Genome-wide association study identifies four pan-ancestry loci for suicidal ideation in the Million Veteran Program.

Suicidal ideation (SI) often precedes and predicts suicide attempt and death, is the most common suicidal phenotype and is over-represented in veterans. The genetic architecture of SI in the absence of suicide attempt (SA) is unknown, yet believed to have distinct and overlapping risk with other suicidal behaviors. We performed the first GWAS of SI without SA in the Million Veteran Program (MVP), identifying 99,814 SI cases from electronic health records without a history of SA or suicide death (SD) and 512,567 controls without SI, SA or SD. GWAS was performed separately in the four largest ancestry groups, controlling for sex, age and genetic substructure. Ancestry-specific results were combined via meta-analysis to identify pan-ancestry loci. Four genome-wide significant (GWS) loci were identified in the pan-ancestry meta-analysis with loci on chromosomes 6 and 9 associated with suicide attempt in an independent sample. Pan-ancestry gene-based analysis identified GWS associations with DRD2, DCC, FBXL19, BCL7C, CTF1, ANNK1, and EXD3. Gene-set analysis implicated synaptic and startle response pathways (q's<0.05). European ancestry (EA) analysis identified GWS loci on chromosomes 6 and 9, as well as GWS gene associations in EXD3, DRD2, and DCC. No other ancestry-specific GWS results were identified, underscoring the need to increase representation of diverse individuals. The genetic correlation of SI and SA within MVP was high (rG = 0.87; p = 1.09e-50), as well as with post-traumatic stress disorder (PTSD; rG = 0.78; p = 1.98e-95) and major depressive disorder (MDD; rG = 0.78; p = 8.33e-83). Conditional analysis on PTSD and MDD attenuated most pan-ancestry and EA GWS signals for SI without SA to nominal significance, with the exception of EXD3 which remained GWS. Our novel findings support a polygenic and complex architecture for SI without SA which is largely shared with SA and overlaps with psychiatric conditions frequently comorbid with suicidal behaviors.

Longitudinal Influence of Prescribed Antidepressants on Fecal and Oral Microbiomes Among Veterans With Major Depressive Disorder.

OBJECTIVE: The purpose of this study was to evaluate the influence of a new course of antidepressant monotherapy on gut and oral microbiomes and the relationship to depressive symptoms. METHODS: Longitudinal microbiome samples obtained from 10 U.S. veterans were analyzed. Baseline samples were taken before a new course of antidepressant monotherapy (either switching from a previous treatment or starting a new treatment). Targeted genomic sequencing of the microbiome samples was used to analyze changes in taxonomy and diversity across participants, medications, and medication class. Associations between these changes and Patient Health Questionnaire-9 (PHQ-9) scores were analyzed. RESULTS: Taxonomic variability was observed across participants, with the individual being the main microbial community driver. In terms of the fecal microbiome, antidepressants were associated with shifts toward Bacteroides being less abundant and Blautia, Pseudomonas, or Faecalibacterium being more abundant. Likewise, the composition of the oral microbiome was variable, with individual participants being the primary drivers of community composition. In the oral samples, the relative abundance of Haemophilus decreased after antidepressants were started. Increases in Blautia and decreases in Bacteroides were associated with lower PHQ-9 scores. CONCLUSIONS: Antidepressants were found to influence fecal and oral microbiomes such that a new course of antidepressant monotherapy was associated with taxonomic alterations toward healthier states in both fecal and oral microbiomes, which were associated with decreases in depressive symptoms. Additional longitudinal research is required to increase understanding of microbiomes and symptom-based changes, with a particular focus on potential differences between medication classes and underlying mechanisms.

The impact of substance use on posttraumatic stress disorder symptoms and treatment discontinuation.

