Adverse childhood experiences (ACEs) are associated with physical and mental health difficulties in adulthood. This study examines the associations of ACEs with functional impairment and life stress among military personnel, a population disproportionately affected by ACEs. We also evaluate the extent to which the associations of ACEs with functional outcomes are mediated through internalizing and externalizing disorders.

The sample included 4,666 STARRS Longitudinal Study (STARRS-LS) participants who provided information about ACEs upon enlistment in the US Army (2011–2012). Mental disorders were assessed in wave 1 (LS1; 2016–2018), and functional impairment and life stress were evaluated in wave 2 (LS2; 2018–2019) of STARRS-LS. Mediation analyses estimated the indirect associations of ACEs with physical health-related impairment, emotional health-related impairment, financial stress, and overall life stress at LS2 through internalizing and externalizing disorders at LS1.

ACEs had significant indirect effects via mental disorders on all functional impairment and life stress outcomes, with internalizing disorders displaying stronger mediating effects than externalizing disorders (explaining 31–92% vs 5–15% of the total effects of ACEs, respectively). Additionally, ACEs exhibited significant direct effects on emotional health-related impairment, financial stress, and overall life stress, implying ACEs are also associated with these longer-term outcomes via alternative pathways.

This study indicates ACEs are linked to functional impairment and life stress among military personnel in part because of associated risks of mental disorders, particularly internalizing disorders. Consideration of ACEs should be incorporated into interventions to promote psychosocial functioning and resilience among military personnel.

Functional cognitive disorder is an increasingly recognised subtype of functional neurological disorder for which treatment options are currently limited. We have developed a brief online group acceptance and commitment therapy (ACT)-based intervention.

To assess the feasibility of conducting a randomised controlled trial of this intervention versus treatment as usual (TAU).

The study was a parallel-group, single-blind randomised controlled trial, with participants recruited from cognitive neurology, neuropsychiatry and memory clinics in London. Participants were randomised into two groups: ACT + TAU or TAU alone. Feasibility was assessed on the basis of recruitment and retention rates, the acceptability of the intervention, and signal of efficacy on the primary outcome measure (Acceptance and Action Questionnaire II (AAQ-II)) score, although the study was not powered to demonstrate this statistically. Outcome measures were collected at baseline and at 2, 4 and 6 months post-intervention, including assessments of quality of life, memory, anxiety, depression and healthcare use.

We randomised 44 participants, with a participation rate of 51.1% (95% CI 40.8–61.5%); 36% of referred participants declined involvement, but retention was high, with 81.8% of ACT participants attending at least four sessions, and 64.3% of ACT participants reported being ‘satisfied’ or ‘very satisfied’ compared with 0% in the TAU group. Psychological flexibility as measured using the AAQ-II showed a trend towards modest improvement in the ACT group at 6 months. Other measures (quality of life, mood, memory satisfaction) also demonstrated small to modest positive trends.

It has proven feasible to conduct a randomised controlled trial of ACT versus TAU.

The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients.

Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub’s EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis.

One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%–98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation.

Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

The antipsychotic aripiprazole is often used in the treatment of first-episode psychosis. Measuring aripiprazole blood levels provides an objective measure of treatment adherence, but this currently involves taking a venous blood sample and sending to a laboratory for analysis.

To detail the development, validation and utility of a new point of care (POC) test for finger-stick capillary blood concentrations of aripiprazole.

Analytical performance (sensitivity, precision, recovery and linearity) of the assay were established using spiked whole blood and control samples of varying aripiprazole concentration. Assay validation was performed over a 14-month period starting in July 2021. Eligible patients were asked to provide a finger-stick capillary sample in addition to their usual venous blood sample. Capillary blood samples were tested by the MyCare™ Insite POC analyser, which provided measurement of aripiprazole concentration in 6 min, and the venous blood sample was tested by the standard laboratory method.

