Polypharmacy and pharmacogenomics in high-acuity behavioral health care for autism spectrum disorder: A retrospective study

Polypharmacy and pharmacogenomics in high-acuity behavioral health care for autism spectrum 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 Polypharmacy and pharmacogenomics in high-acuity behavioral health care for autism spectrum disorder: A retrospective study Sheldon R. Garrison, Sophie A. Schweinert, Matthew W. Boyer, Maharaj Singh, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5753717/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study evaluated pharmacogenomic (PGx) testing in children and adolescents with autism spectrum disorder (ASD). ASD frequently presents with co-occurring depression and anxiety. This complex phenotype often results in psychotropic medication polypharmacy. Incorporating PGx testing into the medical work-up may reduce polypharmacy and improve quality of life with symptom reduction. Methods A retrospective electronic health record review between January 2017 to May 2023. Individuals either received PGx testing or treatment as usual (TAU). The co-primary outcomes were polypharmacy and the Pediatric Quality of Life Enjoyment and Satisfaction Questionnaire (PQ-LES-Q). Secondary outcomes included length of stay and assessments measuring severity or behavioral impact. Results A total of 99 individuals with ASD were analyzed. At the time of admission, 93% of individuals were prescribed at least one psychotropic medication and over half of these individuals were prescribed medications with potential gene-drug interactions. Following PGx testing, there was an overall reduction in prescribed medications with a potential gene-drug interaction. Quality of life and symptom assessments of depression, anxiety, obsessive-compulsive disorder and body-focused repetitive behaviors revealed similar improvements in the PGx and TAU groups. Subanalysis comparing congruent (“use as directed”) or incongruent (“use with caution”), as well as analysis of only CYP2D6 and CYP2C19 gene-drug interactions, were observed to have a similar profile. Conclusion Combinatorial PGx testing was utilized as a clinical decision-making tool for medication selection and dosage adjustment. As a result, all treatment groups were able to achieve similar levels of polypharmacy, improvement in quality of life and symptom reduction. Psychiatry Autism CYP2D6 ASD depression anxiety pharmacogenomics PGx GeneSight polypharmacy antipsychotics antidepressants Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Autism spectrum disorder (ASD) is a neurological and developmental disorder affecting an estimated 2.9% of the population (US Department of Health and Human Services 2021 ). Symptoms typically manifest before age three and include deficits in communication and social interaction, repetitive behaviors, fixated interests and inflexible routines (American Psychiatric Association 2013). Depression, anxiety disorders, obsessive-compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD), and tics also frequently co-occur with ASD (Hollocks et al. 2019 ; Lai et al. 2019 ). Medication management for individuals with ASD often includes psychotropic medication polypharmacy, even in young children, underscoring the need to optimize medication selection to increase efficacy and minimize side effect burden. Medication management for individuals with ASD can be complicated, with no approved treatments targeting the underlying etiology of ASD, as well as reports of side effects and limited response in children with ASD compared to children not diagnosed with ASD (Bose-Brill et al. 2017 ; Bowers et al. 2015 ; Patel et al. 2020 ). This is further complicated by underlying clinically significant genetic variations affecting an individual’s neurophysiology as well as heterogeneous symptomatic presentation of individuals with ASD that commonly require psychotropic polypharmacy with medications across different drug classes (Taniguchi et al. 2022 ), including antidepressants, antipsychotics, mood stabilizers and stimulants. One approach to streamline medication trialing and optimize dosing has been to incorporate pharmacogenomic (PGx) testing (Cecchin and Stocco 2020 ). More specifically, PGx tests analyze genetics to predict pharmacokinetic (PK) and pharmacodynamic (PD) response to medications to address the limited response rates to first-line medications, which are reported to range from 42–53% in depression alone (Cipriani et al. 2018 ; Khan et al. 2017 ). Thus, PGx-guided drug selection and dosing may help to reduce medication side effects (Greden et al. 2019 ) and improve medication compliance (Taj and Khan 2005 ), and thereby reduce the need for medication changes. PGx testing may also lead to reduced emergency room visits, readmissions and cost (Elliott et al. 2017 ; Ghanbarian et al. 2023 ; Groessl et al. 2018 ) and may be particularly relevant to persons receiving treatment at higher levels of care, such as in residential, partial hospitalization and intensive outpatient behavioral healthcare treatment programs. Emerging data has demonstrated that when used for individuals with ASD, PGx testing can provide clinically meaningful information to guide medication management and improve clinical outcomes (Yoshida et al. 2021 ). This includes global improvement in clinical symptoms when PGx is incorporated into medication management decisions for those with ASD who failed to respond to at least two psychotropic medications (Arranz et al. 2022 ). Additional studies for PGx-guided psychotropic medication management have followed guidelines by Clinical Pharmacogenetics Implementation Consortium (CPIC), the American Psychiatric Association Council of Research (APA-COR) or the Association for Molecular Pathology Clinical Practice Committee's Pharmacogenomics (PGx) Working Group and others (Bousman et al. 2023 ; Hicks et al. 2015 ; Pratt et al. 2021 ; Zeier et al. 2018 ). These focus on specific PK-related enzymes that are effective in ameliorating some ASD symptoms, including CYP2D6, which plays a role in the metabolism of aripiprazole and risperidone (Bousman et al. 2023 ; Pratt et al. 2021 ). Genetic variants of PK-related enzymes may confer ultra-rapid, intermediate or poor metabolism of select psychotropic medications. Taking these medications may result in poor therapeutic response to altered plasma concentrations and delayed drug clearance (Bousman et al. 2023 ). Although these genetic variants occur frequently in individuals with ASD (Biswas et al. 2023 ; Goodson et al. 2023 ), there are currently no ASD-specific PGx guidelines for commercially available PGx products (Biswas et al. 2023 ). As a result, PGx utilization continues to be limited for ASD and continued investigation is warranted. Therefore, a retrospective study analyzing 99 persons diagnosed with ASD was conducted to determine whether PGx-guided medication management would improve medication congruence with potential gene-drug interactions, reduce polypharmacy and decrease symptom severity. METHODS Participants and Procedure A retrospective electronic health record (EHR) review was conducted for children and adolescents with ASD treated acutely in the anxiety and mood disorders partial hospitalization (PHP) and intensive outpatient (IOP) programs within a national behavioral health care system. All testing was ordered on a case-by-case basis at the prescriber’s discretion, although 13% (6/46) of the PGx reports were provided by patients or their parent/guardian at the time of admission. Individuals included in the current study received daily behavioral therapy as part of their treatment program. Classification of medication congruence with PGx testing Any psychotropic medication-related PGx test was eligible for inclusion in this study; however, all extracted reports for this study were the GeneSight® Psychotropic test (Myriad Genetics, Inc., Salt Lake City, UT). Data was extracted from individual PGx reports and analyzed. Determinations of gene-drug interactions were based on both pharmacokinetic (PK) and pharmacodynamic (PD) genes, as well as further determination by the PK-specific genes, CYP2D6 and CYP2C19. The PGx treatment group was classified as “incongruent” (PGx-I) when individuals were prescribed at least one daily psychotropic medication that was reported to have “moderate” or “significant” gene-drug interactions. Individuals were classified as “congruent” (PGx-C) when prescribed medications that were all reported with use as directed, or “normal”, with no suspected gene-drug interactions. Individuals were also classified as congruent when medications in the moderate and significant categories were discontinued or titrated down before discontinuing following the PGx report. PK subanalysis was manually conducted to include only the CYP2D6 and CYP2C19 enzymes, which are two clinically actionable genes specifically referenced by CPIC, the Food and Drug Administration (FDA) and other consortia (Bousman et al. 2023 ; Palumbo et al. 2024 ). CPIC has recognized CYP2D6 and CYP2C19 as specific genes that may be evaluated as part of the psychotropic medication selection decision, particularly around certain selective serotonin reuptake inhibitors (SSRI), serotonin-norepinephrine reuptake inhibitors (SNRI) and atypical antipsychotics due to the potential of increased side effect risk and reduced efficacy (Bousman et al. 2023 ). While debate continues about which specific genes, variants, and combinations thereof, have a strength of evidence to guide psychotropic medication management, analysis for the current study was kept broad to include any psychotropic medication metabolized by CYP2D6 or CYP2C19. If the PGx report indicated either gene as having an ultrarapid, poor or intermediate metabolizer status for at least one prescribed psychotropic medication it was classified as incongruent (PK-I). If the report indicated ‘normal’ or ‘not reported’ for these genes the medication was classified as congruent (PK-C). Detailed results for all treatment groups are included in Table 1 and Table 2. Table 1 Demographic characteristics TAU PGx P Value n 53 46 Age (y) (mean (SEM) [range]) 14.2 (0.3) [10–20] 14.3 (0.4) [9–18] 0.819 Sex (%) 0.885 Female 39.6 (21) 41.3 (19) Male 60.4 (32) 58.7 (27) Level of care (%) Intensive Outpatient 3.8 (2) 10.9 (5) Partial Hospitalization 96.2 (51) 89.1 (41) Overall comorbidities (n) Comorbidities % (n) Anxiety disorders 96.2 (51) 93.5 (43) Attention-deficit / hyperactivity disorder 54.7 (29) 56.5 (26) Eating disorders 7.5 (4) 2.2 (1) Mood disorders 84.9 (45) 69.6 (32) Obsessive-compulsive and related disorders 41.5 (22) 63.0 (29) Post-traumatic stress disorder 9.4 (5) 6.6 (3) Tourette's disorder, Tic disorder 9.4 (5) 15.2 (7) Treatment response, comparison between groups at admission and discharge. Admission Discharge Variable [mean (SEM), n] TAU PGx P value TAU PGx P value PQ-LES-Q 44.4 (1.7), 42 45.4 (1.8), 39 0.664 52.4 (1.5), 22 51.8 (1.7), 26 0.805 PROMIS-D 15.3 (1.6), 32 14.5 (1.3), 38 0.671 8.1 (1.3), 27 10.5 (1.5), 24 0.223 LSAS-CA 58.3 (5.5), 32 58.3 (5.2), 38 > 0.999 34.5 (6.6), 19 43.5 (6.6), 25 0.339 RBBS Hairpulling 4.6 (1.3), 29 6.4 (1.4), 37 0.382 1.1 (0.6), 27 3.8 (1.5), 24 0.110 Skin picking 12.2 (2.1), 29 10.8 (1.8), 37 0.609 6.5 (1.6), 27 7.5 (1.9), 24 0.697 Nail biting 9.1 (1.6), 29 6.6 (1.3), 37 0.222 4.1 (1.1), 27 3.8 (1.2), 24 0.844 Length of Stay (days) (SEM) [range] 37.1 (3.3) [1-130] 36.7 (1.8) [10–66] 0.911 Table 2. Treatment response, comparison between PGx-Congruent and PGx-Incongruent at admission and discharge. Admission Discharge Variable [mean (SEM), n] PGx-C PGx-I P value PGx-C PGx-I P value Age 14.7 (0.6) 14.1 (0.5) Medications (average) 2.5 (0.3), 20 2.8 (0.2), 23 0.197 3.0 (0.2), 20 3.6 (0.3), 26 0.985 PQ-LES-Q 41.4 (2.4), 16 48.2 (2.4), 23 0.059 49.1 (2.3), 11 53.7 (2.4), 15 0.188 PROMIS-D 17.3 (2.1), 15 12.6 (1.6), 23 0.079 12.5 (2.5), 10 9.1 (1.9), 14 0.295 LSAS-CA 64.9 (7.3), 15 54.0 (7.1), 23 0.309 48.2 (10.2), 11 39.9 (6.53), 14 0.483 RBBS Hairpulling 9.4 (2.8), 14 5.1 (1.5), 23 0.188 6.2 (3.2), 10 2.1 (1.2), 14 0.197 Skin picking 14.2 (3.0), 14 8.6 (2.2), 23 0.143 10.0 (3.5), 10 5.7 (2.1), 14 0.282 Nail biting 7.8, (2.5), 14 5.9 (1.4), 23 0.485 4.0 (2.6), 10 3.6 (1.1), 14 0.867 Treatment response, comparison between PK-Congruent and PK-Incongruent (CYP2D6 and CYP2C19) at admission and discharge. Admission Discharge Variable [mean (SEM), n] PK-C PK-I P value PK-C PK-I P value Age 14.4 (0.6), 25 14.2 (0.5), 21 Medications (average) 2.8 (0.3), 25 2.6 (0.2), 21 0.295 3.2 (0.2), 25 3.4 (0.3), 21 0.467 PQ-LES-Q 44.8 (2.2), 20 46.0 (2.9), 19 0.727 53.6 (2.7), 13 49.9 (2.1), 13 0.292 PROMIS-D 15.4 (2.0), 20 13.4 (1.7), 18 0.442 11.2 (2.7), 12 9.8 (1.6), 12 0.660 LSAS-CA 61.8 (7.4), 19 54.8 (7.3), 19 0.505 39.2 (8.0), 12 47.5 (8.4), 13 0.486 RBBS Hairpulling 5.0 (2.0), 20 7.9 (2.0), 17 0.327 3.2 (2.1), 12 4.5 (2.3), 12 0.672 Skin picking 11.5 (2.7), 20 9.9 (2.4), 17 0.666 10 (3.2), 12 5 (2.0), 12 0.201 Nail biting 6.4 (1.5), 20 6.9 (2.1), 17 0.854 2.4 (1.3), 12 5.1 (2.0), 12 0.285 Outcomes Two co-primary outcomes were evaluated for this study. The first co-primary outcome was the average number of medications prescribed following the PGx report. Due to the lack of a consensus definition for polypharmacy (Wang et al. 2023 ), two criteria were used to evaluate the impact of PGx testing on psychotropic polypharmacy. Polypharmacy was defined as: 1) three or more concurrent psychotropic medications or 2) two or more medications prescribed from the same class (e.g., serotonin reuptake inhibitor). The average number of medications before and after the PGx report, as well as comparison between the PGx-C and PGx-I subgroups, were evaluated. Non-psychotropic medications were not reported. The second co-primary outcome was the Pediatric Quality of Life Enjoyment and Satisfaction Questionnaire (PQ-LES-Q). For the PQ-LES-Q, respondents were scored as a percentage of the total possible score and categorized as a low-quality of life (< 65%), average quality of life 65–83%, and high quality of life (84% and above) (Anderson et al. 2022 ; Endicott et al. 1993 ). The secondary outcomes included rate of readmission, length of stay (LOS) and assessments. These secondary outcome assessments included: Patient-Reported Outcomes Measurement Information System Pediatric Depressive scale (PROMIS-D) was used to measure depression severity. Individuals rate the degree to which they experienced a given symptom (e.g., “I felt sad”) over the past week from 0 ( never ) to 4 ( almost always ), such that total scores range from 0–32 with higher scores reflecting greater depression (Cheng et al. 2023 ). Liebowitz Social Anxiety Scale for Children and Adolescents (LSAS-CA) is a 24-item self-report that assesses social anxiety severity (Masia-Warner et al. 2003 ). Repetitive Body-Focused Behavior Scale (RBBS). The RBBS is broken down to assess the presence, severity, and associated consequences of skin picking, nail biting, and hair pulling (Zavrou and Storch 2017 ). The exploratory outcomes included the following and were reported in Supplementary Table S2. Penn State Worry Questionnaire for Children (PSWQ-C) is a 14-item self-report that assesses generalized worry (Pestle et al. 2008 ). Childhood Anxiety Sensitivity Index (CASI) is a self-report assessment of anxiety sensitivity on a scale ranging from 18 to 54 (Allan et al. 2014 ). Children’s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) is an assessment designed to rate the severity of obsessions and compulsions using a scale ranging from 0–40. OCD symptom severity was scored as subclinical (0–7), mild (8–15), moderate (16–23), severe (24–31) and extreme (32–40) (Conelea et al. 2012 ; Scahill et al. 1997 ). Matching In this study, all individuals in the anxiety and mood disorders program for children and adolescents with ASD with PGx testing were included. Records were excluded if individuals discharged prior to the return of the PGx results. To get a randomized control group, treatment as usual (TAU), others in the same program were randomly selected using the MatchIt package within R, version 4.3.3 (Ho et al. 2011 ; R Core Team 2021 ). Matching criteria included medical service line, ASD diagnosis and level of care, which was partial hospitalization or intensive outpatient so that a similar profile would be included within the TAU group. Statistical Analysis Categorical variables were described as count and percentages. Continuous variables were summarized as mean and standard deviation for normally distributed data, and as median and interquartile range for non-normally distributed data. The sample size (n) represented all individuals in the EHR meeting eligibility criteria. Group comparisons were performed using student’s t-test for independent samples for normally distributed continuous variables; two-way repeated measures ANOVA with group (between-subjects factor) by time (admission to discharge; repeated measures factor), followed by Bonferroni post-hoc tests; and, Chi-Square or Fisher’s Exact test, were used for categorical data comparisons, as appropriate. For all statistical tests, an alpha level of 0.05 was used to determine significance. Data were reported as means ± standard error of the mean (SEM) for continuous variables. All analyses were conducted using R software (version 4.3.3). RESULTS Demographics and characteristics There were no differences in demographic and clinical characteristics between the TAU and PGx groups (Table 1 ). The average length of stay (LOS) was similar between the TAU (35.6 ± 1.7 days) and PGx (31.2 ± 2.2 days) groups (P = 0.911). There was insufficient racial and ethnic diversity to report, with only individuals self-identifying as White and Non-Hispanic having ≥ 10 individuals per group. Quality of life at admission was low for both TAU (44.7 ± 1.4) and PGx (46.4 ± 1.5) groups (P = 0.664; Table 1 ), as assessed using the PQ-LES-Q. Depression scores at admission, measured using PROMIS-D, indicated moderate levels of depressive symptoms in both the TAU (15.3 ± 1.6) and PGx (14.5 ± 1.3) groups (P = 0.671; Table 1 ). LSAS-CA scores at admission reflected moderate levels of social anxiety for both the TAU (58.3 ± 5.5) and PGx (58.3 ± 5.2) groups (P = > 0.999; Table 1 ). The presence and severity of repetitive body-focused behavioral symptoms, measured using the RBBS, were similar between the TAU and PGx groups for skin-picking (P = 0.609), hair pulling (P = 0.382) and nail biting (P = 0.222) (Table 1 ). Exploratory outcomes included the PSWQ, CASI and CY-BOCS assessments. Both the TAU and PGx groups had clinically elevated levels of worry at admission as measured with the PSWQ (P = 0.424; Table S2 ). Anxiety sensitivity, measured using CASI, revealed similarly elevated scores at admission in both TAU and PGx groups (P = 0.420; Table S2 ). Obsessive-compulsive disorder (OCD) symptoms, measured with the CY-BOCS, indicated that the symptoms at admission for both TAU and PGx groups were severe (P = 0.113; Table S2 ). Medication changes following PGx testing PGx-guided psychotropic medication selection was anticipated to result in decreased polypharmacy and improved quality of life. Medications were frequently changed, or dosages adjusted, following the PGx report, particularly when the admitting medication had a potential gene-drug interaction. At the time of admission to either PHP or IOP levels of care, 96% (44/46) of individuals in the PGx group were already prescribed at least one psychotropic. The average number of medications was 2.7 at admission compared to 3.4 at discharge, although this increase was not significant (P = 0.969; Fig. 1 A). Of these medications, 57% (26/46) of individuals were prescribed at least one incongruent medication. Following PGx testing, over half of those initially prescribed an incongruent medication had switched to a congruent medication by the time of discharge. Multiple medications are often prescribed to treat the ASD-associated symptoms and symptoms of co-occurring psychiatric conditions. Polypharmacy was common in this cohort, with 53% of individuals prescribed three or more psychotropic medications at admission and 74% at discharge. This was then stratified by medication congruency. Incongruent medications were expected to result in non-therapeutic plasma drug levels, requiring polypharmacy to manage symptoms. Individuals prescribed incongruent medications (PGx-I) at discharge averaged 3.6 ± 0.3 medications, compared to 3.0 ± 0.2 for those prescribed congruent medications (PGx-C) (P = 0.985; Fig. 1 B). The PGx-C and PGx-I subgroups had a similar average number of medications from admission to discharge ( Table 2 ). Similar results were found when focused on PK, with individuals prescribed medications incongruent (PK-I) with their CYP2D6 and CYP2C19 metabolizer status averaging 3.4 ± 0.3 psychotropic medications at discharge compared to 3.2 ± 0.2 for those prescribed congruent psychotropic medications (PK-C) (P = 0.467; Fig. 1 C). The PK-C and PK-I subgroups had a similar average number of medications from admission to discharge ( Table 2 ). Rates of antidepressant and antipsychotic polypharmacy were also calculated and reported in Table S1 . Efficacy comparison between TAU and PGx revealed similar levels of improvement Quality of life, anxiety and depression assessments were utilized to evaluate whether PGx testing improved outcomes compared to TAU. Between admission and discharge the co-primary efficacy variable, the PQ-LES-Q score, showed that both groups had improved from low to average life satisfaction (P < 0.001; Fig. 2 A). The PROMIS-D scores similarly demonstrated that both groups improved from none to slight levels of depressive symptoms between admission and discharge (P < 0.001; Fig. 2 B). Social anxiety scores, as measured with LSAS-CA, also showed that both groups improved from moderate to mild levels of social anxiety between admission and discharge (P < 0.001; Fig. 2 C). The within-group comparison showed improvement from admission to discharge for the TAU and PGx groups for skin-picking (P < 0.001), hair pulling (P = 0.017) and nail biting (P < 0.001) (Table 1 ). Scores for both the TAU and PGx groups across these measures did not differ at admission and discharge (Table 1 ). Additional measures of the CASI (P < 0.001), PSWQ (P < 0.001) and CY-BOCS (P < 0.001) revealed symptom improvement for both the TAU and PGx groups between admission and discharge across each measure ( Table S2 ). When scores for TAU and PGx were compared at discharge, the TAU and PGx groups reached similar levels of improvement of anxiety sensitivity and worry, as measured by the CASI and PSWQ assessments, respectively. However, the TAU group improved to subclinical level (7.2 ± 1.3) while the PGx group continued to report a mild level of OCD symptoms (11.9 ± 1.6) (P = 0.028; Table S2 ). PGx congruency comparison To better understand how PGx results may be integrated into clinical decision-making for medications, we next investigated outcomes between the individuals prescribed congruent medications compared to those prescribed one or more incongruent medication(s). Individuals either remained on an incongruent psychotropic medication (n = 26) or they either remained on, or switched to, a congruent medication (n = 20) following the PGx test results for the remainder of their treatment. Quality of life, anxiety and depression were compared between individuals taking congruent medications (PGx-C) and those taking incongruent (PGx-I) medications. Between admission and discharge the co-primary efficacy variable, the PQ-LES-Q score, showed that both subgroups had improved from low to average life satisfaction (P = 0.001; Fig. 3 A). The PROMIS-D scores suggested that both subgroups improved from none to slight levels of depressive symptoms