JAMA Psychiatry

This study examined arrays offered by commercial pharmacogenomic (PGx) testing services for mental health care in Australia and the United States, with a focus on utility for non-European populations. Seven of the 14 testing services we identified provided the manifests of their arrays. We examined allele frequencies for each variant using data from the Allele Frequency Aggregator1 (ALFA), genome Aggregation Database2 (gnomAD), Exome Aggregation Consortium2 (ExAC), and Japanese Multi Omics Reference Panel3, and examined genetic heterogeneity. We also analyzed meta-data from the Pharmacogenomic Knowledge Base4 (PharmGKB) and explored the biogeographical origin of supporting evidence for clinical annotations. Most arrays included the minimum allele set recommended by Bousman et al5. However, few arrays included HLA-A or HLA-B. The most diverse allele frequencies were seen for variants in CYP3A5, ADRA2A and GNB3, with European and African populations showing the largest differences. Most evidence listed in PharmGKB originated from European or unknown ancestry samples.

Drug addiction is accompanied by enhanced salience attributed to drug over nondrug cues. This objectively measured bias is reliable yet underutilized in informing clinical endpoints, as clinical trials largely employ subjective (i.e., self-report or interview-based drug use and craving) or simple categorical (e.g., drug in urine) measures, with limited success. Having previously demonstrated their utility in cocaine addiction, we investigated whether behavioral picture choice (a lab-simulated drug seeking measure) and verbal fluency similarly reveal drug bias in 59 abstinent, inpatient individuals with opioid use disorder (iOUD) compared to 29 healthy controls (HC). Using a hierarchical regression, and compared to subjective measures, we then tested whether these objective markers can better inform prospective treatment completion--a clinically relevant and measurable outcome. As expected, results showed that the iOUD exhibited higher simulated drug seeking (ps<0.036) and drug fluency (p=0.008) compared to the HC. Importantly, after dimensionality reduction, while the self-reported years of regular opioid use and cue-induced craving showed null results (|{beta}|<0.47, p>0.290), and controlling for demographics, drug choice was associated with treatment completion {beta} =-0.75, p=0.036), explaining greater variability in its likelihood compared to the subjective measures (model comparison:{Delta} R2=0.102, p=0.027). Extending drug-biased choice and fluency from cocaine to opioid addiction, results further indicate that these objective measures of drug bias outperform the commonly employed subjective drug use and craving in informing a clinical outcome; unlike drug urine tests, they show important variability in abstinent iOUD. Results implicate these cognitive-behavioral tasks as powerful markers of drug bias and predictors of treatment outcome.

ObjectiveDespite recommendations that young people at clinical high risk (CHR) for psychosis receive stepped treatment, few programs have published details of their clinical models or outcomes. This study describes the preliminary effectiveness of a risk calculator-informed stepped care model used at the Yale PRIME Clinic, a specialized outpatient clinic for young people at CHR. MethodsSeventy-one individuals (ages 12-25) at CHR enrolled in Yales PRIME Clinic during the first four years of the treatment program. Participants completed clinical assessments at six timepoints over two years of treatment within a care model informed by an empirically grounded psychosis risk calculator. Linear mixed-effect models were fit to examine changes in clinical symptoms over time, and sensitivity analyses evaluated differences in clinical trajectories between completers and non-completers. ResultsIndividuals engaged in treatment demonstrated significant and sustained improvements in positive, negative, general, disorganized, and depressive symptoms. Improvements in positive symptoms emerged by 6 months and continued to improve across most subsequent timepoints (6, 12, and 24 months). Pattern mixture analyses suggested that clinical trajectories did not significantly differ between completers and non-completers, though non-completers possessed more heterogeneous trajectories. ConclusionsA stepped care model informed by individualized risk calculator scores was feasible for delivery in a specialized outpatient setting, and was associated with broad symptom improvement for young people at CHR. Further controlled studies with blinded raters are needed to further confirm the efficacy of stepped care models and isolate the active components of treatment. HighlightsO_LIParticipants at clinical high risk for psychosis experienced significant reductions in attenuated psychotic symptoms and improvements in mood while enrolled in a risk-calculator-informed stepped care treatment model. C_LIO_LIParticipants who disengaged from treatment did not have significantly different clinical trajectories than those who remained in care. C_LIO_LIThe results suggest preliminary evidence for the feasibility of implementing a risk-calculator-informed stepped care model. C_LI

