Identifying neuroimaging biomarkers of antidepressant response may help guide treatment decisions and advance precision medicine.

To examine the relationship between anhedonia and functional neurocircuitry in key reward processing brain regions in people with major depressive disorder receiving aripiprazole adjunct therapy with escitalopram.

Data were collected as part of the CAN-BIND-1 study. Participants experiencing a current major depressive episode received escitalopram for 8 weeks; escitalopram non-responders received adjunct aripiprazole for an additional 8 weeks. Functional magnetic resonance imaging (on weeks 0 and 8) and clinical assessment of anhedonia (on weeks 0, 8 and 16) were completed. Seed-based correlational analysis was employed to examine the relationship between baseline resting-state functional connectivity (rsFC), using the nucleus accumbens (NAc) and anterior cingulate cortex (ACC) as key regions of interest, and change in anhedonia severity after adjunct aripiprazole.

Anhedonia severity significantly improved after treatment with adjunct aripiprazole.

There was a positive correlation between anhedonia improvement and rsFC between the ACC and posterior cingulate cortex, ACC and posterior praecuneus, and NAc and posterior praecuneus. There was a negative correlation between anhedonia improvement and rsFC between the ACC and anterior praecuneus and NAc and anterior praecuneus.

Eight weeks of aripiprazole, adjunct to escitalopram, was associated with improved anhedonia symptoms. Changes in functional connectivity between key reward regions were associated with anhedonia improvement, suggesting aripiprazole may be an effective treatment for individuals experiencing reward-related deficits. Future studies are required to replicate our findings and explore their generalisability, using other agents with partial dopamine (D2) agonism and/or serotonin (5-HT2A) antagonism.

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.

Many clinical trials leverage real-world data. Typically, these data are manually abstracted from electronic health records (EHRs) and entered into electronic case report forms (CRFs), a time and labor-intensive process that is also error-prone and may miss information. Automated transfer of data from EHRs to eCRFs has the potential to reduce data abstraction and entry burden as well as improve data quality and safety.

We conducted a test of automated EHR-to-CRF data transfer for 40 participants in a clinical trial of hospitalized COVID-19 patients. We determined which coordinator-entered data could be automated from the EHR (coverage), and the frequency with which the values from the automated EHR feed and values entered by study personnel for the actual study matched exactly (concordance).

The automated EHR feed populated 10,081/11,952 (84%) coordinator-completed values. For fields where both the automation and study personnel provided data, the values matched exactly 89% of the time. Highest concordance was for daily lab results (94%), which also required the most personnel resources (30 minutes per participant). In a detailed analysis of 196 instances where personnel and automation entered values differed, both a study coordinator and a data analyst agreed that 152 (78%) instances were a result of data entry error.

An automated EHR feed has the potential to significantly decrease study personnel effort while improving the accuracy of CRF data.

Blast related characteristics may contribute to the diversity of findings on whether mild traumatic brain injury sustained during war zone deployment has lasting cognitive effects. This study aims to evaluate whether a history of blast exposure at close proximity, defined as exposure within 30 feet, has long-term or lasting influences on cognitive outcomes among current and former military personnel.

One hundred participants were assigned to one of three groups based on a self-report history of blast exposure during combat deployments: 47 close blast, 14 non-close blast, and 39 comparison participants without blast exposure. Working memory, processing speed, verbal learning/memory, and cognitive flexibility were evaluated using standard neuropsychological tests. In addition, assessment of combat exposure and current post-concussive, posttraumatic stress, and depressive symptoms, and headache was performed via self-report measures. Variables that differed between groups were controlled as covariates.

No group differences survived Bonferroni correction for family-wise error rate; the close blast group did not differ from non-close blast and comparison groups on measures of working memory, processing speed, verbal learning/memory, or cognitive flexibility. Controlling for covariates did not alter these results.

No evidence emerged to suggest that a history of close blast exposure was associated with decreased cognitive performance when comparisons were made with the other groups. Limited characterization of blast contexts experienced, self-report of blast distance, and heterogeneity of injury severity within the groups are the main limitations of this study.

