Cost-effectiveness analysis of HLA-B*5801 testing in preventing allopurinol-induced SJS/TEN in Thai population

Abstract

Background: Stevens-Johnson syndrome (SJS) and Toxic Epidermal Necrolysis (TEN), caused by allopurinol therapy, are strongly associated with the human leukocyte antigen (HLA), HLA-B*5801. Identification of HLA-B*5801 genotype before prescribing allopurinol offers the possibility of avoiding allopurinol-induced SJS/TEN. As there is a paucity of evidence about economic value of such testing, this study aims to determine the cost-effectiveness of HLA-B*5801 testing compared with usual care (no genetic testing) before allopurinol administration in Thailand.

Methods and finding: A decision analytical and Markov model was used to estimate life time costs and outcomes represented as quality adjusted life years (QALYs) gained. The model was populated with relevant information of the association between gene and allopurinol-induced SJS/TEN, test characteristics, costs, and epidemiologic data for Thailand from a societal perspective. Input data were obtained from the literature and a retrospective database analysis. The results were expressed as incremental cost per QALY gained. A base-case analysis was performed for patients at age 30. A series of sensitivity analyses including scenario, one-way, and probabilistic sensitivity analyses were constructed to explore the robustness of the findings. Based on a hypothetical cohort of 1,000 patients, the incremental total cost was 923,919 THB (USD 29,804) and incremental QALY was 5.89 with an ICER of 156,937.04 THB (USD 5,062) per QALY gained. The cost of gout management, incidence of SJS/TEN, case fatality rate of SJS/TEN, and cost of genetic testing are considered very influential parameters on the cost-effectiveness value of HLA-B*5801 testing.

Conclusions: The genetic testing for HLA-B*5801 before allopurinol administration is considered a highly potential cost-effective intervention in Thailand. The findings are sensitive to a number of factors. In addition to cost-effectiveness findings, consideration of other factors including ethical, legal, and social implications is needed for an informed policy decision making.

Figure 1. Decision analytic model.

SJS =  indicated Stevens-Johnson syndrome; TEN =  toxic epidermal necrolysis; M =  markov model.

Figure 2. Decision tree for calculating the effectiveness of diagnostic test.

p = probability; P = prevalence; S = sensitivity; Sp = specificity; PPV = positive predictive value; NPV = negative predictive value.

Figure 3. Tornado diagram showing a series of one-way sensitivity analyses comparing genetic testing 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. The wider the horizon bar, the more uncertainty that parameter introduces. The vertical line represents the base-case ICER. X-axis indicated the ICER. THB = Thai baht. The low and high parameter values are based on the range specified in Table 1.

Figure 4. Cost-effectiveness scatter plot.

Each point represents incremental cost (year 2013 values) and Quality adjusted life year gained (QALYs) between genetic testing and usual care from Monte Carlo simulation with varying model parameters.

Figure 5. Cost-effectiveness acceptability curve.

The curves provide the probability of the different strategy being the most cost-effective option at any willingness to pay value for an additional quality -life year gained for genetic testing.