Respiratory virus testing is routinely performed and ways to obtain specimens aside from a nasopharyngeal swab are needed for pandemic preparedness. The main objective is to validate a self-collected oral-nasal swab for the detection of Influenza and respiratory syncytial virus (RSV).

Diagnostic test validation of a self-collected oral nasal swab as compared to a provider-collected nasopharyngeal swab.

Emergency Department at Michael Garron Hospital.

Consecutive individuals who presented to the Emergency Department with a suspected viral upper respiratory tract infection were included if they self-collected an oral-nasal swab. Individuals testing positive for Influenza or RSV along with randomly selected participants who tested negative were eligible for inclusion.

All participants had the paired oral-nasal swab tested using a multiplex respiratory virus polymerase chain reaction for the three respiratory pathogens and compared to the nasopharyngeal swab.

48 individuals tested positive for Influenza, severe acute respiratory coronavirus virus 2 (SARS-CoV-2) or RSV along with 80 who tested negative. 110 were symptomatic with the median time from symptom onset to testing of 1 day (interquartile range 2–5 days). Using the clinical nasopharyngeal swab as the reference standard, the sensitivity was 0.75 (95% CI, 0.43–0.95) and specificity was 0.99 (95% CI, 0.93–1.00) for RSV, sensitivity is 0.67 (95% CI, 0.49–0.81) and specificity is 0.96 (95% CI, 0.89–0.99) for Influenza.

Multiplex testing with a self-collected oral-nasal swab for Influenza and RSV is not an acceptable substitute for a healthcare provider collected nasopharyngeal swab primarily due to suboptimal Influenza test characteristics.

Late-life affective disorders (LLADs) are common and are projected to increase by 2050. There have been several studies linking late-life depression to an increased risk of dementia, but it is unclear if bipolar affective disorder or anxiety disorders pose a similar risk.

We aimed to compare the risk of LLADs progressing to all-cause dementia, and the demographic and clinical variables mediating the risk.

We used the South London and Maudsley National Health Service Foundation Trust Clinical Records Interactive Search system to identify patients aged 60 years or older with a diagnosis of any affective disorder. Cox proportional hazard models were used to determine differences in dementia risk between late-life anxiety disorders versus late-life depression, and late-life bipolar disorder versus late-life depression. Demographic and clinical characteristics associated with the risk of dementia were investigated.

Some 5695 patients were identified and included in the final analysis. Of these, 388 had a diagnosis of bipolar affective disorder, 1365 had a diagnosis of an anxiety disorder and 3942 had a diagnosis of a depressive disorder. Bipolar affective disorder was associated with a lower hazard of developing dementia compared to depression (adjusted model including demographics and baseline cognition, hazard ratio: 0.60; 95% CI: 0.41–0.87). Anxiety disorders had a similar hazard of developing dementia (adjusted hazard ratio: 1.05; 95% CI: 0.90–1.22). A prior history of a depressive disorder reduced the risk of late-life depression progressing to dementia – suggesting the new onset of a depressive disorder in later life is associated with higher risk – but a prior history of anxiety disorders or bipolar affective disorder did not alter risk.

LLADs have a differential risk of developing all-cause dementia, with demographic- and illness-related factors influencing the risk. Further prospective cohort studies are needed to explore the link between LLADs and dementia development, and mediators of the lower risk of dementia associated with late-life bipolar disorder compared to late-life depression.

Estimating the risk of developing bipolar disorder (BD) in children and adolescents (C&A) with depressive disorders is important to optimize prevention and early intervention efforts. We aimed to quantitatively examine the risk of developing BD from depressive disorders and identify factors which moderate this development.

In this systematic review and meta-analysis (PROSPERO:CRD42023431301), PubMed and Web-of-Science databases were searched for longitudinal studies reporting the percentage of C&A with ICD/DSM-defined depressive disorders who developed BD during follow-up. Data extraction, random-effects meta-analysis, between-study heterogeneity analysis, quality assessment, sub-group analyses, and meta-regressions were conducted.

Thirty-nine studies were included, including 72,371 individuals (mean age=13.9 years, 57.1% females); 14.7% of C&A with a depressive disorder developed BD after 20.4–288 months: 9.5% developed BD-I (95% CI=4.7 to 18.1); 7.7% developed BD-II (95% CI=3.2% to 17.3%); 19.8% (95% CI=9.9% to 35.6%) of C&A admitted into the hospital with a depressive disorder developed BD. Studies using the DSM (21.6%, 95% CI=20.2% to 23.1%) and studies evaluating C&A with a major depressive disorder only (19.8%, 95% CI=16.8% to 23.1%) found higher rates of development of BD. Younger age at baseline, a history of hospitalization and recruitment from specialized clinics were associated with an increased risk of developing BD at follow-up. Quality of included studies was good in 76.9% of studies.