Background and hypothesesHospitalization is common during first-episode psychosis (FEP) and is linked to functional decline, stigma, and healthcare burden. Coordinated Specialty Care (CSC) programs aim to reduce hospitalization and improve outcomes through early, multidisciplinary intervention. This study examined hospitalization outcomes and predictors among participants in the Academic Community Early Psychosis Intervention Network (AC-EPINET), a multisite CSC hub in the United States. Study designParticipants with FEP (N = 701; mean age = 21.6 years, 64% male) were followed after CSC admission, with analyses restricted to the first 24 months. Primary outcomes included time to first hospitalization, number of hospitalizations, and length of stay (LOS). Kaplan-Meier survival and multivariable Cox regression examined predictors of time to first hospitalization, while negative binomial regression assessed hospitalization frequency and LOS. Study resultsHospitalization rates declined after CSC enrollment. Females had shorter time to first hospitalization (HR = 2.96, 95% CI [1.24-7.10]) and more frequent admissions (IRR = 1.38, 95% CI [1.06-1.79]) than males. Younger age also predicted earlier (HR = 0.80, 95% CI [0.67-0.95]) and more frequent hospitalizations (IRR = 0.70 per 5 years, 95% CI [0.58-0.84]). Prior hospitalization predicted more admissions (IRR = 4.83, p < .0001) and longer LOS (RR = 10.72, p < .0001). Black/African American participants had longer LOS than White participants (RR = 1.67, p = .01). ConclusionsWhile CSC reduces overall hospitalization risk, females, younger individuals, and those with prior admissions remain at elevated risk. These findings underscore the need for tailored strategies to mitigate disparities and optimize early psychosis care.

AimSchizophrenia is associated with subtle brain structural alterations but separating disease from medication effects is challenging. Antipsychotic dopamine D2 receptor (D2R) antagonism has been associated with striatal volume increases, but effects of partial D2R agonism by newer antipsychotics are largely unexplored. This study aimed to compare short-term brain changes associated with either D2R antagonism or partial D2R agonism using normative modeling. Secondarily, the study aimed to explore long-term effects following naturalistic treatment. MethodsPatients received 6 weeks of monotherapy with either amisulpride (D2R antagonist) (N=41) or aripiprazole (partial D2R agonist) (N=45). All patients underwent structural magnetic resonance imaging before and after 6 weeks of treatment. A subset was re-scanned at 6 months, 1 year, and 2 years of naturalistic treatment. A pre-trained normative model was applied to 186 subcortical and cortical regions. We used Wilcoxon signed-rank tests to identify longitudinal structural deviations. ResultsAmisulpride and aripiprazole were associated with striatal volume increases after six weeks. No cortical effects were observed with amisulpride. Thinning of the temporal lobe was observed with aripiprazole. After 6 months, and 1 and 2 years, the striatal changes abated but cortical thinning in the frontal lobes emerged. ConclusionsBoth partial D2R agonism and D2R antagonism appear linked to striatal volume increases, however changes appear transient. Conversely, frontal thinning occurs over time and appears less closely linked to antipsychotic treatment. Interpretating structural brain changes in patients with psychosis require consideration of short-term pharmacological effects as well as factors related to illness progression.

Opioid dependence (OD) is epidemic in the United States and it is associated with a variety of adverse health effects. Its estimated heritability is [~]50%, and recent genome-wide association studies have identified more than a dozen common risk variants. However, there are no published studies of rare OD risk variants. In this study, we analyzed whole-exome sequencing data from the Yale-Penn cohort, comprising 2,100 participants of European ancestry (EUR; 1,321 OD cases) and 1,790 of African ancestry (AFR; 864 cases). A novel low-frequency variant (rs746301110) in the RUVBL2 gene was identified in EUR (p=6.59x10-10). Suggestive associations (p<1x10-5) were observed in TMCO3 in EUR, in NEIL2 and CFAP44 in AFR, and in FAM210B in the cross-ancestry meta-analysis. Gene-based collapsing tests identified SLC22A10, TMCO3, FAM90A1, DHX58, CHRND, GLDN, PLAT, H1-4, COL3A1, GPHB5 and QPCTL as top genes (p<1x10-4) with most associations attributable to rare variants and driven by the burden of predicted loss-of-function and missense variants. This study begins to fill the gap in our understanding of the genetic architecture of OD, providing insights into the contribution of rare coding variants and potential targets for future functional studies and drug development.

