This project developed and validated an automated pipeline for prostate treatments to accurately determine which patients could benefit from adaptive radiotherapy (ART) using synthetic CTs (sCTs) generated from on-treatment cone-beam CT (CBCT) images.

The automated pipeline converted CBCTs to sCTs utilising deep-learning, for accurate dose recalculation. Deformable image registration mapped contours from the planning CT to the sCT, with the treatment plan recalculated. A pass/fail assessment used relevant clinical goals. A fail threshold indicated ART was required. All acquired CBCTs (230 sCTs) for 31 patients (6 who had ART) were assessed for pipeline accuracy and clinical viability, comparing clinical outcomes to pipeline outcomes.

The pipeline distinguished patients requiring ART; 74·4% of sCTs for ART patients were red (failure) results, compared to 6·4% of non-ART sCTs. The receiver operator characteristic area under curve was 0·98, demonstrating high performance. The automated pipeline was statistically significantly (p < 0·05) quicker than the current clinical assessment methods (182·5s and 556·4s, respectively), and deformed contour accuracy was acceptable, with 96·6% of deformed clinical target volumes (CTVs) clinically acceptable.

The automated pipeline identified patients who required ART with high accuracy while reducing time and resource requirements. This could reduce departmental workload and increase efficiency and personalisation of patient treatments. Further work aims to apply the pipeline to other treatment sites and investigate its potential for taking into account dose accumulation.

Early intervention in psychosis (EIP) services improve outcomes for young people, but approximately 30% disengage.

To test whether a new motivational engagement intervention would prolong engagement and whether it was cost-effective.

We conducted a multicentre, single-blind, parallel-group, cluster randomised controlled trial involving 20 EIP teams at five UK National Health Service (NHS) sites. Teams were randomised using permuted blocks stratified by NHS trust. Participants were all young people (aged 14–35 years) presenting with a first episode of psychosis between May 2019 and July 2020 (N = 1027). We compared the novel Early Youth Engagement (EYE-2) intervention plus standardised EIP (sEIP) with sEIP alone. The primary outcome was time to disengagement over 12–26 months. Economic outcomes were mental health costs, societal costs and socio-occupational outcomes over 12 months. Assessors were masked to treatment allocation for primary disengagement and cost-effectiveness outcomes. Analysis followed intention-to-treat principles. The trial was registered at ISRCTN51629746.

Disengagement was low at 15.9% overall in standardised stand-alone services. The adjusted hazard ratio for EYE-2 + sEIP (n = 652) versus sEIP alone (n = 375) was 1.07 (95% CI 0.76–1.49; P = 0.713). The health economic evaluation indicated lower mental healthcare costs linked to reductions in unplanned mental healthcare with no compromise of clinical outcomes, as well as some evidence for lower societal costs and more days in education, training, employment and stable accommodation in the EYE-2 group.

We found no evidence that EYE-2 increased time to disengagement, but there was some evidence for its cost-effectiveness. This is the largest study to date reporting positive engagement, health and cost outcomes in a total EIP population sample. Limitations included high loss to follow-up for secondary outcomes and low completion of societal and socio-occupational data. COVID-19 affected fidelity and implementation. Future engagement research should target engagement to those in greatest need, including in-patients and those with socio-occupational goals.

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.

Problem Management Plus (PM+) has been effective in reducing mental health problems among refugees at three-month follow-up, but there is a lack of research on its long-term effectiveness. This study examined the effectiveness of PM+ in reducing symptoms of common mental disorders at 12-month follow-up among Syrian refugees in the Netherlands.

This single-blind, parallel, controlled trial randomised 206 adult Syrians who screened positive for psychological distress and impaired functioning to either PM+ in addition to care as usual (PM+/CAU) or CAU alone. Assessments were at baseline, 1 week and 3 months after the intervention and 12 months after baseline. Outcomes were psychological distress (Hopkins Symptom Checklist [HSCL-25]), depression (HSCL-25 subscale), anxiety (HSCL-25 subscale), posttraumatic stress disorder symptoms (PCL-5), functional impairment (WHODAS 2.0) and self-identified problems (PSYCHLOPS).

In March 2019–December 2022, 103 participants were assigned to PM+/CAU and 103 to CAU of which 169 (82.0%) were retained at 12 months. Intention-to-treat analyses showed greater reductions in psychological distress at 12 months for PM+/CAU compared to CAU (adjusted mean difference −0.17, 95% CI −0.310 to −0.027; p = 0.01, Cohen’s d = 0.28). Relative to CAU, PM+/CAU participants also showed significant reductions on anxiety (−0.19, 95% CI −0.344 to −0.047; p = 0.01, d = 0.31) but not on any of the other outcomes.

PM+ is effective in reducing psychological distress and symptoms of anxiety over a period up to 1 year. Additional support such as booster sessions or additional (trauma-focused) modules may be required to prolong and consolidate benefits gained through PM+ on other mental health and psychosocial outcomes.

