Search results

Medication Exposure and Mortality in Patients With Schizophrenia.

IMPORTANCE: The use of antipsychotics, antidepressants, and benzodiazepines may influence the risk of mortality in people with schizophrenia. However, many observational studies have not accounted for immortal time bias (ITB), which occurs when there is a period during which patients in the exposed group are necessarily alive and misclassified as exposed (the period between start of follow-up and initiation of drug). Ignoring ITB may lead to misinterpretation of the association between these drugs and mortality. OBJECTIVES: To examine whether the cumulative dose of antipsychotics, antidepressants, and benzodiazepines is associated with mortality risk in patients with schizophrenia and discuss the potential impacts of ignoring ITB. DESIGN, SETTING, AND PARTICIPANTS: This cohort study used administrative data from Québec, Canada, including patients aged 17 to 64 years diagnosed with schizophrenia between January 1, 2002, and December 31, 2012. Data analysis was performed from June 22, 2022, to September 30, 2024. MAIN OUTCOMES AND MEASURES: The primary outcome was all-cause mortality, with follow-up from January 1, 2013, to December 31, 2017, or until death. Mortality risk was assessed for low, moderate, and high exposure to antipsychotics, antidepressants, and benzodiazepines. Cox proportional hazards regression models with time-fixed exposure (not controlling for ITB) and time-dependent exposure (controlling for ITB) were performed. RESULTS: The cohort included 32 240 patients (mean [SD] age, 46.1 [11.6] years; 19 776 [61.3%] men), of whom 1941 (6.0%) died during follow-up. No dose-response association was found for antipsychotics with mortality using the time-fixed method. However, high-dose antipsychotic use was associated with increased mortality after correcting for ITB (adjusted hazard ratio [AHR], 1.28; 95% CI, 1.07-1.55; P = .008). Antidepressants showed a reduced mortality risk using the time-fixed method, but only at high doses when correcting for ITB (AHR, 0.86; 95% CI, 0.74-1.00; P = .047). Benzodiazepines were associated with increased mortality risk regardless of the method. CONCLUSIONS AND RELEVANCE: The findings of this study do not dispute the known efficacy of antipsychotics in schizophrenia, but they call into question the magnitude of long-term mortality benefits.

Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN.

Several multivariate prognostic models have been published to predict outcomes in patients with first episode psychosis (FEP), but it remains unclear whether those predictions generalize to independent populations. Using a subset of demographic and clinical baseline predictors, we aimed to develop and externally validate different models predicting functional outcome after a FEP in the context of a schizophrenia-spectrum disorder (FES), based on a previously published cross-validation and machine learning pipeline. A crossover validation approach was adopted in two large, international cohorts (EUFEST, n = 338, and the PSYSCAN FES cohort, n = 226). Scores on the Global Assessment of Functioning scale (GAF) at 12 month follow-up were dichotomized to differentiate between poor (GAF current 

Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study.

BACKGROUND: Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for changes in risk over time. METHODS: We included 158,139 patients (5007 events) who received a first index diagnosis of a nonorganic and nonpsychotic mental disorder within electronic health records from the South London and Maudsley National Health Service Foundation Trust between January 1, 2008, and October 8, 2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement. The dynamic model included 24 predictors extracted at 9 landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): 3 demographic, 1 clinical, and 20 natural language processing-based symptom and substance use predictors. Performance was compared with a static Cox regression model with all predictors assessed at baseline only and indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation. RESULTS: The dynamic model improved discrimination performance from baseline compared with the static model (dynamic: C-index = 0.9; static: C-index = 0.87) and the final landmark point (dynamic: C-index = 0.79; static: C-index = 0.76). The dynamic model was also significantly better calibrated (calibration slope = 0.97-1.1) than the static model at later landmark points (≥24 months). Net benefit was higher for the dynamic than for the static model at later landmark points (≥24 months). CONCLUSIONS: These findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.

Individualized pretest risk estimates to guide treatment decisions in patients with clinical high risk for psychotic disorders.

