Cost-Effectiveness of Combinatorial Pharmacogenomic Testing for Treatment-Resistant Major Depressive Disorder Patients

Using a state-transition probability analysis, this study assessed the long-term outcomes and economic implications of combinatorial pharmacogenomic testing for managing patients with major depressive disorder who were nonresponsive to treatments.
Published Online: July 21, 2015
John Hornberger, MD, MS, FACP; Qianyi Li, MS; and Bruce Quinn, MD, PhD

Objectives: To describe the lifetime outcomes and economic implications of combinatorial pharmacogenomic (CPGx) testing versus treatment as usual (TAU) psychopharmacologic medication selection for a representative major depressive disorder patient who has not responded to previous treatment(s).

Study Design: Markov state-transition analysis based on clinical studies.

Methods: Clinical validity and utility were based on published findings in prospective clinical studies of a commercially available CPGx test. Data for quality of life, direct costs, and indirect costs were extracted from meta-analyses of published literature on clinical studies and claims databases. Outcomes were assessed from a societal perspective, and included differences between the CPGx and the TAU strategies in quality-adjusted life-years (QALYs), cumulative direct and indirect costs, and cost per QALY gained.

Results: CPGx improved the treatment response rate by 70% (1.7 times as high as that with TAU), increasing QALYs by 0.316 years. With these health benefits, CPGx is expected to save $3711 in direct medical costs per patient and $2553 in work productivity costs per patient over the lifetime. The cost-effectiveness of CPGx testing was robust over a wide range of variation in the input parameters, including the scenario when testing efficacy was set to its lower limit.

Conclusions: CPGx testing has been shown by prospective studies to modify treatment decisions for patients nonresponsive to previous treatment(s), with increased rates of treatment response. These effects are projected to increase quality-adjusted survival, and to save both direct and indirect costs to individual patients and society generally.

Am J Manag Care. 2015;21(6):e357-e365
Take-Away Points
Combinatorial pharmacogenomic (CPGx) testing dominates the treatment-as-usual approach in managing major depressive disorder patients who did not respond to previous treatments.
  • Prospective clinical studies across multiple settings have shown that CPGx testing helps to guide treatment decision-making, matching patients with the treatments that are most likely to be safe and effective.
  • Further analysis indicates that CPGx testing is related to improved quality of life and cost savings.
Major depressive disorder (MDD) is associated with significant clinical and economic burden worldwide, with the United States1 experiencing a 16.6% lifetime prevalence rate.2 Although guidelines recommend antidepressants as well-validated treatment options, not all individuals will respond and/or achieve remission with an initial treatment and little evidence exists to guide next-step treatment, resulting in a trial and error approach to drug selection.3 In the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial, the rate of response to an initial antidepressant treatment was only 49.6%, which declined with an increasing number of treatment trials.4 A systematic review of published studies found that patients nonresponsive to 1 or more treatments have a 15% likelihood of suicide ideation, an approximately 17% likelihood of a suicide attempt, and a 10% incidence of severe adverse events; the net effect being a 0.26 absolute reduction in health utility compared with treatment-responsive patients.5 Moreover, the annual medical costs for managing a patient nonresponsive to treatments were nearly $10,000 more than those for a treatment-responsive patient, in 2012 US$.5
Studies have indicated significant association of pharmacogenomic (PGx) stratification with patients’ response to treatment, quality of life (QoL), work productivity, and the related costs.6-12 Perlis et al found that a hypothetical PGx test for a newly diagnosed MDD patient was associated with a relative risk of recovery of 1.28, and the cost/quality-adjusted life-year (QALY) gained varied from $1000 to more than $50,000, depending on input parameters.11 Because some studies indicated a weak association between certain single-nucleotide polymorphisms and antidepressant response,9,13 an integrated analysis of multiple polymorphisms has been proposed as a solution to the pharmacotherapy “treatment odyssey,” because this may allow the assessment of genetic variations that influence the activity of multiple enzymes in drug metabolism. Clinical data and adjunctive analyses are needed to estimate efficacy and to enable pharmacoeconomic analysis of combinatorial tests.11,14-16
Prospective studies have recently provided evidence of improved antidepressant response rates associated with the use of a multi-gene combinatorial pharmacogenomic (CPGxTM) test in real-world settings.17-19 A retrospective analysis indicated a decrease in the use of healthcare and employer resources in patients treated with medications reported as likely to be effective.20 The panel test assesses 6 genes (CYP2D6, CYP2C19, CYP2C9, CYP1A2, SLC6A4, and HTR2A) frequently shown to be important for neuropsychiatric disorders, from which an integrated report is generated to guide selection of 38 FDA-approved antidepressant and antipsychotic medications.21 Given this new clinical data supporting CPGx testing, the aim of this study was to estimate the lifetime QoL effects and economic implications of CPGx testing compared with the treatment as usual (TAU) approach for medication selection. We focused on patients who were shown to be nonresponsive to at least 1 prior treatment, and primary outcome measures included QALYs, direct and indirect costs, and incremental cost-effectiveness ratio (ICER).
The research methods and analytic framework followed the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Practices for Outcomes Research22 and the Consolidated Health Economic Evaluation Reporting Standards (CHEERS).23 The data were drawn from published clinical evidence and were validated by experts’ opinions where published evidence was limited. The authors had independence in the design of the analytic framework, data sources, and interpretation.
Target Population
The analysis targeted patients nonresponsive to 1 or more treatments. The base-case scenario was a representative patient tested at age 44 years, the average age of participants in the 3 clinical studies.17-19
Analytic Framework
CPGx testing-guided treatment was compared with TAU from a societal perspective. TAU represents clinical decision making after evaluation of treatment history, physical examination, and interpretation of appropriate laboratory results.
A generalized, state-transition probability analysis was conducted of 4 states based on survival and treatment response (Figure 1), assessed quarterly in the first year and annually in subsequent years. Health outcomes assessed included QALYs and probability of death from suicide over the patient’s lifetime. The patient’s total QALYs were calculated as the sum of the QoL in the health states in each cycle within the time horizon.
Economic implications were evaluated by differences in direct medical costs and indirect costs, which included labor participation and employee productivity costs. Both benefits and costs were discounted at an annual fixed rate of 3%, correcting for accrued value at future dates. The ICER was calculated as the ratio of the difference in costs to the difference in QALYs.
Input Parameters and Data Sources
Published peer-reviewed literature on MDD epidemiology and cost-effectiveness analyses was reviewed to identify data sources with relevant estimates. Gaps in this literature were augmented by additional searches in PubMed (National Center for Biotechnology Information, US National Library of Medicine). The quality of data was evaluated for inclusion using a hierarchy of evidence sources, ranging from literature review to population surveys and authors’ assumptions (Table 1).
Response Rates
To estimate treatment response rates, we conducted a meta-analysis of 3 prospective clinical studies of the CPGx test carried out at different psychiatric outpatient clinics (eAppendix A, available at STATA version 9.2 (Stata Corp, College Station, Texas) was used for all analyses. In the first quarter, the response rate was 24.7% for TAU and 42.2% for CPGx. Both rates increased during the next 2 quarters until reaching the plateau as the treating physicians tried different medications, based on the STAR*D response rates at different treatment levels.24-26