Patients with schizophrenia suffer from increased mortality rates equivalent to 15-20 years shorter life expectancy. Up to 60% of this excess mortality can be explained by preventable, somatic conditions like cardiovascular, metabolic, and respiratory comorbidities. As forensic psychiatric (FP) patients often experience the triple stigmatization of mental illness, substance misuse and criminal conviction, the risk of suboptimal diagnosis and treatment may be high. Although benefits from the addition of general practitioner (GP) services to non-FP wards have been shown elsewhere, this cross-sectoral approach has never been attempted in a Danish FP ward.

One purpose of this project is to evaluate the associations between self-reported quality of life and objective measures of somatic health.

A clinical intervention in which a GP consults patients in all medium secure wards in the Central Denmark Region (N=72). The consultation includes a physical examination, medication review, and evaluation of blood samples. Data is collected from: electronic patient files and questionnaires regarding quality of life (SF-12), lifestyle, and attitude towards GP services.

The population will be described in regards to socio-demographic, clinical, and forensic characteristics. Associations will be made between quality of life (SF-12), metabolic syndrome, blood markers, and heart-SCORE risk. Risk profiles for endocrinologic and coronary illness will be examined.

Results may guide future health interventions and will be used as a basis for adjustments to the current project.

No significant relationships.

Impairment in decision-making capacity is a serious consequence of executive dysfunction secondary to serious mental disorders like schizophrenia. Functional mental capacity (FMC) refers to an individual’s ability to make and communicate legally competent decisions autonomously. Studies have shown that FMC is dependent on severity of psychosis and can improve with treatment.

To ascertain the correlation between the scores on a structured judgement tool, namely the Dundrum Capacity Ladders (DCL) with level of acuity of treatment setting and length of stay in a secure forensic hospital.

Sixty-two patients were interviewed using the DCL across three domains – healthcare, welfare and finances. Correlation between DCL scores, length of hospital stay and level of acuity of treatment setting was assessed.

As patients moved from higher to lower dependency wards, mean DCL score increased, indicating a higher level of capacity. Patients in high dependency wards were most impaired while those in the low dependency wards performed significantly better (rs=0.472, p<0.001). The longer the patients stayed in the hospital, up until five years, the higher the mean welfare domain score (rs=0.402, p=0.011) and mean DCL score (rs=0.376, p=0.018). Beyond five years of hospital stay, those who had lower DCL scores and did not improve had longer length of stay.

Patients’ FMC improve as they progress from high to low level of acuity of treatment setting. However, this is dependent on the length of hospital stay. FMC may be a measure of recovery in the forensic setting.

No significant relationships.

Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers.

In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively.

A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction.

A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.

Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.

The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Secure forensic mental health services have a dual role, to treat mental illness and reduce violent recidivism. Those admitted to secure forensic services have a significant history of violence and treatment needs in multiple domains including psychiatric illness, violence and other areas such as substance misuse and physical health.

The aim of this study was to ascertain if the units in a medium secure forensic hospital are stratified according to individual risks and needs. We also aimed to clarify if there were differences in the symptom level, risks and needs of those with and without community leave and to clarify the risks and needs of the female patients and ID patients.

This is a cross sectional study a cohort of patients (n=138) in a secure forensic hospital.

There was a total of 138 patients, the majority of whom were male (n=123, 89.1%). The most common diagnosis was schizophrenia (n=99, 71.7%). Placements in the care pathway of the medium secure forensic hospital were associated with level of symptomatology (PANSS positive), dynamic violence risk (F=26.880,P<0.001), DUNDRUM-3 therapeutic programme completion (F=44.067,P<0.001), and DUNDRUM 4 recovery (F=59.629,P<0.001). Patients with community leave had better scores than those without leave on violence risk (F=77.099, P<0.001), therapeutic programme completion (F=116.072, P<0.001) and recovery (F=172.211, P<0.001).

Stratifying secure forensic psychiatric hospitals according to individual risks and needs provides in-patient care in the least restrictive setting appropriate for individuals, however niche groups such as female forensic patients and ID patients may need special consideration.

