1-year health outcomes associated with systemic corticosteroids for COVID-19: a longitudinal cohort study.

Background: In patients with coronavirus disease 2019 (COVID-19) requiring supplemental oxygen, dexamethasone reduces acute severity and improves survival, but longer-term effects are unknown. We hypothesised that systemic corticosteroid administration during acute COVID-19 would be associated with improved health-related quality of life (HRQoL) 1 year after discharge. Methods: Adults admitted to hospital between February 2020 and March 2021 for COVID-19 and meeting current guideline recommendations for dexamethasone treatment were included using two prospective UK cohort studies (Post-hospitalisation COVID-19 and the International Severe Acute Respiratory and emerging Infection Consortium). HRQoL, assessed by the EuroQol-Five Dimensions-Five Levels utility index (EQ-5D-5L UI), pre-hospital and 1 year after discharge were compared between those receiving corticosteroids or not after propensity weighting for treatment. Secondary outcomes included patient-reported recovery, physical and mental health status, and measures of organ impairment. Sensitivity analyses were undertaken to account for survival and selection bias. Findings: Of the 1888 participants included in the primary analysis, 1149 received corticosteroids. There was no between-group difference in EQ-5D-5L UI at 1 year (mean difference 0.004, 95% CI -0.026-0.034). A similar reduction in EQ-5D-5L UI was seen at 1 year between corticosteroid exposed and nonexposed groups (mean±sd change -0.12±0.22 versus -0.11±0.22). Overall, there were no differences in secondary outcome measures. After sensitivity analyses modelled using a cohort of 109 318 patients admitted to hospital with COVID-19, EQ-5D-5L UI at 1 year remained similar between the two groups. Interpretation: Systemic corticosteroids for acute COVID-19 have no impact on the large reduction in HRQoL 1 year after hospital discharge. Treatments to address the persistent reduction in HRQoL are urgently needed.

Artificial intelligence and cybercrime: implications for individuals and the healthcare sector.

The malicious use of artificial intelligence is growing rapidly, creating major security threats for individuals and the healthcare sector. Individuals with mental illness may be especially vulnerable. Healthcare provider data are a prime target for cybercriminals. There is a need to improve cybersecurity to detect and prevent cyberattacks against individuals and the healthcare sector, including the use of artificial intelligence predictive tools.

Cognitive and psychiatric symptom trajectories 2-3 years after hospital admission for COVID-19: a longitudinal, prospective cohort study in the UK.