A total of 101 patients agreed to measurements by the two methods. Venous blood aripiprazole concentrations as assessed by the laboratory method ranged from 17 to 909 ng/mL, and from 1 to 791 ng/mL using POC testing. The correlation coefficient between the two methods (r) was 0.96 and there was minimal bias (slope 0.91, intercept 4 ng/ml).

The MyCare Insite POC analyser is sufficiently accurate and reliable for clinical use. The availability of this technology will improve the assessment of adherence to aripiprazole and the optimising of aripiprazole dosing.

Lumateperone (LUMA) is an FDA-approved antipsychotic to treat schizophrenia and depressive episodes associated with bipolar I or bipolar II disorder. An open-label study (Study 303) evaluated the safety and tolerability of LUMA in outpatients with stable schizophrenia who switched from previous antipsychotic (AP) treatment. This post hoc analysis of Study 303 investigated the safety and tolerability of LUMA stratified by previous AP in patients who switched to LUMA treatment for 6 weeks.

Adult outpatients (≥18 years) with stable schizophrenia were switched from previous AP to LUMA 42 mg once daily for 6 weeks followed by switching to another approved AP for 2 weeks follow-up. Post hoc analyses were stratified by most common previous AP: risperidone or paliperidone (RIS/PAL); quetiapine (QET); aripiprazole or brexpiprazole (ARI/BRE); olanzapine (OLA). Safety analyses included adverse events (AE), vital signs, and laboratory tests. Efficacy was assessed using the Positive and Negative Syndrome Scale (PANSS) and the Clinical Global Impressions-Severity (CGI-S) scale.

The safety population comprised 301 patients, of which 235 (78.1%) were previously treated with RIS/PAL (n=95), QET (n=60), ARI/BRE (n=43), or OLA (n=37). Rates of treatment-emergent AEs (TEAEs) while on LUMA were similar between previous AP groups (44.2%-55.8%). TEAEs with incidences of ≥5% in any AP group were dry mouth, somnolence, sedation, headache, diarrhea, cough, and insomnia. Most TEAEs were mild or moderate in severity for all groups. Rates of serious TEAEs were low and similar between groups (0%–7.0%).

Statistically significant (P<.05) decreases from baseline were observed in the OLA group that switched to LUMA in total cholesterol and low-density lipoprotein cholesterol with significant decreases thereafter on LUMA. Statistically significant decreases in prolactin levels were observed in both the RIS/PAL (P<.0001) and OLA (P<.05) groups. Patients switched from RIS/PAL to LUMA showed significant (P<.05) decreases for body mass index, waist circumference, and weight. At follow-up, 2 weeks after patients switched back from LUMA to another AP, none of the decreases in laboratory parameters or body morphology observed while on LUMA maintained significance.

Those switching from QET had significant improvements from baseline at Day 42 in PANSS Total score (mean change from baseline −3.47; 95% confidence interval [CI] −5.27, −1.68; P<.001) and CGI-S Total score (mean change from baseline −0.24; 95% CI, −0.38, −0.10; P<.01).

In outpatients with stable schizophrenia, LUMA 42 mg treatment was well tolerated in patients switching from a variety of previous APs. Patients switching from RIS/PAL or OLA to LUMA had significant improvements in cardiometabolic and prolactin parameters. These data further support the favorable safety, tolerability, and efficacy of LUMA in patients with schizophrenia.

Intra-Cellular Therapies, Inc.

Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

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 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.

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.

There is evidence that the COVID-19 pandemic has negatively affected mental health, but most studies have been conducted in the general population.

To identify factors associated with mental health during the COVID-19 pandemic in individuals with pre-existing mental illness.

Participants (N = 2869, 78% women, ages 18–94 years) from a UK cohort (the National Centre for Mental Health) with a history of mental illness completed a cross-sectional online survey in June to August 2020. Mental health assessments were the GAD-7 (anxiety), PHQ-9 (depression) and WHO-5 (well-being) questionnaires, and a self-report question on whether their mental health had changed during the pandemic. Regressions examined associations between mental health outcomes and hypothesised risk factors. Secondary analyses examined associations between specific mental health diagnoses and mental health.

