Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determining effective personalized treatments.

To identify a data-driven pattern of clinical improvement in MDD and to quantify neural-to-symptom relationships according to antidepressant treatment, we performed a secondary analysis of the publicly available dataset EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care). In EMBARC, participants with MDD were treated either by sertraline or placebo for 8 weeks (Stage 1), and then switched to bupropion according to clinical response (Stage 2). We computed a univariate measure of clinical improvement through a principal component (PC) analysis on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas, and manic-like symptoms. We then investigated how initial clinical and neural factors predicted this measure during Stage 1 by running a linear model for each brain parcel’s resting-state global brain connectivity (GBC) with individual improvement scores during Stage 1.

The first PC (PC1) was similar across treatment groups at stages 1 and 2, suggesting a shared pattern of symptom improvement. PC1 patients’ scores significantly differed according to treatment, whereas no difference in response was evidenced between groups with the Clinical Global Impressions Scale. Baseline GBC correlated with Stage 1 PC1 scores in the sertraline but not in the placebo group.

Using data-driven reduction of symptom scales, we identified a common profile of symptom improvement with distinct intensity between sertraline and placebo.

Mapping from data-driven symptom improvement onto neural circuits revealed treatment-responsive neural profiles that may aid in optimal patient selection for future trials.

Clinical trials provide the “gold standard” evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources – data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor.

Three examples of real-world trials that leverage different types of data sources – wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived.

Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity.

Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.

Patients with Functional Neurological Disorder (FND) experience neurological symptoms which may impair motor control, sensory function, or awareness. Long waiting lists before treatment mean the risk of relapse during this period is high. A lack of knowledge around FND also results in a lower quality of life. Therefore, it is important patients with FND receive appropriate psychoeducation to empower them to understand and manage their symptoms. We aimed to strengthen our symptom self-management booklet for patients in a community neuropsychiatry setting, using a co-production model and taking forward improvements into a digital audiovisual format.

We used co-production as part of a quality improvement project (QIP) at East Kent Neuropsychiatry Service to identify improvements to our existing symptom self-management booklet and apply these in the production of a digital resource. Initially, the symptom self-management booklet was distributed to 10 patients, awaiting further assessment and treatment, chosen by the multidisciplinary team following triage appointments. Two weeks later, 7 patients reviewed the booklet with 4 medical students by phone and qualitative and quantitative feedback was obtained from patients and carers. Quantitative feedback was collected using an adapted 20-point Ensuring Quality Information for Patients (EQIP) tool. Informed by this feedback, scripts were developed for the audiovisual resource. The scripts were further reviewed by a medical student, 2 multidisciplinary team members and 3 Trust Communications Department members.

The first QIP cycle highlighted the importance of the symptom self-management booklet. Most patients had used the booklet. Patients found it a helpful source of information. Two patients noticed a considerable improvement in their quality of life, others did not due to the short length of booklet use. . EQIP tool demonstrated an improved score of 80.51% compared to previous round of feedback (53.33%). Carers identified the booklet as reassuring. Additional links to external information was identified as an area for development.

Patient feedback informed the development of scripts for the audiovisual resource. Consultation with the Trust Communications Department identified three themes of improvement: accessibility to patients, increased clarity and concise language, and an appropriate visual format, therefore scripts were further refined.

Our QIP shows the value of a psychoeducation and symptom self-management tool for FND patients which was positively received by patients and carers. Collaborating with patients in the digitalisation of this information allows for a more accessible resource which effectively addresses patient concerns and empowers symptom self-management.

The Domain-Specific Risk Taking scale (DOSPERT) has been recommended as a tool for measuring risk attitudes in medical studies, but does not contain items specific to health care. Butler, et al. (2012) developed a medical risk domain subscale for DOSPERT.

To characterize medical risk attitudes in a nationally-representative U.S. sample using the full DOSPERT scale with the medical risk domain add-on (DOSPERT+M), and examine associations with other risk domains.

Members of a nationally-representative online panel (KnowledgePanel®) were randomized to complete pairs of DOSPERT+M tasks (risk attitude, risk perception, expected benefits). We explored relationships among domains through correlational and factor analysis; we tested the hypothesis that the medical risk domain and DOSPERT’s health/safety domains were not highly correlated.

Three hundred forty-four panelists.

The medical risk domain subscale had low inter-item reliability in the risk-taking task and moderate inter-item reliability in the other tasks. Medical risk domain scores were poorly correlated with the DOSPERT health/safety domain. Exploratory factor analysis largely recovered the expected DOSPERT domain structure.

Attitudes toward risky medical activities may constitute a distinct domain from those measured by the standard DOSPERT items. Additional work is required to develop a medical risk subscale with higher inter-item reliability.

The Domain-Specific Risk Taking scale (DOSPERT) has been used to measure risk perceptions and attitudes in several nations and cultures. Takahashi translated DOSPERT to Japanese but DOSPERT responses from Japan have never been reported. Butler et al. (2012) developed an additional medical risk domain subscale to be added to DOSPERT to form DOSPERT+M.

To describe the translation of the medical risk domain subscale to Japanese and to characterize domain-specific risk attitudes in Tokyo.

