Pharmacogenomic-Guided Prescribing and Polypharmacy Across Age Groups in Obsessive-Compulsive Disorder: A Retrospective Study

Pharmacogenomic-Guided Prescribing and Polypharmacy Across Age Groups in Obsessive-Compulsive Disorder: A Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pharmacogenomic-Guided Prescribing and Polypharmacy Across Age Groups in Obsessive-Compulsive Disorder: A Retrospective Study Sheldon R. Garrison, Matthew W. Boyer, Anthony W. Zoghbi, Rachel A. Schwartz, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8182802/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background This study evaluated medication utilization in children, adolescents, and adults with obsessive-compulsive disorder (OCD), a chronic psychiatric condition characterized by intrusive thoughts and repetitive behaviors. Although first-line treatments include selective serotonin reuptake inhibitors (SSRIs) and cognitive behavioral therapy (CBT), the heterogeneous biological underpinnings contribute to suboptimal outcomes, with 40–60% of individuals not responding to SSRIs. This complex phenotype often leads to psychotropic polypharmacy, which may be mitigated by incorporating combinatorial pharmacogenomic (PGx) testing into protocol-based care to identify potential gene-drug interactions. Methods A retrospective review was conducted of individuals with OCD aged 8 to 65 years who received either PGx testing or treatment as usual (TAU). Co-primary outcomes were polypharmacy rate and quality of life. Secondary outcomes included length of stay, medication utilization, and OCD and depression severity. Individuals prescribed at least one daily psychotropic medication with a gene-drug interaction were classified as “incongruent” (PGx-I). Individuals without gene-drug interactions for all prescribed psychotropic medications were categorized as “congruent” (PGx-C). Results A total of 363 individuals with OCD were analyzed. Of these 241 received TAU and 122 underwent PGx testing. Within the PGx cohort, 67% were prescribed medications with potential gene-drug interactions at discharge. The polypharmacy rate was 71% in the PGx-I cohort, compared with 35% in the PGx-C cohort. Quality-of-life measures revealed similar levels of improvement in the PGx-C and PGx-I cohorts. Conclusions Psychotropic polypharmacy rates were higher among individuals prescribed at least one medication with a gene-drug interaction, most notably among adults, while all cohorts showed similar improvement. These findings suggest that incorporating combinatorial PGx testing into the medical evaluation particularly where polypharmacy is a concern may help optimize medication selection, while maintaining effectiveness. OCD CYP2D6 depression anxiety pharmacogenomics PGx GeneSight polypharmacy antipsychotics antidepressants Figures Figure 1 Figure 2 BACKGROUND Obsessive-compulsive disorder (OCD) is a chronic and debilitating psychiatric condition characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions). It affects an estimated 2.3% of the population and is associated with significant economic burden, with annual costs exceeding $ 10 billion in the United States alone. 9,10 Although the etiology of OCD is not fully understood, accumulating evidence implicates genetic predisposition, oxidative damage, and dysregulation of serotonin and glutamate signaling [ 1 – 4 ]. First-line treatments include cognitive behavioral therapy (CBT) with exposure and response prevention (ERP) and pharmacotherapy with selective serotonin reuptake inhibitors (SSRIs) [ 5 ]. However, these strategies remain suboptimal with only 40–60% of individuals with OCD responding to first-line SSRIs in standard care [ 6 – 8 ]. Non-responding individuals typically trial another SSRI, a serotonin–norepinephrine reuptake inhibitor (SNRI), clomipramine, or add adjunctive therapies such as atypical antipsychotics and glutamatergic agents [ 4 , 9 , 10 ]. Consequently, there is increasing interest in identifying biomarkers and genetic factors that may better define OCD subtypes and guide medication selection [ 11 ]. Co-occurring psychiatric conditions complicate the challenge of medication optimization [ 12 ]. An estimated 90% of individuals with a lifetime diagnosis of OCD meet the diagnostic criteria for at least one other psychiatric disorder [ 12 ]. Generalized anxiety disorder and major depressive disorder are among the most common co-occurring conditions [ 13 ], and are typically treated with SSRIs [ 14 – 16 ]. These co-occurring conditions often share neural networks and neurochemical perturbations with OCD, which can complicate medication management [ 17 ]. Multiple psychotropic medications are often prescribed to treat OCD, primarily SSRIs, but also atypical antipsychotics and other adjunctive agents. This frequently raises the possibility for polypharmacy to effectively manage the constellation of symptoms. Moreover, these combinations have rarely been evaluated in clinical trials, leaving prescribers with limited evidence to guide decisions, and little insight into which symptoms or mechanisms each medication is targeting. Therefore, optimizing medication selection and dosing to reduce polypharmacy may have substantial therapeutic benefit. Polypharmacy also raises concerns about potential gene-drug interactions, drug-drug interactions and adverse effects, posing challenges for future medication management. Risks increase with age and the addition of nonpsychiatric medications [ 18 ]. Psychotropic polypharmacy is linked to multiple issues, including medication nonadherence [ 19 ], amplification of side effects when multiple medications are prescribed from the same drug class [ 20 ], difficulties in selecting and tapering medications when discontinued [ 21 ], and significant annual cost per medication [ 22 , 23 ]. Although there is emerging evidence that multiple drug classes may be required to treat co-occurring psychiatric conditions [ 24 ], and suggestions that low-dose polypharmacy may result in fewer side effects compared to high-dose monotherapy [ 25 , 26 ], the general consensus is that fewer medications are preferred when possible to maximize efficacy [ 27 ]. Accordingly, polypharmacy requires careful monitoring, thoughtful selection of drug combinations and may benefit from clinical tools that help guide optimization. Pharmacogenomics (PGx) has been proposed as one approach to optimize medication selection and decrease time to remission [ 28 ]. PGx testing uses an individual’s genetics profile to predict the pharmacokinetic (PK) and pharmacodynamic (PD) profiles and alert prescribers, including psychiatrists and primary care providers [ 29 ], to potential liabilities that may affect treatment response. The most robust PGx data for psychotropic medications is focused on the metabolizer status of the CYP2D6 , CYP2C19 and CYP2B6 genes [ 30 , 31 ]. When integrated into prescribing decisions, PGx testing for the treatment of depression has been associated with reduced side effect burden [ 32 ], improved adherence [ 33 ], and decreases in emergency room visits, hospital readmissions and overall cost [ 34 – 36 ]. However, gene-drug associations that inform medication recommendations are not well established for OCD, and commercial combinatorial PGx products have not been adequately tested in this patient population [ 28 , 37 , 38 ]. Limited real-world analysis of neuropsychiatric-related PGx reports indicate that most ordered tests are combinatorial [ 39 ]. suggesting that evaluating combinatorial PGx testing in OCD may provide practical insights with broader clinical relevance. This is important as combinatorial PGx testing utilizes proprietary weighted algorithms that integrates multiple PK and PD genes, as opposed to single- or multi-gene panels to predict gene-drug interactions [ 32 , 40 , 41 ]. Understanding the complex genetic and signaling mechanisms involved in psychotropic medication metabolism is therefore essential to developing effective pharmacologic strategies and minimizing the number of medications required to treat OCD. The present study addresses these gaps by evaluating the clinical utility of combinatorial PGx testing as an adjunctive decision-making tool in the treatment of OCD across developmental stages. Using retrospective data from individuals in a naturalistic OCD treatment environment, those prescribed medications with potential gene-drug interactions were compared to a matched control cohort to evaluate potential differences in polypharmacy and other clinical outcomes. This study builds upon the existing literature in several ways by examining the effectiveness of PGx testing for OCD specifically in both adult and child and adolescent populations. Importantly, by evaluating PGx testing in adult and pediatric individuals, this study adds to the limited understanding of the clinical utility of adjunctive PGx testing for younger individuals. Furthermore, the outcomes were stratified in multiple ways to provide a nuanced analysis of PGx testing’s impact in a real-world setting. The findings are intended to inform the development of clinical guidelines and advance precision medicine approaches in psychiatry and primary care. METHODS Participants and procedure A retrospective electronic health record (EHR) review, approved by an institutional review board (IRB), was conducted that included 363 individuals aged 8–65 with a confirmed diagnosis of OCD between January 2017 and May 2023. All individuals received treatment in a large behavioral health care system with residential, partial hospitalization (PHP) and intensive outpatient (IOP) programs. Upon admission, clinicians conducted comprehensive evaluations that included symptom and functional assessments, alongside diagnostic interviews with a psychiatrist and clinical psychologist following DSM-5 guidelines. These findings were integrated to create a personalized treatment plan, which encompassed decisions regarding medication management and therapeutic approaches. Participants engaged in daily sessions through either the residential program, the PHP program (6–7 hours per day) or the IOP program (3 hours per day). ERP was a core component of the treatment, delivered by trained clinicians skilled in both CBT and pharmacotherapy. To maintain consistency in treatment delivery, each program across the healthcare system followed established treatment manuals. Individuals who transitioned to a lower level of care within seven days of discharge from a higher level of care were included for the total duration of their stay. For individuals who transitioned to a higher level of care, or who stepped down or were readmitted more than seven days after discharge, only the stay during which PGx testing was completed was analyzed. PGx testing was ordered at the prescribing provider’s discretion with the exception of two individuals or their guardian who provided the report at the time of admission. Individuals were excluded if they discharged before the PGx results were available or if no psychotropic medications were prescribed at both admission and discharge. All medications prescribed within 30 days before the report or during the stay were included in the main analysis. Classification of medication congruence with PGx testing Results from any commercial psychotropic PGx test for psychotropic medications were eligible for inclusion. All tests were