BackgroundThe STAR*D trial is the most influential study of sequential antidepressant treatment strategies. However, major STAR*D publications deviated from the protocol-defined analytic plan. Prior re-analyses found lower cumulative remission rates than STAR*D publications reported, sustained remission rates of only 3.1 to 8.4% at 12 months, and high rates of treatment-emergent suicidal ideation (TESI) during medication-switch therapy. A similar reanalysis is warranted for STAR*Ds augmentation study in which citalopram was augmented with sustained-release bupropion or buspirone. MethodsWe reanalyzed STAR*Ds patient-level augmentation dataset with fidelity to the original protocol or relevant STAR*D publications where the protocol did not prespecify an analytic plan. ResultsThis reanalysis identified 124 patients (21.9% of enrolled subjects) who were inappropriately included in the original STAR*D analysis, including 54 who were in protocol-defined remission before starting augmentation therapy. Remission rates as defined in the protocol were lower than reported in the original publication for bupropion SR (25.0% vs 29.7%) and buspirone (25.8% vs. 30.1%). Using a secondary definition of remission, bupropion SRs rate was significantly lower than reported in original publications (29.2% vs. 39.0%). Sustained remission through 12 months was low (4.9-12.5%). TESI rates were significantly higher for buspirone (13.9%) than bupropion SR (3.6%) augmentation. ConclusionCompared with the original STAR*D publication, our reanalysis identified inflated remission rates, low sustained remission, and marked differences in TESI risk between augmentation strategies. These findings suggest that both treatments offer lower acute and sustained benefit than is widely understood, with buspirone associated with more TESI.

Changes in brain volume are a common finding in Magnetic Resonance Imaging (MRI) studies of people with psychosis and numerous longitudinal studies suggest that volume deficits progress with illness duration. However, a major unresolved question concerns whether these changes are driven by the underlying illness or represent iatrogenic effects of antipsychotic medication. Here, we report MRI findings from a triple-blind randomised placebo-controlled study where 62 antipsychotic-naive patients with first episode psychosis (FEP) received either an atypical antipsychotic or a placebo pill over a treatment period of 6 months. Both FEP groups received intensive psychosocial therapy. A healthy control group (n=27) was also recruited. Structural MRI scans were obtained at baseline, 3-months and 12- months. Our primary aim was to differentiate illness-related brain volume changes from medication-related changes within the first 3 months of treatment. We secondarily investigated long-term effects at the 12-month timepoint. From baseline to 3 months, we observed a significant group x time interaction in the pallidum (p < 0.05 FWE-corrected), such that patients receiving antipsychotic medication showed increased volume, patients on placebo showed decreased volume, and healthy controls showed no change. In patients, a greater increase in pallidal grey matter volume over 3 months was associated with a greater reduction in symptom severity. We additionally found preliminary evidence for illness- related volume reductions in prefrontal cortices at 12 months and medication-related volume reductions in cerebellum at both 3-months and 12-months. Our findings indicate that psychotic illness and antipsychotic exposure exert distinct and spatially distributed effects on brain volume. Our results align with prior work in suggesting that the therapeutic efficacy of antipsychotic medications may be primarily mediated through their effects on the basal ganglia.

Neurodevelopmental disorder-risk CNVs (NDD CNVs) are associated with complex neuropsychiatric phenotypes. These CNVs also confer risk for a host of medical outcomes in adults yet the long-term health consequences in the context of comorbid psychiatric illness have not been well documented. Twenty-four psychiatric inpatients with treatment-resistant psychosis were identified as carriers of NDD CNVs as part of a larger Pennsylvania State Hospital genomics study. Comprehensive life course phenotyping was performed through review of medical records, specialized neurobehavioral evaluation, and synthesis of data using the Human Phenotype Ontology. Phenotypes were examined across the cohort and within sets of individuals with CNVs in common. Retrospective phenotyping indicated comorbid medical manifestations across multiple organ systems. Cardiovascular disorders were present in 96% of cases and motor disorders in 92%. All cases had multiple organ system involvement, and most organ systems (12/17 systems) were affected in 50% or more of cases culminating in a high degree of individual-level comorbidity. Potentially novel health outcomes are described for individual CNV loci. Our descriptive case series supports a complex and multidimensional course of illness. Thorough reporting on the long-term implications of these variants is the first step toward advancing clinical care for these complex psychiatric patients carrying NDD CNVs.