between admission and discharge (P < 0.001; Fig. 3 B). The LSAS-CA, also showed that both subgroups improved from moderate to mild levels of social anxiety between admission and discharge (P = 0.015; Fig. 3 C). The RBBS scores showed improvement from admission to discharge for the PGx-C and PGx-I subgroups for skin-picking (P = 0.047) and hair pulling (P = 0.038), but not for nail biting (P = 0.068) ( Table 2 ). Scores for both the PGx-C and PGx-I subgroups across all three measures did not differ at admission and discharge ( Table 2 ). Additional measures of the CASI (P = 0.086), PSWQ (P = 0.052) and CY-BOCS (P < 0.01) revealed symptom improvement between admission and discharge for only the CY-BOCS ( Table S2 ). When compared at discharge, the PGx-C and PGx-I subgroups reached similar levels of improvement of anxiety sensitivity, worry and OCD symptoms as measured by the CASI and PSWQ and CY-BOCS assessments, respectively ( Table S2 ). PK-restricted medication management using CYP2D6 and CYP2C19 Metabolizer status of CYP2D6 and CYP2C19 was used to assess PK response to SSRI and other psychotropic exposure. Individuals prescribed medications incongruent with CYP2D6 and/or CYP2C19 (PK-I) were compared to those only prescribed congruent psychotropic medications (PK-C). Over half (52%) of the individuals in the current study had an intermediate, poor or ultrarapid metabolizer status for CYP2D6 and/or CYP2C19, and 39% admitted on a psychotropic medication that was incongruent with the metabolizer status of these two genes. Quality of life, anxiety and depression assessments were next analyzed to evaluate how PK-C and PK-I subgroups changed over the course of treatment. Between admission and discharge the co-primary efficacy variable, the PQ-LES-Q score, indicated that both subgroups had improved from low to average life satisfaction (P = 0.001; Fig. 3 D). The PROMIS-D scores similarly demonstrated that both subgroups improved from none to slight levels of depressive symptoms between admission and discharge (P < 0.001; Fig. 3 E ). LSAS-CA scores suggested that the PK-C and PK-I subgroups did not improve between admission and discharge (P = 0.377; Fig. 3 F). Scores for both the PK-C and PK-I subgroups across all three measures did not differ at admission and discharge ( Table 2 ). The RBBS scores indicated nonsignificant trends in improvement from admission to discharge for the PK-C and PK-I subgroups for skin-picking (P = 0.075), hair pulling (P = 0.066) and nail biting (P = 0.074; Table 2 ). The additional measures of the PSWQ (P = 0.026) and CY-BOCS (P < 0.001), but not for the CASI (P = 0.057), revealed symptom improvement between admission and discharge for both the PK-C and PK-I subgroups ( Table S2 ). DISCUSSION Implications of research findings from the current study This study highlights the potential role of incorporating PGx testing into clinical workflows to optimize medication management for individuals with ASD. Notably, 57% of the study population admitted on at least one medication with a potential gene-drug interaction. With co-occurring conditions frequently affecting persons with ASD, medication regimens commonly include the use of SSRIs, antipsychotics, and psychostimulants (Sturman et al. 2017 ). Polypharmacy in the current study was common, with over half of individuals were prescribed three or more psychotropic medications at the time of admission, and approximately half of those individuals were prescribed at least one incongruent medication. Prescribers did appear to utilize the recommendations provided within the PGx report to either switch medications or adjust dosing those admitting on incongruent medications, suggesting potential utility of the PGx test. Polypharmacy is common with ASD in general (Ritter et al. 2021 ), and remains a challenge that patients and prescribers must navigate to minimize side effects and drug-drug interactions, and to promote medication adherence. As individuals with ASD age into adulthood, the polypharmacy concerns grow as non-psychiatric co-occurring conditions develop (Espadas et al. 2020 ; McCarthy and Chaplin 2022 ). While we hypothesized that individuals taking congruent medications would be prescribed fewer medications compared to those prescribed incongruent medications, the data did not support this. Overall, prescribers frequently changed medications or adjusted dose when the PGx report indicated a significant or moderate potential for gene-drug interactions, switching to alternatives with a decreased likelihood of gene-drug interactions. Given the high level of polypharmacy in this population, there is an opportunity to integrate PGx testing in the medical workup conducted at admission to reduce the number of incongruent medications. This study also highlights that PGx testing is only one of multiple factors guiding medication management decisions. The retrospective study design makes it unclear to what degree decisions were informed by guidelines and algorithms, historical family data, patient and family preference, prescriber preference, insurance reimbursement, or careful use of the PGx report to avoid potential incongruent medications. One of the more important findings of the study was that all individuals improved similarly irrespective of access to PGx testing. To further explore the potential of PGx testing for individuals with ASD, we compared the impact of the combinatorial panel with PK-focused panels. Did evaluating CYP2D6 and CYP2C19 improve utility for clinical decision support over the combinatorial PGx panel? PGx remains of great interest when prescribing psychotropic medications, yet there is no consensus as to which test or specific genes provide the most utility as part of the psychotropic medication selection decision. Some stakeholders advocate for a combinatorial test report (Greden et al. 2019 ), inclusive of a range of PK- and PD-related genes, while others focus on specific genes, such as CYP2D6, CYP2C19, CYP2B6 and others (Bousman et al. 2023 ; Jukic et al. 2018 ; Pratt et al. 2021 ). Previous comparisons between both approaches reported the combinatorial PGx test outperformed individual genes with major depression (Shelton et al. 2020 ); however, little is known about this comparison in ASD. To better understand how these testing approaches can be used for children and adolescents with ASD, the current study evaluated both a comprehensive PGx panel and a PK-focused panel inclusive of only CYP2D6 and CYP2C19 to determine the impact on medication changes and assessment outcomes. The resulting data did not support one approach over another. Taken together with the complex factors informing medication changes, it may be most impactful for prescribers to order the combinatorial PGx panels, as the recommendations when narrowing to CYP2D6 and CYP2C19 were similar to the more comprehensive commercial product. Commercial PGx products also tend to have short turnaround times, which can be helpful in high acuity settings. These panels typically report the metabolizer status of specific genes, such as CYP2D6 and CYP2C19, allowing prescriber discretion on which clinical decision tool to utilize as part of the overall medication selection decision. Clinical implications The study’s co-primary outcome of polypharmacy rate did not differ between treatment groups. Indeed, medication selection and dosing decisions are complex and multifactorial. Prescribers must navigate guidelines and algorithms, family history, past medication trials, co-occurring conditions, allergies, patient and family preferences, insurance reimbursement and other factors. Disentangling the impact of the results from PGx reports in such a complex clinical decision-making context is challenging, particularly when patient outcomes were analyzed retrospectively. Moreover, the retrospective nature of the study did not allow us to understand how the PGx tests were used by prescribers and how the data were weighted in decision-making, e.g., moderate versus significant. Clinical assessment outcomes were modest across all treatment groups. The PQ-LES-Q, showed improvement from low to average quality of life across all groups. Improvements were also observed for depressive symptoms, social anxiety and repetitive behaviors, regardless of treatment group. The modest overall improvements for all treatment groups, lack of separation between any treatment groups when stratified by combinatorial PGx test and medication congruency (PGx-C versus PGx-I; PK-C versus PK-I), and complexity of medication decision-making suggest that longitudinal prospective trials that incorporate long-term follow-up are necessary. Therefore, a large prospective, randomized clinical trial is recommended to comprehensively evaluate the utility of PGx for individuals with ASD. Conclusion The current study evaluated the utility of PGx testing for individuals with ASD in a high-acuity depression and anxiety program. Medication selection or dose changes appeared to be adjusted based on the PGx results, suggesting that there may have been some benefit to using this clinical decision-making tool when making medication decisions. Group analysis, including those comparing congruency, polypharmacy, and CYP2D6 and CYP2C19 enzymatic profiles, did not yield different outcomes. Quality of life and symptom severity improved across all groups. Prospective, controlled trials are necessary to determine the specific patient populations or individual patient profiles that would benefit most from PGx testing. Limitations Due to the retrospective nature of the study, medication compliance was dependent on patient self-report and prescription refills, and history of failed medication trials were not evaluated. The current study sample size may limit detection of statistical differences, particularly within the subgroups. The assessments used for the current study are validated in pediatric populations. However, the clinical program used these assessments for individuals up to the age of 20 who were also evaluated with these assessments. The average length of stay was relatively brief, averaging under 40 days, decreasing the likelihood that medication-related differences in assessment outcomes would be observed. The binning of medications based on CYP2D6 and CYP2C19 by any non-normal metabolizer status may not align with specific recommendations that may require the inclusion of other genes (e.g., CYP3A4), or specific metabolizer status (e.g., ultrarapid metabolizer) for an actionable recommendation, which may have affected findings within the PK subgroup analysis. Declarations Ethics The Rogers Behavioral Health Institutional Review Board approved this retrospective study (RBH-2023-01). Patient and Public Involvement Patient and members of the public were not involved in the design, management, and conduct of this retrospective study. Conflict of interest The authors declare no conflicts of interest. Acknowledgements The authors thank Amaya Ramos, MD for her clinical guidance with study design, Isaac Seigel for helpful discussion and comments on the manuscript, and Ella C. Patty, Lily E. Mantsch, Sladjana Strbac and Rachel Lopez for their assistance in manual data extraction from the pharmacogenomics reports. Funding statement This research was supported by the Rogers Behavioral Health Foundation and the Lynn S. Nicholas Foundation. References Allan NP, Raines AM, Capron DW, Norr AM, Zvolensky MJ, Schmidt NB (2014) Identification of anxiety sensitivity classes and clinical cut-scores in a sample of adult smokers: results from a factor mixture model. J Anxiety Disord 28:696–703. 10.1016/j.janxdis.2014.07.006 Diagnostic and statistical manual of mental disorders: DSM-5™, 5th edition. edn. 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Int J Environ Res Public Health 19. 10.3390/ijerph192315974 Palumbo S, Mariotti V, Pellegrini S (2024) A Narrative Review on Pharmacogenomics in Psychiatry: Scientific Definitions, Principles, and Practical Resources. J Clin Psychopharmacol 44:49–56. 