A total of 60% of participants reported that mental health had worsened during the pandemic. Younger age, difficulty accessing mental health services, low income, income affected by COVID-19, worry about COVID-19, reduced sleep and increased alcohol/drug use were associated with increased depression and anxiety symptoms and reduced well-being. Feeling socially supported by friends/family/services was associated with better mental health and well-being. Participants with a history of anxiety, depression, post-traumatic stress disorder or eating disorder were more likely to report that mental health had worsened during the pandemic than individuals without a history of these diagnoses.

We identified factors associated with worse mental health during the COVID-19 pandemic in individuals with pre-existing mental illness, in addition to specific groups potentially at elevated risk of poor mental health during the pandemic.

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.

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.

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.

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.

Retrospective self-report is typically used for diagnosing previous pediatric traumatic brain injury (TBI). A new semi-structured interview instrument (New Mexico Assessment of Pediatric TBI; NewMAP TBI) investigated test–retest reliability for TBI characteristics in both the TBI that qualified for study inclusion and for lifetime history of TBI.

One-hundred and eight-four mTBI (aged 8–18), 156 matched healthy controls (HC), and their parents completed the NewMAP TBI within 11 days (subacute; SA) and 4 months (early chronic; EC) of injury, with a subset returning at 1 year (late chronic; LC).

The test–retest reliability of common TBI characteristics [loss of consciousness (LOC), post-traumatic amnesia (PTA), retrograde amnesia, confusion/disorientation] and post-concussion symptoms (PCS) were examined across study visits. Aside from PTA, binary reporting (present/absent) for all TBI characteristics exhibited acceptable (≥0.60) test–retest reliability for both Qualifying and Remote TBIs across all three visits. In contrast, reliability for continuous data (exact duration) was generally unacceptable, with LOC and PCS meeting acceptable criteria at only half of the assessments. Transforming continuous self-report ratings into discrete categories based on injury severity resulted in acceptable reliability. Reliability was not strongly affected by the parent completing the NewMAP TBI.

Categorical reporting of TBI characteristics in children and adolescents can aid clinicians in retrospectively obtaining reliable estimates of TBI severity up to a year post-injury. However, test–retest reliability is strongly impacted by the initial data distribution, selected statistical methods, and potentially by patient difficulty in distinguishing among conceptually similar medical concepts (i.e., PTA vs. confusion).

This study aimed to investigate general factors associated with prognosis regardless of the type of treatment received, for adults with depression in primary care.

We searched Medline, Embase, PsycINFO and Cochrane Central (inception to 12/01/2020) for RCTs that included the most commonly used comprehensive measure of depressive and anxiety disorder symptoms and diagnoses, in primary care depression RCTs (the Revised Clinical Interview Schedule: CIS-R). Two-stage random-effects meta-analyses were conducted.

Twelve (n = 6024) of thirteen eligible studies (n = 6175) provided individual patient data. There was a 31% (95%CI: 25 to 37) difference in depressive symptoms at 3–4 months per standard deviation increase in baseline depressive symptoms. Four additional factors: the duration of anxiety; duration of depression; comorbid panic disorder; and a history of antidepressant treatment were also independently associated with poorer prognosis. There was evidence that the difference in prognosis when these factors were combined could be of clinical importance. Adding these variables improved the amount of variance explained in 3–4 month depressive symptoms from 16% using depressive symptom severity alone to 27%. Risk of bias (assessed with QUIPS) was low in all studies and quality (assessed with GRADE) was high. Sensitivity analyses did not alter our conclusions.

When adults seek treatment for depression clinicians should routinely assess for the duration of anxiety, duration of depression, comorbid panic disorder, and a history of antidepressant treatment alongside depressive symptom severity. This could provide clinicians and patients with useful and desired information to elucidate prognosis and aid the clinical management of depression.