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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.

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

ABSTRACT BACKGROUND Alcohol use disorder is one of the leading causes of disability worldwide. Despite the availability of efficacious treatments, few individuals with an alcohol use disorder are actively engaged in treatment. Available evidence suggests that primary care may play a crucial role in the identification of patients with an alcohol use disorder, delivery of interventions, and the success of treatment. OBJECTIVE The principal aims of this study were to test the effectiveness of a primary care-based Alcohol Care Management (ACM) program for alcohol use disorder and treatment engagement in veterans. DESIGN The design of the study was a 26-week single-blind randomized clinical trial. The study was conducted in the primary care practices at three VA medical centers. Participants were randomly assigned to treatment in ACM or standard treatment in a specialty outpatient addiction treatment program. PARTICIPANTS One hundred and sixty-three alcohol-dependent veterans were randomized. INTERVENTION ACM focused on the use of pharmacotherapy and psychosocial support. ACM was delivered in-person or by telephone within the primary care clinic. MAIN MEASUREMENTS Engagement in treatment and heavy alcohol consumption. KEY RESULTS The ACM condition had a significantly higher proportion of participants engaged in treatment over the 26 weeks [OR = 5.36, 95 % CI = (2.99, 9.59)]. The percentage of heavy drinking days were significantly lower in the ACM condition [OR = 2.16, 95 % CI = (1.27, 3.66)], while overall abstinence did not differ between groups. CONCLUSIONS Results demonstrate that treatment for an alcohol use disorder can be delivered effectively within primary care, leading to greater rates of engagement in treatment and greater reductions in heavy drinking.

Background Mental health care lags behind other forms of medical care in its reliance on subjective clinician assessment. Although routine use of standardized patient-reported outcome measures, measurement-based care (MBC), can improve patient outcomes and engagement, clinician efficiency, and, collaboration across care team members, full implementation of this complex practice change can be challenging. This study seeks to understand whether and how an intensive facilitation strategy can be effective in supporting the implementation of MBC. Implementation researchers partnering with US Department of Veterans Affairs (VA) leaders are conducting the study within the context of a national initiative to support MBC implementation throughout VA mental health services. This study will focus specifically on VA Primary Care-Mental Health Integration (PCMHI) programs. Methods A mixed-methods, multiple case study design will include 12 PCMHI sites recruited from the 23 PCMHI programs that volunteered to participate in the VA national initiative. Guided by a study partnership panel, sites are clustered into similar groups using administrative metrics. Site pairs are recruited from within these groups. Within pairs, sites are randomized to the implementation facilitation strategy (external facilitation plus QI team) or standard VA national support. The implementation strategy provides an external facilitator and MBC experts who work with intervention sites to form a QI team, develop an implementation plan, and, identify and overcome barriers to implementation. The RE-AIM framework guides the evaluation of the implementation facilitation strategy which will utilize data from administrative, medical record, and primary qualitative and quantitative sources. Guided by the iPARIHS framework and using a mixed methods approach, we will also examine factors associated with implementation success. Finally, we will explore whether implementation of MBC increases primary care team communication and function related to the care of mental health conditions. Discussion MBC has significant potential to improve mental health care but it represents a major change in practice. Understanding factors that can support MBC implementation is essential to attaining its potential benefits and spreading these benefits across the health care system.

Am J Manag Care. 2023;29(10):499-502. https://doi.org/10.37765/ajmc.2023.89438 _____ Takeaway Points Modest spending on the collaborative care model is a worthwhile investment. * Modest spending on collaborative care services to address the behavioral health needs of patients did not increase overall health care costs. * The new collaborative care billing codes provide greater financial sustainability for the model. * Coverage of the billing codes affords improved access to mental health care without increasing overall spending, which should reassure insurers. * Investment in collaborative care is important because it allows psychiatric expertise to reach more patients than specialty mental health settings can accommodate and may be more convenient and less stigmatizing for patients. _____ The collaborative care model (CoCM) integrates mental health care into primary care to effectively treat common mental health conditions.1 Multiple randomized controlled trials have demonstrated the efficacy of CoCM, most notably for depression and anxiety.1-3 CoCM is delivered by a team comprising a primary care provider (PCP), a behavioral health care manager (BHCM), and a psychiatric consultant. Members were matched on demographics, Census variables, selected chronic conditions, total medical costs for each month leading up to the intervention, risk scores, inpatient utilization, insurance product type (ie, health maintenance organization or preferred provider organization), insurance contract type (ie, fully insured or self-funded plan), and whether they had any behavioral health carve-out claims. The difference-in-differences model was specified as a 2 × 2 structure, with the group's period costs averaged to the per-member per-month (PMPM) level. Treated members realized a nonsignificant reduction of $91.34 PMPM (95% CI, –$319.32 to $136.63) in inpatient costs (Table 2). Because we observed an increase in cost for the control group in the last month of the study period (month 11), we removed month 11, reestimated the model, and obtained a new point estimate of $39.02.