10.1097/JCP.0000000000001795 Patel JN, Mueller MK, Guffey WJ, Stegman J (2020) Drug Prescribing and Outcomes After Pharmacogenomic Testing in a Developmental and Behavioral Health Pediatric Clinic. J Dev Behav Pediatr 41:65–70. 10.1097/DBP.0000000000000746 Pestle S, Chorpita B, Schiffman J (2008) Psychometric Properties of the Penn State Worry Questionnaire for Children in a Large Clinical Sample. Journal of clinical child and adolescent psychology: the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53 37, 465 – 71 10.1080/15374410801955896 Pratt VM et al (2021) Recommendations for Clinical CYP2D6 Genotyping Allele Selection: A Joint Consensus Recommendation of the Association for Molecular Pathology, College of American Pathologists, Dutch Pharmacogenetics Working Group of the Royal Dutch Pharmacists Association, and the European Society for Pharmacogenomics and Personalized Therapy. J Mol Diagn 23:1047–1064. 10.1016/j.jmoldx.2021.05.013 Core Team R (2021) R. A Language and Environment for Statistical Computing. R version 4.3.3 edn. Vienna, Austria Ritter C, Hewitt K, McMorris CA (2021) Psychotropic Polypharmacy Among Children and Youth with Autism: A Systematic Review. J Child Adolesc Psychopharmacol 31:244–258. 10.1089/cap.2020.0110 Scahill L et al (1997) Children's Yale-Brown Obsessive Compulsive Scale: reliability and validity. J Am Acad Child Adolesc Psychiatry 36:844–852. 10.1097/00004583-199706000-00023 Shelton RC et al (2020) Combinatorial Pharmacogenomic Algorithm is Predictive of Citalopram and Escitalopram Metabolism in Patients with Major Depressive Disorder. Psychiatry Res 290:113017. 10.1016/j.psychres.2020.113017 Sturman N, Deckx L, Van Driel ML (2017) Methylphenidate for children and adolescents with autism spectrum disorder. Cochrane Database of Systematic Reviews 2017. 10.1002/14651858.cd011144.pub2 Taj R, Khan S (2005) A study of reasons of non-compliance to psychiatric treatment. J Ayub Med Coll Abbottabad 17:26–28 Taniguchi E, Conant K, Keller K, Kim SJ (2022) A Retrospective Chart Review of Factors Impacting Psychotropic Prescribing Patterns and Polypharmacy Rates in Youth with Autism Spectrum Disorder during the COVID-19 Pandemic. J Clin Med 11. 10.3390/jcm11164855 US Department of Health and Human, Services H (2021) 2020–2021 National Survey of Children’s Health data query Child and Adolescent Health Measurement Initiative. Washington DC: US Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Bureau Wang X et al (2023) Prevalence and trends of polypharmacy in U.S. adults, 1999–2018. Glob Health Res Policy 8:25. 10.1186/s41256-023-00311-4 Yoshida K et al (2021) Pharmacogenomic Studies in Intellectual Disabilities and Autism Spectrum Disorder: A Systematic Review. Can J Psychiatry 66:1019–1041. 10.1177/0706743720971950 Zavrou S, Storch EA (2017) Body focused repetitive behaviors among Cypriot teenage dancers: preliminary incidence and clinical correlates. Journal of Sport Behavior 40, 331+ Zeier Z et al (2018) Clinical Implementation of Pharmacogenetic Decision Support Tools for Antidepressant Drug Prescribing. Am J Psychiatry 175:873–886. 10.1176/appi.ajp.2018.17111282 Additional Declarations The authors declare no competing interests. Supplementary Files TableS1.xlsx TableS2.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5753717","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":397061001,"identity":"b0ab8a18-0d2d-47aa-842d-9f45365c6b6a","order_by":0,"name":"Sheldon R. 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Boyer","email":"","orcid":"https://orcid.org/0009-0000-2476-8573","institution":"Rogers Behavioral Health","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"W.","lastName":"Boyer","suffix":""},{"id":397061004,"identity":"3556c88e-d82e-4256-8586-36431d87c6b1","order_by":3,"name":"Maharaj Singh","email":"","orcid":"https://orcid.org/0000-0001-7736-1291","institution":"Rogers Behavioral Health","correspondingAuthor":false,"prefix":"","firstName":"Maharaj","middleName":"","lastName":"Singh","suffix":""},{"id":397061005,"identity":"3b41997e-ff77-4440-994e-8db391e0ce89","order_by":4,"name":"Sreya Vadapalli","email":"","orcid":"","institution":"Rogers Behavioral Health","correspondingAuthor":false,"prefix":"","firstName":"Sreya","middleName":"","lastName":"Vadapalli","suffix":""},{"id":397061006,"identity":"0bbfc514-0080-4bef-bfee-e79dba38de77","order_by":5,"name":"Jeffery M. 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Hartig","email":"","orcid":"https://orcid.org/0009-0000-5688-8377","institution":"Rogers Behavioral Health","correspondingAuthor":false,"prefix":"","firstName":"Madeline","middleName":"M.","lastName":"Hartig","suffix":""}],"badges":[],"createdAt":"2025-01-02 18:59:24","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5753717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5753717/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73085625,"identity":"4e36d92b-196f-414f-95a8-1c3289aeeadc","added_by":"auto","created_at":"2025-01-06 14:52:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1508328,"visible":true,"origin":"","legend":"\u003cp\u003eThe average number of medications was similar between treatment groups. (A) Within the PGx group, the average number of medications did not differ between admission (2.7 ± 0.2) and discharge (3.2 ± 0.2) (P=0.969). (B) At admission, the average number of medications did not differ between PGx-C (2.5 ± 0.3) and PGx-I (2.8 ± 0.2) (P=0.197). At discharge the average number of medications did not differ between PGx-C (3.0 ± 0.2) and PGx-I (3.6 ± 0.3) (P=0.985). The average number of medications did not differ over time for PGx-C (P=0.912) and PGx-I (P=0.996). (C) At admission, the average number of medications did not differ between PK-C (2.8 ± 0.3) and PK-I (2.6 ± 0.2) (P=0.295). At discharge the average number of medications did not differ between PK-C (3.2 ± 0.2) and PK-I (3.4 ± 0.3) (P=0.467). The average number of medications did not differ over time for PK-C (P=0.977) and PK-I (P=0.963). n.s. = not significant. Data reported as mean ± SEM.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753717/v1/4db698b81426783bdffd4050.jpg"},{"id":73085627,"identity":"ab558a24-2bb5-47f8-93db-4f87fbf16f4a","added_by":"auto","created_at":"2025-01-06 14:52:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1317721,"visible":true,"origin":"","legend":"\u003cp\u003eQuality of life, anxiety and depression improved between admission and discharge for both the TAU and PGx groups. (A) PQ-LES-Q scores at admission were similar between the TAU and PGx groups (P=0.664). Quality of life for both groups improved to reach similar levels at the time of discharge (P=0.805), with both groups reporting average life satisfaction. (B) LSAS-CA scores at admission were also similar between the TAU and PGx groups (P \u0026gt;0.999). Social anxiety for both groups improved to similar levels at the time of discharge (P=0.339), with both groups reporting mild levels of social anxiety. (C) PROMIS-D scores at admission did not differ between the TAU and PGx groups (P=0.671). Depressive symptom severity for both groups improved to reach similar levels at the time of discharge (P=0.223), with both groups reporting none to slight levels of depressive symptoms. *** P\u003cem\u003e \u003c/em\u003e\u0026lt;0.001; n.s. = not significant. Data reported as mean ± SEM.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753717/v1/3917f42dfaf6e4cba54c6c6d.jpg"},{"id":73087238,"identity":"44b1a4e2-46f3-4748-aecc-c1f35d728e88","added_by":"auto","created_at":"2025-01-06 15:00:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2166781,"visible":true,"origin":"","legend":"\u003cp\u003eQuality of life, anxiety and depression improved between admission and discharge for both PK-C and PK-I. A) PQ-LES-Q scores at admission were similar between PGx-C and PGx-I (P = 0.059), with quality of life improving for both subgroups at discharge (P = 0.188) and reporting average life satisfaction. (B) LSAS-CA scores at admission were similar between PGx-C and PGx-I (P = 0.309), with both subgroups reporting improvements in social anxiety to mild levels by discharge (P = 0.483). (C) PROMIS-D scores at admission did not differ significantly between PGx-C and PGx-I (P = 0.079), with depressive symptom severity improving for both subgroups to none to slight levels by discharge (P = 0.295). (D) PQ-LES-Q scores at admission were comparable between PK-C and PK-I (P = 0.727), with quality of life improving to average life satisfaction for both subgroups by discharge (P = 0.658). (E) LSAS-CA scores at admission were similar between PK-C and PK-I (P = 0.505), with improvements in social anxiety to mild levels by discharge for both subgroups (P = 0.486). (F) PROMIS-D scores at admission did not differ significantly between PK-C and PK-I (P = 0.442), with depressive symptom severity improving for both subgroups to none to slight levels by discharge (P = 0.658). ***P \u0026lt; 0.001; **P \u0026lt; 0.01; *P \u0026lt; 0.05; n.s. = not significant. Data reported as mean ± SEM.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753717/v1/0713e28690042a9420c8cab9.jpg"},{"id":73087261,"identity":"b3e5ecd7-c2bd-498e-bec9-b70a2e4b3225","added_by":"auto","created_at":"2025-01-06 15:00:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5910586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5753717/v1/69759073-41ff-48f7-afe8-6b56007fd25f.pdf"},{"id":73087239,"identity":"5fdd286c-7cbe-4ec3-9d04-6e10a6b47e66","added_by":"auto","created_at":"2025-01-06 15:00:47","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19267,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5753717/v1/8af0ebae23c505cb2d049b3a.xlsx"},{"id":73085636,"identity":"c2f4819c-dde2-4a06-b61c-4bd49149d75d","added_by":"auto","created_at":"2025-01-06 14:52:47","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20309,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5753717/v1/ec50a512d718e84efec68791.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePolypharmacy and pharmacogenomics in high-acuity behavioral health care for autism spectrum disorder: A retrospective study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAutism spectrum disorder (ASD) is a neurological and developmental disorder affecting an estimated 2.9% of the population (US Department of Health and Human Services \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Symptoms typically manifest before age three and include deficits in communication and social interaction, repetitive behaviors, fixated interests and inflexible routines (American Psychiatric Association 2013). Depression, anxiety disorders, obsessive-compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD), and tics also frequently co-occur with ASD (Hollocks et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lai et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Medication management for individuals with ASD often includes psychotropic medication polypharmacy, even in young children, underscoring the need to optimize medication selection to increase efficacy and minimize side effect burden.