There is a substantial risk of developing BD in C&A with depressive disorders. This is particularly the case for C&A with MDD, DSM-diagnosed depressive disorders, and C&A admitted into the hospital. Research exploring additional predictors and preventive interventions is crucial.

Motor neuron disease (MND) is a progressive, fatal, neurodegenerative condition that affects motor neurons in the brain and spinal cord, resulting in loss of the ability to move, speak, swallow and breathe. Acceptance and commitment therapy (ACT) is an acceptance-based behavioural therapy that may be particularly beneficial for people living with MND (plwMND). This qualitative study aimed to explore plwMND’s experiences of receiving adapted ACT, tailored to their specific needs, and therapists’ experiences of delivering it.

Semi-structured qualitative interviews were conducted with plwMND who had received up to eight 1:1 sessions of adapted ACT and therapists who had delivered it within an uncontrolled feasibility study. Interviews explored experiences of ACT and how it could be optimised for plwMND. Interviews were audio recorded, transcribed and analysed using framework analysis.

Participants were 14 plwMND and 11 therapists. Data were coded into four over-arching themes: (i) an appropriate tool to navigate the disease course; (ii) the value of therapy outweighing the challenges; (iii) relevance to the individual; and (iv) involving others. These themes highlighted that ACT was perceived to be acceptable by plwMND and therapists, and many participants reported or anticipated beneficial outcomes in the future, despite some therapeutic challenges. They also highlighted how individual factors can influence experiences of ACT, and the potential benefit of involving others in therapy.

Qualitative data supported the acceptability of ACT for plwMND. Future research and clinical practice should address expectations and personal relevance of ACT to optimise its delivery to plwMND.

1. (1) To understand the views of people living with motor neuron disease (plwMND) and therapists on acceptance and commitment therapy (ACT) for people living with this condition.

2. (2) To understand the facilitators of and barriers to ACT for plwMND.

3. (3) To learn whether ACT that has been tailored to meet the specific needs of plwMND needs to be further adapted to potentially increase its acceptability to this population.

At the start of a new community perinatal mental health service in Scotland we sought the opinions and aspirations of professional and lay stakeholders. A student elective project supported the creation of an anonymous 360-degree online survey of a variety of staff and people with lived experience of suffering from or managing perinatal mental health problems. The survey was designed and piloted with trainees and volunteer patients.

A rich variety of opinions was gathered from the 60 responses, which came from a reasonably representative sample. Respondents provided specific answers to key questions and wrote free-text recommendations and concerns to inform service development.

There is clear demand for the new expanded service, with strong support for provision of a mother and baby unit in the North of Scotland. The digital survey method could be adapted to generate future surveys to review satisfaction with service development and generate ideas for further change.

Clinical trials that fail prematurely due to poor design are a waste of resources and deprives us of data for evaluating potentially effective interventions. This study used machine learning modelling to predict clinical trials’ success or failure and to understand feature contributions driving this result. Features to power the modelling were engineered using data collected from the National Institute for Health and Care Research Innovation Observatory’s ScanMedicine database.

Using ScanMedicine, a large dataset containing 641,079 clinical trial records from 11 global clinical trial registries, was extracted. Sixteen features were generated from the data based on fields relating to trial design and eligibility. Trials were labeled positive if they were completed (or target recruitment was achieved) or negative if terminated/withdrawn (or target recruitment was not achieved). To achieve optimal performance, phase-specific datasets were generated, and we focused on a subsample of Phase 2 trials (n=70,167). Ensemble models using bagging and boosting algorithms, including balanced random forest and extreme gradient boosting classifiers were used for training and evaluating predictive performance. Shapley Additive Explanations was used to explain the output of the best performing model and calculate feature contributions for individual studies.

We achieved a weighted F1-score of 0.88, Receiver Operator Characteristic Area under the Curve score of 0.75, and balanced accuracy of 0.75 on the test set with the xgBoost model. This result shows that the model can successfully distinguish between classes to predict if a trial will succeed or fail and subsequently output the features driving this outcome. The number of primary outcomes, whether the study was randomized, target sample size and number of exclusion criteria were the most important features affecting the model’s prediction.

This study is the first to use predictive modelling on a large sample of clinical trial data obtained from 11 international trial registries. The prediction outcomes achieved by our novel approach, which uses phase-specific trained models, outperforms previous modelling in this space.