Implementation of pharmacogenetic testing to guide drug prescribing has potential to improve drug response and prevent adverse events. Robust data exist for more than 30 gene-drug pairs linking genotype to drug response phenotypes; however, it is unclear which pharmacogenetic tests, if implemented, would provide the greatest utility for a given patient population. To project the proportion of veterans in the US Veterans Health Administration (VHA) with actionable pharmacogenetic variants and evaluate how testing might be associated with prescribing decisions. This cross-sectional study included veterans who used national VHA pharmacy services from October 1, 2011, to September 30, 2017. Data analyses began April 26, 2018, and were completed February 6, 2019. Receipt of level A drugs based on VHA pharmacy dispensing records. Projected prevalence of actionable pharmacogenetic variants among VHA pharmacy users based on variant frequencies from the 1000 Genomes Project and veteran demographic characteristics; incident number of level A prescriptions, and proportion of new level A drug recipients projected to carry an actionable pharmacogenetic variant. During the study, 7 769 359 veterans (mean [SD] age, 58.1 [17.8] years; 7 021 504 [90.4%] men) used VHA pharmacy services. It was projected that 99% of VHA pharmacy users would carry at least 1 actionable pharmacogenetic variant. Among VHA pharmacy users, 4 259 153 (54.8%) received at least 1 level A drug with 1 188 124 (15.3%) receiving 2 drugs, and 912 189 (11.7%) receiving 3 or more drugs. The most common incident prescriptions during the study were tramadol (923 671 new recipients), simvastatin (533 928 new recipients), citalopram (266 952 new recipients), and warfarin (205 177 new recipients). Gene-drug interactions projected to have substantial clinical impacts in the VHA population include the interaction of SLCO1B1 with simvastatin (1 988 956 veterans [25.6%]), CYP2D6 with tramadol (318 544 veterans [4.1%]), and CYP2C9 or VKORC1 with warfarin (7 163 349 veterans [92.2%]). Clinically important pharmacogenetic variants are highly prevalent in the VHA population. Almost all veterans would carry an actionable variant, and more than half of the population had been exposed to a drug affected by these variants. These results suggest that pharmacogenetic testing has the potential to affect pharmacotherapy decisions for commonly prescribed outpatient medications for many veterans.

BackgroundPhysician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes.ObjectiveTo examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment.DesignPatient self-report surveys at treatment initiation linked to medical records.SettingVeterans Health Administration (VA) clinics nationally, 2018–2020.ParticipantsA total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White)Main MeasuresPatient 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 ResultsMore 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.ConclusionsMismatches 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.

In patients with major depressive disorder (MDD), achieving remission and/or response may take many months because of the lengthy trial-and-error process often needed to identify effective medication. Pharmacogenomic testing is a prescribing tool that has been shown to improve remission and response rates for MDD patients, but data describing its impact over time is limited. The objective of this study was to determine whether pharmacogenomic-guided treatment increases the rate of remission and response over time in patients with MDD, and if so, to assess the persistence of that effect. This study was a prespecified analysis of the PRIME Care (Precision Medicine in Mental Healthcare) randomized clinical trial, a pragmatic trial that compared pharmacogenomic-guided treatment with usual care among veterans with depression. Participants were recruited at 22 Department of Veterans Affairs medical centers by 676 clinicians and were randomized to the pharmacogenomic-guided arm or the usual care arm. Multivariate Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations between study arm (pharmacogenomic-guided treatment or usual care) and the first instance of response or remission as assessed by the Patient Health Questionnaire-9 (PHQ-9) scale. 1,764 (90.7%) of the 1,944 veterans who participated in the PRIME Care trial had sufficient visit data to be included in this analysis. Patients who received pharmacogenomic-guided treatment had higher rates of remission (HR [95% CI] = 1.27 [1.05, 1.53]; p = 0.015) and response (HR [95% CI] = 1.21 [1.05, 1.40]; p = 0.010) at any time relative to patients receiving usual care. Schoenfeld residuals tests were not statistically significant for remission (p = 0.931) or response (p = 0.112), providing no evidence that the benefit due to pharmacogenomic-guided treatment changed over the 24-week period. Pharmacogenomic-guided treatment led to faster initial remission and response in patients with MDD, and this benefit persisted over 6 months with no evidence of changing over time.

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). A 2018-2020 national sample of = 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. 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. 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.