\u003c/p\u003e \u003cp\u003eMedication management for individuals with ASD can be complicated, with no approved treatments targeting the underlying etiology of ASD, as well as reports of side effects and limited response in children with ASD compared to children not diagnosed with ASD (Bose-Brill et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bowers et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Patel et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is further complicated by underlying clinically significant genetic variations affecting an individual\u0026rsquo;s neurophysiology as well as heterogeneous symptomatic presentation of individuals with ASD that commonly require psychotropic polypharmacy with medications across different drug classes (Taniguchi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), including antidepressants, antipsychotics, mood stabilizers and stimulants. One approach to streamline medication trialing and optimize dosing has been to incorporate pharmacogenomic (PGx) testing (Cecchin and Stocco \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). More specifically, PGx tests analyze genetics to predict pharmacokinetic (PK) and pharmacodynamic (PD) response to medications to address the limited response rates to first-line medications, which are reported to range from 42\u0026ndash;53% in depression alone (Cipriani et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thus, PGx-guided drug selection and dosing may help to reduce medication side effects (Greden et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and improve medication compliance (Taj and Khan \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), and thereby reduce the need for medication changes. PGx testing may also lead to reduced emergency room visits, readmissions and cost (Elliott et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ghanbarian et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Groessl et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and may be particularly relevant to persons receiving treatment at higher levels of care, such as in residential, partial hospitalization and intensive outpatient behavioral healthcare treatment programs.\u003c/p\u003e \u003cp\u003eEmerging data has demonstrated that when used for individuals with ASD, PGx testing can provide clinically meaningful information to guide medication management and improve clinical outcomes (Yoshida et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This includes global improvement in clinical symptoms when PGx is incorporated into medication management decisions for those with ASD who failed to respond to at least two psychotropic medications (Arranz et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additional studies for PGx-guided psychotropic medication management have followed guidelines by Clinical Pharmacogenetics Implementation Consortium (CPIC), the American Psychiatric Association Council of Research (APA-COR) or the Association for Molecular Pathology Clinical Practice Committee's Pharmacogenomics (PGx) Working Group and others (Bousman et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hicks et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pratt et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zeier et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These focus on specific PK-related enzymes that are effective in ameliorating some ASD symptoms, including CYP2D6, which plays a role in the metabolism of aripiprazole and risperidone (Bousman et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pratt et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenetic variants of PK-related enzymes may confer ultra-rapid, intermediate or poor metabolism of select psychotropic medications. Taking these medications may result in poor therapeutic response to altered plasma concentrations and delayed drug clearance (Bousman et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although these genetic variants occur frequently in individuals with ASD (Biswas et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Goodson et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), there are currently no ASD-specific PGx guidelines for commercially available PGx products (Biswas et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a result, PGx utilization continues to be limited for ASD and continued investigation is warranted. Therefore, a retrospective study analyzing 99 persons diagnosed with ASD was conducted to determine whether PGx-guided medication management would improve medication congruence with potential gene-drug interactions, reduce polypharmacy and decrease symptom severity.\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 was conducted for children and adolescents with ASD treated acutely in the anxiety and mood disorders partial hospitalization (PHP) and intensive outpatient (IOP) programs within a national behavioral health care system. All testing was ordered on a case-by-case basis at the prescriber\u0026rsquo;s discretion, although 13% (6/46) of the PGx reports were provided by patients or their parent/guardian at the time of admission. Individuals included in the current study received daily behavioral therapy as part of their treatment program.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClassification of medication congruence with PGx testing\u003c/h3\u003e\n\u003cp\u003eAny psychotropic medication-related PGx test was eligible for inclusion in this study; however, all extracted reports for this study were the GeneSight\u0026reg; Psychotropic test (Myriad Genetics, Inc., Salt Lake City, UT). Data was extracted from individual PGx reports and analyzed. Determinations of gene-drug interactions were based on both pharmacokinetic (PK) and pharmacodynamic (PD) genes, as well as further determination by the PK-specific genes, CYP2D6 and CYP2C19. The PGx treatment group was classified as \u0026ldquo;incongruent\u0026rdquo; (PGx-I) when individuals were prescribed at least one daily psychotropic medication that was reported to have \u0026ldquo;moderate\u0026rdquo; or \u0026ldquo;significant\u0026rdquo; gene-drug interactions. Individuals were classified as \u0026ldquo;congruent\u0026rdquo; (PGx-C) when prescribed medications that were all reported with use as directed, or \u0026ldquo;normal\u0026rdquo;, with no suspected gene-drug interactions. Individuals were also classified as congruent when medications in the moderate and significant categories were discontinued or titrated down before discontinuing following the PGx report.\u003c/p\u003e \u003cp\u003ePK subanalysis was manually conducted to include only the CYP2D6 and CYP2C19 enzymes, which are two clinically actionable genes specifically referenced by CPIC, the Food and Drug Administration (FDA) and other consortia (Bousman et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Palumbo et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). CPIC has recognized CYP2D6 and CYP2C19 as specific genes that may be evaluated as part of the psychotropic medication selection decision, particularly around certain selective serotonin reuptake inhibitors (SSRI), serotonin-norepinephrine reuptake inhibitors (SNRI) and atypical antipsychotics due to the potential of increased side effect risk and reduced efficacy (Bousman et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While debate continues about which specific genes, variants, and combinations thereof, have a strength of evidence to guide psychotropic medication management, analysis for the current study was kept broad to include any psychotropic medication metabolized by CYP2D6 or CYP2C19. If the PGx report indicated either gene as having an ultrarapid, poor or intermediate metabolizer status for at least one prescribed psychotropic medication it was classified as incongruent (PK-I). If the report indicated \u0026lsquo;normal\u0026rsquo; or \u0026lsquo;not reported\u0026rsquo; for these genes the medication was classified as congruent (PK-C). Detailed results for all treatment groups are included in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDemographic characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePGx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y) (mean (SEM) [range])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.2 (0.3) [10\u0026ndash;20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3 (0.4) [9\u0026ndash;18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.6 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.3 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.4 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.7 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of care (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensive Outpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial Hospitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.2 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.1 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall comorbidities (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.2 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.5 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttention-deficit / hyperactivity \u003c/p\u003e \u003cp\u003e disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.7 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.5 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEating disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMood disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.9 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.6 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObsessive-compulsive and \u003c/p\u003e \u003cp\u003e related disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.5 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-traumatic stress disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTourette's disorder, Tic disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment response, comparison between groups at admission and discharge.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdmission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eDischarge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable [mean (SEM), n]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTAU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePGx\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTAU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ePGx\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ-LES-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.4 (1.7), 42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.4 (1.8), 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.4 (1.5), 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.8 (1.7), 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROMIS-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.3 (1.6), 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.5 (1.3), 38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1 (1.3), 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.5 (1.5), 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSAS-CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.3 (5.5), 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.3 (5.2), 38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.5 (6.6), 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.5 (6.6), 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHairpulling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6 (1.3), 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4 (1.4), 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1 (0.6), 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8 (1.5), 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin picking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.2 (2.1), 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.8 (1.8), 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5 (1.6), 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.5 (1.9), 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNail biting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.1 (1.6), 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (1.3), 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1 (1.1), 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8 (1.2), 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of Stay (days) (SEM) [range]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.1 (3.3) [1-130]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.7 (1.8) [10\u0026ndash;66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;2.