BackgroundSuicidality, including suicidal ideation (SI), attempt (SA), and death (SD), represents complex and partially overlapping phenotypes. This complexity contributes to study population heterogeneity in suicidality research, impeding replication efforts and data consolidation by research consortia. The standardization of suicidality definitions would help but has been insufficiently addressed in existing literature. Here, the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) provides International Classification of Disease (ICD) definitions, a critical real-world data source, for SA and SI. MethodsThe PGC Suicide Workgroup used published definitions coupled with expert consensus to develop ICD lists to serve as suicidality phenotype definitions. One SI and two SA lists were produced and evaluated for performance against patient screening responses in two independent cohorts (N = 9,151 and 12,621) with differing ascertainment strategies. OutcomesICD list suicidality definitions were produced. Evaluation of generated ICD lists versus patient responses across two cohorts demonstrated varied sensitivity (15{middle dot}4% to 71{middle dot}1%), specificity (67{middle dot}6% to 96{middle dot}3%), and positive predictive values (0{middle dot}57-0{middle dot}92). SI ICD code performance also varied in sensitivity (29{middle dot}4%-86{middle dot}1%), specificity (64{middle dot}2% to 90{middle dot}6%), and positive predictive values (0{middle dot}67 to 0{middle dot}98). InterpretationGuidelines were developed to provide more consistent and comparable suicidality definitions. However, real-world application of ICD codes leads to a wide range of performance, dependent on cohort characteristics, that will need to be carefully considered in implementation. Future efforts would benefit from consistent training in use of ICD codes between sites to improve generalizability, and should include validation in diverse populations. FundingThis work was funded by NIMH R01MH132733 (Mullins), R01MH132733 (Ruderfer), R01MH123619 (Docherty), R01MH123489 (Coon), R01MH124839 (PGC4), R01MH118233 and MH117599 (Smoller), Brain and Behavior Research Foundation No. 31248 (Monson), the Huntsman Mental Health Institute, National Science Foundation Graduate Research Fellowship Program Grant #1842169, and by grant # I01BX005881 and #IK6BX006523 (Kimbrel) from the Department of Veterans Affairs.

IntroductionSchizophrenia-spectrum disorders are debilitating and contribute to a substantial economic burden. Clinicians have historically underutilized clozapine, an atypical antipsychotic traditionally reserved for use in treatment-resistant schizophrenia, due to the medications adverse effect profile and associated management requirements, concerns of poor treatment adherence, and poor training/exposure to the use. In addition to alleviating schizophrenia symptoms when multiple other medications have failed, clozapine has other unique benefits that compel its use such as its use being associated with reduced suicide ideation and action, aggression, substance use, and all-cause mortality. MethodsThis study aimed to characterize clozapine utilization by US Medicare patients from 2015-20. Additionally, we identified the states that prescribed significantly different amounts than the national average. ResultsWe observed a steady decrease in clozapine use adjusted for population (-18.0%) and spending (-24.9%) over time. For all years, there was significant geographic heterogeneity (average: nine-fold) in population-corrected clozapine use. Massachusetts (2015-20: 95.4, 82.7, 76.8, 72.2, 71.2, 63.7 prescriptions per thousand enrollees) and South Dakota (2015-20: 78.0, 77.4, 78.4, 75.6, 72.0, 71.6) were the only states that prescribed significantly more than average, and none prescribed significantly less. DiscussionClozapine use by US Medicare patients is low, decreasing, and concerning for underutilization--patterns likewise seen for the US Medicaid recipients. Further study of the reasons for the state variation is needed. Education interventions, training reform, and devices that ease required routine blood monitoring are all practical solutions to optimize clozapine use.