combinatorial, which evaluated gene-drug interactions based on specific pharmacokinetic (PK) and pharmacodynamic (PD) gene variants. Congruency classification followed previously described methods [ 42 ]. Due to the retrospective nature of the study and inability to determine how providers specifically utilized the test results, when at least one daily psychotropic medication was prescribed and reported to have ‘use with caution’, or moderate, or ‘use with increased caution with recommended frequent monitoring’, or ‘significant’ gene-drug interactions, the individual was classified as ‘incongruent’ (PGx-I). Individuals were categorized as ‘congruent’ (PGx-C) if all prescribed psychotropic medications were 'without reported gene-drug interactions’, or ‘normal’. At admission, individuals not prescribed psychotropic medications were classified as PGx-C. Outcomes The co-primary outcomes were polypharmacy and quality of life (QOL). Polypharmacy was defined as ≥ 3 psychotropic medications concurrently prescribed, or ≥ 2 antidepressants or antipsychotics. Because polypharmacy is a binary metric and provides limited information to understand the degree of separation between cohorts, the average number of medications was also analyzed to gain a more granular understanding of how gene-drug interactions contributed to the observed polypharmacy differences between the PGx-C and PGx-I cohorts. QOL was measured using the Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q) in adults (≥ 18 years) and the Pediatric Quality of Life Enjoyment and Satisfaction Questionnaire (PQ-LES-Q) [ 43 , 44 ] in children and adolescents (5–17 years). Secondary outcomes included length of stay (LOS), average number of psychotropic medications, and age-appropriate assessments of symptom severity. In adults, these included the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) [ 45 , 46 ] and the Quick Inventory of Depression Symptomology (QIDS) [ 47 ]. In adolescents, the assessments included the QIDS and the Children’s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) [ 48 , 49 ]. And in children the assessments included the Patient-Reported Outcomes Measurement Information System Pediatric Depressive scale (PROMIS-D) [ 50 ] and the CY-BOCS. Matching The treatment as usual (TAU) cohort was randomized by randomly selecting individuals in the same treatment programs using the MatchIt package within R, version 4.3.3 [ 51 , 52 ]. Matching criteria included medical service line and OCD diagnosis so that a similar profile would be included within the TAU cohort. Statistical Analysis Categorical variables were described as counts and percentages. Continuous variables were described as means ± standard error of the mean (SEM). The sample size (n) represents individuals in the electronic medical record who met eligibility criteria. Cohort comparisons were performed using Student’s t test for independent samples for normally distributed continuous variables; one-way ANOVA followed by Bonferroni post hoc tests or two-way repeated-measures ANOVA with cohort by time, followed by Bonferroni post-hoc tests as appropriate. Chi-square or Fisher’s Exacts test were used for categorical comparisons as appropriate. Spearman correlation coefficients and negative binomial regression models were used to assess the relationship between gene-drug incongruence with the total psychotropic medications. Statistical significance was set, at α = 0.05 (two-sided). All analyses were carried out using the "stats" and "psych" packages in R. RESULTS Demographics and clinical characteristics Demographic and clinical characteristics were generally similar between the TAU (n = 241) and PGx (n = 122) cohorts (Table 1). Average age did not differ significantly between TAU (20.9 ± 0.7 years) and PGx (23.2 ± 1.1 years) cohorts (P = 0.079, Table 1 ). Reported sex distribution was comparable between TAU (56% female; 44% male) and PGx (58% female; 42% male) cohorts (P = 0.777, Table 1 ). The average length of stay (LOS) was also similar (TAU: 42.6 ± 1.7 days; PGx: 43.4 ± 1.6 days; P = 0.749, Table 1). Level of care differed between cohorts (P = 0.001, Table 1 ), though most individuals were treated in partial hospitalization (74% TAU; 70% PGx). Racial and ethnic data were insufficient for analysis. At admission, QOL and symptom severity were similar across cohorts. Adult Q-LES-Q scores were similarly low for TAU (43.2 ± 1.9) and PGx (43.2 ± 2.1) (P = 0.997; Table 1 ). For children and adolescents, PQ-LES-Q scores were also low and comparable (TAU: 42.4 ± 1.2; PGx: 40.7 ± 1.5; P = 0.373; Table 1). Adult OCD symptom severity measured by Y-BOCS indicated severe symptoms in both TAU (25.1 ± 0.8) and PGx (25.0 ± 0.9) cohorts (P = 0.993; Table 1 ). CY-BOCS scores for children and adolescents were likewise similar (TAU: 21.9 ± 0.9; PGx: 23.9 ± 0.9; P = 0.130; Table 1). Depressive symptoms, assessed by QIDS, indicated moderate severity in adults and adolescents (TAU: 13.4 ± 0.5; PGx: 13.3 ± 0.6; P = 0.831; Table 1 ). PROMIS-D scores in children were moderate and comparable between groups (TAU: 16.9 ± 2.0; PGx: 17.1 ± 2.3; P = 0.951; Table 1 ). Efficacy comparison revealed similar levels of improvement QOL improved in adults between admission and discharge as measured by Q-LES-Q scores. Both TAU and PGx cohorts demonstrated similar improvement (P = 0.882; Fig. 1 a). Children and adolescents also showed comparable gains in PQ-LES-Q scores (P = 0.250; Fig. 1 b). OCD symptom severity improved between admission and discharge. In adults, Y-BOCS scores decreased for both TAU (18.3 ± 0.9) and PGx (17.5 ± 1.1) cohorts (P = 0.582; Table 1 ), with no between-group differences at interim assessments (Fig. 1 c). Among children and adolescents, CY-BOCS scores for both the TAU and PGx groups improved to mild severity at discharge ( Table 1 ), though PGx scores remained higher at all timepoints (Fig. 1 d). Depression severity declined similarly in both groups. QIDS scores improved to mild depression by discharge (TAU: 8.9 ± 0.5; PGx: 8.8 ± 0.5; Fig. 1 e). PROMIS-D scores in children improved to “none to slight” for both TAU (9.9 ± 1.9) and PGx (9.2 ± 2.8; Fig. 1 f). Polypharmacy rates for psychotropic medications at admission were comparable (TAU: 37%; PGx: 36%; P = 0.999; Table 2 ). Stratified by medication class, antidepressant and antipsychotic polypharmacy were also similar. By discharge, polypharmacy increased in the PGx cohort (50%) but not in TAU (32%) (P = 0.002; Table 2 ). To better understand the impact of PGx testing, further analyses focused on gene-drug interactions and outcomes. Psychotropic medication selection, all ages PGx-guided psychotropic medication use was analyzed for polypharmacy and the total number of prescribed medications. Polypharmacy differed by gene-drug congruence. At admission, 15% of PGx-C participants met polypharmacy criteria versus 42% of PGx-I participants (P = 0.017; Fig. 2 a). By discharge, rates increased to 55% for PGx-I and 35% for PGx-C (P = 0.062; Fig. 2 b). Antidepressant polypharmacy differed between groups at discharge (P = 0.011; Fig. 2 b), but no difference was observed for antipsychotics (P = 0.268; Table 2 ). The mean number of psychotropic medications was lower for PGx-C (1.6) than PGx-I (2.5; P = 0.003; Table 2 ). By discharge, the mean increased for both groups but remained lower in PGx-C (2.3) compared to PGx-I (3.1; P = 0.005; Table 2 ). Similar trends were observed for antidepressants and antipsychotics ( Table 2 ). Medication utilization in adults In adults, gene-drug incongruency was associated with higher polypharmacy. At admission, 56% (31/55) of PGx-I participants were prescribed ≥ 3 psychotropic medications (Fig. 2 c), increasing to 72% (38/53) at discharge (P = 0.003; Fig. 2 d). In contrast, PGx-C participants showed lower polypharmacy rates (12% [2/17] at admission; 36% [8/22] at discharge; Figs. 2 c, 2 d). Antidepressant polypharmacy was similar at admission (PGx-C: 6%; PGx-I: 26%) but diverged by discharge (PGx-C: 14%; PGx-I: 42%). Antipsychotic polypharmacy remained low at both timepoints. Average medication counts were reported in Table 2 . Medication utilization in children and adolescents Congruency with gene-drug interactions appeared to play a diminished role in prescribing for children and adolescents. At admission, psychotropic polypharmacy was 24% (9/37) in PGx-I compared to 20% (2/10) in PGx-C (Fig. 2 e). By discharge, rates were 31% (9/29) and 33% (6/18), respectively (Fig. 2 f). Antidepressant polypharmacy increased from 14% to 28% in PGx-I and from 10% to 11% in PGx-C, with no statistical difference. Antipsychotic polypharmacy remained low and similar across groups. Average medication numbers did not differ except for antipsychotics at discharge ( Table 2 ). Correlation of the number of incongruent medications with total medications Across all participants, the number of incongruent psychotropic medications correlated with the total number prescribed (ρ = 0.48; P < 0.001), and a similar correlation was seen for antidepressants (ρ = 0.47; P < 0.001). Among adults, these correlations remained significant for both psychotropics (ρ = 0.53; P < 0.001) and antidepressants (ρ = 0.49; P < 0.001). In children and adolescents, a positive trend was observed for total psychotropics (ρ = 0.25; P = 0.094) and a significant correlation for antidepressants (ρ = 0.40; P = 0.006) ( Table S2 ). Assessment outcomes by congruency Medication congruency did not appear to affect clinical outcomes. Across all age groups, QOL, OCD and depression symptom severity improved similarly in PGx-C and PGx-I cohorts. Adults improved from low to average Q-LES-Q scores ( Table S3 ). Children and adolescents showed comparably improved PQ-LES-Q scores ( Table S3 ). OCD severity (Y-BOCS and CY-BOCS), depression severity (QIDS, PROMIS-D) improved similarly showed no between-group differences, with a comparable LOS ( Table S3 ). Utilization of PGx test results Medication changes based on PGx results were minimal, regardless of congruency with gene-drug interactions. Among PGx-I individuals, 55% (52/95) remained on at least one incongruent psychotropic at discharge, and 42% (22/52) were prescribed at least one additional incongruent medication. Only 18% (17/95) were switched fully to congruent alternatives ( Table S1 ). Age-stratified analyses revealed more changes in children and adolescents: 62% (23/37) switched to an alternative medication, of whom 61% received a congruent option. Among adults, 35% (20/58) switched medications, and only 20% (4/20) were switched fully to congruent alternatives (data not shown). Additional PGx considerations Variants affecting CYP2D6, CYP2C19, and CYP2B6 metabolism were identified in 58% (71/122) of participants ( Table S1 ), though not further analyzed. Psychiatric comorbidities and OCD symptom severity at admission were not correlated with total psychotropic (P = 0.944) or antidepressant (P = 0.339) counts (data not shown). To reduce confounding from medication changes, analyses limited to individuals maintained exclusively on congruent or incongruent regimens showed no outcome differences ( Table S4 ). Assessment outcomes were also compared between TAU and PGx-I cohorts, with no between-group differences ( Table S4 ). However, an increased average number of psychotropic medications were observed in adults, and an increased in the average number of antipsychotics in