This study examined the impact of ongoing substance use during posttraumatic stress disorder (PTSD) and substance use disorder (SUD) treatment on PTSD symptoms and treatment discontinuation. The study represents a secondary analysis of U.S. military veterans (N = 183) who participated in a randomized clinical trial for the treatment of both PTSD and SUD. Veterans mostly identified as Black (53.8%) or White (41.9%) and male (92.4%). Substance use, PTSD symptoms, and treatment discontinuation were measured at 4-week intervals throughout treatment. Predictors were the percentage of days with alcohol, cannabis, and other substance use (primarily cocaine and opioids) and the average number of alcoholic drinks per drinking day. Outcomes were PTSD symptoms and treatment discontinuation at concurrent and prospective assessments. Multilevel models accounted for the nested structure of the longitudinal data. Alcohol, cannabis, and other substance use did not predict PTSD symptoms or treatment discontinuation prospectively. Concurrently, we observed that as a participant's percentage of drinking days increased by 34.7% (i.e., 1 standard deviation), PTSD symptoms during the same period were 0.07 standard deviations higher (i.e., 1 point on the PCL), B = 0.03, p = .033. No other substances were related to PTSD symptoms concurrently. The findings demonstrate that PTSD symptoms improved regardless of substance use during exposure-based PTSD and SUD treatment, and treatment discontinuation was not associated with substance use. This study suggests that substance use during treatment cannot directly explain the poorer treatment outcomes observed in the literature on comorbid PTSD/SUD compared to PTSD-only populations.

Clusters Based on Within-Treatment Symptom Trajectories as Predictors of Dropout in Treatment for Posttraumatic Stress Disorder and Substance Use Disorder.

Objective: Dropout rates are high in treatments for co-occurring posttraumatic stress disorder (PTSD) and substance use disorders (SUDs). We examined dropout predictors in PTSD-SUD treatment. Methods: Participants were 183 veterans receiving integrated or phased motivational enhancement therapy and prolonged exposure. Using survival models, we examined demographics and symptom trajectories as dropout predictors. Using latent trajectory analysis, we incorporated clusters based on symptom trajectories to improve dropout prediction. Results: Hispanic ethnicity (integrated arm), Black or African American race (phased arm), and younger age (phased arm) predicted dropout. Clusters based on PTSD and substance use trajectories improved dropout prediction. In integrated treatment, participants with consistently-high use and low-and-improving use had the highest dropout. In phased treatment, participants with the highest and lowest PTSD symptoms had lower dropout; participants with the lowest substance use had higher dropout. Conclusions: Identifying within-treatment symptom trajectories associated with dropout can help clinicians intervene to maximize outcomes. ClinicalTrials.gov Identifier: NCT01211106.

Development of a model to predict psychotherapy response for depression among Veterans.

BACKGROUND: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Development of a model to predict antidepressant treatment response for depression among Veterans.

BACKGROUND: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

A genome-wide association study of suicide attempts in the million veterans program identifies evidence of pan-ancestry and ancestry-specific risk loci.

To identify pan-ancestry and ancestry-specific loci associated with attempting suicide among veterans, we conducted a genome-wide association study (GWAS) of suicide attempts within a large, multi-ancestry cohort of U.S. veterans enrolled in the Million Veterans Program (MVP). Cases were defined as veterans with a documented history of suicide attempts in the electronic health record (EHR; N = 14,089) and controls were defined as veterans with no documented history of suicidal thoughts or behaviors in the EHR (N = 395,064). GWAS was performed separately in each ancestry group, controlling for sex, age and genetic substructure. Pan-ancestry risk loci were identified through meta-analysis and included two genome-wide significant loci on chromosomes 20 (p = 3.64 × 10-9) and 1 (p = 3.69 × 10-8). A strong pan-ancestry signal at the Dopamine Receptor D2 locus (p = 1.77 × 10-7) was also identified and subsequently replicated in a large, independent international civilian cohort (p = 7.97 × 10-4). Additionally, ancestry-specific genome-wide significant loci were also detected in African-Americans, European-Americans, Asian-Americans, and Hispanic-Americans. Pathway analyses suggested over-representation of many biological pathways with high clinical significance, including oxytocin signaling, glutamatergic synapse, cortisol synthesis and secretion, dopaminergic synapse, and circadian rhythm. These findings confirm that the genetic architecture underlying suicide attempt risk is complex and includes both pan-ancestry and ancestry-specific risk loci. Moreover, pathway analyses suggested many commonly impacted biological pathways that could inform development of improved therapeutics for suicide prevention.