Antipsychotic efficacy in schizophrenia spectrum disorders (SSD) is commonly evaluated using static measures that fail to capture the dynamic evolution of symptoms and the unfolding impact of treatment over time. Network Intervention Analysis (NIA) is a novel approach that models pharmacological treatments as active nodes within longitudinal symptom networks, capturing both direct and indirect treatment effects. This study aimed to investigate how the receptor-binding profile of antipsychotics influence symptom trajectories over six weeks. NIA was used to characterise the evolving impact of treatment with muscarinic antagonists, serotonergic/dopaminergic antagonists, and adrenergic agents with low dopaminergic antagonism within dynamic symptom networks. We hypothesised that NIA would reveal distinct patterns of symptom change, reflecting the pharmacodynamic mechanisms specific to each drug class. Forty-seven patients with SSD underwent baseline assessments including neuropsychological tests and five symptom rating scales from the Manual for the Assessment and Documentation of Psychopathology in Psychiatry (AMDP). They were then followed weekly for six additional weeks evaluating them with the AMDP-based symptom ratings, providing a comprehensive, standardised, and fine-grained measure of psychopathological changes. Muscarinic antagonists initially targeted self-disorder, then shifted to delusions, reducing symptom interconnectivity and increasing network resilience. Serotonergic/dopaminergic antagonists primarily influenced hallucinations but showed a late stage rebound, with increased network density and reduced treatment influence. Adrenergic agents exhibited a stabilising effect, preserving network structure with minimal symptom reduction. These findings demonstrate the utility of NIA in capturing the temporal dynamics of antipsychotic effects based on receptor affinity, supporting the development of phase-specific, network-informed, and personalised interventions.

IntroductionPatients recently discharged from psychiatric hospitalization are at increased risk of intentional self-harm, including suicide. Using linked population-based registry data from Catalonia, Spain, we developed machine learning-based prediction models for post-discharge intentional self-harm across different follow-up horizons, sex, and age groups, and evaluated their generalizability and robustness with multiple validation strategies. MethodsRetrospective cohort study including 41,827 individuals accounting for 71,865 psychiatric hospitalizations with discharge at age [&ge;]10 years, between January 1, 2015, and December 31, 2018, in Catalonia, Spain, with follow-up until December 31, 2019. Primary outcome was intentional self-harm (fatal or non-fatal) within 7, 30, 90, 180, and 365 days post-discharge. Models incorporated 247 predictors from electronic health records, including sociodemographic characteristics, mental and physical disorder categories, categories of dispensed psychotropic medication, and history of self-harm and psychiatric hospitalization. Model performance was evaluated using the area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR). Predictor importance was assessed using Shapley Additive Explanations (SHAP). ResultsWithin 365 days, 4,901 hospitalizations (6.8%) were followed by intentional self-harm. The 365-day model trained on the full cohort achieved a AUCROC of 0.819, in the test sample with adjusted AUCPR indicating a median 5.4-fold improvement over baseline prevalence. This model generalized well across event horizons and sex-age strata, outperforming subgroup-specific models when data sparsity limited performance. Separate models trained by event horizons, and stratified by sex, and sex-age groups achieved a median AUCROC of 0.775 (IQR 0.764-0.808), with adjusted AUCPR indicating a median 5.4-fold improvement over baseline prevalence (IQR 4.5-6.2). Key predictors included the recency of the last registered diagnosis of depressive episodes, recurrent depression, adjustment disorders, and schizophrenia, as well as recent SSRI dispensation and the number of childhood-onset disorder and musculoskeletal disease diagnoses in the previous five years. Predictor importance varied considerably across sex-age strata, with smaller differences across horizons. Subject-level and temporal split validation strategies reduced performance (AUCROC 0.711-0.746), though estimates remained clinically informative (2.8-3.1-fold improvement over baseline prevalence). ConclusionsMachine learning models using routinely collected health records predicted intentional self-harm after psychiatric hospitalization with good discrimination and clinically meaningful precision-recall performance. A single 365-day model generalized well across horizons and demographic groups, suggesting that one broadly trained model may provide a pragmatic and scalable approach for clinical implementation.

ObjectivePersons with substance use disorders (SUD) often suffer from additional comorbidities. Researchers have explored this overlap via phenome wide association studies (PheWAS). However, PheWAS are largely cross-sectional, limiting our understanding of whether diagnoses predate development of an SUD. We characterize whether polygenic scores (PGS) are associated with time to comorbid diagnoses in electronic health records (EHR) after the first documented SUD diagnosis. MethodsUsing data from All of Us (N = 393,596), we explored: 1) whether social determinants of health (SDoH) are associated with lifetime risk of SUD (N cases = 42,568) and 2) within a subset those with a diagnosed SUD and available genetic data SUD (N = 21,357), whether PGS for alcohol use disorders, cannabis use disorders, depression, externalizing, post-traumatic stress disorder, and schizophrenia were associated with subsequent diagnoses via a phenome-wide survival analysis. ResultsMultiple SDoH were associated with lifetime SUD diagnosis, with annual household income having the largest overall associations (e.g., <$10K annually vs $100K-$150K annually: OR = 3.89, 95% CI = 3.66, 4.13). There were 101 phenome-wide significant PGS associations with subsequent diagnoses across various bodily systems. PGSs for alcohol use disorders, post-traumatic stress disorder, and schizophrenia were each associated with time to their respective diagnoses. ConclusionsSocial determinants, especially those related to income, have profound associations with lifetime SUD risk. Additionally, PGS for psychiatric conditions are associated with multiple post-SUD diagnoses within those with a SUD, suggesting PGS may capture information beyond lifetime risk, including timing and severity of comorbidities related to SUD.