the child and adolescent subgroup ( Table S5 ). DISCUSSION Overall implications This study advances the understanding of how PGx testing may be incorporated into clinical workflows to optimize medication management for individuals with OCD. While both the PGx and TAU cohorts demonstrated similar levels of symptom improvement overall, PGx subgroup analysis revealed important differences in prescribing, particularly with polypharmacy. Specifically, adults had lower polypharmacy rates when prescribed medications were congruent with their PGx results (PGx-C subgroup) compared to those in the PG-I subgroup. Medication congruency with gene-drug interactions was consistently associated with reduced polypharmacy and a lower average number of psychotropic medications. Individuals prescribed psychotropic medications that were congruent with their gene-drug interactions (PGx-C) were able to achieve the same outcomes as PGx-I with fewer prescribed psychotropic medications. In contrast, gene-drug interactions appeared to have less of an impact on prescribing decisions in children and adolescents, where polypharmacy rates were similar regardless of congruency. These findings suggest that PGx testing may have greater clinical benefit for adults. Interestingly, although providers ordered the PGx tests in the current study, in most cases prescribing decisions did not seem to rely solely on the PGx test results. Only 18% of individuals prescribed an incongruent psychotropic medication switched to a congruent alternative, suggesting that prescribers placed limited weight on gene-drug interactions when making medication decisions. This limited use underscores barriers to clinical implementation when treating individuals with OCD. Indeed, medication selection is a complex process involving treatment guidelines, evidence-based practice for usage of certain medications without gene-drug interactions, family history, prior medication trials, patient and family preferences, insurance coverage, co-occurring conditions, allergies and other factors. This is not unexpected when treating OCD, as most neuropsychiatric PGx testing research has focused on antidepressants, with the strongest evidence in depression,[ 53 ] and less evidence available for other indications such as OCD.[ 38 ] Nevertheless, the decision to order PGx testing without routinely acting on the results raises important questions about its clinical utility for both prescribers and patients. Combinatorial PGx testing for OCD To date, utilization of PGx testing for OCD remains limited,[ 38 ] and no prior studies have evaluated combinatorial PGx testing in this population. Most available evidence comes from studies of specific genes, such as CYP2D6 and CYP2C19 [ 54 – 56 ], which may affect the metabolism of SSRIs and other medications commonly prescribed for OCD. Findings from these gene-specific studies have been mixed. While PGx testing to guide medication selection for escitalopram, citalopram and sertraline may be beneficial [ 30 , 57 ], there remains a need for comprehensive investigation encompassing other first-line and adjunctive medications commonly prescribed for OCD [ 38 ]. Therefore, the current study adds to this literature by demonstrating that combinatorial PGx testing may reduce polypharmacy in adults when individuals are switched to congruent medications. Future directions Broader adoption of PGx testing as a clinical decision support tool for OCD medication management may depend on outcomes prioritized by prescribers, patients and families. The current findings suggest that polypharmacy and average number of medications may be important metrics when evaluating PGx efficacy, particularly given the limited evidence base for PGx usage in OCD. Adults were prescribed an average of 3.6 psychotropic medications, a level rarely evaluated in randomized, controlled clinical trials [ 58 ], and for which data on side effects or drug-drug interactions remains limited. This is also notable because adults, particularly older adults, are more likely to be prescribed other medications for unrelated medical conditions [ 59 – 61 ]. PGx testing may be therefore useful to help reduce polypharmacy while maintaining similar clinical outcomes. Future studies should include randomized, controlled trials with longitudinal follow-up to assess the sustained impact of prescribing medications with gene-drug interactions and to evaluate sustained outcomes following discharge from residential, partial hospitalization and intensive outpatient settings. This will help to minimize systematic differences that may exist between individuals whose prescribers ordered PGx testing compared to TAU, a confounding factor in the current study. Child and adolescent-focused studies are also warranted to clarify the utility for PGx testing in younger individuals. Limitations As a retrospective study, this research is subject to several limitations. Medications were classified as either congruent or incongruent without further consideration of dosage or titration rate. Additionally, three individuals admitted without a documented psychotropic medication at intake and were classified as congruent. Medication compliance was based on patient self-report and prescription refill history; available data did not include information on prior failed medication trials. Only psychotropic medications were evaluated, so the potential influence of drug-drug interactions with non-psychotropic prescriptions on prescribing decisions remains unaddressed. Cross-tapering across multiple psychotropic medications was not evaluated, which could complicate polypharmacy interpretation. The relatively short average length of stay, averaging less than 45 days, reduced the likelihood of detecting medication-related differences in assessment outcomes. Longitudinal data from standard outpatient settings may better capture differences in assessments, polypharmacy, and average medication use that were not observable in the higher levels of care studied here. While data extraction included any PGx test, the data revealed that prescribers exclusively used the GeneSight test. Because of proprietary algorithms and other differences across commercial tests, the findings may not be inherently generalizable across all commercial neuropsychiatric PGx tests. Although adverse effects were monitored as part of standard care, they were not systematically evaluated or compared between groups. Conclusions This study evaluated the clinical utility of PGx testing for children, adolescents and adults with OCD. Overall, combinatorial PGx testing identified psychotropic medications with gene-drug interactions and highlighted associations with polypharmacy patterns. Importantly, individuals receiving PGx testing achieved similar quality of life and symptom severity compared to TAU. Taken together, these findings highlight the potential benefit of PGx testing as an adjunctive clinical decision support tool that may help to reduce polypharmacy while maintaining therapeutic effectiveness. Abbreviations Children's Yale-Brown Obsessive-Compulsive Scale CY-BOCS Cognitive behavioral therapy CBT Cytochrome P450 2B6 CYP2B6 Cytochrome P450 2C19 CYP2C19 Cytochrome P450 2D6 CYP2D6 Electronic Health Record EHR Exposure and response prevention ERP Institutional Review Board IRB Intensive Outpatient Program IOP Length of Stay LOS Obsessive-Compulsive Disorder OCD Partial Hospitalization Program PHP Patient-Reported Outcomes Measurement Information System Pediatric Depressive scale PROMIS-D Pediatric Quality of Life Enjoyment and Satisfaction Questionnaire PQ-LES-Q Pharmacodynamic PD Pharmacogenomics PGx Pharmacogenomic congruent PGx-C Pharmacogenomic incongruent PGx-I Pharmacokinetic PK Quick Inventory of Depression Symptomology QIDS Quality of life QOL Quality of Life Enjoyment and Satisfaction Questionnaire Q-LES-Q Selective Serotonin Reuptake Inhibitor SSRI Serotonin-norepinephrine Reuptake Inhibitor SNRI Standard Error of the Mean SEM Treatment as Usual TAU Yale-Brown Obsessive-Compulsive Scale Y-BOCS Declarations Ethics approval and consent to participate The study protocol was approved by the Rogers Behavioral Health Institutional Review Board (approved protocol number: RBH-2023-01). The study was designed in accordance with the Helsinki Declaration. This retrospective study utilized de-identified data, and a HIPAA (Health Insurance Portability and Accountability Act) waiver of informed consent was approved. Availability of data and materials The data used in this retrospective study were obtained from the electronic health record system and are not publicly available due to patient privacy. Data are available upon reasonable request. Researchers interested in accessing de-identified data for further analysis may contact the corresponding author to discuss potential data sharing under the approval of the Institutional Review Board and with appropriate data access agreements in place. Competing interests The authors declare no competing interests. Funding This research was supported by the Rogers Behavioral Health Foundation and the Lynn S. Nicholas Foundation. Clinical trial number Not applicable. Consent for publication Not applicable. Author’s contributions Conceptualization: S.R.G., S.V., M.M.H., A.W.Z., M.W.B., N.W.; Data curation: S.V.; Formal analysis: S.R.G., M.W.B., M.S., S.V., M.M.H.; Funding acquisition: S.R.G.; Investigation: S.R.G., R.A.S., M.W.B. M.S., S.V., R.A.S., M.M.H.; Methodology: S.R.G., R.A.S., M.S., S.V., M.M.H.; Project administration: S.R.G.; Software: M.S., S.V., M.M.H.; Supervision: S.R.G.; Validation: S.R.G., M.M.H., M.S.; Visualization: S.R.G.; Writing-original draft: S.R.G., R.A.S., M.S., A.W.Z., M.W.B., N.W., M.E.F.; Writing-review & editing: S.R.G., M.W.B., M.S., S.V., R.A.S., M.M.H., N.W., M.E.F., A.W.Z., S.A.S. Acknowledgements The authors thank Amaya Ramos, MD for her clinical guidance with study design. We thank Sophie Schweinert, Zaira Chavez, Rachel Lopez, Lily Mantsch, Ella Patty, and Sladjana Strbac for their assistance in manual data extraction from the pharmacogenomics reports. We thank Jeffery Engelmann, PhD for his guidance with data extraction for this project. 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Greden JF, Parikh SV, Rothschild AJ, Thase ME, Dunlop BW, DeBattista C, Conway CR, Forester BP, Mondimore FM, Shelton RC et al : Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: A large, patient- and rater-blinded, randomized, controlled study . J Psychiatr Res 2019, 111 :59-67. Taj R, Khan S: A study of reasons of non-compliance to psychiatric treatment . J Ayub Med Coll Abbottabad 2005, 17 (2):26-28. Elliott LS, Henderson JC, Neradilek MB, Moyer NA, Ashcraft KC, Thirumaran RK: Clinical impact of pharmacogenetic profiling with a clinical decision support tool in polypharmacy home health patients: A prospective pilot randomized controlled trial . PLoS One 2017, 12 (2):e0170905. Ghanbarian S, Wong GWK, Bunka M, Edwards L, Cressman S, Conte T, Price M, Schuetz C, Riches L, Landry G et al : Cost-effectiveness of pharmacogenomic-guided treatment for major depression . CMAJ 2023, 195 (44):E1499-E1508. Groessl EJ, Tally SR, Hillery N, Maciel A, Garces JA: Cost-Effectiveness of a Pharmacogenetic Test to Guide Treatment for Major Depressive Disorder . J Manag Care Spec Pharm 2018, 24 (8):726-734. Zanardi R, Manfredi E, Montrasio C, Colombo C, Serretti A, Fabbri C: Pharmacogenetic ‐ guided treatment of depression: Real ‐ world clinical applications, challenges, and perspectives . Clinical Pharmacology & Therapeutics 2021, 110 (3):573-581. Zai G: Pharmacogenetics of Obsessive-Compulsive Disorder: An Evidence-Update . Curr Top Behav Neurosci 2021, 49 :385-398. Zhang L, Tholkes AJ, Jones KC, Yang LJ, Sieger GK, Cullen KR, Gunlicks-Stoessel ML, Mroz P, Farley JF, Johnson SG et al : Real-World Characterization of Psychiatric Pharmacogenomic Test Ordering and Clinical Relevance in Adults and Children . Clin Transl Sci 2025, 18 (10):e70297. 