BACKGROUND: COVID-19 is known to be associated with increased risks of cognitive and psychiatric outcomes after the acute phase of disease. We aimed to assess whether these symptoms can emerge or persist more than 1 year after hospitalisation for COVID-19, to identify which early aspects of COVID-19 illness predict longer-term symptoms, and to establish how these symptoms relate to occupational functioning. METHODS: The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a prospective, longitudinal cohort study of adults (aged ≥18 years) who were hospitalised with a clinical diagnosis of COVID-19 at participating National Health Service hospitals across the UK. In the C-Fog study, a subset of PHOSP-COVID participants who consented to be recontacted for other research were invited to complete a computerised cognitive assessment and clinical scales between 2 years and 3 years after hospital admission. Participants completed eight cognitive tasks, covering eight cognitive domains, from the Cognitron battery, in addition to the 9-item Patient Health Questionnaire for depression, the Generalised Anxiety Disorder 7-item scale, the Functional Assessment of Chronic Illness Therapy Fatigue Scale, and the 20-item Cognitive Change Index (CCI-20) questionnaire to assess subjective cognitive decline. We evaluated how the absolute risks of symptoms evolved between follow-ups at 6 months, 12 months, and 2-3 years, and whether symptoms at 2-3 years were predicted by earlier aspects of COVID-19 illness. Participants completed an occupation change questionnaire to establish whether their occupation or working status had changed and, if so, why. We assessed which symptoms at 2-3 years were associated with occupation change. People with lived experience were involved in the study. FINDINGS: 2469 PHOSP-COVID participants were invited to participate in the C-Fog study, and 475 participants (191 [40·2%] females and 284 [59·8%] males; mean age 58·26 [SD 11·13] years) who were discharged from one of 83 hospitals provided data at the 2-3-year follow-up. Participants had worse cognitive scores than would be expected on the basis of their sociodemographic characteristics across all cognitive domains tested (average score 0·71 SD below the mean [IQR 0·16-1·04]; p<0·0001). Most participants reported at least mild depression (263 [74·5%] of 353), anxiety (189 [53·5%] of 353), fatigue (220 [62·3%] of 353), or subjective cognitive decline (184 [52·1%] of 353), and more than a fifth reported severe depression (79 [22·4%] of 353), fatigue (87 [24·6%] of 353), or subjective cognitive decline (88 [24·9%] of 353). Depression, anxiety, and fatigue were worse at 2-3 years than at 6 months or 12 months, with evidence of both worsening of existing symptoms and emergence of new symptoms. Symptoms at 2-3 years were not predicted by the severity of acute COVID-19 illness, but were strongly predicted by the degree of recovery at 6 months (explaining 35·0-48·8% of the variance in anxiety, depression, fatigue, and subjective cognitive decline); by a biocognitive profile linking acutely raised D-dimer relative to C-reactive protein with subjective cognitive deficits at 6 months (explaining 7·0-17·2% of the variance in anxiety, depression, fatigue, and subjective cognitive decline); and by anxiety, depression, fatigue, and subjective cognitive deficit at 6 months. Objective cognitive deficits at 2-3 years were not predicted by any of the factors tested, except for cognitive deficits at 6 months, explaining 10·6% of their variance. 95 of 353 participants (26·9% [95% CI 22·6-31·8]) reported occupational change, with poor health being the most common reason for this change. Occupation change was strongly and specifically associated with objective cognitive deficits (odds ratio [OR] 1·51 [95% CI 1·04-2·22] for every SD decrease in overall cognitive score) and subjective cognitive decline (OR 1·54 [1·21-1·98] for every point increase in CCI-20). INTERPRETATION: Psychiatric and cognitive symptoms appear to increase over the first 2-3 years post-hospitalisation due to both worsening of symptoms already present at 6 months and emergence of new symptoms. New symptoms occur mostly in people with other symptoms already present at 6 months. Early identification and management of symptoms might therefore be an effective strategy to prevent later onset of a complex syndrome. Occupation change is common and associated mainly with objective and subjective cognitive deficits. Interventions to promote cognitive recovery or to prevent cognitive decline are therefore needed to limit the functional and economic impacts of COVID-19. FUNDING: National Institute for Health and Care Research Oxford Health Biomedical Research Centre, Wolfson Foundation, MQ Mental Health Research, MRC-UK Research and Innovation, and National Institute for Health and Care Research.

Implications of Online Self-Diagnosis in Psychiatry.

Online self-diagnosis of psychiatric disorders by the general public is increasing. The reasons for the increase include the expansion of Internet technologies and the use of social media, the rapid growth of direct-to-consumer e-commerce in healthcare, and the increased emphasis on patient involvement in decision making. The publicity given to artificial intelligence (AI) has also contributed to the increased use of online screening tools by the general public. This paper aims to review factors contributing to the expansion of online self-diagnosis by the general public, and discuss both the risks and benefits of online self-diagnosis of psychiatric disorders. A narrative review was performed with examples obtained from the scientific literature and commercial articles written for the general public. Online self-diagnosis of psychiatric disorders is growing rapidly. Some people with a positive result on a screening tool will seek professional help. However, there are many potential risks for patients who self-diagnose, including an incorrect or dangerous diagnosis, increased patient anxiety about the diagnosis, obtaining unfiltered advice on social media, using the self-diagnosis to self-treat, including online purchase of medications without a prescription, and technical issues including the loss of privacy. Physicians need to be aware of the increase in self-diagnosis by the general public and the potential risks, both medical and technical. Psychiatrists must recognize that the general public is often unaware of the challenging medical and technical issues involved in the diagnosis of a mental disorder, and be ready to treat patients who have already obtained an online self-diagnosis.