A Collaborative-Care Telephone-Based Intervention for Depression, Anxiety, and at-Risk Drinking in Primary Care: The PARTNERs Randomized Clinical Trial.

BACKGROUND: Collaborative care (CC) could improve outcomes in primary care patients with common mental conditions. We assessed the effectiveness of a transdiagnostic model of telephone-based CC (tCC) delivered by lay providers to primary care patients with depression, anxiety, or at-risk drinking. METHODS: PARTNERS was a pragmatic trial in 502 primary care adults presenting with depressive symptoms, anxiety symptoms, or at-risk drinking randomized to (1) usual care by primary care providers (PCPs) enhanced with the results of computer-assisted telephone-based assessments (at baseline and 4, 8, and 12 months later) (enhanced usual care [eUC]) or (2) tCC consisting of eUC plus frequent telephone coaching and psychoeducation provided by mental health technicians who also communicated to the PCP recommendations from a psychiatrist for evidence-based pharmacotherapy, psychotherapy, or, when indicated, referrals to mental health services. The primary analysis compared the change on the 9-item Patient Health Questionnaire (PHQ-9) in participants presenting with depression (n = 366) randomized to tCC versus eUC. Secondary analyses compared changes on the Generalized Anxiety Disorder-7 scale (GAD-7) in those presenting with anxiety (n = 298); or change in the number of weekly drinks in those presenting with at-risk drinking (n = 176). RESULTS: There were no treatment or time×treatment effects between tCC and eUC on PHQ-9 scores for patients with depression during the 12-month follow-up. However, there was a treatment effect (tCC > eUC) on GAD-7 scores in those with anxiety and a time×treatment interaction effect on the number of weekly drinks (tCC > eUC) in those with at-risk drinking. CONCLUSION: Implementing transdiagnostic tCC for common mental disorders using lay providers appears feasible in Canadian primary care. While tCC was not better than eUC for depression, there were some benefits for those with anxiety or at-risk drinking. Future studies will need to confirm whether tCC differentially benefits patients with depression, anxiety, or at-risk drinking.

Addressing the Increasing Mental Health Distress and Mental Illness Among Young Adults in the United States.

ABSTRACT: Recent surveys show rising numbers of young people who report anxiety and depression. Although much attention has focused on mental health of adolescent youth, less attention has been paid to young people as they transition into adulthood. Multiple factors may have contributed to this steady increase: greater exposure to social media, information, and distressing news via personal electronic devices; increased concerns regarding social determinants of health and climate change; and changing social norms due to increased mental health literacy and reduced stigma. The COVID-19 pandemic may have temporarily exacerbated symptoms and impacted treatment availability. Strategies to mitigate causal factors for depression and anxiety in young adults may include education and skills training for cognitive, behavioral, and social coping strategies, as well as healthier use of technology and social media. Policies must support the availability of health insurance and treatment, and clinicians can adapt interventions to encompass the specific concerns and needs of young adults.

The Management of Major Depressive Disorder: Synopsis of the 2022 U.S. Department of Veterans Affairs and U.S. Department of Defense Clinical Practice Guideline.