BackgroundKetamine is a rapid-acting treatment for patients with treatment refractory depression (TRD), however treatment responses are often transient and ketamines antidepressant action lacks robust clinical durability. Little is known about which patient characteristics are associated with faster or more durable ketamine responses. Ketamines antidepressant mechanism is proposed to involve modulation of glutamatergic signaling leading to long term potentiation (LTP) and synaptogenesis, and these neuroplasticity pathways have been shown to be attenuated with older age. We therefore investigated the impact of patient age on the speed and durability of ketamines antidepressant effects in veterans receiving serial intravenous ketamine infusions for TRD. MethodsBeck Depression Inventory (BDI-II) scores from 49 veterans receiving six ketamine infusions (twice weekly) were examined from a retrospective case series. Percent change in BDI-II scores across the infusion series were assessed with respect to patient age using a mixed-linear model. Follow-up analyses examined the age x infusion number interaction effect at each assessment time point. To assess treatment durability, BDI-II change scores three weeks following the sixth infusion were correlated with age. ResultsThere was a significant age x infusion number interaction (F=3.01, p=.0274) across the six infusions. Beta estimates at each infusion showed a significant effect of age at infusion #4 (B=.88% +/-.29%, t=3.02, p=. 004) and a trend towards significance at infusion #5 (B=.62% +/-.31%, t=1.95, p=.057). There was no significant correlation between percent change in BDI-II and age at three-week follow-up. ConclusionsOlder age is associated with an altered trajectory of antidepressant response across serial ketamine infusions, with a model-predicted difference of 8.8% less improvement in BDI-II score for each decade in age mid-way through the infusion course. In contrast, antidepressant durability at three-week follow-up was not related to age. These data suggest age is an important moderating factor of patient response to ketamine, and that differing mechanisms may underlie speed and durability of ketamines antidepressant activity.

BackgroundPoor patient retention and family engagement compromise the effectiveness of coordinated specialty care (CSC) for first-episode psychosis (FEP). This mixed methods study aimed to identify program-level characteristics (CSC fidelity and engagement strategies) associated with patient retention and family engagement in Massachusetts CSC programs. MethodsPrimary outcomes were rates of patient retention and family engagement ([&ge;]1 evidence-based family intervention session), based on CSC program census (October 2022 - September 2023). Quantitative analyses explored program characteristics (EPINET Program-Level Core Assessment Battery) and fidelity ratings (Massachusetts Psychosis Fidelity Scale) as predictors using t-tests or univariate linear regressions. Thematic analysis of program interviews compared patient and family engagement strategies employed by high versus low performing programs. ResultsAcross nine programs, mean patient retention was 86% (range: 58-97%) and family engagement was 40% (range: 12-100%). Higher fidelity to evidence-based services (e.g., individual therapy, family intervention, and supported education/employment) was significantly associated with both outcomes (p<.05; R2 range: .51-.72). Mixed-methods analysis showed that high performing programs used case management-related supports to meet service users practical needs. Factors associated with higher patient retention included having comprehensive intake assessments, provider visits during hospitalization, and periodic treatment reviews. Programs that conducted benefits counseling and proactively recommended family services as standard care had higher family engagement. ConclusionsHigher fidelity CSC programs had better patient retention and family engagement. Case management-related supports addressed treatment barriers. Strategies designed to strengthen therapeutic alliance and goal alignment may promote patient engagement, while family engagement may benefit from proactive recommendation of family intervention.