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTreatment response, comparison between PGx-Congruent and PGx-Incongruent at admission and discharge.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable [mean (SEM), n]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePGx-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePGx-I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePGx-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ePGx-I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedications (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5 (0.3), 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8 (0.2), 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0 (0.2), 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6 (0.3), 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ-LES-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.4 (2.4), 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.2 (2.4), 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.1 (2.3), 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.7 (2.4), 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROMIS-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.3 (2.1), 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.6 (1.6), 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5 (2.5), 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.1 (1.9), 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSAS-CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.9 (7.3), 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.0 (7.1), 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.2 (10.2), 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.9 (6.53), 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHairpulling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4 (2.8), 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1 (1.5), 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.2 (3.2), 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1 (1.2), 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin picking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.2 (3.0), 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6 (2.2), 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 (3.5), 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7 (2.1), 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNail biting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8, (2.5), 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9 (1.4), 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0 (2.6), 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6 (1.1), 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment response, comparison between PK-Congruent and PK-Incongruent (CYP2D6 and CYP2C19) at admission and discharge.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdmission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eDischarge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable [mean (SEM), n]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePK-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePK-I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePK-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ePK-I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.4 (0.6), 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.2 (0.5), 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedications (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8 (0.3), 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 (0.2), 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2 (0.2), 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4 (0.3), 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ-LES-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.8 (2.2), 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.0 (2.9), 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.6 (2.7), 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.9 (2.1), 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROMIS-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.4 (2.0), 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.4 (1.7), 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2 (2.7), 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.8 (1.6), 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSAS-CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.8 (7.4), 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.8 (7.3), 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.2 (8.0), 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.5 (8.4), 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHairpulling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 (2.0), 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9 (2.0), 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2 (2.1), 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5 (2.3), 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin picking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.5 (2.7), 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9 (2.4), 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (3.2), 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (2.0), 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNail biting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.4 (1.5), 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9 (2.1), 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4 (1.3), 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1 (2.0), 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eTwo co-primary outcomes were evaluated for this study. The first co-primary outcome was the average number of medications prescribed following the PGx report. Due to the lack of a consensus definition for polypharmacy (Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), two criteria were used to evaluate the impact of PGx testing on psychotropic polypharmacy. Polypharmacy was defined as: 1) three or more concurrent psychotropic medications or 2) two or more medications prescribed from the same class (e.g., serotonin reuptake inhibitor). The average number of medications before and after the PGx report, as well as comparison between the PGx-C and PGx-I subgroups, were evaluated. Non-psychotropic medications were not reported. The second co-primary outcome was the Pediatric Quality of Life Enjoyment and Satisfaction Questionnaire (PQ-LES-Q). For the PQ-LES-Q, respondents were scored as a percentage of the total possible score and categorized as a low-quality of life (\u0026lt;\u0026thinsp;65%), average quality of life 65\u0026ndash;83%, and high quality of life (84% and above) (Anderson et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Endicott et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The secondary outcomes included rate of readmission, length of stay (LOS) and assessments. These secondary outcome assessments included:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePatient-Reported Outcomes Measurement Information System Pediatric Depressive scale (PROMIS-D) was used to measure depression severity. Individuals rate the degree to which they experienced a given symptom (e.g., \u0026ldquo;I felt sad\u0026rdquo;) over the past week from 0 (\u003cem\u003enever\u003c/em\u003e) to 4 (\u003cem\u003ealmost always\u003c/em\u003e), such that total scores range from 0\u0026ndash;32 with higher scores reflecting greater depression (Cheng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLiebowitz Social Anxiety Scale for Children and Adolescents (LSAS-CA) is a 24-item self-report that assesses social anxiety severity (Masia-Warner et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRepetitive Body-Focused Behavior Scale (RBBS). The RBBS is broken down to assess the presence, severity, and associated consequences of skin picking, nail biting, and hair pulling (Zavrou and Storch \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe exploratory outcomes included the following and were reported in Supplementary Table S2.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePenn State Worry Questionnaire for Children (PSWQ-C) is a 14-item self-report that assesses generalized worry (Pestle et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eChildhood Anxiety Sensitivity Index (CASI) is a self-report assessment of anxiety sensitivity on a scale ranging from 18 to 54 (Allan et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eChildren\u0026rsquo;s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) is an assessment designed to rate the severity of obsessions and compulsions using a scale ranging from 0\u0026ndash;40. OCD symptom severity was scored as subclinical (0\u0026ndash;7), mild (8\u0026ndash;15), moderate (16\u0026ndash;23), severe (24\u0026ndash;31) and extreme (32\u0026ndash;40) (Conelea et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Scahill et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eMatching\u003c/h3\u003e\n\u003cp\u003eIn this study, all individuals in the anxiety and mood disorders program for children and adolescents with ASD with PGx testing were included. Records were excluded if individuals discharged prior to the return of the PGx results. To get a randomized control group, treatment as usual (TAU), others in the same program were randomly selected using the MatchIt package within R, version 4.3.3 (Ho et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; R Core Team \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Matching criteria included medical service line, ASD diagnosis and level of care, which was partial hospitalization or intensive outpatient so that a similar profile would be included within the TAU group.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were described as count and percentages. Continuous variables were summarized as mean and standard deviation for normally distributed data, and as median and interquartile range for non-normally distributed data. The sample size (n) represented all individuals in the EHR meeting eligibility criteria. Group comparisons were performed using student\u0026rsquo;s t-test for independent samples for normally distributed continuous variables; two-way repeated measures ANOVA with group (between-subjects factor) by time (admission to discharge; repeated measures factor), followed by Bonferroni post-hoc tests; and, Chi-Square or Fisher\u0026rsquo;s Exact test, were used for categorical data comparisons, as appropriate. For all statistical tests, an alpha level of 0.05 was used to determine significance. Data were reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM) for continuous variables. All analyses were conducted using R software (version 4.3.3).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDemographics and characteristics\u003c/h2\u003e \u003cp\u003eThere were no differences in demographic and clinical characteristics between the TAU and PGx groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average length of stay (LOS) was similar between the TAU (35.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 days) and PGx (31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 days) groups (P\u0026thinsp;=\u0026thinsp;0.911). There was insufficient racial and ethnic diversity to report, with only individuals self-identifying as White and Non-Hispanic having\u0026thinsp;\u0026ge;\u0026thinsp;10 individuals per group. Quality of life at admission was low for both TAU (44.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4) and PGx (46.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5) groups (P\u0026thinsp;=\u0026thinsp;0.664; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), as assessed using the PQ-LES-Q. Depression scores at admission, measured using PROMIS-D, indicated moderate levels of depressive symptoms in both the TAU (15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6) and PGx (14.