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Genetic Testing Decision Analysis Model for Antidepressant Treatment
Jeffrey M Pyne, MD
Central Arkansas Veterans Healthcare System Eugene J. Towbin Healthcare Center, Little Rock, AR
No. Little Rock, AR
Funding Period: May 2008 - September 2008
Genomic medicine holds great promise for improving treatment and outcomes for veterans with mental health disorders. Genetic testing to determine metabolic genotypes and personalize pharmacotherapy for depression is in the early stages of implementation. Genetic testing could be used to personalize antidepressant medication treatment with the goal of reducing the risk side effects, increasing medication adherence, and ultimately improving treatment outcomes. The cost of genetic testing is expensive and it is unclear at what price the testing becomes cost-saving or cost-effective. To address the value of genetic testing, there will be an increasing number of effectiveness and cost-effectiveness studies conducted. Decision analysis modeling is an ideal method for utilizing the existing evidence base to identify the factors that influence cost-effectiveness, generate hypotheses, and inform study designs for future intervention studies.
Incorporate probabilities for CYP450 polymorphisms, costs, and outcomes into an existing depression treatment decision analysis model.
Estimate the cost-effectiveness of conducting genetic testing for CYP450 polymorphisms at initiation of antidepressant treatment and after first antidepressant treatment failure.
Conduct sensitivity analyses for a range of probabilities for CYP450 polymorphisms, side effects, adherence outcomes, and genetic testing cost.
Conduct a break even analysis to determine the cost of genetic testing at which the genetic test becomes cost effective.
A deterministic decision analysis model which was inspired by a model developed by Sullivan et al 2004 was created to estimate the cost effectiveness of initiating antidepressant therapy. The model was built using Tree Age Pro and validated with the original Sullivan model inputs and adapted to model three treatment strategies; initiate treatment with paroxetine, initiate treatment with citalopram, conduct a genetic test to test CYP2D6 polymorphisms where fast metabolizers are prescribed paroxetine and slow metabolizers are prescribed citalopram. The model used a 6 month time horizon to incorporate all costs and utilities. Model inputs were obtained from published sources and clinical assumptions. Since polymorphisms of the CYP2D6 vary across race, race specific cost effectiveness estimates were estimated. One way sensitivity analyses were conducted to explore the robustness of the findings.
The preliminary base case results show that initiating therapy with citalopram was expected to cost $3,790 and produce 0.378 QALYs over the 6 month time horizon and this option dominated the other two strategies. When the cost of citalopram exceeded $120/month gene testing became the most cost effective strategy using an incremental cost-effectiveness threshold of $50,000/QALY. When the clinical decision was simplified to two treatment strategies, initiate paroxetine or the gene testing strategy, the gene testing strategy dominated the no gene testing strategy up until the cost of genetic testing exceeded $100/patient.
Because citalopram has a lower adverse event rate than paroxetine with similar monthly costs, it is the preferred agent across a range of sensitivity analyses. Metabolic genetic testing may be a cost effective strategy in some clinical situations, particularly when adverse event rates vary substantially with genetic polymorphisms and where there may be substantial cost differences between drugs.
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DRA: Mental, Cognitive and Behavioral Disorders, Health Systems
DRE: Pathology, Diagnosis
Keywords: Cost effectiveness, Depression, Genomics
MeSH Terms: none