Methadone, a long-acting opioid agonist commonly used in the treatment of opiate dependence, has been reported to cause QTc interval prolongation, increasing the risk of a fatal cardiac arrhythmia – Torsades-de-Pointes (TdP). This effect seems to be attributable to methadone's inhibitory effect on the cardiac “hERG”-K+ ion channel and is dose-dependent. There is a lack of consensus regarding when to perform an ECG for patients on methadone.

Identifying other TdPPRFs in a cohort of patients receiving ≥ 85 mg (high dose) methadone daily to inform local clinical safety guidelines.

Our outpatient caseload was filtered to select opiate-dependent patients receiving more than 85 mg methadone daily. Primary care summaries and laboratory results databases were analysed for the presence of other TdPPRFs: female sex a documented history of ECG abnormalities, electrolyte imbalance, liver or renal failure, and concomitant use of other QT prolonging medication or stimulants.

Fourteen opiate-dependent patients (10.29% of patients on methadone) were maintained on ≥ 85 mg methadone daily. Gender distribution was F:M = 1:1.8; 64% misused illicit stimulants; 57% were prescribed other QTc prolonging medication and 29% had a documented history of liver/renal failure or electrolyte imbalance. Only 14% had previous ECGs documented in primary care summaries. Of patients on high dose methadone, 85.7% had at least one TdPPRFs present and 64.3% had at least two.

These results demonstrate an increased rate of TdPPRFs in this patient group and highlight the importance of ECG monitoring which ideally should be offered to patients receiving even lower doses of methadone.

The authors have not supplied their declaration of competing interest.

Patients with major depressive disorder (MDD) display cognitive deficits in acutely depressed and remitted states. Childhood maltreatment is associated with cognitive dysfunction in adults, but its impact on cognition and treatment related cognitive outcomes in adult MDD has received little consideration. We investigate whether, compared to patients without maltreatment and healthy participants, adult MDD patients with childhood maltreatment display greater cognitive deficits in acute depression, lower treatment-associated cognitive improvements, and lower cognitive performance in remission.

Healthy and acutely depressed MDD participants were enrolled in a multi-center MDD predictive marker discovery trial. MDD participants received 16 weeks of standardized antidepressant treatment. Maltreatment and cognition were assessed with the Childhood Experience of Care and Abuse interview and the CNS Vital Signs battery, respectively. Cognitive scores and change from baseline to week 16 were compared amongst MDD participants with (DM+, n = 93) and without maltreatment (DM−, n = 90), and healthy participants with (HM+, n = 22) and without maltreatment (HM−, n = 80). Separate analyses in MDD participants who remitted were conducted.

DM+ had lower baseline global cognition, processing speed, and memory v. HM−, with no significant baseline differences amongst DM−, HM+, and HM− groups. There were no significant between-group differences in cognitive change over 16 weeks. Post-treatment remitted DM+, but not remitted DM−, scored significantly lower than HM− in working memory and processing speed.

Childhood maltreatment was associated with cognitive deficits in depressed and remitted adults with MDD. Maltreatment may be a risk factor for more severe and persistent cognitive deficits in adult MDD.

There is renewed interest in the inverse association between psychiatric hospital and prison places, with reciprocal time trends shown in more than one country. We hypothesised that the numbers of admissions to psychiatric hospitals and committals to prisons in Ireland would also correlate inversely over time (i.e. dynamic measures of admission and committal rather than static, cross-sectional numbers of places).

Publicly available activity statistics for psychiatric hospitals and prisons in Ireland were collated from 1986 to 2010.

There was a reciprocal association between psychiatric admissions and prison committals (Pearson r=−0.788, p<0.001), an increase of 91 prison committals for every 100 psychiatric hospital admissions foregone.

Penrose’s hypothesis applies to admissions to psychiatric hospitals and prisons in Ireland over time (dynamic measures), just as it does to the numbers of places in psychiatric hospitals and prisons in Ireland and elsewhere (static, cross-sectional measures). Although no causal connection can be definitively established yet, mentally disordered prisoners are usually known to community mental health services. Psychiatric services for prisons and the community should be linked to ensure that the needs of those currently accessing care through prisons can also be met in the community.