DESCRIPTION: In February 2022, the U.S. Department of Veterans Affairs (VA) and U.S. Department of Defense (DoD) approved a joint clinical practice guideline (CPG) for the management of major depressive disorder (MDD). This synopsis summarizes key recommendations. METHODS: Senior leaders within the VA and the DoD assembled a team to update the 2016 CPG for the management of MDD that included clinical stakeholders and conformed to the National Academy of Medicine's tenets for trustworthy CPGs. The guideline panel developed key questions, systematically searched and evaluated the literature, created two 1-page algorithms, and distilled 36 recommendations for care using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system. Select recommendations that were identified by the authors to represent key changes from the prior CPG are presented in this synopsis. RECOMMENDATIONS: The scope of the CPG is diverse; however, this synopsis focuses on key recommendations that the authors identified as important new evidence and changes to prior recommendations on pharmacologic management, pharmacogenomics, psychotherapy, complementary and alternative therapies, and the use of telemedicine.

Are Veterans Getting Their Preferred Depression Treatment? A National Observational Study in the Veterans Health Administration.

BACKGROUND: Physician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes. OBJECTIVE: To examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment. DESIGN: Patient self-report surveys at treatment initiation linked to medical records. SETTING: Veterans Health Administration (VA) clinics nationally, 2018-2020. PARTICIPANTS: A total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White) MAIN MEASURES: Patient self-reported preferences for medication and psychotherapy on 0-10 self-anchoring visual analog scales (0="completely unwilling"; 10="completely willing"). Treatment receipt and adherence (refilling medications; attending 3+ psychotherapy sessions) over 3 months. Logistic regression models controlled for socio-demographics and geographic variables. KEY RESULTS: More patients reported strong preferences (10/10) for psychotherapy than medication (51.2% versus 36.7%, McNemar χ21=175.3, p<0.001). A total of 32.1% of patients who preferred (7-10/10) medication and 21.8% who preferred psychotherapy did not receive these treatments. Patients who strongly preferred medication were substantially more likely to receive medication than those who had strong negative preferences (odds ratios [OR]=17.5; 95% confidence interval [CI]=12.5-24.5). Compared with patients who had strong negative psychotherapy preferences, those with strong psychotherapy preferences were about twice as likely to receive psychotherapy (OR=1.9; 95% CI=1.0-3.5). Patients who strongly preferred psychotherapy were more likely to adhere to psychotherapy than those with strong negative preferences (OR=3.3; 95% CI=1.4-7.4). Treatment preferences were not associated with medication or combined treatment adherence. Patients in primary care settings had lower odds of receiving (but not adhering to) psychotherapy than patients in specialty mental health settings. Depression severity was not associated with treatment receipt or adherence. CONCLUSIONS: Mismatches between treatment preferences and treatment type received were common and associated with worse treatment adherence for psychotherapy. Future research could examine ways to decrease mismatch between patient preferences and treatments received and potential effects on patient outcomes.

Continuous-time probabilistic models for longitudinal electronic health records.

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. We present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time. This method works with arbitrary sampling patterns and captures the joint probability distribution between variable measurements and the time intervals between them. Inference algorithms are derived that can be used to evaluate the likelihood of future using under a trained model. As an example, we consider data from the United States Veterans Health Administration (VHA) in the areas of diabetes and depression. Likelihood ratio maps are produced showing the likelihood of risk for moderate-severe vs minimal depression as measured by the Patient Health Questionnaire-9 (PHQ-9).

Structural Racism and Psychiatric Practice: A Call for Sustained Change.

ABSTRACT: Structural racism has received renewed focus over the past year, fueled by the convergence of major political and social events. Psychiatry as a field has been forced to confront a legacy of systemic inequities. Here, we use examples from our clinical and supervisory work to highlight the urgent need to integrate techniques addressing racial identity and racism into psychiatric practice and teaching. This urgency is underlined by extensive evidence of psychiatry's long-standing systemic inequities. We argue that our field suffers not from a lack of available techniques, but rather a lack of sustained commitment to understand and integrate those techniques into our work; indeed, there are multiple published examples of strategies to address racism and racial identity in psychiatric clinical practice. We conclude with recommendations geared toward more firmly institutionalizing a focus on racism and racial identity in psychiatry, and suggest applications of existing techniques to our initial clinical examples.