INTRODUCTION: Clinical high risk for psychosis (CHR) states are associated with an increased risk of transition to psychosis. However, the predictive value of CHR screening interviews is dependent on pretest risk enrichment in referred patients. This poses a major obstacle to CHR outreach campaigns since they invariably lead to risk dilution through enhanced awareness. A potential compensatory strategy is to use estimates of individual pretest risk as a 'gatekeeper' for specialized assessment. We aimed to test a risk stratification model previously developed in London, UK (OASIS) and to train a new predictive model for the Swiss population. METHOD: The sample was composed of 513 individuals referred for CHR assessment from six Swiss early psychosis detection services. Sociodemographic variables available at referral were used as predictors whereas the outcome variable was transition to psychosis. RESULTS: Replication of the risk stratification model developed in OASIS resulted in poor performance (Harrel's c=0.51). Retraining resulted in moderate discrimination (Harrel's c=0.67) which significantly differentiated between different risk groups. The lowest risk group had a cumulative transition incidence of 6.4% (CI: 0-23.1%) over two years. CONCLUSION: Failure to replicate the OASIS risk stratification model might reflect differences in the public health care systems and referral structures between Switzerland and London. Retraining resulted in a model with adequate discrimination performance. The developed model in combination with CHR assessment result, might be useful for identifying individuals with high pretest risk, who might benefit most from specialized intervention.

Duration of Untreated Psychosis and Outcomes in First-Episode Psychosis: Systematic Review and Meta-analysis of Early Detection and Intervention Strategies.

BACKGROUND: The role of duration of untreated psychosis (DUP) as an early detection and intervention target to improve outcomes for individuals with first-episode psychosis is unknown. STUDY DESIGN: PRISMA/MOOSE-compliant systematic review to identify studies until February 1, 2023, with an intervention and a control group, reporting DUP in both groups. Random effects meta-analysis to evaluate (1) differences in DUP in early detection/intervention services vs the control group, (2) the efficacy of early detection strategies regarding eight real-world outcomes at baseline (service entry), and (3) the efficacy of early intervention strategies on ten real-world outcomes at follow-up. We conducted quality assessment, heterogeneity, publication bias, and meta-regression analyses (PROSPERO: CRD42020163640). STUDY RESULTS: From 6229 citations, 33 intervention studies were retrieved. The intervention group achieved a small DUP reduction (Hedges' g = 0.168, 95% CI = 0.055-0.283) vs the control group. The early detection group had better functioning levels (g = 0.281, 95% CI = 0.073-0.488) at baseline. Both groups did not differ regarding total psychopathology, admission rates, quality of life, positive/negative/depressive symptoms, and employment rates (P > .05). Early interventions improved quality of life (g = 0.600, 95% CI = 0.408-0.791), employment rates (g = 0.427, 95% CI = 0.135-0.718), negative symptoms (g = 0.417, 95% CI = 0.153-0.682), relapse rates (g = 0.364, 95% CI = 0.117-0.612), admissions rates (g = 0.335, 95% CI = 0.198-0.468), total psychopathology (g = 0.298, 95% CI = 0.014-0.582), depressive symptoms (g = 0.268, 95% CI = 0.008-0.528), and functioning (g = 0.180, 95% CI = 0.065-0.295) at follow-up but not positive symptoms or remission (P > .05). CONCLUSIONS: Comparing interventions targeting DUP and control groups, the impact of early detection strategies on DUP and other correlates is limited. However, the impact of early intervention was significant regarding relevant outcomes, underscoring the importance of supporting early intervention services worldwide.

Why we need to pursue both universal and targeted prevention to reduce the incidence of affective and psychotic disorders: Systematic review and meta-analysis.

The effectiveness of universal preventive approaches in reducing the incidence of affective/psychotic disorders is unclear. We therefore aimed to synthesise the available evidence from randomised controlled trials. For studies reporting change in prevalence, we simulated all possible scenarios for the proportion of individuals with the disorder at baseline and at follow-up to exclude them. We then combined these data with studies directly measuring incidence and conducted random effects meta-analysis with relative risk (RR) to estimate the incidence in the intervention group compared to the control group. Eighteen studies (k=21 samples) were included investigating the universal prevention of depression in 66,625 individuals. No studies were available investigating universal prevention on the incidence of bipolar/psychotic disorders. 63 % of simulated scenarios showed a significant preventive effect on reducing the incidence of depression (k=9 - 19, RR=0.75-0.94, 95 %CIs=0.55-0.87,0.93-1.15, p=0.007-0.246) but did not survive sensitivity analyses. There is some limited evidence for the effectiveness of universal interventions for reducing the incidence of depression but not for bipolar/psychotic disorders.