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Anderson JR, Killian M, Fuller A, Hughes JL, Byerly M, Lindow J, John Rush A, Trivedi MH: Psychometric Evaluation of the Pediatric Quality of Life Enjoyment and Satisfaction Questionnaire in a General Youth Population . Child Psychiatry Hum Dev 2022, 53 (3):546-553. Endicott J, Nee J, Harrison W, Blumenthal R: Quality of Life Enjoyment and Satisfaction Questionnaire: a new measure . Psychopharmacol Bull 1993, 29 (2):321-326. Goodman WK, Price LH, Rasmussen SA, Mazure C, Delgado P, Heninger GR, Charney DS: The Yale-Brown Obsessive Compulsive Scale. II. Validity . Arch Gen Psychiatry 1989, 46 (11):1012-1016. Goodman WK, Price LH, Rasmussen SA, Mazure C, Fleischmann RL, Hill CL, Heninger GR, Charney DS: The Yale-Brown Obsessive Compulsive Scale. I. Development, use, and reliability . Arch Gen Psychiatry 1989, 46 (11):1006-1011. Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, Markowitz JC, Ninan PT, Kornstein S, Manber R et al : The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression . Biol Psychiatry 2003, 54 (5):573-583. Scahill L, Riddle MA, McSwiggin-Hardin M, Ort SI, King RA, Goodman WK, Cicchetti D, Leckman JF: Children's Yale-Brown Obsessive Compulsive Scale: reliability and validity . J Am Acad Child Adolesc Psychiatry 1997, 36 (6):844-852. Conelea CA, Schmidt ER, Leonard RC, Riemann BC, Cahill S: The Children's Yale–Brown Obsessive Compulsive Scale: Clinician versus self-report format in adolescents in a residential treatment facility . Journal of Obsessive-Compulsive and Related Disorders 2012, 1 (2):69-72. Cheng AL, Downs DL, Brady BK, Hong BA, Park P, Prather H, Hunt DM: Interpretation of PROMIS Depression and Anxiety Measures Compared with DSM-5 Diagnostic Criteria in Musculoskeletal Patients . JB JS Open Access 2023, 8 (1). Team RC: R: A Language and Environment for Statistical Computing . In . , R version 4.3.3 edn. Vienna, Austria: R Foundation for Statistical Computing; 2021. Ho D, Imai K, King G, Stuart EA: MatchIt: Nonparametric Preprocessing for Parametric Causal Inference . Journal of Statistical Software 2011, 42 (8):1 - 28. Wang X, Wang C, Zhang Y, An Z: Effect of pharmacogenomics testing guiding on clinical outcomes in major depressive disorder: a systematic review and meta-analysis of RCT . BMC Psychiatry 2023, 23 (1):334. Muller DJ, Brandl EJ, Hwang R, Tiwari AK, Sturgess JE, Zai CC, Lieberman JA, Kennedy JL, Richter MA: The AmpliChip(R) CYP450 test and response to treatment in schizophrenia and obsessive compulsive disorder: a pilot study and focus on cases with abnormal CYP2D6 drug metabolism . Genet Test Mol Biomarkers 2012, 16 (8):897-903. Brandl EJ, Tiwari AK, Zhou X, Deluce J, Kennedy JL, Muller DJ, Richter MA: Influence of CYP2D6 and CYP2C19 gene variants on antidepressant response in obsessive-compulsive disorder . Pharmacogenomics J 2014, 14 (2):176-181. Brown JT, Schneiderhan M, Eum S, Bishop JR: Serum clomipramine and desmethylclomipramine levels in a CYP2C19 and CYP2D6 intermediate metabolizer . Pharmacogenomics 2017, 18 (7):601-605. Jukic MM, Haslemo T, Molden E, Ingelman-Sundberg M: Impact of CYP2C19 Genotype on Escitalopram Exposure and Therapeutic Failure: A Retrospective Study Based on 2,087 Patients . Am J Psychiatry 2018, 175 (5):463-470. Kukreja S, Kalra G, Shah N, Shrivastava A: Polypharmacy in psychiatry: a review . Mens Sana Monogr 2013, 11 (1):82-99. Sharp CN, Linder MW, Valdes R, Jr.: Polypharmacy: a healthcare conundrum with a pharmacogenetic solution . Crit Rev Clin Lab Sci 2019, 57 (3):161-180. Garfinkel D, Levy Y: Optimizing clinical outcomes in polypharmacy through poly-de-prescribing: a longitudinal study . Front Med (Lausanne) 2024, 11 :1365751. Delara M, Murray L, Jafari B, Bahji A, Goodarzi Z, Kirkham J, Chowdhury M, Seitz DP: Correction: Prevalence and factors associated with polypharmacy: a systematic review and meta-analysis . BMC Geriatr 2022, 22 (1):742. Tables Tables 1 and 2 are available in the supplementary files section Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Table2.xlsx TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviews received at journal 21 Jan, 2026 Reviews received at journal 11 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviews received at journal 06 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviewers invited by journal 29 Dec, 2025 Editor assigned by journal 22 Dec, 2025 Editor invited by journal 28 Nov, 2025 Submission checks completed at journal 26 Nov, 2025 First submitted to journal 26 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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(a) Q-LES-Q scores at admission were similar between the TAU and PGx groups (P = 0.997). Quality of life for both groups improved to reach similar levels at the time of discharge (P = 0.952). (b) PQ-LES-Q scores at admission were similar between the TAU and PGx groups (P = 0.373). Quality of life for both groups improved to reach similar levels at the time of discharge (P = 0.845). (c) Y-BOCS scores at admission were also similar between the TAU and PGx groups (P = 0.993). OCD symptom severity for both groups improved to a similar level at discharge (P = 0.582). (d) CY-BOCS scores at admission were higher in PGx group compared to TAU (P \u0026lt;0.05). While both groups improved, OCD symptom severity continued to be higher in the PGx group at Week 2 (P \u0026lt;0.01), Week 4 (P \u0026lt;0.01) and discharge (P \u0026lt;0.05). (e) QIDS scores at admission did not differ between the TAU and PGx groups (P=0.831). Depressive symptom severity for both groups improved to reach similar levels at the time of discharge (P=0.845). (f) PROMIS-D scores at admission did not differ between the TAU and PGx groups (P=0.951). Depressive symptom severity for both groups improved to reach similar levels at the time of discharge (P=0.825). For all measures, with the exception of the CY-BOCS, no differences were observed at the Week 2 and Week 4 assessment timepoints. * P\u003cem\u003e \u003c/em\u003e\u0026lt;0.05; ** P\u003cem\u003e \u003c/em\u003e\u0026lt;0.01; n.s. = not significant. Data reported as mean ± SEM.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-8182802/v1/9847c8aa1f0da42545175092.png"},{"id":99292486,"identity":"f8ffabec-e3dd-4c84-a14a-891b7118f4e0","added_by":"auto","created_at":"2025-12-31 10:44:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40175,"visible":true,"origin":"","legend":"\u003cp\u003ePolypharmacy rates for the PGx-C and PGx-I cohorts were evaluated at admission and discharge for all ages, adults, and child/adolescent groups. (a) At admission across all ages, psychotropic polypharmacy differed between PGx-C (15%, 4/27) and PGx-I (42%, 40/95) (P = 0.017). Antidepressant polypharmacy did not differ (P = 0.215). (b) At discharge across all ages, psychotropic polypharmacy was similar between PGx-C (35%, 14/40) and PGx-I (55%, 45/82) (P = 0.062). Antidepressant polypharmacy differed (P = 0.011). (c) At admission for adults, psychotropic polypharmacy differed between PGx-C (12%, 2/17) and PGx-I (56%, 31/55) (P = 0.003). Antidepressant polypharmacy did not differ (P = 0.163). (d) At discharge for adults, psychotropic polypharmacy differed between PGx-C (36%, 8/22) and PGx-I (72%, 38/53) (P = 0.009), and antidepressant polypharmacy also differed (P = 0.039). (e) At admission or the child/adolescent cohort, psychotropic polypharmacy was similar between PGx-C (20%, 2/10) and PGx-I (24%, 9/37) (P = 0.999). Antidepressant polypharmacy did not differ (P = 0.999). (f) At discharge for the child/adolescent cohort, psychotropic polypharmacy was similar between PGx-C (20%, 6/18) and PGx-I (24%, 9/29) (P = 0.999). Antidepressant polypharmacy did not differ (P = 0.330). *P \u0026lt; 0.05; **P \u0026lt; 0.01; n.s. = not significant. Data reported as mean ± SEM.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-8182802/v1/9b085cba0f1fca0aa935c7ab.png"},{"id":99787922,"identity":"5d6b69c8-272e-4256-b829-e761b222f3f3","added_by":"auto","created_at":"2026-01-08 12:41:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3744291,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8182802/v1/1a5ad305-910c-4bb5-955b-a6bb8859f4d4.pdf"},{"id":99320889,"identity":"24e1cd33-4592-483a-9056-c80eb5390ac2","added_by":"auto","created_at":"2025-12-31 16:38:56","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18140,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8182802/v1/2ec3531a32b3026911bc65dc.xlsx"},{"id":99321407,"identity":"d3c31125-2af5-4e27-b427-5fd1c0b7ab36","added_by":"auto","created_at":"2025-12-31 16:39:23","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18936,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8182802/v1/c6f24d435ba74ff1c7cb5603.xlsx"},{"id":99292488,"identity":"0e4ffb84-94c9-4d35-a2cf-e99307e1aa57","added_by":"auto","created_at":"2025-12-31 10:44:17","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19707,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8182802/v1/c3801c991a57b89e64e74f5b.xlsx"},{"id":99292489,"identity":"5f5327c9-9806-4ada-b3ea-e05cc959a8e4","added_by":"auto","created_at":"2025-12-31 10:44:17","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21081,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8182802/v1/20860a89f65b44ddc06b11d9.xlsx"},{"id":99321134,"identity":"cedb5c76-8e7f-407c-8e17-79052d366fce","added_by":"auto","created_at":"2025-12-31 16:39:13","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":21348,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8182802/v1/af74c92dd8c8abc371401d9c.xlsx"},{"id":99320587,"identity":"a0df836a-0cfe-4ea4-92ae-a3b6898d606c","added_by":"auto","created_at":"2025-12-31 16:38:46","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":22925,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8182802/v1/15fa2b598fa6a870b952bc95.xlsx"},{"id":99292505,"identity":"3f52e16a-012b-4737-9745-2c98c8acd266","added_by":"auto","created_at":"2025-12-31 10:44:17","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":21678,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8182802/v1/40e100b0522201940cacbdb4.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pharmacogenomic-Guided Prescribing and Polypharmacy Across Age Groups in Obsessive-Compulsive Disorder: A Retrospective Study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eObsessive-compulsive disorder (OCD) is a chronic and debilitating psychiatric condition characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions). It affects an estimated 2.3% of the population and is associated with significant economic burden, with annual costs exceeding \u003cspan\u003e$\u003c/span\u003e10\u0026nbsp;billion in the United States alone.