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3) groups (P\u0026thinsp;=\u0026thinsp;0.671; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). LSAS-CA scores at admission reflected moderate levels of social anxiety for both the TAU (58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5) and PGx (58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2) groups (P\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;0.999; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The presence and severity of repetitive body-focused behavioral symptoms, measured using the RBBS, were similar between the TAU and PGx groups for skin-picking (P\u0026thinsp;=\u0026thinsp;0.609), hair pulling (P\u0026thinsp;=\u0026thinsp;0.382) and nail biting (P\u0026thinsp;=\u0026thinsp;0.222) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExploratory outcomes included the PSWQ, CASI and CY-BOCS assessments. Both the TAU and PGx groups had clinically elevated levels of worry at admission as measured with the PSWQ (P\u0026thinsp;=\u0026thinsp;0.424; \u003cb\u003eTable S2\u003c/b\u003e). Anxiety sensitivity, measured using CASI, revealed similarly elevated scores at admission in both TAU and PGx groups (P\u0026thinsp;=\u0026thinsp;0.420; \u003cb\u003eTable S2\u003c/b\u003e). Obsessive-compulsive disorder (OCD) symptoms, measured with the CY-BOCS, indicated that the symptoms at admission for both TAU and PGx groups were severe (P\u0026thinsp;=\u0026thinsp;0.113; \u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMedication changes following PGx testing\u003c/h3\u003e\n\u003cp\u003ePGx-guided psychotropic medication selection was anticipated to result in decreased polypharmacy and improved quality of life. Medications were frequently changed, or dosages adjusted, following the PGx report, particularly when the admitting medication had a potential gene-drug interaction. At the time of admission to either PHP or IOP levels of care, 96% (44/46) of individuals in the PGx group were already prescribed at least one psychotropic. The average number of medications was 2.7 at admission compared to 3.4 at discharge, although this increase was not significant (P\u0026thinsp;=\u0026thinsp;0.969; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Of these medications, 57% (26/46) of individuals were prescribed at least one incongruent medication. Following PGx testing, over half of those initially prescribed an incongruent medication had switched to a congruent medication by the time of discharge.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMultiple medications are often prescribed to treat the ASD-associated symptoms and symptoms of co-occurring psychiatric conditions. Polypharmacy was common in this cohort, with 53% of individuals prescribed three or more psychotropic medications at admission and 74% at discharge. This was then stratified by medication congruency. Incongruent medications were expected to result in non-therapeutic plasma drug levels, requiring polypharmacy to manage symptoms. Individuals prescribed incongruent medications (PGx-I) at discharge averaged 3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 medications, compared to 3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 for those prescribed congruent medications (PGx-C) (P\u0026thinsp;=\u0026thinsp;0.985; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The PGx-C and PGx-I subgroups had a similar average number of medications from admission to discharge (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). Similar results were found when focused on PK, with individuals prescribed medications incongruent (PK-I) with their CYP2D6 and CYP2C19 metabolizer status averaging 3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 psychotropic medications at discharge compared to 3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 for those prescribed congruent psychotropic medications (PK-C) (P\u0026thinsp;=\u0026thinsp;0.467; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The PK-C and PK-I subgroups had a similar average number of medications from admission to discharge (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). Rates of antidepressant and antipsychotic polypharmacy were also calculated and reported in \u003cb\u003eTable S1\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEfficacy comparison between TAU and PGx revealed similar levels of improvement\u003c/h2\u003e \u003cp\u003eQuality of life, anxiety and depression assessments were utilized to evaluate whether PGx testing improved outcomes compared to TAU. Between admission and discharge the co-primary efficacy variable, the PQ-LES-Q score, showed that both groups had improved from low to average life satisfaction (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The PROMIS-D scores similarly demonstrated that both groups improved from none to slight levels of depressive symptoms between admission and discharge (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Social anxiety scores, as measured with LSAS-CA, also showed that both groups improved from moderate to mild levels of social anxiety between admission and discharge (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The within-group comparison showed improvement from admission to discharge for the TAU and PGx groups for skin-picking (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hair pulling (P\u0026thinsp;=\u0026thinsp;0.017) and nail biting (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Scores for both the TAU and PGx groups across these measures did not differ at admission and discharge (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditional measures of the CASI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PSWQ (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CY-BOCS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) revealed symptom improvement for both the TAU and PGx groups between admission and discharge across each measure (\u003cb\u003eTable S2\u003c/b\u003e). When scores for TAU and PGx were compared at discharge, the TAU and PGx groups reached similar levels of improvement of anxiety sensitivity and worry, as measured by the CASI and PSWQ assessments, respectively. However, the TAU group improved to subclinical level (7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3) while the PGx group continued to report a mild level of OCD symptoms (11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6) (P\u0026thinsp;=\u0026thinsp;0.028; \u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePGx congruency comparison\u003c/h2\u003e \u003cp\u003eTo better understand how PGx results may be integrated into clinical decision-making for medications, we next investigated outcomes between the individuals prescribed congruent medications compared to those prescribed one or more incongruent medication(s). Individuals either remained on an incongruent psychotropic medication (n\u0026thinsp;=\u0026thinsp;26) or they either remained on, or switched to, a congruent medication (n\u0026thinsp;=\u0026thinsp;20) following the PGx test results for the remainder of their treatment.\u003c/p\u003e \u003cp\u003eQuality of life, anxiety and depression were compared between individuals taking congruent medications (PGx-C) and those taking incongruent (PGx-I) medications. Between admission and discharge the co-primary efficacy variable, the PQ-LES-Q score, showed that both subgroups had improved from low to average life satisfaction (P\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The PROMIS-D scores suggested that both subgroups improved from none to slight levels of depressive symptoms between admission and discharge (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The LSAS-CA, also showed that both subgroups improved from moderate to mild levels of social anxiety between admission and discharge (P\u0026thinsp;=\u0026thinsp;0.015; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The RBBS scores showed improvement from admission to discharge for the PGx-C and PGx-I subgroups for skin-picking (P\u0026thinsp;=\u0026thinsp;0.047) and hair pulling (P\u0026thinsp;=\u0026thinsp;0.038), but not for nail biting (P\u0026thinsp;=\u0026thinsp;0.068) (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). Scores for both the PGx-C and PGx-I subgroups across all three measures did not differ at admission and discharge (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditional measures of the CASI (P\u0026thinsp;=\u0026thinsp;0.086), PSWQ (P\u0026thinsp;=\u0026thinsp;0.052) and CY-BOCS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) revealed symptom improvement between admission and discharge for only the CY-BOCS (\u003cb\u003eTable S2\u003c/b\u003e). When compared at discharge, the PGx-C and PGx-I subgroups reached similar levels of improvement of anxiety sensitivity, worry and OCD symptoms as measured by the CASI and PSWQ and CY-BOCS assessments, respectively (\u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePK-restricted medication management using CYP2D6 and CYP2C19\u003c/h2\u003e \u003cp\u003eMetabolizer status of CYP2D6 and CYP2C19 was used to assess PK response to SSRI and other psychotropic exposure. Individuals prescribed medications incongruent with CYP2D6 and/or CYP2C19 (PK-I) were compared to those only prescribed congruent psychotropic medications (PK-C). Over half (52%) of the individuals in the current study had an intermediate, poor or ultrarapid metabolizer status for CYP2D6 and/or CYP2C19, and 39% admitted on a psychotropic medication that was incongruent with the metabolizer status of these two genes.\u003c/p\u003e \u003cp\u003eQuality of life, anxiety and depression assessments were next analyzed to evaluate how PK-C and PK-I subgroups changed over the course of treatment. Between admission and discharge the co-primary efficacy variable, the PQ-LES-Q score, indicated that both subgroups had improved from low to average life satisfaction (P\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The PROMIS-D scores similarly demonstrated that both subgroups improved from none to slight levels of depressive symptoms between admission and discharge (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e).\u003c/b\u003e LSAS-CA scores suggested that the PK-C and PK-I subgroups did not improve between admission and discharge (P\u0026thinsp;=\u0026thinsp;0.377; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Scores for both the PK-C and PK-I subgroups across all three measures did not differ at admission and discharge (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). The RBBS scores indicated nonsignificant trends in improvement from admission to discharge for the PK-C and PK-I subgroups for skin-picking (P\u0026thinsp;=\u0026thinsp;0.075), hair pulling (P\u0026thinsp;=\u0026thinsp;0.066) and nail biting (P\u0026thinsp;=\u0026thinsp;0.074; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). The additional measures of the PSWQ (P\u0026thinsp;=\u0026thinsp;0.026) and CY-BOCS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not for the CASI (P\u0026thinsp;=\u0026thinsp;0.057), revealed symptom improvement between admission and discharge for both the PK-C and PK-I subgroups (\u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplications of research findings from the current study\u003c/h2\u003e \u003cp\u003eThis study highlights the potential role of incorporating PGx testing into clinical workflows to optimize medication management for individuals with ASD. Notably, 57% of the study population admitted on at least one medication with a potential gene-drug interaction. With co-occurring conditions frequently affecting persons with ASD, medication regimens commonly include the use of SSRIs, antipsychotics, and psychostimulants (Sturman et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Polypharmacy in the current study was common, with over half of individuals were prescribed three or more psychotropic medications at the time of admission, and approximately half of those individuals were prescribed at least one incongruent medication. Prescribers did appear to utilize the recommendations provided within the PGx report to either switch medications or adjust dosing those admitting on incongruent medications, suggesting potential utility of the PGx test.