Members of a probability-weighted online panel representative of the Tokyo metro area were randomized to complete pairs of DOSPERT+M tasks (risk attitude, risk perception, benefit perception). We explored relationships among domains through correlational and factor analysis; we tested the hypothesis that the medical risk domain and DOSPERT’s health/safety domains were uncorrelated.

One hundred eighty panelists.

Six of the original DOSPERT items (two each in the ethics, health/safety, and financial domains) are not useable in Japan according to the Japanese Marketing Research Association code because they ask about participation in illegal activities; we thus used abbreviated versions of those domains leaving out these items. The DOSPERT+M items generally did not cluster cleanly into the expected domains, although items within the same domain usually were intercorrelated. Participants demonstrated domain-specific conventional risk attitudes, although nearly half of those assessed were perceived-risk neutral in all domains. Unlike our recently reported findings in the U.S. population, DOSPERT+M medical domain scores were associated with health/safety domain scores, although they were often more strongly associated with scores in other domains, such as recreational activities.

The DOSPERT (and DOSPERT+M) instruments are problematic in Japan but Japanese citizens may also differ from those of other nations in their risk attitudes and perceptions.

To examine the costs and cost-effectiveness of mirtazapine compared to placebo over 12-week follow-up.

Economic evaluation in a double-blind randomized controlled trial of mirtazapine vs. placebo.

Community settings and care homes in 26 UK centers.

People with probable or possible Alzheimer’s disease and agitation.

Primary outcome included incremental cost of participants’ health and social care per 6-point difference in CMAI score at 12 weeks. Secondary cost-utility analyses examined participants’ and unpaid carers’ gain in quality-adjusted life years (derived from EQ-5D-5L, DEMQOL-Proxy-U, and DEMQOL-U) from the health and social care and societal perspectives.

One hundred and two participants were allocated to each group; 81 mirtazapine and 90 placebo participants completed a 12-week assessment (87 and 95, respectively, completed a 6-week assessment). Mirtazapine and placebo groups did not differ on mean CMAI scores or health and social care costs over the study period, before or after adjustment for center and living arrangement (independent living/care home). On the primary outcome, neither mirtazapine nor placebo could be considered a cost-effective strategy with a high level of confidence. Groups did not differ in terms of participant self- or proxy-rated or carer self-rated quality of life scores, health and social care or societal costs, before or after adjustment.

On cost-effectiveness grounds, the use of mirtazapine cannot be recommended for agitated behaviors in people living with dementia. Effective and cost-effective medications for agitation in dementia remain to be identified in cases where non-pharmacological strategies for managing agitation have been unsuccessful.

The most common treatment for major depressive disorder (MDD) is antidepressant medication (ADM). Results are reported on frequency of ADM use, reasons for use, and perceived effectiveness of use in general population surveys across 20 countries.

Face-to-face interviews with community samples totaling n = 49 919 respondents in the World Health Organization (WHO) World Mental Health (WMH) Surveys asked about ADM use anytime in the prior 12 months in conjunction with validated fully structured diagnostic interviews. Treatment questions were administered independently of diagnoses and asked of all respondents.

3.1% of respondents reported ADM use within the past 12 months. In high-income countries (HICs), depression (49.2%) and anxiety (36.4%) were the most common reasons for use. In low- and middle-income countries (LMICs), depression (38.4%) and sleep problems (31.9%) were the most common reasons for use. Prevalence of use was 2–4 times as high in HICs as LMICs across all examined diagnoses. Newer ADMs were proportionally used more often in HICs than LMICs. Across all conditions, ADMs were reported as very effective by 58.8% of users and somewhat effective by an additional 28.3% of users, with both proportions higher in LMICs than HICs. Neither ADM class nor reason for use was a significant predictor of perceived effectiveness.

ADMs are in widespread use and for a variety of conditions including but going beyond depression and anxiety. In a general population sample from multiple LMICs and HICs, ADMs were widely perceived to be either very or somewhat effective by the people who use them.

Emergency Medical Services (EMS) are designed to respond to and manage patients experiencing life-threatening emergencies; however, not all emergency calls are necessarily emergent and of high acuity. Emergency responses to low-acuity patients affect not only EMS, but other areas of the health care system. However, definitions of low-acuity calls are vague and subjective; therefore, it was necessary to provide a clear description of the low-acuity patient in EMS.

The goal of this study was to develop descriptors for “low-acuity EMS patients” through expert consensus within the EMS environment.

A Modified Delphi survey was used to develop call-out categories and descriptors of low acuity through expert opinion of practitioners within EMS. Purposive, snowball sampling was used to recruit 60 participants, of which 29 completed all three rounds. An online survey tool was used and offered both binary and free-text options to participants. Consensus of 75% was accepted on the binary options while free text offered further proposals for consideration during the survey.

On completion of round two, consensus was obtained on 45% (70/155) of the descriptors, and a further 30% (46/155) consensus was obtained in round three. Experts felt that respiratory distress, unconsciousness, chest pain, and severe hemorrhage cannot be considered low acuity. For other emergency response categories, specific descriptors were offered to denote a case as low acuity.

Descriptors of low acuity in EMS are provided in both medical and trauma cases. These descriptors may not only assist in the reduction of unnecessary response and transport of patients, but also assist in identifying the most appropriate response of EMS resources to call-outs. Further development and validation are required of these descriptors in order to improve accuracy and effectiveness within the EMS dispatch environment.