Pharmacogenetic-Guided Psychiatric Intervention Associated With Increased Adherence and Cost Savings
Published Online: May 20, 2014
Jesen Fagerness, JD; Eileen Fonseca, MS; Gregory P. Hess, MD, MBA, MSc; Rachel Scott, PharmD; Kathryn R. Gardner, MS; Michael Koffler, MBA; Maurizio Fava, MD; Roy H. Perlis, MD, MSc; Francis X. Brennan, PhD; and Jay Lombard, DO
Depression and other psychiatric illnesses have substantial costs in human and financial terms, accounting for 4 of the top 10 causes of disability worldwide.1 In the United States, it is estimated that mental health disorders account for 6.2% of the nation’s healthcare spending.2 A major driver of depression costs is treatment-resistant depression (TRD),3 defined as a failure to reach symptomatic remission despite at least 2 adequate treatment trials.4 An estimated two-thirds of patients with depression will not respond to first-line treatment and more than one-third of patients will become treatment resistant.5-7 These individuals pose an even more striking cost burden, approximately 40% higher, compared with patients without TRD.8,9 Treatment for psychiatric disorders is further complicated by variation in medication response across populations. Known pharmacodynamic and pharmacokinetic variations exist across ethnic groups impacting response to common psychiatric medications.10 There is abundant evidence that genetic variations influence drug disposition, metabolism, transport, and response, resulting in changes to efficacy and tolerability of psychotropics.11-13 An estimated 40% of the interindividual differences in antidepressant response are explained by common genetic variations.14 Genetic variations may compound already elevated costs by increasing the risk for intolerable side effects or poor efficacy, which may contribute to nonadherence.15-19
Identifying genetic variations involved in treatment response may facilitate more targeted evidence-based interventions in order to improve the likelihood of remission. While a theoretical basis for expecting benefit from genetic testing exists, the actual impact on outcomes—and potential cost savings in particular—has not been well established. The use of claims data to establish these benefits provides advantages through the efficiency of data collection, ability to observe effectiveness in real-world clinical practice, and the ability to directly measure costs. While such methods pose a risk of confounding, modern analysis and matching techniques, including propensity score matching, can be used to reduce these potential sources of bias. The objective of the current study was to determine whether patient and clinician access to genetic information during psychiatric treatment selection would influence medication adherence and healthcare costs among psychiatric patients.
MATERIALS AND METHODS
This retrospective, observational study used patients’ claims data from September 2010 through September 2012. Patients were divided into 2 groups: (1) cases whose treating clinicians ordered genetic testing and (2) a matched set of controls whose treating clinicians did not have access to genetic information, treating patients as usual.
The genetic test used in this study, the Genecept Assay, analyzed variations in 5 pharmacodynamic and 2 pharmacokinetic genes associated with treatment response, side effects, metabolism, tolerability, and overall efficacy of many psychiatric medications.15,20-24 Pharmacokinetic genes included cytochrome P450 2D6 (CYP2D6) and 2C19 (CYP2C19). Pharmacodynamic genes included the serotonin transporter (SLC6A4), calcium channel subunit (CACNA1C), dopamine receptor subtype (DRD2), catechol- O-methyl transferase (COMT), and methylenetetrahydrofolate reductase (MTHFR).
The saliva-based test was administered to cases at their clinician’s office. Clinicians were provided with instructions and telephone support. Samples were sent to a Clinical Laboratory Improvement Amendments (CLIA) certified lab for analysis. After genotyping via polymerase chain reaction (PCR) Taqman, a results report was provided to clinicians with a summary of clinical interpretation and implications for each variant supported by peer-reviewed published literature. An algorithm that was designed based on the functional significance of each variant and the associated effects on treatment outcomes was applied to generate a unique report for each patient. Professionals with experience in medical genetics and psychopharmacology were available to assist clinicians with interpretation of results.
Healthcare utilization and cost data were extracted from IMS Health longitudinal patient-level databases which included pharmacy claims, private practitioner medical claims, and hospital detail charge master records. Available data included all claims submitted to third-party payers including commercial insurers, Medicare and Medicaid via Centers for Medicare & Medicaid Services (CMS) 1500 forms and others (eg, Tricare) in all 50 US states, and cash claims. Prescriptions were analyzed using the National Council for Prescription Drug Programs claim forms, which included approximately 2 billion dispensed claims per year within the database. Patients were assigned a synthetic identifier, and the databases were certified as being compliant with the Health Insurance Portability and Accountability Act. This study was deemed exempt from additional consent requirements by Chesapeake IRB (Pro00008398), as data were completely deidentified.
Sample Measures and Selection Criteria
Cases were included if their practitioner received their test results between May 1, 2011, and March 31, 2012. The index date for cases was defined as the date the Genecept Assay results were available to clinicians. Additional inclusion criteria for cases required a psychotropic prescription be dispensed between 5 and 60 days postindex (Appendix A) as well as pharmacy activity between 5 and 8 months pre-index, as evidenced by claims data. Cases were required to have a psychiatric diagnosis listed in the claims database (Appendix B). Additional selection criteria were applied to create a final pool of cases utilized in propensity score matching (PSM) (Table 1).
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