We tested two metabotropic glutamate receptor 2/3 (mGluR2/3) agonist prodrugs - pomaglumetad (POMA) and TS-134 - including a high-dose of POMA that was four times the dose tested in the failed phase schizophrenia III trials - in two proof of mechanism, Phase Ib studies using identical pharmacoBOLD target-engagement methodology. The POMA study was a double-blind, NIMH-sponsored, 10-day study of 80 or 320 mg/d POMA or placebo (1:1:1 ratio), designed to detect d>0.8 sd between-group effect-size differences. The TS-134 study was a single-blind, industry-sponsored, 6-day study of 20 or 60 mg/d TS-134 or placebo (5:5:2 ratio), designed to permit effect-size estimation for future studies. Primary outcomes were ketamine-induced changes in pharmacoBOLD in the dorsal anterior cingulate cortex (dACC) and Brief Psychiatric Rating Scale (BPRS). 95 healthy controls were randomized to POMA and 63 to TS-134. High-dose POMA had significant within and between-group reduction in ketamine-induced BPRS total symptoms (p<0.01, d=-0.41; p=0.04, d=-0.44, respectively) but neither POMA dose significantly suppressed ketamine-induced dACC pharmacoBOLD. In contrast, low-dose TS-134 had significant/trend level, moderate to large within and between group effects on BPRS positive symptoms (p=0.02, d=-0.36; p=0.008, d=-0.82, respectively) and dACC pharmacoBOLD (p=0.004, d=-0.56; p=0.079, d=-0.50, respectively) using pooled across-study placebo data. High-dose POMA exerted significant effects on clinical symptoms, but not on target engagement, suggesting a higher dose may yet be needed. TS-134 20 mg showed evidence of symptom reduction and target engagement, indicating a curvilinear dose-response curve. These results warrant further investigation of mGluR2/3 and other glutamate-targeted treatments for schizophrenia.

BACKGROUNDPredicting outcomes in schizophrenia spectrum disorders is challenging due to the variability of individual trajectories. While machine learning (ML) shows promise in outcome prediction, is has not yet been integrated into clinical practice. Understanding how ML models (MLMs) can complement psychiatrists predictions and bridge the gap between MLM capabilities and practical use is key. OBJECTIVEThis study aims to compare the performance of psychiatrists and MLMs in predicting short-term symptomatic and functional remission in patients with first-episode psychosis and explore whether MLMs can improve psychiatrists prognostic accuracy. METHODTwenty-four psychiatrists predicted symptomatic and functional remission probabilities based on written baseline information from 66 patients in the OPTiMiSE trial. ML-generated predictions were then shared with psychiatrists, allowing them to adjust their estimates. A questionnaire assessed trust in MLMs, perceived information gaps, and psychiatrists self-assessed predictive accuracy, which was compared to actual accuracy. FINDINGSThe predictive accuracy of the MLM was comparable to that of psychiatrists for symptomatic remission (MLM: 0.50, psychiatrists: 0.52) and functional remission (MLM: 0.72, psychiatrists: 0.79). Interrater agreement was low but comparable for psychiatrists and the MLM. Although the MLM did not improve overall predictive accuracy, it showed potential in aiding psychiatrists with difficult-to-predict cases. However, psychiatrists struggled to recognize when to rely on the models output and we were unable to determine a clear pattern in these cases based on their characteristics. Psychiatrists could not reliably estimate their predictive accuracy. Psychiatrists expressed moderate to high trust in MLMs for prognostic prediction, but highlighted concerns about the lack of transparency and interpretability of model outputs. CONCLUSIONSMLMs are a promising tool for supporting psychiatric decision-making, particularly in challenging cases. However, their potential remains underutilized due to limitations in predictive accuracy and a lack of clarity in how predictions are generated. Addressing these issues is essential to build trust and foster integration into clinical practice. CLINICAL IMPLICATIONSMLMs are best suited as supplementary tools, providing a second opinion while psychiatrists retain decision-making autonomy. Integrating predictions from both sources may help reduce individual biases and improve accuracy. This approach leverages the strengths of MLMs without compromising clinical responsibility. SUMMARY BOXO_ST_ABSWhat is already known on this topicC_ST_ABSWhile machine learning models (MLMs) show promise in predicting outcomes in psychotic disorders, they have yet to be integrated into clinical practice. Evidence on the predictive accuracy of psychiatrists for these disorders is limited, with only two small studies published before 1990 suggesting moderate accuracy. Comparisons of MLMs and psychiatrists in this context have not been previously conducted. What this study addsThis is the first study to compare the predictive accuracy of psychiatrists with that of an MLM for psychotic disorders and to assess whether an MLM can enhance psychiatrists performance. It highlights that while MLMs do not improve overall accuracy, they may support psychiatrists in difficult cases. Insights into psychiatrists trust in MLMs and the challenges of implementing these models are also provided. How this study might affect research, practice, or policyThe findings emphasize the need for advancements in MLM accuracy, interpretability, and strategies to identify cases where MLMs are most beneficial. These improvements could foster effective integration of MLMs as supplementary tools in clinical practice, aiding psychiatrists in decision-making while maintaining their autonomy.