\u003csup\u003e9,10\u003c/sup\u003e Although the etiology of OCD is not fully understood, accumulating evidence implicates genetic predisposition, oxidative damage, and dysregulation of serotonin and glutamate signaling [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. First-line treatments include cognitive behavioral therapy (CBT) with exposure and response prevention (ERP) and pharmacotherapy with selective serotonin reuptake inhibitors (SSRIs) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, these strategies remain suboptimal with only 40\u0026ndash;60% of individuals with OCD responding to first-line SSRIs in standard care [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Non-responding individuals typically trial another SSRI, a serotonin\u0026ndash;norepinephrine reuptake inhibitor (SNRI), clomipramine, or add adjunctive therapies such as atypical antipsychotics and glutamatergic agents [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, there is increasing interest in identifying biomarkers and genetic factors that may better define OCD subtypes and guide medication selection [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCo-occurring psychiatric conditions complicate the challenge of medication optimization [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. An estimated 90% of individuals with a lifetime diagnosis of OCD meet the diagnostic criteria for at least one other psychiatric disorder [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Generalized anxiety disorder and major depressive disorder are among the most common co-occurring conditions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and are typically treated with SSRIs [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These co-occurring conditions often share neural networks and neurochemical perturbations with OCD, which can complicate medication management [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiple psychotropic medications are often prescribed to treat OCD, primarily SSRIs, but also atypical antipsychotics and other adjunctive agents. This frequently raises the possibility for polypharmacy to effectively manage the constellation of symptoms. Moreover, these combinations have rarely been evaluated in clinical trials, leaving prescribers with limited evidence to guide decisions, and little insight into which symptoms or mechanisms each medication is targeting. Therefore, optimizing medication selection and dosing to reduce polypharmacy may have substantial therapeutic benefit.\u003c/p\u003e \u003cp\u003ePolypharmacy also raises concerns about potential gene-drug interactions, drug-drug interactions and adverse effects, posing challenges for future medication management. Risks increase with age and the addition of nonpsychiatric medications [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Psychotropic polypharmacy is linked to multiple issues, including medication nonadherence [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], amplification of side effects when multiple medications are prescribed from the same drug class [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], difficulties in selecting and tapering medications when discontinued [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and significant annual cost per medication [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Although there is emerging evidence that multiple drug classes may be required to treat co-occurring psychiatric conditions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and suggestions that low-dose polypharmacy may result in fewer side effects compared to high-dose monotherapy [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the general consensus is that fewer medications are preferred when possible to maximize efficacy [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Accordingly, polypharmacy requires careful monitoring, thoughtful selection of drug combinations and may benefit from clinical tools that help guide optimization.\u003c/p\u003e \u003cp\u003ePharmacogenomics (PGx) has been proposed as one approach to optimize medication selection and decrease time to remission [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. PGx testing uses an individual\u0026rsquo;s genetics profile to predict the pharmacokinetic (PK) and pharmacodynamic (PD) profiles and alert prescribers, including psychiatrists and primary care providers [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], to potential liabilities that may affect treatment response. The most robust PGx data for psychotropic medications is focused on the metabolizer status of the \u003cem\u003eCYP2D6\u003c/em\u003e, \u003cem\u003eCYP2C19\u003c/em\u003e and \u003cem\u003eCYP2B6\u003c/em\u003e genes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. When integrated into prescribing decisions, PGx testing for the treatment of depression has been associated with reduced side effect burden [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], improved adherence [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and decreases in emergency room visits, hospital readmissions and overall cost [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, gene-drug associations that inform medication recommendations are not well established for OCD, and commercial combinatorial PGx products have not been adequately tested in this patient population [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Limited real-world analysis of neuropsychiatric-related PGx reports indicate that most ordered tests are combinatorial [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. suggesting that evaluating combinatorial PGx testing in OCD may provide practical insights with broader clinical relevance. This is important as combinatorial PGx testing utilizes proprietary weighted algorithms that integrates multiple PK and PD genes, as opposed to single- or multi-gene panels to predict gene-drug interactions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Understanding the complex genetic and signaling mechanisms involved in psychotropic medication metabolism is therefore essential to developing effective pharmacologic strategies and minimizing the number of medications required to treat OCD.\u003c/p\u003e \u003cp\u003eThe present study addresses these gaps by evaluating the clinical utility of combinatorial PGx testing as an adjunctive decision-making tool in the treatment of OCD across developmental stages. Using retrospective data from individuals in a naturalistic OCD treatment environment, those prescribed medications with potential gene-drug interactions were compared to a matched control cohort to evaluate potential differences in polypharmacy and other clinical outcomes. This study builds upon the existing literature in several ways by examining the effectiveness of PGx testing for OCD specifically in both adult and child and adolescent populations. Importantly, by evaluating PGx testing in adult and pediatric individuals, this study adds to the limited understanding of the clinical utility of adjunctive PGx testing for younger individuals. Furthermore, the outcomes were stratified in multiple ways to provide a nuanced analysis of PGx testing\u0026rsquo;s impact in a real-world setting. The findings are intended to inform the development of clinical guidelines and advance precision medicine approaches in psychiatry and primary care.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and procedure\u003c/h2\u003e \u003cp\u003eA retrospective electronic health record (EHR) review, approved by an institutional review board (IRB), was conducted that included 363 individuals aged 8\u0026ndash;65 with a confirmed diagnosis of OCD between January 2017 and May 2023. All individuals received treatment in a large behavioral health care system with residential, partial hospitalization (PHP) and intensive outpatient (IOP) programs. Upon admission, clinicians conducted comprehensive evaluations that included symptom and functional assessments, alongside diagnostic interviews with a psychiatrist and clinical psychologist following DSM-5 guidelines. These findings were integrated to create a personalized treatment plan, which encompassed decisions regarding medication management and therapeutic approaches. Participants engaged in daily sessions through either the residential program, the PHP program (6\u0026ndash;7 hours per day) or the IOP program (3 hours per day). ERP was a core component of the treatment, delivered by trained clinicians skilled in both CBT and pharmacotherapy. To maintain consistency in treatment delivery, each program across the healthcare system followed established treatment manuals.\u003c/p\u003e \u003cp\u003eIndividuals who transitioned to a lower level of care within seven days of discharge from a higher level of care were included for the total duration of their stay. For individuals who transitioned to a higher level of care, or who stepped down or were readmitted more than seven days after discharge, only the stay during which PGx testing was completed was analyzed. PGx testing was ordered at the prescribing provider\u0026rsquo;s discretion with the exception of two individuals or their guardian who provided the report at the time of admission. Individuals were excluded if they discharged before the PGx results were available or if no psychotropic medications were prescribed at both admission and discharge. All medications prescribed within 30 days before the report or during the stay were included in the main analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClassification of medication congruence with PGx testing\u003c/h3\u003e\n\u003cp\u003eResults from any commercial psychotropic PGx test for psychotropic medications were eligible for inclusion. All tests were combinatorial, which evaluated gene-drug interactions based on specific pharmacokinetic (PK) and pharmacodynamic (PD) gene variants. Congruency classification followed previously described methods [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Due to the retrospective nature of the study and inability to determine how providers specifically utilized the test results, when at least one daily psychotropic medication was prescribed and reported to have \u0026lsquo;use with caution\u0026rsquo;, or moderate, or \u0026lsquo;use with increased caution with recommended frequent monitoring\u0026rsquo;, or \u0026lsquo;significant\u0026rsquo; gene-drug interactions, the individual was classified as \u0026lsquo;incongruent\u0026rsquo; (PGx-I). Individuals were categorized as \u0026lsquo;congruent\u0026rsquo; (PGx-C) if all prescribed psychotropic medications were 'without reported gene-drug interactions\u0026rsquo;, or \u0026lsquo;normal\u0026rsquo;. At admission, individuals not prescribed psychotropic medications were classified as PGx-C.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe co-primary outcomes were polypharmacy and quality of life (QOL). Polypharmacy was defined as \u0026ge;\u0026thinsp;3 psychotropic medications concurrently prescribed, or \u0026ge;\u0026thinsp;2 antidepressants or antipsychotics. Because polypharmacy is a binary metric and provides limited information to understand the degree of separation between cohorts, the average number of medications was also analyzed to gain a more granular understanding of how gene-drug interactions contributed to the observed polypharmacy differences between the PGx-C and PGx-I cohorts.