 USD) per QALY threshold. Consistently, similar findings were observed in all four previous economic analyses where RR of major bleeding, efficacy of PGx test and its cost introduced great impact in their models [18,28,30,52]. Although the RR of major bleeding in VKORC1 variant used in the model was derived from a meta-analysis of Caucasians-dominant studies and the prevalence of VKORC1 variant genotype in Thailand is quite high, its effect on ICER is minimal due to the low likelihood of the RR for major bleeding in VKORC1 variant genotype to be as high as 4.17. It is also important to note that the impact of cost of major bleeding on cost-effectiveness findings of the PGx test is not significant. This is because the use of PGx test can only prevent 10 episodes out of 1,000 patients, although it represents 38.4% reduction (from 26 in UC to 16 using PGx). This figure can be explained by the assumption made on the efficacy of PGx test where it reduces the risk of major bleeding only at the first 3 months of warfarin initiation. The key strengths of this study should be highlighted. First, input parameters were considered highest quality data as substantial efforts were spent in systematical literature search and meta-analysis to synthesize all best available evidences for the inputs in the cost-effectiveness model. In addition, we attempted this analysis using input parameters derived from Thai or Asian publications/databases to best reflect the Asian population, in turn, these findings could be applied in this region. Second, the decision analytic model used is constructed in accordance with the Thai HTA recommendations. Thus, model structure can adequately represent country-specific clinical practice in Thailand with assured face validity. Third, all model parameter uncertainties were carefully examined using a series of comprehensive sensitivity analyses and the robustness of the results was subsequently demonstrated.

Table 2 Results of base-case analysis (n = 1,000)*. Strategy

Cost (THB)

Effectiveness (QALYs)

Incremental Cost (ΔTHB)

Incremental Effectiveness (ΔQALYs)

ICER (ΔTHB/ΔQALYs)

Health system perspective Usual Care 31,878,125.39 PGx 34,837,740.84

39,418.94 39,420.94

2,959,615.45

2.00

1,477,042.04

Societal perspective Usual Care PGx

39,418.94 39,420.94

2,953,223.27

2.00

1,473,851.92

49,509,852.00 52,463,075.27

* Discounted at 3% per year. THB, Thai Baht; QALY, quality-adjusted life-year; PGx, pharmacogenetic testing; ICER, incremental cost-effectiveness ratio.

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Fig. 2. Tornado diagram showing a series of one-way sensitivity analyses comparing PGx and usual care. The horizon bars represent the range of the incremental cost-effectiveness ratio (ICER) for one-way sensitivity over the range of parameters in parenthesis. The wider the horizon bar, the more uncertainty that parameter introduces. The vertical line represents the base-case ICER. PGx, pharmacogenetic testing; RR, risk ratio; QALY, quality-adjusted life year.

There are a few limitations. First, some input parameters were extrapolated from other regions e.g. Western and European countries (e.g. probability of major bleeding in CYP2C9 or VKORC1 variant, probability of sequelae, utility) which can affect the results and not represent the situation in Thailand. However, as shown in the oneway sensitivity analysis and threshold analysis, these parameters did not influence the cost-effectiveness of PGx test. Second, some input parameters (e.g. cost of TE and major bleeding, death rate) were derived from a single tertiary hospital in Thailand, so the generalizability to other levels of setting may be limited. Third, although PGx test theoretically can shorten time to optimal warfarin management, potentially leading to the reduction of TE risks in early period of warfarin initiation especially those with wild genotypes, there remains an absence of clear evidence to support it. The difference in TE events between control and PGx arm was either insignificant or not reported in the 5 randomized trials comparing PGx and standard warfarin dosing [55–59].

Despite our research is very specific to Thailand, there are a number of lessons learned which could be applied to other countries. Based on the similarity of these cost-effectiveness findings, it is highly suggestive that the PGx-guided warfarin dosing may provide a small clinical benefit with significant uncertainty in economic value in other settings as well. The main factor contributing to the minimal economic value of PGx test is the marginal benefits of the PGxguided warfarin demonstrated by current evidence. However, it is important to note that most evidence is derived from studies conducted in western countries where the quality of existing anticoagulation management is high and established. Thus, the relative efficacy of PGx test resulted in these studies may be underestimation. Consequently, there remains a need of evidence generated for different settings, patients with high risk for bleeding, or even in different ethnic groups. Last but not least, it is worth noting that there are some other factors beyond health economic message which could affect the policy recommendation of implementing this PGx test. Indeed, even in clinical situations where economic advantages can be demonstrated, it may be difficult to fund such population-based testing in a managed care environment when possible future benefits have to be paid for in the present [60]. Conclusion On the basis of currently best available data, this CEA study suggests that PGx-guided warfarin dosing in unlikely a cost-effective intervention in Thailand compared to standard warfarin dosing in the usual care. This information can be used to assist policy makers and clinicians in efficiently allocating limited resources. Conflicts of interest statement No potential conflicts of interest exist. Appendix A. Supplementary data

Fig. 3. Cost-effectiveness acceptability curve from health system and societal perspective. The curves provide the probability of PGx being the cost-effective at any willingness to pay value for an additional QALY. The curve was generated from the Monte Carlo simulation. PGx, pharmacogenetic testing; QALY, quality-adjusted life year; GDP, gross domestic product; THB, Thai Baht.

Additional Supporting Information may be found in the online version of this article at http://dx.doi.org/10.1016/j.thromres.2014. 10.006.

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