Effect of Pharmacogenomic Testing for Drug-Gene Interactions on Medication Selection and Remission of Symptoms in Major Depressive Disorder: The PRIME Care Randomized Clinical Trial.

Importance: Selecting effective antidepressants for the treatment of major depressive disorder (MDD) is an imprecise practice, with remission rates of about 30% at the initial treatment. Objective: To determine whether pharmacogenomic testing affects antidepressant medication selection and whether such testing leads to better clinical outcomes. Design, Setting, and Participants: A pragmatic, randomized clinical trial that compared treatment guided by pharmacogenomic testing vs usual care. Participants included 676 clinicians and 1944 patients. Participants were enrolled from 22 Department of Veterans Affairs medical centers from July 2017 through February 2021, with follow-up ending November 2021. Eligible patients were those with MDD who were initiating or switching treatment with a single antidepressant. Exclusion criteria included an active substance use disorder, mania, psychosis, or concurrent treatment with a specified list of medications. Interventions: Results from a commercial pharmacogenomic test were given to clinicians in the pharmacogenomic-guided group (n = 966). The comparison group received usual care and access to pharmacogenomic results after 24 weeks (n = 978). Main Outcomes and Measures: The co-primary outcomes were the proportion of prescriptions with a predicted drug-gene interaction written in the 30 days after randomization and remission of depressive symptoms as measured by the Patient Health Questionnaire-9 (PHQ-9) (remission was defined as PHQ-9 ≤ 5). Remission was analyzed as a repeated measure across 24 weeks by blinded raters. Results: Among 1944 patients who were randomized (mean age, 48 years; 491 women [25%]), 1541 (79%) completed the 24-week assessment. The estimated risks for receiving an antidepressant with none, moderate, and substantial drug-gene interactions for the pharmacogenomic-guided group were 59.3%, 30.0%, and 10.7% compared with 25.7%, 54.6%, and 19.7% in the usual care group. The pharmacogenomic-guided group was more likely to receive a medication with a lower potential drug-gene interaction for no drug-gene vs moderate/substantial interaction (odds ratio [OR], 4.32 [95% CI, 3.47 to 5.39]; P < .001) and no/moderate vs substantial interaction (OR, 2.08 [95% CI, 1.52 to 2.84]; P = .005) (P < .001 for overall comparison). Remission rates over 24 weeks were higher among patients whose care was guided by pharmacogenomic testing than those in usual care (OR, 1.28 [95% CI, 1.05 to 1.57]; P = .02; risk difference, 2.8% [95% CI, 0.6% to 5.1%]) but were not significantly higher at week 24 when 130 patients in the pharmacogenomic-guided group and 126 patients in the usual care group were in remission (estimated risk difference, 1.5% [95% CI, -2.4% to 5.3%]; P = .45). Conclusions and Relevance: Among patients with MDD, provision of pharmacogenomic testing for drug-gene interactions reduced prescription of medications with predicted drug-gene interactions compared with usual care. Provision of test results had small nonpersistent effects on symptom remission. Trial Registration: ClinicalTrials.gov Identifier: NCT03170362.

Addressing Common Challenges in the Implementation of Collaborative Care for Mental Health: The Penn Integrated Care Program.

PURPOSE: We developed and implemented a new model of collaborative care that includes a triage and referral management system. We present initial implementation metrics using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework. METHODS: Primary care clinicians in 8 practices referred patients with any unmet mental health needs to the Penn Integrated Care program. Assessments were conducted using validated measures. Patients were primarily triaged to collaborative care (26%) or specialty mental health care with active referral management (70%). We conducted 50 qualitative interviews to understand the implementation process and inform program refinement. Our primary outcomes were reach and implementation metrics, including referral and encounter rates derived from the electronic health record. RESULTS: In 12 months, 6,124 unique patients were referred. Assessed patients reported symptoms consistent with a range of conditions from mild to moderate depression and anxiety to serious mental illnesses including psychosis and acute suicidal ideation. Among patients enrolled in collaborative care, treatment entailed a mean of 7.2 (SD 5.1) encounters over 78.1 (SD 51.3) days. Remission of symptoms was achieved by 32.6% of patients with depression and 39.5% of patients with anxiety. Stakeholders viewed the program favorably and had concrete suggestions to ensure sustainability. CONCLUSIONS: The Penn Integrated Care program demonstrated broad reach. Implementation was consistent with collaborative care as delivered in seminal studies of the model. Our results provide insight into a model for launching and implementing collaborative care to meet the needs of a diverse group of patients with the full range of mental health conditions seen in primary care.