\u003c/p\u003e \u003cp\u003eQOL was measured using the Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q) in adults (\u0026ge;\u0026thinsp;18 years) and the Pediatric Quality of Life Enjoyment and Satisfaction Questionnaire (PQ-LES-Q) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] in children and adolescents (5\u0026ndash;17 years).\u003c/p\u003e \u003cp\u003eSecondary outcomes included length of stay (LOS), average number of psychotropic medications, and age-appropriate assessments of symptom severity. In adults, these included the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and the Quick Inventory of Depression Symptomology (QIDS) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In adolescents, the assessments included the QIDS and the Children\u0026rsquo;s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. And in children the assessments included the Patient-Reported Outcomes Measurement Information System Pediatric Depressive scale (PROMIS-D) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and the CY-BOCS.\u003c/p\u003e\n\u003ch3\u003eMatching\u003c/h3\u003e\n\u003cp\u003eThe treatment as usual (TAU) cohort was randomized by randomly selecting individuals in the same treatment programs using the MatchIt package within R, version 4.3.3 [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Matching criteria included medical service line and OCD diagnosis so that a similar profile would be included within the TAU cohort.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were described as counts and percentages. Continuous variables were described as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). The sample size (n) represents individuals in the electronic medical record who met eligibility criteria. Cohort comparisons were performed using Student\u0026rsquo;s t test for independent samples for normally distributed continuous variables; one-way ANOVA followed by Bonferroni post hoc tests or two-way repeated-measures ANOVA with cohort by time, followed by Bonferroni post-hoc tests as appropriate. Chi-square or Fisher\u0026rsquo;s Exacts test were used for categorical comparisons as appropriate. Spearman correlation coefficients and negative binomial regression models were used to assess the relationship between gene-drug incongruence with the total psychotropic medications. Statistical significance was set, at α\u0026thinsp;=\u0026thinsp;0.05 (two-sided). All analyses were carried out using the \"stats\" and \"psych\" packages in R.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDemographics and clinical characteristics\u003c/h2\u003e \u003cp\u003eDemographic and clinical characteristics were generally similar between the TAU (n\u0026thinsp;=\u0026thinsp;241) and PGx (n\u0026thinsp;=\u0026thinsp;122) cohorts (Table\u0026nbsp;1). Average age did not differ significantly between TAU (20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 years) and PGx (23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 years) cohorts (P\u0026thinsp;=\u0026thinsp;0.079, \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). Reported sex distribution was comparable between TAU (56% female; 44% male) and PGx (58% female; 42% male) cohorts (P\u0026thinsp;=\u0026thinsp;0.777, \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). The average length of stay (LOS) was also similar (TAU: 42.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 days; PGx: 43.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 days; P\u0026thinsp;=\u0026thinsp;0.749, Table\u0026nbsp;1). Level of care differed between cohorts (P\u0026thinsp;=\u0026thinsp;0.001, \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e), though most individuals were treated in partial hospitalization (74% TAU; 70% PGx). Racial and ethnic data were insufficient for analysis.\u003c/p\u003e \u003cp\u003eAt admission, QOL and symptom severity were similar across cohorts. Adult Q-LES-Q scores were similarly low for TAU (43.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9) and PGx (43.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1) (P\u0026thinsp;=\u0026thinsp;0.997; \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). For children and adolescents, PQ-LES-Q scores were also low and comparable (TAU: 42.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2; PGx: 40.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5; P\u0026thinsp;=\u0026thinsp;0.373; Table\u0026nbsp;1). Adult OCD symptom severity measured by Y-BOCS indicated severe symptoms in both TAU (25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8) and PGx (25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9) cohorts (P\u0026thinsp;=\u0026thinsp;0.993; \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). CY-BOCS scores for children and adolescents were likewise similar (TAU: 21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9; PGx: 23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9; P\u0026thinsp;=\u0026thinsp;0.130; Table\u0026nbsp;1). Depressive symptoms, assessed by QIDS, indicated moderate severity in adults and adolescents (TAU: 13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5; PGx: 13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6; P\u0026thinsp;=\u0026thinsp;0.831; \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). PROMIS-D scores in children were moderate and comparable between groups (TAU: 16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0; PGx: 17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3; P\u0026thinsp;=\u0026thinsp;0.951; \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEfficacy comparison revealed similar levels of improvement\u003c/h3\u003e\n\u003cp\u003eQOL improved in adults between admission and discharge as measured by Q-LES-Q scores. Both TAU and PGx cohorts demonstrated similar improvement (P\u0026thinsp;=\u0026thinsp;0.882; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Children and adolescents also showed comparable gains in PQ-LES-Q scores (P\u0026thinsp;=\u0026thinsp;0.250; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOCD symptom severity improved between admission and discharge. In adults, Y-BOCS scores decreased for both TAU (18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9) and PGx (17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1) cohorts (P\u0026thinsp;=\u0026thinsp;0.582; \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e), with no between-group differences at interim assessments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Among children and adolescents, CY-BOCS scores for both the TAU and PGx groups improved to mild severity at discharge (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e), though PGx scores remained higher at all timepoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eDepression severity declined similarly in both groups. QIDS scores improved to mild depression by discharge (TAU: 8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5; PGx: 8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). PROMIS-D scores in children improved to \u0026ldquo;none to slight\u0026rdquo; for both TAU (9.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9) and PGx (9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003ePolypharmacy rates for psychotropic medications at admission were comparable (TAU: 37%; PGx: 36%; P\u0026thinsp;=\u0026thinsp;0.999; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). Stratified by medication class, antidepressant and antipsychotic polypharmacy were also similar. By discharge, polypharmacy increased in the PGx cohort (50%) but not in TAU (32%) (P\u0026thinsp;=\u0026thinsp;0.002; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). To better understand the impact of PGx testing, further analyses focused on gene-drug interactions and outcomes.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePsychotropic medication selection, all ages\u003c/h2\u003e \u003cp\u003ePGx-guided psychotropic medication use was analyzed for polypharmacy and the total number of prescribed medications. Polypharmacy differed by gene-drug congruence. At admission, 15% of PGx-C participants met polypharmacy criteria versus 42% of PGx-I participants (P\u0026thinsp;=\u0026thinsp;0.017; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). By discharge, rates increased to 55% for PGx-I and 35% for PGx-C (P\u0026thinsp;=\u0026thinsp;0.062; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Antidepressant polypharmacy differed between groups at discharge (P\u0026thinsp;=\u0026thinsp;0.011; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), but no difference was observed for antipsychotics (P\u0026thinsp;=\u0026thinsp;0.268; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mean number of psychotropic medications was lower for PGx-C (1.6) than PGx-I (2.5; P\u0026thinsp;=\u0026thinsp;0.003; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). By discharge, the mean increased for both groups but remained lower in PGx-C (2.3) compared to PGx-I (3.1; P\u0026thinsp;=\u0026thinsp;0.005; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). Similar trends were observed for antidepressants and antipsychotics (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMedication utilization in adults\u003c/h2\u003e \u003cp\u003eIn adults, gene-drug incongruency was associated with higher polypharmacy. At admission, 56% (31/55) of PGx-I participants were prescribed\u0026thinsp;\u0026ge;\u0026thinsp;3 psychotropic medications (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), increasing to 72% (38/53) at discharge (P\u0026thinsp;=\u0026thinsp;0.003; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). In contrast, PGx-C participants showed lower polypharmacy rates (12% [2/17] at admission; 36% [8/22] at discharge; Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eAntidepressant polypharmacy was similar at admission (PGx-C: 6%; PGx-I: 26%) but diverged by discharge (PGx-C: 14%; PGx-I: 42%). Antipsychotic polypharmacy remained low at both timepoints. Average medication counts were reported in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMedication utilization in children and adolescents\u003c/h2\u003e \u003cp\u003eCongruency with gene-drug interactions appeared to play a diminished role in prescribing for children and adolescents. At admission, psychotropic polypharmacy was 24% (9/37) in PGx-I compared to 20% (2/10) in PGx-C (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). By discharge, rates were 31% (9/29) and 33% (6/18), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Antidepressant polypharmacy increased from 14% to 28% in PGx-I and from 10% to 11% in PGx-C, with no statistical difference. Antipsychotic polypharmacy remained low and similar across groups. Average medication numbers did not differ except for antipsychotics at discharge (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of the number of incongruent medications with total medications\u003c/h2\u003e \u003cp\u003eAcross all participants, the number of incongruent psychotropic medications correlated with the total number prescribed (ρ\u0026thinsp;=\u0026thinsp;0.48; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a similar correlation was seen for antidepressants (ρ\u0026thinsp;=\u0026thinsp;0.47; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among adults, these correlations remained significant for both psychotropics (ρ\u0026thinsp;=\u0026thinsp;0.53; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and antidepressants (ρ\u0026thinsp;=\u0026thinsp;0.49; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In children and adolescents, a positive trend was observed for total psychotropics (ρ\u0026thinsp;=\u0026thinsp;0.25; P\u0026thinsp;=\u0026thinsp;0.094) and a significant correlation for antidepressants (ρ\u0026thinsp;=\u0026thinsp;0.40; P\u0026thinsp;=\u0026thinsp;0.006) (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssessment outcomes by congruency\u003c/h2\u003e \u003cp\u003eMedication congruency did not appear to affect clinical outcomes. Across all age groups, QOL, OCD and depression symptom severity improved similarly in PGx-C and PGx-I cohorts. Adults improved from low to average Q-LES-Q scores (\u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). Children and adolescents showed comparably improved PQ-LES-Q scores (\u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). OCD severity (Y-BOCS and CY-BOCS), depression severity (QIDS, PROMIS-D) improved similarly showed no between-group differences, with a comparable LOS (\u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eUtilization of PGx test results\u003c/h2\u003e \u003cp\u003eMedication changes based on PGx results were minimal, regardless of congruency with gene-drug interactions. Among PGx-I individuals, 55% (52/95) remained on at least one incongruent psychotropic at discharge, and 42% (22/52) were prescribed at least one additional incongruent medication. Only 18% (17/95) were switched fully to congruent alternatives (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Age-stratified analyses revealed more changes in children and adolescents: 62% (23/37) switched to an alternative medication, of whom 61% received a congruent option. Among adults, 35% (20/58) switched medications, and only 20% (4/20) were switched fully to congruent alternatives (data not shown).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAdditional PGx considerations\u003c/h2\u003e \u003cp\u003eVariants affecting CYP2D6, CYP2C19, and CYP2B6 metabolism were identified in 58% (71/122) of participants (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), though not further analyzed.\u003c/p\u003e \u003cp\u003ePsychiatric comorbidities and OCD symptom severity at admission were not correlated with total psychotropic (P\u0026thinsp;=\u0026thinsp;0.944) or antidepressant (P\u0026thinsp;=\u0026thinsp;0.339) counts (data not shown). To reduce confounding from medication changes, analyses limited to individuals maintained exclusively on congruent or incongruent regimens showed no outcome differences (\u003cb\u003eTable \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e). Assessment outcomes were also compared between TAU and PGx-I cohorts, with no between-group differences (\u003cb\u003eTable \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e). However, an increased average number of psychotropic medications were observed in adults, and an increased in the average number of antipsychotics in the child and adolescent subgroup (\u003cb\u003eTable \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOverall implications\u003c/h2\u003e \u003cp\u003eThis study advances the understanding of how PGx testing may be incorporated into clinical workflows to optimize medication management for individuals with OCD. While both the PGx and TAU cohorts demonstrated similar levels of symptom improvement overall, PGx subgroup analysis revealed important differences in prescribing, particularly with polypharmacy. Specifically, adults had lower polypharmacy rates when prescribed medications were congruent with their PGx results (PGx-C subgroup) compared to those in the PG-I subgroup.\u003c/p\u003e \u003cp\u003eMedication congruency with gene-drug interactions was consistently associated with reduced polypharmacy and a lower average number of psychotropic medications. Individuals prescribed psychotropic medications that were congruent with their gene-drug interactions (PGx-C) were able to achieve the same outcomes as PGx-I with fewer prescribed psychotropic medications. In contrast, gene-drug interactions appeared to have less of an impact on prescribing decisions in children and adolescents, where polypharmacy rates were similar regardless of congruency. These findings suggest that PGx testing may have greater clinical benefit for adults.\u003c/p\u003e \u003cp\u003eInterestingly, although providers ordered the PGx tests in the current study, in most cases prescribing decisions did not seem to rely solely on the PGx test results. Only 18% of individuals prescribed an incongruent psychotropic medication switched to a congruent alternative, suggesting that prescribers placed limited weight on gene-drug interactions when making medication decisions. This limited use underscores barriers to clinical implementation when treating individuals with OCD. Indeed, medication selection is a complex process involving treatment guidelines, evidence-based practice for usage of certain medications without gene-drug interactions, family history, prior medication trials, patient and family preferences, insurance coverage, co-occurring conditions, allergies and other factors. This is not unexpected when treating OCD, as most neuropsychiatric PGx testing research has focused on antidepressants, with the strongest evidence in depression,[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and less evidence available for other indications such as OCD.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] Nevertheless, the decision to order PGx testing without routinely acting on the results raises important questions about its clinical utility for both prescribers and patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCombinatorial PGx testing for OCD\u003c/h2\u003e \u003cp\u003eTo date, utilization of PGx testing for OCD remains limited,[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and no prior studies have evaluated combinatorial PGx testing in this population. Most available evidence comes from studies of specific genes, such as \u003cem\u003eCYP2D6\u003c/em\u003e and \u003cem\u003eCYP2C19\u003c/em\u003e [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], which may affect the metabolism of SSRIs and other medications commonly prescribed for OCD. Findings from these gene-specific studies have been mixed. While PGx testing to guide medication selection for escitalopram, citalopram and sertraline may be beneficial [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], there remains a need for comprehensive investigation encompassing other first-line and adjunctive medications commonly prescribed for OCD [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, the current study adds to this literature by demonstrating that combinatorial PGx testing may reduce polypharmacy in adults when individuals are switched to congruent medications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFuture directions\u003c/h2\u003e \u003cp\u003eBroader adoption of PGx testing as a clinical decision support tool for OCD medication management may depend on outcomes prioritized by prescribers, patients and families. The current findings suggest that polypharmacy and average number of medications may be important metrics when evaluating PGx efficacy, particularly given the limited evidence base for PGx usage in OCD. Adults were prescribed an average of 3.6 psychotropic medications, a level rarely evaluated in randomized, controlled clinical trials [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and for which data on side effects or drug-drug interactions remains limited. This is also notable because adults, particularly older adults, are more likely to be prescribed other medications for unrelated medical conditions [\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. PGx testing may be therefore useful to help reduce polypharmacy while maintaining similar clinical outcomes.\u003c/p\u003e \u003cp\u003eFuture studies should include randomized, controlled trials with longitudinal follow-up to assess the sustained impact of prescribing medications with gene-drug interactions and to evaluate sustained outcomes following discharge from residential, partial hospitalization and intensive outpatient settings. This will help to minimize systematic differences that may exist between individuals whose prescribers ordered PGx testing compared to TAU, a confounding factor in the current study. Child and adolescent-focused studies are also warranted to clarify the utility for PGx testing in younger individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eAs a retrospective study, this research is subject to several limitations. Medications were classified as either congruent or incongruent without further consideration of dosage or titration rate. Additionally, three individuals admitted without a documented psychotropic medication at intake and were classified as congruent. Medication compliance was based on patient self-report and prescription refill history; available data did not include information on prior failed medication trials. Only psychotropic medications were evaluated, so the potential influence of drug-drug interactions with non-psychotropic prescriptions on prescribing decisions remains unaddressed. Cross-tapering across multiple psychotropic medications was not evaluated, which could complicate polypharmacy interpretation. The relatively short average length of stay, averaging less than 45 days, reduced the likelihood of detecting medication-related differences in assessment outcomes. Longitudinal data from standard outpatient settings may better capture differences in assessments, polypharmacy, and average medication use that were not observable in the higher levels of care studied here. While data extraction included any PGx test, the data revealed that prescribers exclusively used the GeneSight test. Because of proprietary algorithms and other differences across commercial tests, the findings may not be inherently generalizable across all commercial neuropsychiatric PGx tests. Although adverse effects were monitored as part of standard care, they were not systematically evaluated or compared between groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study evaluated the clinical utility of PGx testing for children, adolescents and adults with OCD. Overall, combinatorial PGx testing identified psychotropic medications with gene-drug interactions and highlighted associations with polypharmacy patterns. Importantly, individuals receiving PGx testing achieved similar quality of life and symptom severity compared to TAU. Taken together, these findings highlight the potential benefit of PGx testing as an adjunctive clinical decision support tool that may help to reduce polypharmacy while maintaining therapeutic effectiveness.