Cannabidiol does not attenuate acute delta-9-tetrahydrocannabinol-induced attentional bias in healthy volunteers: A randomised, double-blind, cross-over study.

AIMS: To test how attentional bias and explicit liking are influenced by delta-9-tetrahydrocannabinol (THC) and whether these effects are moderated by cannabidiol (CBD). DESIGN: Double-blind, randomised, within-subjects cross-over study. SETTING: NIHR Wellcome Trust Clinical Research Facility at King's College Hospital, London, United Kingdom. PARTICIPANTS/CASES: Forty-six infrequent cannabis users (cannabis use <1 per week). INTERVENTION(S): Across four sessions, participants inhaled vaporised cannabis containing 10 mg of THC and either 0 mg (0:1 CBD:THC), 10 mg (1:1), 20 mg (2:1) or 30 mg (3:1) of CBD, administered in a randomised order and counter-balanced across participants (a total of 24 order groups). MEASUREMENTS: Participants completed two tasks: (1) Attentional Bias (AB), comparing reaction times toward visual probes presented behind 28 target stimuli (cannabis/food) compared with probes behind corresponding non-target (neutral) stimuli. Participants responding more quickly to probes behind target than non-target stimuli would indicate greater attentional bias to cannabis/food; (2) Picture Rating (PR), where all AB stimuli were rated on a 7-point pleasantness scale, measuring explicit liking. FINDINGS: During the AB task, participants were more biased toward cannabis stimuli in the 0:1 condition compared with baseline (mean difference = 12.2, 95% confidence intervals [CIs] = 1.20-23.3, d = 0.41, P = 0.03). No other significant AB or PR differences were found between cannabis and food stimuli between baseline and 0:1 condition (P > 0.05). No significant CBD effect was found on AB or PR task performance at any dose (P > 0.05). There was additionally no cumulative effect of THC exposure on AB or PR outcomes (P > 0.05). CONCLUSIONS: A double-blind, randomised, cross-over study among infrequent cannabis users found that inhaled delta-9-tetrahydrocannabinol increased attentional bias toward cannabis in the absence of explicit liking, a marker of liability toward cannabis use disorder. At the concentrations normally found in legal and illegal cannabis, cannabidiol had no influence on this effect.

The effects of acute hyperglycaemia on sports and exercise performance in type 1 diabetes: A systematic review and meta-analysis.

OBJECTIVES: People with type 1 diabetes (T1D) are advised by health care professionals to target mild hyperglycaemia before and during exercise, to reduce the risk of hypoglycaemia. This review aimed to summarise the available evidence on the effects of acute hyperglycaemia on sports and exercise performance in T1D. DESIGN: Systematic review and meta-analysis. METHODS: Medline, EMBASE, CENTRAL, and Web of Science were searched until 29th May 2023 for studies investigating the effects of acute hyperglycaemia on any sports or exercise performance outcome in T1D. Random-effects meta-analysis was performed using standardised mean differences (SMD) when more than one study reported data for similar outcomes. Certainty of evidence for each outcome was assessed using GRADE. RESULTS: Seven studies were included in the review, comprising data from 119 people with T1D. Meta-analysis provided moderate-certainty evidence that acute hyperglycaemia does not significantly affect aerobic exercise performance (SMD -0.17; 95 % CI -0.59, 0.26; p = 0.44). There is low- or very-low certainty evidence that acute hyperglycaemia has no effect on anaerobic (two outcomes), neuromuscular (seven outcomes) or neurocognitive performance (three outcomes), except impaired isometric knee extension strength. One study provided low-certainty evidence that the performance effects of hyperglycaemia may depend on circulating insulin levels. CONCLUSIONS: Acute hyperglycaemia before or during exercise appears unlikely to affect aerobic performance to an extent that is relevant to most people with T1D, based on limited evidence. Future research in this field should focus on anaerobic, neuromuscular and neurocognitive performance, and examine the relevance of circulating insulin levels.