\u003c/p\u003e \u003cp\u003ePolypharmacy is common with ASD in general (Ritter et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and remains a challenge that patients and prescribers must navigate to minimize side effects and drug-drug interactions, and to promote medication adherence. As individuals with ASD age into adulthood, the polypharmacy concerns grow as non-psychiatric co-occurring conditions develop (Espadas et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; McCarthy and Chaplin \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While we hypothesized that individuals taking congruent medications would be prescribed fewer medications compared to those prescribed incongruent medications, the data did not support this. Overall, prescribers frequently changed medications or adjusted dose when the PGx report indicated a significant or moderate potential for gene-drug interactions, switching to alternatives with a decreased likelihood of gene-drug interactions. Given the high level of polypharmacy in this population, there is an opportunity to integrate PGx testing in the medical workup conducted at admission to reduce the number of incongruent medications.\u003c/p\u003e \u003cp\u003eThis study also highlights that PGx testing is only one of multiple factors guiding medication management decisions. The retrospective study design makes it unclear to what degree decisions were informed by guidelines and algorithms, historical family data, patient and family preference, prescriber preference, insurance reimbursement, or careful use of the PGx report to avoid potential incongruent medications. One of the more important findings of the study was that all individuals improved similarly irrespective of access to PGx testing. To further explore the potential of PGx testing for individuals with ASD, we compared the impact of the combinatorial panel with PK-focused panels.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDid evaluating CYP2D6 and CYP2C19 improve utility for clinical decision support over the combinatorial PGx panel?\u003c/em\u003e \u003c/p\u003e \u003cp\u003ePGx remains of great interest when prescribing psychotropic medications, yet there is no consensus as to which test or specific genes provide the most utility as part of the psychotropic medication selection decision. Some stakeholders advocate for a combinatorial test report (Greden et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), inclusive of a range of PK- and PD-related genes, while others focus on specific genes, such as CYP2D6, CYP2C19, CYP2B6 and others (Bousman et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jukic et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pratt et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous comparisons between both approaches reported the combinatorial PGx test outperformed individual genes with major depression (Shelton et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); however, little is known about this comparison in ASD. To better understand how these testing approaches can be used for children and adolescents with ASD, the current study evaluated both a comprehensive PGx panel and a PK-focused panel inclusive of only CYP2D6 and CYP2C19 to determine the impact on medication changes and assessment outcomes. The resulting data did not support one approach over another. Taken together with the complex factors informing medication changes, it may be most impactful for prescribers to order the combinatorial PGx panels, as the recommendations when narrowing to CYP2D6 and CYP2C19 were similar to the more comprehensive commercial product. Commercial PGx products also tend to have short turnaround times, which can be helpful in high acuity settings. These panels typically report the metabolizer status of specific genes, such as CYP2D6 and CYP2C19, allowing prescriber discretion on which clinical decision tool to utilize as part of the overall medication selection decision.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eClinical implications\u003c/h2\u003e \u003cp\u003eThe study\u0026rsquo;s co-primary outcome of polypharmacy rate did not differ between treatment groups. Indeed, medication selection and dosing decisions are complex and multifactorial. Prescribers must navigate guidelines and algorithms, family history, past medication trials, co-occurring conditions, allergies, patient and family preferences, insurance reimbursement and other factors. Disentangling the impact of the results from PGx reports in such a complex clinical decision-making context is challenging, particularly when patient outcomes were analyzed retrospectively. Moreover, the retrospective nature of the study did not allow us to understand how the PGx tests were used by prescribers and how the data were weighted in decision-making, e.g., moderate versus significant.\u003c/p\u003e \u003cp\u003eClinical assessment outcomes were modest across all treatment groups. The PQ-LES-Q, showed improvement from low to average quality of life across all groups. Improvements were also observed for depressive symptoms, social anxiety and repetitive behaviors, regardless of treatment group. The modest overall improvements for all treatment groups, lack of separation between any treatment groups when stratified by combinatorial PGx test and medication congruency (PGx-C versus PGx-I; PK-C versus PK-I), and complexity of medication decision-making suggest that longitudinal prospective trials that incorporate long-term follow-up are necessary. Therefore, a large prospective, randomized clinical trial is recommended to comprehensively evaluate the utility of PGx for individuals with ASD.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study evaluated the utility of PGx testing for individuals with ASD in a high-acuity depression and anxiety program. Medication selection or dose changes appeared to be adjusted based on the PGx results, suggesting that there may have been some benefit to using this clinical decision-making tool when making medication decisions. Group analysis, including those comparing congruency, polypharmacy, and CYP2D6 and CYP2C19 enzymatic profiles, did not yield different outcomes. Quality of life and symptom severity improved across all groups. Prospective, controlled trials are necessary to determine the specific patient populations or individual patient profiles that would benefit most from PGx testing.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDue to the retrospective nature of the study, medication compliance was dependent on patient self-report and prescription refills, and history of failed medication trials were not evaluated.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe current study sample size may limit detection of statistical differences, particularly within the subgroups.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe assessments used for the current study are validated in pediatric populations. However, the clinical program used these assessments for individuals up to the age of 20 who were also evaluated with these assessments.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe average length of stay was relatively brief, averaging under 40 days, decreasing the likelihood that medication-related differences in assessment outcomes would be observed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe binning of medications based on CYP2D6 and CYP2C19 by any non-normal metabolizer status may not align with specific recommendations that may require the inclusion of other genes (e.g., CYP3A4), or specific metabolizer status (e.g., ultrarapid metabolizer) for an actionable recommendation, which may have affected findings within the PK subgroup analysis.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Rogers Behavioral Health Institutional Review Board approved this retrospective study (RBH-2023-01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatient and Public Involvement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient and members of the public were not involved in the design, management, and conduct of this retrospective study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\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, Isaac Seigel for helpful discussion and comments on the manuscript, and Ella C. 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Am J Psychiatry 175:873\u0026ndash;886. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1176/appi.ajp.2018.17111282\u003c/span\u003e\u003cspan address=\"10.1176/appi.ajp.2018.17111282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Rogers Behavioral Health","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Autism, CYP2D6, ASD, depression, anxiety, pharmacogenomics, PGx, GeneSight, polypharmacy, antipsychotics, antidepressants","lastPublishedDoi":"10.21203/rs.3.rs-5753717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5753717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study evaluated pharmacogenomic (PGx) testing in children and adolescents with autism spectrum disorder (ASD). ASD frequently presents with co-occurring depression and anxiety. This complex phenotype often results in psychotropic medication polypharmacy. Incorporating PGx testing into the medical work-up may reduce polypharmacy and improve quality of life with symptom reduction.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective electronic health record review between January 2017 to May 2023. Individuals either received PGx testing or treatment as usual (TAU). The co-primary outcomes were polypharmacy and the Pediatric Quality of Life Enjoyment and Satisfaction Questionnaire (PQ-LES-Q). Secondary outcomes included length of stay and assessments measuring severity or behavioral impact.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 99 individuals with ASD were analyzed. At the time of admission, 93% of individuals were prescribed at least one psychotropic medication and over half of these individuals were prescribed medications with potential gene-drug interactions. Following PGx testing, there was an overall reduction in prescribed medications with a potential gene-drug interaction. Quality of life and symptom assessments of depression, anxiety, obsessive-compulsive disorder and body-focused repetitive behaviors revealed similar improvements in the PGx and TAU groups. Subanalysis comparing congruent (\u0026ldquo;use as directed\u0026rdquo;) or incongruent (\u0026ldquo;use with caution\u0026rdquo;), as well as analysis of only CYP2D6 and CYP2C19 gene-drug interactions, were observed to have a similar profile.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCombinatorial PGx testing was utilized as a clinical decision-making tool for medication selection and dosage adjustment. As a result, all treatment groups were able to achieve similar levels of polypharmacy, improvement in quality of life and symptom reduction.\u003c/p\u003e","manuscriptTitle":"Polypharmacy and pharmacogenomics in high-acuity behavioral health care for autism spectrum disorder: A retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-06 14:52:34","doi":"10.21203/rs.3.rs-5753717/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbe53ea2-4935-4998-ad61-01a302bcd1d7","owner":[],"postedDate":"January 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42301176,"name":"Psychiatry"}],"tags":[],"updatedAt":"2025-01-06T14:52:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-06 14:52:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5753717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5753717","identity":"rs-5753717","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}