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChildren's Yale-Brown Obsessive-Compulsive Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCY-BOCS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCognitive behavioral therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCBT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCytochrome P450 2B6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCYP2B6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCytochrome P450 2C19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCYP2C19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCytochrome P450 2D6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCYP2D6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElectronic Health Record\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEHR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExposure and response prevention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eERP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInstitutional Review Board\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIRB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntensive Outpatient Program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIOP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLength of Stay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObsessive-Compulsive Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOCD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePartial Hospitalization Program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePHP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePatient-Reported Outcomes Measurement Information System Pediatric\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Depressive scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePROMIS-D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePediatric Quality of Life Enjoyment and Satisfaction Questionnaire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePQ-LES-Q\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePharmacodynamic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePharmacogenomics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePGx\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePharmacogenomic congruent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePGx-C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePharmacogenomic incongruent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePGx-I\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePharmacokinetic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuick Inventory of Depression Symptomology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQIDS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuality of life\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQOL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuality of Life Enjoyment and Satisfaction Questionnaire\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ-LES-Q\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelective Serotonin Reuptake Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSSRI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSerotonin-norepinephrine Reuptake Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard Error of the Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSEM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTreatment as Usual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTAU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYale-Brown Obsessive-Compulsive Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eY-BOCS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Rogers Behavioral Health Institutional Review Board (approved protocol number: RBH-2023-01). The study was designed in accordance with the Helsinki Declaration.\u0026nbsp;This retrospective study utilized de-identified data, and a HIPAA (Health Insurance Portability and Accountability Act) waiver of informed consent was approved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this retrospective study were obtained from the electronic health record system and are not publicly available due to patient privacy. Data are available upon reasonable request. Researchers interested in accessing de-identified data for further analysis may contact the corresponding author to discuss potential data sharing under the approval of the Institutional Review Board and with appropriate data access agreements in place.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Rogers Behavioral Health Foundation and the Lynn S. Nicholas Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical trial number\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor’s contributions\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: S.R.G., S.V., M.M.H., A.W.Z., M.W.B., N.W.; Data curation: S.V.; Formal analysis: S.R.G., M.W.B., M.S., S.V., M.M.H.; Funding acquisition: S.R.G.; Investigation: S.R.G., R.A.S., M.W.B. M.S., S.V., R.A.S., M.M.H.; Methodology: S.R.G., R.A.S., M.S., S.V., M.M.H.; Project administration: S.R.G.; Software: M.S., S.V., M.M.H.; Supervision: S.R.G.; Validation: S.R.G., M.M.H., M.S.; Visualization: S.R.G.; Writing-original draft: S.R.G., R.A.S., M.S., A.W.Z., M.W.B., N.W., M.E.F.; Writing-review \u0026amp; editing: S.R.G., M.W.B., M.S., S.V., R.A.S., M.M.H., N.W., M.E.F., A.W.Z., S.A.S.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Amaya Ramos, MD for her clinical guidance with study design. We thank Sophie Schweinert, Zaira Chavez, Rachel Lopez, Lily Mantsch, Ella Patty, and Sladjana Strbac for their assistance in manual data extraction from the pharmacogenomics reports. We thank Jeffery Engelmann, PhD for his guidance with data extraction for this project.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMahjani B, Klei L, Hultman CM, Larsson H, Devlin B, Buxbaum JD, Sandin S, Grice DE: \u003cstrong\u003eMaternal Effects as Causes of Risk for Obsessive-Compulsive Disorder\u003c/strong\u003e. \u003cem\u003eBiol Psychiatry\u0026nbsp;\u003c/em\u003e2020, \u003cstrong\u003e87\u003c/strong\u003e(12):1045-1051.\u003c/li\u003e\n \u003cli\u003eFernandez TV, Leckman JF, Pittenger C: \u003cstrong\u003eGenetic susceptibility in obsessive-compulsive disorder\u003c/strong\u003e. \u003cem\u003eHandb Clin Neurol\u0026nbsp;\u003c/em\u003e2018, \u003cstrong\u003e148\u003c/strong\u003e:767-781.\u003c/li\u003e\n \u003cli\u003eKurhan F, Alp HH, Isik M, Atan YS: \u003cstrong\u003eThe Evaluation of Thiol/Disulfide Homeostasis and Oxidative DNA Damage in Patients with Obsessive Compulsive Disorder\u003c/strong\u003e. \u003cem\u003eClin Psychopharmacol Neurosci\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e20\u003c/strong\u003e(2):240-247.\u003c/li\u003e\n \u003cli\u003eHadi F, Kashefinejad S, Kamalzadeh L, Hoobehfekr S, Shalbafan M: \u003cstrong\u003eGlutamatergic medications as adjunctive therapy for moderate to severe obsessive-compulsive disorder in adults: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eBMC Pharmacol Toxicol\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e22\u003c/strong\u003e(1):69.\u003c/li\u003e\n \u003cli\u003eAmerican Psychiatric Association A: \u003cstrong\u003ePractice guideline for the treatment of patients with obsessive-compulsive disorder\u003c/strong\u003e. 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Med (Lausanne)\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e11\u003c/strong\u003e:1365751.\u003c/li\u003e\n \u003cli\u003eDelara M, Murray L, Jafari B, Bahji A, Goodarzi Z, Kirkham J, Chowdhury M, Seitz DP: \u003cstrong\u003eCorrection: Prevalence and factors associated with polypharmacy: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eBMC Geriatr\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(1):742.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"OCD, CYP2D6, depression, anxiety, pharmacogenomics, PGx, GeneSight, polypharmacy, antipsychotics, antidepressants","lastPublishedDoi":"10.21203/rs.3.rs-8182802/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8182802/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study evaluated medication utilization in children, adolescents, and adults with obsessive-compulsive disorder (OCD), a chronic psychiatric condition characterized by intrusive thoughts and repetitive behaviors. Although first-line treatments include selective serotonin reuptake inhibitors (SSRIs) and cognitive behavioral therapy (CBT), the heterogeneous biological underpinnings contribute to suboptimal outcomes, with 40\u0026ndash;60% of individuals not responding to SSRIs. This complex phenotype often leads to psychotropic polypharmacy, which may be mitigated by incorporating combinatorial pharmacogenomic (PGx) testing into protocol-based care to identify potential gene-drug interactions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A retrospective review was conducted of individuals with OCD aged 8 to 65 years who received either PGx testing or treatment as usual (TAU). Co-primary outcomes were polypharmacy rate and quality of life. Secondary outcomes included length of stay, medication utilization, and OCD and depression severity. Individuals prescribed at least one daily psychotropic medication with a gene-drug interaction were classified as \u0026ldquo;incongruent\u0026rdquo; (PGx-I). Individuals without gene-drug interactions for all prescribed psychotropic medications were categorized as \u0026ldquo;congruent\u0026rdquo; (PGx-C).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 363 individuals with OCD were analyzed. Of these 241 received TAU and 122 underwent PGx testing. Within the PGx cohort, 67% were prescribed medications with potential gene-drug interactions at discharge. The polypharmacy rate was 71% in the PGx-I cohort, compared with 35% in the PGx-C cohort. Quality-of-life measures revealed similar levels of improvement in the PGx-C and PGx-I cohorts.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePsychotropic polypharmacy rates were higher among individuals prescribed at least one medication with a gene-drug interaction, most notably among adults, while all cohorts showed similar improvement. These findings suggest that incorporating combinatorial PGx testing into the medical evaluation particularly where polypharmacy is a concern may help optimize medication selection, while maintaining effectiveness.\u003c/p\u003e","manuscriptTitle":"Pharmacogenomic-Guided Prescribing and Polypharmacy Across Age Groups in Obsessive-Compulsive Disorder: A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 10:44:12","doi":"10.21203/rs.3.rs-8182802/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T12:18:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-21T06:09:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-11T05:53:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54000393098304184907892720697671706000","date":"2026-01-07T12:13:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T03:23:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239238558783561264079276597907718184804","date":"2026-01-05T07:48:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84260454787227567282670412764276920318","date":"2025-12-29T22:59:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-29T10:20:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-22T20:22:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-28T08:59:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-26T19:16:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-11-26T19:09:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbe53ea2-4935-4998-ad61-01a302bcd1d7","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T12:27:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 10:44:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8182802","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8182802","identity":"rs-8182802","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}