AJMC

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
Objectives

Pharmacogenetic testing as a means of guiding treatment decisions is beginning to see wider clinical use in psychiatry. The utility of this genetic information as it pertains to clinical decision making, treatment effectiveness, cost savings, and patient perception has not been fully characterized.

Study Design

In this retrospective study, we examined health claims data in order to assess medication adherence rates and healthcare costs for psychiatric patients.

Methods

Individuals for whom pharmacogenetic testing was ordered (cases) were contrasted with those who did not undergo such testing (controls). Cases and controls were propensity score matched in order to minimize risk of confounding in this nonrandomized study. An initial analysis of 111 cases and 222 controls examined both adherence and healthcare costs. A replication study of 116 cases and 232 controls examined adherence alone, as cost data was not available for this latter cohort.

Results

Overall, individuals with assay-guided treatment were significantly more medication adherent (P = 1.56 3 10–3; Cohen’s d = 0.511) than patients with standard treatment and demonstrated a relative cost savings of 9.5% in outpatient costs over a 4-month follow-up period, or $562 in total savings.

Conclusions

The data show the utility of pharmacogenetic testing in everyday psychiatric clinical practice, as it can lead to improved patient adherence and decreased healthcare costs.

Am J Manag Care. 2014;20(5):e146-e156
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

Design


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.

Data Source

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).

The pool of controls was created by selecting patients available in the claims database with matching year of birth, sex, and psychiatric condition to any cases. The index date for the corresponding case was assigned to all matching controls. The matching controls must have received any dispensed psychotropic agent within +/– 7 days from the date of a case’s in both the pre- and postindex periods. Additional selection criteria were applied to the controls to further limit the pool utilized in PSM (Table 1). Figure A shows a temporal depiction of inclusion characteristics. PSM was performed on the refined pools of cases and controls, matching 2 controls to every case.

PSM was computed using a logistic regression model that adjusted for covariates of patient age, sex, payer type, US Census region, all psychiatric condition(s), all medication type(s), Charlson Comorbidity Index score, and treating practitioner’s specialty.25-28 Binary variables were assigned for presence or absence of each central nervous system (CNS) diagnosis of interest as determined by the International Classification of Diseases, Ninth Revision, supplied at the time of testing for cases or the diagnosis code associated with private practitioner visits between 4 months pre to 60 days postindex (Appendix B). Separate binary variables were assigned for each pharmacologic category of interest (Appendix A). A standard mean difference between the 2 samples of less than 10% pre-post matching was considered indicative of good balance.29,30 Figure B shows a temporal depiction of matching.

A separate replication analysis of medication adherence, which included different cases and controls, was conducted using the same selection criteria, with the exception of observation within medical claims following index date (Table 1). This criterion was necessary for cost evaluation in the primary analysis, but did not impact inclusion in a replication analysis regarding medication adherence. Adherence Methods Adherence was calculated using the medication possession ratio (MPR).31,32 MPR was calculated at the active ingredient or generic drug level for each psychiatric product the patient filled during the respective periods: (1) preindex included dispensed scripts between 4 and 8 months pre-index (each product was tracked for 120 days from the time the first script was filled within this period); and (2) postindex included dispensed scripts between days 5 and 120 postindex. Dispensed dates that did not allow for a 4-month observation window were excluded. At the generic molecule and patient level, the days of supply was summed over the respective observation period to represent the MPR numerator. The MPR denominator was the observation period duration of 120 days. Figure 1B shows a temporal depiction of pharmacy claims data utilized to determine MPR.

Overlapping days were considered early fills. Any prescription’s days of supply that extended beyond the 120- day observation period was truncated to end on the 120th day of observation. MPR was calculated for all distinct drugs at the generic level, and the patient’s maximum MPR among all products was used to represent the overall MPR for that period. Within sample comparison of the mean MPR 4-month pre-index versus 4-month postindex analysis was conducted. Additionally, a case versus control approach was utilized.

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Issue: May 2014
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