Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact.

We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations.

BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI.

We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

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.

The aim of this QIP was to identify whether the information obtained during routine physical health reviews was being adequately handed over during discharge from the Sheffield Home Treatment Team within discharge summaries. Individuals with serious mental illness have a significantly higher all-cause mortality rate than those without, much of which is due to preventable physical health conditions. Due to this, the Home Treatment Team aims to complete a Physical Health Review (PHR) for every patient under their care as per guidelines. It is important that these examinations are performed, however, it is equally important that the results of PHRs are communicated to the patient's primary care physician upon discharge. Communication between services is vital for continuity of care and to ensure that identified problems are managed effectively. On discharge, relevant data from these investigations should be communicated to the service user's registered GP surgery for appropriate follow-up care. Performing investigations without informing the service user's primary physician is an inefficient use of resources and may result in unnecessary repeated investigations and procedures. There is not currently an official system in place to assure that the investigations and results of PHRs are summarised and communicated upon discharge.

There were two steps taken in this stage of the QIP. First, a questionnaire was distributed to all members of the Sheffield Home Treatment Team, including medics, nurses, and STR workers. The responses were compiled and analysed to form the criteria and standards for an audit of previous discharges. Following this, an audit was performed for the months of June-July 2021, data were kindly collected by junior doctors. This data looked to determine whether previous discharges met the criteria and standards set by the questionnaire.

The results of the audit showed that the discharges did not meet the standards set, with many containing little to no information. Only 49% of the service users with physical health reviews had any information provided on discharge. Of these, the contents of the summaries were varies and inconsistent, resulting in a significant amount of information becoming unavailable to the service user's GPs.

• 1. The current system is insufficient in terms of handing over physical health information collected during investigations performed by the Home Treatment Team.

• 2. A proposed solution will be implemented in the coming months.

• 3. A re-audit will be performed to complete the audit cycle and assess the efficacy of the proposed solution.

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.

To assess the feasibility of iPhone-based teleradiology as a potential solution for the diagnosis of acute cervico-dorsal spine trauma.

We have developed a solution that allows visualization of images on the iPhone. Our system allows rapid, remote, secure, visualization of medical images without storing patient data on the iPhone. This retrospective study is comprised of cervico-dorsal computed tomogram (CT) scan examination of 75 consecutive patients having clinically suspected cervico-dorsal spine fracture. Two radiologists reviewed CT scan images on the iPhone. Computed tomogram spine scans were analyzed for vertebral body fracture and posterior elements fractures, any associated subluxation-dislocation and cord lesion. The total time taken from the launch of viewing application on the iPhone until interpretation was recorded. The results were compared with that of a diagnostic workstation monitor. Inter-rater agreement was assessed.

The sensitivity and accuracy of detecting vertebral body fractures was 80% and 97% by both readers using the iPhone system with a perfect inter-rater agreement (kappa:1). The sensitivity and accuracy of detecting posterior elements fracture was 75% and 98% for Reader 1 and 50% and 97% for Reader 2 using the iPhone. There was good inter-rater agreement (kappa: 0.66) between both readers. No statistically significant difference was noted between time on the workstation and the iPhone system.

iPhone-based teleradiology system is accurate in the diagnosis of acute cervico-dorsal spinal trauma. It allows rapid, remote, secure, visualization of medical images without storing patient data on the iPhone.

To analyze antipsychotic prescribing patterns in a UK high security hospital (HSH) that treats seriously violent men with either schizophrenia or personality disorder and examine how different groups consented to treatment and prescribing for metabolic conditions. We hypothesized that there would be high prevalence of antipsychotic polypharmacy, and high-dose antipsychotic and clozapine prescribing.

HSHs treat seriously violent, mentally disordered offenders, and the extant literature on prescribing patterns in forensic settings is sparse.

Prescribing and clinical data on all 189 patients in a UK HSH were collected from the hospital’s databases. Data were analyzed using SPSS.

The population was split into the following groups: schizophrenia spectrum disorder (SSD-only), personality disorder (PD-only), and comorbid schizophrenia spectrum disorder and PD. The majority (93.7%) of all patients were prescribed at least one antipsychotic, and (27.5%) were on clozapine. Polypharmacy was prevalent in 22.2% and high-dose antipsychotic in 27.5%. Patients on clozapine were more likely to be prescribed antidiabetic, statins, or antihypertensive medication. Patients in the PD-only group were more likely to be deemed to have the capacity to consent to treatment and be prescribed clozapine in contrast to the SSD-only group.

Rates of clozapine and high-dose antipsychotic prescribing were higher than in other psychiatric settings, while polypharmacy prescribing rates were lower. Higher clozapine prescribing rates may be a function of a treatment-resistant and aggressive population. A higher proportion of PD-only patients consented to treatment and received clozapine compared with in-house SSD-only as well as other psychiatric settings. Implications of the findings are discussed.