The cause of most CHD is unknown and considered complex, implicating genetic and environmental factors in disease causation. The Kids Heart BioBank was established in 2003 to accelerate genetic investigations into CHD.

Recruitment includes patients undergoing interventions for CHD at The Children’s Hospital at Westmead. Informed consent is obtained from parents/guardians, and blood is collected at the time of cardiac intervention from which DNA is extracted and stored. Associated detailed clinical information and a family history are stored in the purpose-designed database.

To date, the Kids Heart BioBank contains biospecimens and associated clinical information from over 4,900 patients with CHD and their families. Two-thirds (64.1%) of probands have been included in research studies with 28.9% of participants who underwent genomic sequencing receiving a molecular diagnosis with direct clinical utility. The value of this resource to patients and families is highlighted by the high consent rate (94.6%) and the low withdrawal of consent rate (0.4%). The Kids Heart BioBank has supported many large national and international collaborations and contributed significantly to CHD research.

The Kids Heart BioBank is an invaluable resource and, together with other similar resources, the resulting research has paved the way for clinical genetic testing options for CHD patients, previously not possible. With research in the field moving away from diagnosing monogenic disease, the Kids Heart BioBank is ideally placed to support the next chapter of research efforts into complex disease mechanisms, requiring large patient cohorts with detailed phenotypic information.

Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of suicidal thoughts. We developed an algorithm to identify posts containing suicide-related content on a military-specific social media platform.

Publicly-shared social media posts (n = 8449) from a military-specific social media platform were reviewed and labeled by our team for the presence/absence of suicidal thoughts and behaviors and used to train several machine learning models to identify such posts.

The best performing model was a deep learning (RoBERTa) model that incorporated post text and metadata and detected the presence of suicidal posts with relatively high sensitivity (0.85), specificity (0.96), precision (0.64), F1 score (0.73), and an area under the precision-recall curve of 0.84. Compared to non-suicidal posts, suicidal posts were more likely to contain explicit mentions of suicide, descriptions of risk factors (e.g. depression, PTSD) and help-seeking, and first-person singular pronouns.

Our results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.

A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication.

To develop and evaluate a model that could predict the risk of TRS in routine clinical practice.

We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model.

We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723–0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism.

We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services.

Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities.

We identified a patient cohort with diabetes from the institutional Diabetes Registry (N = 67,699) within the Duke University Health System. Patient-level information (demographics, comorbidities, service utilization, laboratory results, and medications) was extracted to Tableau. Neighborhood-level socioeconomic status was assessed via the Area Deprivation Index (ADI), and geospatial files incorporated additional data related to points of interest (i.e., parks/green space). Interactive Tableau dashboards were developed to understand risk and contextual factors affecting diabetes management at the individual, group, neighborhood, and population levels.

The Tableau-powered digital health tool offers dynamic visualizations, identifying T2DM-related disparities. The dashboard allows for the exploration of contextual factors affecting diabetes management (e.g., food insecurity, built environment) and possesses capabilities to generate targeted patient lists for personalized diabetes care planning.

As part of a broader health equity initiative, this application meets the needs of a diverse range of users. The interactive dashboard, incorporating clinical, sociodemographic, and environmental factors, enhances understanding at various levels and facilitates targeted interventions to address disparities in diabetes care and outcomes. Ultimately, this transformative approach aims to manage SDoH and improve patient care.

Evidence for necrotising otitis externa (NOE) diagnosis and management is limited, and outcome reporting is heterogeneous. International best practice guidelines were used to develop consensus diagnostic criteria and a core outcome set (COS).

The study was pre-registered on the Core Outcome Measures in Effectiveness Trials (COMET) database. Systematic literature review identified candidate items. Patient-centred items were identified via a qualitative study. Items and their definitions were refined by multidisciplinary stakeholders in a two-round Delphi exercise and subsequent consensus meeting.

The final COS incorporates 36 items within 12 themes: Signs and symptoms; Pain; Advanced Disease Indicators; Complications; Survival; Antibiotic regimes and side effects; Patient comorbidities; Non-antibiotic treatments; Patient compliance; Duration and cessation of treatment; Relapse and readmission; Multidisciplinary team management.

Consensus diagnostic criteria include 12 items within 6 themes: Signs and symptoms (oedema, otorrhoea, granulation); Pain (otalgia, nocturnal otalgia); Investigations (microbiology [does not have to be positive], histology [malignancy excluded], positive CT and MRI); Persistent symptoms despite local and/or systemic treatment for at least two weeks; At least one risk factor for impaired immune response; Indicators of advanced disease (not obligatory but mut be reported when present at diagnosis). Stakeholders were unanimous that there is no role for secondary, graded, or optional diagnostic items. The consensus meeting identified themes for future research.

The adoption of consensus-defined diagnostic criteria and COS facilitates standardised research reporting and robust data synthesis. Inclusion of patient and professional perspectives ensures best practice stakeholder engagement.