We assumed TAU would catch up with CPGx after year 3, based on a published systematic review of randomized trials in which pharmacologic impacts have been shown to persist up to 36 months.27
Mortality Rates
Death from suicide was examined separately because suicide ideation and attempt are prevalent among treatment-resistant patients.28,29 Probability of death from suicide was based on a US study that followed treatment-resistant patients treated with or without vagus nerve stimulation (VNS) for 5 years.29 The annual suicide rate in the TAU group from this study was used to estimate the probability of suicide death for nonresponders in our analysis (0.16%), and the rate in the TAU+VNS group was used for responders (0.09%). The assumption that the TAU+VNS group is similar to responsive patients was substantiated by a study showing similar medical costs between VNS-treated patients and patients with managed depression.28
Non–suicide-related mortality was derived by subtracting suicide death from all-cause death. The literature review showed a lack of consistent evidence for an association between treatment resistance and increased all-cause mortality,5,28,29 so all-cause mortality rate for responders and nonresponders was conservatively assumed to be the same as that in the general population (relative risk, 1.0).
Both direct and indirect costs were included in this analysis. The annual costs for management of depression per patient were based on a comprehensive meta-analysis of published claims databases.5,28,30-32 Total costs were calculated by summing the costs from each simulation cycle, which equal the product of costs of the health state and the probability of being in this state in the corresponding cycle. Direct medical costs were composed of the costs of depression and non-depression drugs, inpatient and outpatient physician treatment, psychotherapy, and other costs. Indirect costs included productivity and absenteeism costs. Direct medical costs accumulated over the patient’s lifetime, while indirect costs stopped accruing beyond the youngest full retirement age (65 years old) from the Social Security Administration Retirement Planner, reporting conservative results for the test benefit.33 The list price of the test was included for the CPGx arm (provided by AssureRx Health, Inc, of Mason, OH). All costs were inflated to 2013 US$ using the medical care Consumer Price Index from the United States Bureau of Labor Statistics in 2013.34

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