Psychiatry in the Time of COVID: Credibility, Uncertainty, and Self-Reflection.

ABSTRACT: Public trust in the credibility of medicine and physicians has been severely tested amid the COVID-19 pandemic and growing sociopolitical fissures in the United States. Physicians are being asked to be ambassadors to the public of scientific information. Psychiatrists have an opportunity to help the public understand and accept a "new normal" during a time of such uncertainty. Using a case example, we review the impact of uncertainty and fear on scientific and medical credibility. Although the pandemic provides an opportunity for systemic change, the consequences of any change remain unknown. To help patients navigate the uncertainty, we conclude by offering four guidelines to clinicians: the public has little interest in understanding the scientific method; we need to acknowledge that we do not have all the answers; credibility and trustworthiness are linked to our ability to be trusted, believable messengers; and we can retain scientific credibility while acknowledging uncertainty.

Telehealth and the Community SMI Population: Reflections on the Disrupter Experience of COVID-19.

The novel coronavirus pandemic and the resulting expanded use of telemedicine have temporarily transformed community-based care for individuals with serious mental illness (SMI), challenging traditional treatment paradigms. We review the rapid regulatory and practice shifts that facilitated broad use of telemedicine, the literature on the use of telehealth and telemedicine for individuals with SMI supporting the feasibility/acceptability of mobile interventions, and the more limited evidence-based telemedicine practices for this population. We provide anecdotal reflections on the opportunities and challenges for telemedicine drawn from our daily experiences providing services and overseeing systems for this population during the pandemic. We conclude by proposing that a continued, more prominent role for telemedicine in the care of individuals with SMI be sustained in the post-coronavirus landscape, offering future directions for policy, technical assistance, training, and research to bring about this change.

Feasibility and Acceptability of the HOME Program for Veterans Recently Discharged from a Psychiatric Hospitalization.

Enrollment in the Home-Based Mental Health Evaluation (HOME) Program is associated with higher rates of treatment engagement following psychiatric hospitalization discharge, as compared to enhanced care as usual. We aim to describe feasibility and acceptability data related to implementation of the HOME Program at two Department of Veterans Affairs Medical Centers (VAMCs) to inform future program refinement and implementation. Process evaluation data regarding feasibility and acceptability were collected in the context of an interventional multi-site trial. Data regarding enrollment in the study and the intervention were collected by study staff. Additional acceptability and feasibility data were obtained via the Client Satisfaction Questionnaire-8 (CSQ-8) and Narrative Evaluation of Intervention Interview (NEII). Between 82 and 91% of enrolled Veterans participated in at least one post-discharge telephone contact. Site differences existed with respect to completion of home visits. CSQ-8 results suggested high levels of satisfaction with the HOME Program. Themes identified via the NEII reflect that as a result of participation in the HOME Program, Veterans felt hopeful and cared for and learned how to keep themselves safe following hospital discharge. Process evaluation data from a clinical trial of the HOME Program demonstrated that the intervention was feasible to implement at two VAMCs and was acceptable to participants. These data inform considerations for future research and implementation efforts.Trial Registration ClinicalTrials.gov Identifier: NCT03347552.

Primary care and mental health providers' perceptions of implementation of pharmacogenetics testing for depression prescribing.