Co-design of 'Ways of Being', a web-based experience to optimise online arts and culture for mental health in young people.

AIMS AND METHOD: We aimed to co-design an intervention optimising the benefits of online arts and culture for mental health in young people for subsequent testing in a trial. Co-design followed the double diamond phases of design, discover, define, develop and deliver. RESULTS: Navigating the views of all co-designers to produce a testable resource demanded in-depth understanding, and frequent iterations in multiple modalities of the theoretical basis of the intervention, amplification of youth voice and commitment to a common goal. CLINICAL IMPLICATIONS: Co-design with a broad range of collaborators with a shared vision was valued by young co-designers and produced an effective intervention. Co-design allowed the theoretical basis to be followed and refined to create an engaging, practical and testable web experience, aiming to optimise the mental health benefits of online arts and culture for young people in a randomised controlled trial.

Post-acute COVID-19 neuropsychiatric symptoms are not associated with ongoing nervous system injury.

A proportion of patients infected with severe acute respiratory syndrome coronavirus 2 experience a range of neuropsychiatric symptoms months after infection, including cognitive deficits, depression and anxiety. The mechanisms underpinning such symptoms remain elusive. Recent research has demonstrated that nervous system injury can occur during COVID-19. Whether ongoing neural injury in the months after COVID-19 accounts for the ongoing or emergent neuropsychiatric symptoms is unclear. Within a large prospective cohort study of adult survivors who were hospitalized for severe acute respiratory syndrome coronavirus 2 infection, we analysed plasma markers of nervous system injury and astrocytic activation, measured 6 months post-infection: neurofilament light, glial fibrillary acidic protein and total tau protein. We assessed whether these markers were associated with the severity of the acute COVID-19 illness and with post-acute neuropsychiatric symptoms (as measured by the Patient Health Questionnaire for depression, the General Anxiety Disorder assessment for anxiety, the Montreal Cognitive Assessment for objective cognitive deficit and the cognitive items of the Patient Symptom Questionnaire for subjective cognitive deficit) at 6 months and 1 year post-hospital discharge from COVID-19. No robust associations were found between markers of nervous system injury and severity of acute COVID-19 (except for an association of small effect size between duration of admission and neurofilament light) nor with post-acute neuropsychiatric symptoms. These results suggest that ongoing neuropsychiatric symptoms are not due to ongoing neural injury.

Artificial intelligence and increasing misinformation.

With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI. However, generative AI models may be unreliable, routinely make errors and widely spread misinformation. Misinformation created by generative AI about mental illness may include factual errors, nonsense, fabricated sources and dangerous advice. Psychiatrists need to recognise that patients may receive misinformation online, including about medicine and psychiatry.

Pramipexole Enhances Reward Learning by Preserving Value Estimates.

BACKGROUND: Dopamine D2-like agonists show promise as treatments for depression. They are thought to act by enhancing reward learning; however, the mechanisms by which they achieve this are not clear. Reinforcement learning accounts describe 3 distinct candidate mechanisms: increased reward sensitivity, increased inverse decision-temperature, and decreased value decay. As these mechanisms produce equivalent effects on behavior, arbitrating between them requires measurement of how expectations and prediction errors are altered. We characterized the effects of 2 weeks of the D2-like agonist pramipexole on reward learning and used functional magnetic resonance imaging measures of expectation and prediction error to assess which of these 3 mechanistic processes were responsible for the behavioral effects. METHODS: Forty healthy volunteers (50% female) were randomized to 2 weeks of pramipexole (titrated to 1 mg/day) or placebo in a double-blind, between-subject design. Participants completed a probabilistic instrumental learning task before and after the pharmacological intervention, with functional magnetic resonance imaging data collected at the second visit. Asymptotic choice accuracy and a reinforcement learning model were used to assess reward learning. RESULTS: Pramipexole increased choice accuracy in the reward condition with no effect on losses. Participants who received pramipexole had increased blood oxygen level-dependent response in the orbital frontal cortex during the expectation of win trials but decreased blood oxygen level-dependent response to reward prediction errors in the ventromedial prefrontal cortex. This pattern of results indicates that pramipexole enhances choice accuracy by reducing the decay of estimated values during reward learning. CONCLUSIONS: The D2-like receptor agonist pramipexole enhances reward learning by preserving learned values. This is a plausible mechanism for pramipexole's antidepressant effect.

