Individuals living with severe mental illness can have significant emotional, physical and social challenges. Collaborative care combines clinical and organisational components.

We tested whether a primary care-based collaborative care model (PARTNERS) would improve quality of life for people with diagnoses of schizophrenia, bipolar disorder or other psychoses, compared with usual care.

We conducted a general practice-based, cluster randomised controlled superiority trial. Practices were recruited from four English regions and allocated (1:1) to intervention or control. Individuals receiving limited input in secondary care or who were under primary care only were eligible. The 12-month PARTNERS intervention incorporated person-centred coaching support and liaison work. The primary outcome was quality of life as measured by the Manchester Short Assessment of Quality of Life (MANSA).

We allocated 39 general practices, with 198 participants, to the PARTNERS intervention (20 practices, 116 participants) or control (19 practices, 82 participants). Primary outcome data were available for 99 (85.3%) intervention and 71 (86.6%) control participants. Mean change in overall MANSA score did not differ between the groups (intervention: 0.25, s.d. 0.73; control: 0.21, s.d. 0.86; estimated fully adjusted between-group difference 0.03, 95% CI −0.25 to 0.31; P = 0.819). Acute mental health episodes (safety outcome) included three crises in the intervention group and four in the control group.

There was no evidence of a difference in quality of life, as measured with the MANSA, between those receiving the PARTNERS intervention and usual care. Shifting care to primary care was not associated with increased adverse outcomes.

Nonmedical opioid use (NMOU) has been associated with opioid overdose deaths. This pattern of misuse can be seen in those using opioids for cancer-associated pain. We present a case that highlights the complexities associated with NMOU and a patient’s care at the end of life.

A patient with a metastatic solid tumor malignancy along with co-occurring history of polysubstance abuse was admitted to an acute palliative and supportive care unit (APSCU). The patient demonstrated behaviors concerning for NMOU during her hospital stay but had increased symptom expression concerning for worsening dyspnea while in the APSCU. Unfortunately, she used home opioids, which was unknown to the team at the time along with requesting for higher doses of opioids that were being prescribed. This caused a worsening respiratory status and affected her care. Using an interdisciplinary approach, the providers managed her symptoms and discharged her safely to see her child.

This highlights the complexities of the alleviation of suffering in those with NMOU. It is important to continue to manage NMOU at the end of life due to its effects on quality of life. A multimodal approach is recommended to identify and care for these patients.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

1. (1) To understand the usefulness of a behavioural experiment [a low frustration tolerance (LFT) exercise] for training within a SP/SR framework.

2. (2) To examine the potential for using SP/SR as a form of competency-based training.

3. (3) To demonstrate the benefits of experiential learning through SP/SR in training CBT.

Patients presenting to hospital with suspected coronavirus disease 2019 (COVID-19), based on clinical symptoms, are routinely placed in a cohort together until polymerase chain reaction (PCR) test results are available. This procedure leads to delays in transfers to definitive areas and high nosocomial transmission rates. FebriDx is a finger-prick point-of-care test (PoCT) that detects an antiviral host response and has a high negative predictive value for COVID-19. We sought to determine the clinical impact of using FebriDx for COVID-19 triage in the emergency department (ED).

We undertook a retrospective observational study evaluating the real-world clinical impact of FebriDx as part of an ED COVID-19 triage algorithm.

Emergency department of a university teaching hospital.

Patients presenting with symptoms suggestive of COVID-19, placed in a cohort in a ‘high-risk’ area, were tested using FebriDx. Patients without a detectable antiviral host response were then moved to a lower-risk area.

Between September 22, 2020, and January 7, 2021, 1,321 patients were tested using FebriDx, and 1,104 (84%) did not have a detectable antiviral host response. Among 1,104 patients, 865 (78%) were moved to a lower-risk area within the ED. The median times spent in a high-risk area were 52 minutes (interquartile range [IQR], 34–92) for FebriDx-negative patients and 203 minutes (IQR, 142–255) for FebriDx-positive patients (difference of −134 minutes; 95% CI, −144 to −122; P < .0001). The negative predictive value of FebriDx for the identification of COVID-19 was 96% (661 of 690; 95% CI, 94%–97%).

FebriDx improved the triage of patients with suspected COVID-19 and reduced the time that severe acute respiratory coronavirus virus 2 (SARS-CoV-2) PCR-negative patients spent in a high-risk area alongside SARS-CoV-2–positive patients.

The coronavirus disease 2019 (COVID-19) pandemic has significantly increased depression rates, particularly in emerging adults. The aim of this study was to examine longitudinal changes in depression risk before and during COVID-19 in a cohort of emerging adults in the U.S. and to determine whether prior drinking or sleep habits could predict the severity of depressive symptoms during the pandemic.

Participants were 525 emerging adults from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a five-site community sample including moderate-to-heavy drinkers. Poisson mixed-effect models evaluated changes in the Center for Epidemiological Studies Depression Scale (CES-D-10) from before to during COVID-19, also testing for sex and age interactions. Additional analyses examined whether alcohol use frequency or sleep duration measured in the last pre-COVID assessment predicted pandemic-related increase in depressive symptoms.

The prevalence of risk for clinical depression tripled due to a substantial and sustained increase in depressive symptoms during COVID-19 relative to pre-COVID years. Effects were strongest for younger women. Frequent alcohol use and short sleep duration during the closest pre-COVID visit predicted a greater increase in COVID-19 depressive symptoms.

The sharp increase in depression risk among emerging adults heralds a public health crisis with alarming implications for their social and emotional functioning as this generation matures. In addition to the heightened risk for younger women, the role of alcohol use and sleep behavior should be tracked through preventive care aiming to mitigate this looming mental health crisis.