BACKGROUND: Pharmacogenetic testing (PGx) has the potential to improve the quality of psychiatric prescribing by considering patients' genetic profile. However, there is limited scientific evidence supporting its efficacy or guiding its implementation. The Precision Medicine in Mental Health (PRIME) Care study is a pragmatic randomized controlled trial evaluating the effectiveness of a specific commercially-available pharmacogenetic (PGx) test to inform antidepressant prescribing at 22 sites across the U.S. Simultaneous implementation science methods using the Consolidated Framework for Implementation Research (CFIR) are integrated throughout the trial to identify contextual factors likely to be important in future implementation of PGx. The goal of this study was to understand providers' perceptions of PGx for antidepressant prescribing and implications for future implementation. METHODS: Qualitative focus groups (n = 10) were conducted at the beginning of the trial with Primary Care and Mental Health providers (n = 31) from six PRIME Care sites. Focus groups were audio-recorded and transcribed and data were analyzed using rapid analytic procedures organized by CFIR domains. RESULTS: Analysis revealed themes in the CFIR Intervention Characteristics domain constructs of Evidence, Relative Advantage, Adaptability, Trialability, Complexity, and Design that are important for understanding providers' perceptions of PGx testing. Results indicate: 1) providers had limited experience and knowledge of PGx testing and its evidence base, particularly for psychiatric medications; 2) providers were hopeful that PGx could increase their precision in depression prescribing and improve patient engagement, but were uncertain about how results would influence treatment; 3) providers were concerned about potential misinterpretation of PGx results and how to incorporate testing into their workflow; 4) primary care providers were less familiar and comfortable with application of PGx testing to antidepressant prescribing than psychiatric providers. CONCLUSIONS: Provider perceptions may serve as facilitators or barriers to implementation of PGx for psychiatric prescribing. Incorporating implementation science into the conduct of the RCT adds value by uncovering factors to be addressed in preparing for future implementation, should the practice prove effective. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT03170362 ; Registered 31 May 2017.

Cognitive Behavioral Therapy for Insomnia in Alcohol-Dependent Veterans: A Randomized, Controlled Pilot Study.

BACKGROUND: Insomnia is highly prevalent in individuals recovering from alcohol dependence (AD) and increases their risk of relapse. Two studies evaluating cognitive behavior therapy for insomnia (CBT-I) have demonstrated its efficacy in non-Veterans recovering from AD. The aim of this study was to extend these findings in an 8-week trial of CBT-I in Veterans. METHODS: Veterans recovering from AD were randomly assigned to 8 weeks of treatment with CBT-I (N = 11) or a Monitor-Only (MO; N = 11) condition and were evaluated 3 (N = 21/22) and 6 months posttreatment (N = 18/22). The primary outcome measure was the Insomnia Severity Index (ISI) score. Secondary outcome measures were sleep diary measures, percent days abstinent (PDA), and scores on the Dysfunctional Beliefs and Attitudes About Sleep Scale (DBAS), Sleep Hygiene Index (SHI), Penn Alcohol Craving Scale (PACS), Quick Inventory of Depressive Symptoms (QIDS), State-Trait Anxiety Inventory-Trait (STAI-T) scale, and Short Form 12-item (SF-12). Mixed-effects regression models, adjusted for race, evaluated differences in outcomes between the groups over a 6-month period (clinicaltrials.gov identifier = NCT01603381). RESULTS: Subjects were male, aged 54.5 (SD = 6.9) years, and had 26.4 (SD = 26.3) days of abstinence before their baseline evaluation. CBT-I produced a significantly greater improvement in model-based estimates than MO (mean change at 6 months compared to their baseline) for ISI, sleep latency from a daily sleep diary, DBAS mean score, and SHI total score. PDA and QIDS improved over time, but there was no difference between the groups. PACS, STAI-T, or SF-12 scale did not show any improvement from their baseline scores. CONCLUSIONS: CBT-I treatment demonstrated substantial efficacy in reducing insomnia, associated negative cognitions, and improving sleep hygiene in Veterans during early recovery, though it did not reduce drinking behavior.