Individual-level risk factors for suicide mortality in the general population: an umbrella review.

BACKGROUND: Deaths by suicide remain a major public health challenge worldwide. Identifying and targeting risk factors for suicide mortality is a potential approach to prevention. We aimed to summarise current knowledge on the range and magnitude of individual-level risk factors for suicide mortality in the general population and evaluate the quality of the evidence. METHODS: In this umbrella review, five bibliographic databases were systematically searched for articles published from database inception to Aug 31, 2022. We included meta-analyses of observational studies on individual-level risk factors for suicide mortality in the general population. Biological, genetic, perinatal, and ecological risk factors were beyond the scope of this study. Effect sizes were synthesised and compared across domains. To test robustness and consistency of the findings, evidence for small-study effects and excess significance bias (ie, the ratio between the overall meta-analysis effect size and that of its largest included study) was examined, and prediction intervals were calculated. Risk of bias was assessed by the Risk of Bias in Systematic Reviews instrument. The protocol was pre-registered with PROSPERO (CRD42021230119). FINDINGS: We identified 33 meta-analyses on 38 risk factors for suicide mortality in the general population. 422 (93%) of the 454 primary studies included in the meta-analyses were from high-income countries. A previous suicide attempt and suicidal ideation emerged as strong risk factors (with effect sizes ranging from 6 to 16). Psychiatric disorders were associated with a greatly elevated risk of suicide mortality, with risk ratios in the range of 4-13. Suicide risk for physical illnesses (such as cancer and epilepsy) and sociodemographic factors (including unemployment and low education) were typically increased two-fold. Contact with the criminal justice system, state care in childhood, access to firearms, and parental death by suicide also increased the risk of suicide mortality. Among risk factors for which sex-stratified analyses were available, associations were generally similar for males and females. However, the quality of the evidence was limited by excess significance and high heterogeneity, and prediction intervals suggested poor replicability for almost two-thirds of identified risk factors. INTERPRETATION: A wide range of risk factors were identified across various domains, which underscores suicide mortality as a multifactorial phenomenon. Prevention strategies that span individual and population approaches should account for the identified factors and their relative strengths. Despite the large number of risk factors investigated, few associations were supported by robust evidence. Evidence of causal inference will need to be tested in high-quality study designs. FUNDING: Wellcome Trust.

Count Me In: an inclusive approach towards patient recruitment for clinical research studies in the NHS.

BACKGROUND: Participation in clinical research is associated with better patient outcomes and higher staff retention and satisfaction rates. Nevertheless, patient recruitment to mental health studies is challenging due to a reliance on clinician or patient referrals (standard approach). To empower patients and make healthcare research more equitable, we explored a novel researcher-led approach, called 'Count Me In' (CMI). OBJECTIVE: To evaluate a 12-month implementation of CMI in a routine clinical setting. METHODS: CMI was launched in August 2021 in a mental health National Health Service (NHS) Trust in England. Patients (aged 18+) learnt about CMI at their initial clinical appointment. Unless they opted out, they became contactable for research (via research informatics searches). FINDINGS: After 12 months, 368 patients opted out and 22 741 became contactable through CMI, including 2716 through the standard approach and 20 025 through electronic searches (637% increase). Of those identified via electronic searches, 738 were contacted about specific studies and 270 consented to participate. Five themes were identified based on patient and staff experiences of CMI: 'level of awareness and accessibility of CMI', 'perceptions of research and perceived engagement with CMI', 'inclusive research practice', 'engagement and incentives for research participation', and 'relationships between clinical and research settings'. CONCLUSIONS: CMI (vs standard) led to a larger and diverse patient cohort and was favoured by patients and staff. Yet a shift in the NHS research culture is needed to ensure that this diversity translates to actual research participation. CLINICAL IMPLICATIONS: Through collaboration with other NHS Trusts and services, key funders (National Institute for Health and Care Research) and new national initiatives (Office for Life Sciences Mental Health Mission), CMI has the potential to address recruitment challenges through rapid patient recruitment into time-sensitive country-wide studies.

Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review.

This narrative review discusses how the safe and effective use of clinical artificial intelligence (AI) prediction tools requires recognition of the importance of human intelligence. Human intelligence, creativity, situational awareness, and professional knowledge, are required for successful implementation. The implementation of clinical AI prediction tools may change the workflow in medical practice resulting in new challenges and safety implications. Human understanding of how a clinical AI prediction tool performs in routine and exceptional situations is fundamental to successful implementation. Physicians must be involved in all aspects of the selection, implementation, and ongoing product monitoring of clinical AI prediction tools.

Acute blood biomarker profiles predict cognitive deficits 6 and 12 months after COVID-19 hospitalization.

Post-COVID cognitive deficits, including 'brain fog', are clinically complex, with both objective and subjective components. They are common and debilitating, and can affect the ability to work, yet their biological underpinnings remain unknown. In this prospective cohort study of 1,837 adults hospitalized with COVID-19, we identified two distinct biomarker profiles measured during the acute admission, which predict cognitive outcomes 6 and 12 months after COVID-19. A first profile links elevated fibrinogen relative to C-reactive protein with both objective and subjective cognitive deficits. A second profile links elevated D-dimer relative to C-reactive protein with subjective cognitive deficits and occupational impact. This second profile was mediated by fatigue and shortness of breath. Neither profile was significantly mediated by depression or anxiety. Results were robust across secondary analyses. They were replicated, and their specificity to COVID-19 tested, in a large-scale electronic health records dataset. These findings provide insights into the heterogeneous biology of post-COVID cognitive deficits.

Modeling transit time distributions in microvascular networks.

The time a red blood cell (RBC) spends in the microvasculature is of prime importance for a number of physiological processes. In this work, we present a methodology for computing an approximation of the so-called transit time distribution (TTD), i.e., the probabilistic description of how long a RBC will reside within the network. As a proof of concept, we apply this methodology to three flavors of the mesh networks. We show that each network type supports multiple distinct steady-state configurations and we present tools for analyzing the associated collection of TTDs, ranging from standard measures like mean capillary transit time (MCTT) and capillary transit time heterogeneity (CTTH) to novel metrics.

Telepsychiatry in an Era of Digital Mental Health Startups.

PURPOSE OF REVIEW: Telepsychiatry practiced by psychiatrists is evidence-based, regulated, private, and effective in diverse settings. The use of telemedicine has grown since the COVID-19 pandemic as people routinely obtain more healthcare services online. At the same time, there has been a rapid increase in the number of digital mental health startups that offer various services including online therapy and access to prescription medications. These digital mental health firms advertise directly to the consumer primarily through digital advertising. The purpose of this narrative review is to contrast traditional telepsychiatry and the digital mental health market related to online therapy. RECENT FINDINGS: In contrast to standard telepsychiatry, most of the digital mental health startups are unregulated, have unproven efficacy, and raise concerns related to self-diagnosis, self-medicating, and inappropriate prescribing. The role of digital mental health firms for people with serious mental illness has not been determined. There are inadequate privacy controls for the digital mental health firms, including for online therapy. We live in an age where there is widespread admiration for technology entrepreneurs and increasing emphasis on the role of the patient as a consumer. Yet, the business practices of digital mental health startups may compromise patient safety for profits. There is a need to address issues with the digital mental health startups and to educate patients about the differences between standard medical care and digital mental health products.

Community interventions for anxiety and depression in adults and young people: A systematic review.