BackgroundCannabis use is associated with psychosis development and symptom relapse in persons with schizophrenia spectrum disorders (SCZ). As more U.S. states legalize cannabis and products increase in potency, it is crucial to better understand recent cannabis use patterns in SCZ. MethodsWe conducted a scoping review of research on cannabis use patterns in SCZ after January 2016 and present a case series of cannabis use in six inpatients with psychosis from 2023-2024. ResultsScoping review: Of 672 references, nine studies (775 participants) were included; none were designed to characterize cannabis quantity, frequency, or type of use over time. Cannabis measurement methodology varied widely and most studies did not follow recommendations for measuring cannabis use. Frequency and quantity of use at study baseline were reported by most studies and these ranged widely. At least a minority of participants with SCZ in each study used cannabis very frequently; quantity of used ranged widely from 0.6{+/-}0.6 to 3.4{+/-}2.2 joints/day. One small study detailed cannabis product type among users for THC (93% flower, 80% edibles, 60% concentrates) and CBD (40% flower, 20% edibles, 20% concentrates, 13% oils). Case SeriesParticipants were inpatients (32.0{+/-}14.4 years; 83.3% diagnosed with SCZ) who used cannabis 2.7{+/-}2.1 days/week. All used cannabis leaf (3.1{+/-}2.3 joints/day); half (all heavy users) also used concentrates (33.3%) or edibles (16.7%). ConclusionOnly nine recent studies measured cannabis use patterns in SCZ; methodologies varied. As cannabis legalization expands and product potency increases, further research should characterize cannabis use and its consequences in SCZ.

IntroductionMedical cannabis is increasingly used as a therapy for managing post-traumatic stress disorder (PTSD). Patients with PTSD often have high healthcare utilization rates, particularly for acute services. This study examines the association between medical cannabis treatment and healthcare utilization among patients with PTSD. MethodsWe conducted a retrospective cohort study using cross-sectional data with tem-poral elements, derived from administrative records provided by Leafwell, among patients with PTSD. The cohort was defined based on medical cannabis use: the treated group included patients who had used medical cannabis for at least one year (returning for medical card renewal), while the untreated group consisted of cannabis-naive patients reporting no prior cannabis use. The primary outcomes were healthcare utilization within the past six months, including at least one urgent care visit, emergency department (ED) visit, or hospitalization related to their primary medical condition. We used inverse probability weighting with regression adjustment (IPWRA) to estimate the average treatment effect (ATE) of medical cannabis use on healthcare utilization, controlling for key demographics and health factors, including PTSD severity. Sensitivity analyses were conducted to assess the robustness of our findings. ResultsAmong the 1,946 participants, the treated group (n = 1,261) had significantly lower healthcare utilization rates compared to the untreated group (n = 685). Using the doubly robust IPWRA model, medical cannabis treatment was associated with a significant 35.6% reduction in urgent care visits (coefficient = -0.024, Standard Error (SE) = 0.0117) and a 35.1% reduction in ED visits (coefficient = -0.027, SE = 0.0124). Hospitalization rates were 26.3% lower among the treated group but did not reach statistical significance. Sensitivity analyses utilizing alternative ATE estimation strategies displayed consistent reductions in urgent care and ED visits among cannabis users, though hospitalizations remained non-significant. Adjusting the IPWRA models tolerance levels strengthened the found associations while maintaining strong covariate balance. Fewer than 2% of the treated group reported an adverse event. DiscussionThese findings suggest that medical cannabis treatment among patients with PTSD may be associated with reduced utilization of urgent care and ED services. This relationship remains robust across multiple statistical models and sensitivity analyses, underscoring the potential role of medical cannabis in reducing acute healthcare needs in this population. Further longitudinal research is warranted to explore causality and assess its impact on hospitalization rates.