BACKGROUND: Growing evidence suggests that community-based interventions may be effective for anxiety and depression. This study aimed to describe studies of community interventions delivered to adults and/or young people, either in person or online, evaluated in randomised controlled trials and provide an indication as to their effectiveness, acceptability, quality of data and where possible, mechanisms of action. We included interventions delivered at and/or by museums, art galleries, libraries, gardens, music groups/choirs and sports clubs. METHOD: We developed and followed a preregistered protocol: PROSPERO CRD42020204471. Randomised controlled trials in adults and young people were identified in an extensive search with no date/time, language, document type and publication status limitations. Studies were selected according to predetermined eligibility criteria and data independently extracted and then assessed using Risk of Bias 1. The studies were deemed too heterogeneous for meta-analysis and were therefore reported using a narrative synthesis. RESULTS: Our analysis included 31 studies, with 2898 participants. Community interventions most studied in randomised controlled trials were community music (12 studies, 1432 participants), community exercise (14 studies, 955 participants) and community gardens/gardening (6 studies, 335 participants). The majority of studies were from high-income countries - many were in specific populations (such as those with physical health problems) and were generally of low quality. Dropout rates across the included studies were low (1 participant on average per 100 participants). The inadequate description of interventions limited identification of potential mechanisms of action. DISCUSSION: The uncertainty of the evidence allows only a weak recommendation in support of community interventions for anxiety and depression. The results suggest community engagement is a promising area for wide-reaching interventions to be implemented and evaluated, but more high-quality trials are needed, especially in young people and under-represented communities.

A co-produced online cultural experience compared to a typical museum website for mental health in people aged 16-24: A proof-of-principle randomised controlled trial.

OBJECTIVE: We aimed to compare a co-produced online intervention encompassing the diverse human stories behind art and artefacts, named Ways of Being (WoB), with a typical museum website, the Ashmolean (Ash) on negative affect (NA), positive affect (PA) and psychological distress (K10). METHODS: In this parallel group RCT, 463 YP aged 16-24 were randomly assigned, 231 to WoB and 232 to Ash. RESULTS: Over the intervention phase (an aggregate score including all post-allocation timepoints to day-five) a group difference was apparent in favour of WoB for NA (WoB-Ash n=448, NA -0.158, p=0.010) but no differences were detected for PA or K10 and differences were not detected at week six. Group differences in NA in favour of WoB were detected in specific subgroups, e.g. ethnic minorities and males. Across participants (from both groups) mean K10 and NA improved between baseline and six weeks despite increased COVID-19 restrictions. Trial recruitment was rapid, retention high and feedback positive with broad geographical, occupational and ethnic diversity. CONCLUSIONS: Online engagement with arts and culture has the potential to impact on mental health in a measurable way in YP with high unmet mental health needs.

User perspectives on long-term remote active electronic self-monitoring of mood symptoms in bipolar spectrum disorders.

BACKGROUND: User feedback is crucial in the development of electronic self-monitoring tools for bipolar spectrum disorders (BSD). Previous studies have examined user experiences in small samples self-monitoring over relatively short time periods. We aimed to explore the experiences of a large sample of individuals with BSD engaged in long-term remote active electronic self-monitoring. METHODS: An online survey, containing closed and open questions, was sent to participants with BSD enrolled on the Bipolar Disorder Research Network (BDRN) True Colours mood-monitoring system. Questions related to experiences of using True Colours, including viewing mood graphs, and sharing data with healthcare professionals (HCPs) and/or family/friends. RESULTS: Response rate was 62.7 % (n = 362). 88.4 % reported finding using True Colours helpful. Commonly reported benefits were having a visual record of mood changes, patterns/triggers and identifying early warning signs. Limitations included questions not being comprehensive or revealing anything new. One third had shared their graphs, with 89.9 % finding it helpful to share with HCPs and 78.7 % helpful to share with family/friends. Perceived benefits included aiding communication and limitations included lack of interest/understanding from others. LIMITATIONS: Responder bias may be present. Findings may not be generalisable to all research cohorts. CONCLUSIONS: The majority of participants valued long-term self-monitoring. Personalisation and ease of use were important. A potential challenge is continued use when mood is long-term stable, highlighting the need for measures to be sensitive to small changes. Sharing self-monitoring data with HCPs may enhance communication of the lived experience of those with BSD. Future research should examine HCPs' perspectives.