Biomarkers for Psychosis: Are We There Yet? Umbrella Review of 1478 Biomarkers.

BACKGROUND AND HYPOTHESIS: This umbrella review aims to comprehensively synthesize the evidence of association between peripheral, electrophysiological, neuroimaging, neuropathological, and other biomarkers and diagnosis of psychotic disorders. STUDY DESIGN: We selected systematic reviews and meta-analyses of observational studies on diagnostic biomarkers for psychotic disorders, published until February 1, 2018. Data extraction was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Evidence of association between biomarkers and psychotic disorders was classified as convincing, highly suggestive, suggestive, weak, or non-significant, using a standardized classification. Quality analyses used the Assessment of Multiple Systematic Reviews (AMSTAR) tool. STUDY RESULTS: The umbrella review included 110 meta-analyses or systematic reviews corresponding to 3892 individual studies, 1478 biomarkers, and 392 210 participants. No factor showed a convincing level of evidence. Highly suggestive evidence was observed for transglutaminase autoantibodies levels (odds ratio [OR] = 7.32; 95% CI: 3.36, 15.94), mismatch negativity in auditory event-related potentials (standardized mean difference [SMD] = 0.73; 95% CI: 0.5, 0.96), P300 component latency (SMD = -0.6; 95% CI: -0.83, -0.38), ventricle-brain ratio (SMD = 0.61; 95% CI: 0.5, 0.71), and minor physical anomalies (SMD = 0.99; 95% CI: 0.64, 1.34). Suggestive evidence was observed for folate, malondialdehyde, brain-derived neurotrophic factor, homocysteine, P50 sensory gating (P50 S2/S1 ratio), frontal N-acetyl-aspartate, and high-frequency heart rate variability. Among the remaining biomarkers, weak evidence was found for 626 and a non-significant association for 833 factors. CONCLUSIONS: While several biomarkers present highly suggestive or suggestive evidence of association with psychotic disorders, methodological biases, and underpowered studies call for future higher-quality research.

Parsing neurobiological heterogeneity of the clinical high-risk state for psychosis: A pseudo-continuous arterial spin labelling study.

INTRODUCTION: The impact of the clinical high-risk for psychosis (CHR-P) construct is dependent on accurately predicting outcomes. Individuals with brief limited intermittent psychotic symptoms (BLIPS) have higher risk of developing a first episode of psychosis (FEP) compared to individuals with attenuated psychotic symptoms (APS). Supplementing subgroup stratification with information from candidate biomarkers based on neurobiological parameters, such as resting-state, regional cerebral blood flow (rCBF), may help refine risk estimates. Based on previous evidence, we hypothesized that individuals with BLIPS would exhibit increased rCBF compared to APS in key regions linked to dopaminergic pathways. METHODS: Data from four studies were combined using ComBat (to account for between-study differences) to analyse rCBF in 150 age- and sex-matched subjects (n = 30 healthy controls [HCs], n = 80 APS, n = 20 BLIPS and n = 20 FEP). Global gray matter (GM) rCBF was examined in addition to region-of-interest (ROI) analyses in bilateral/left/right frontal cortex, hippocampus and striatum. Group differences were assessed using general linear models: (i) alone; (ii) with global GM rCBF as a covariate; (iii) with global GM rCBF and smoking status as covariates. Significance was set at p  0.05). All results were robust to addition of covariates (p > 0.05). No significant clusters were identified in whole-brain voxel-wise analyses (p > 0.05FWE). Weak-to-moderate evidence was found for an absence of rCBF differences between APS and BLIPS in Bayesian ROI analyses. CONCLUSION: On this evidence, APS and BLIPS are unlikely to be neurobiologically distinct. Due to this and the weak-to-moderate evidence for the null hypothesis, future research should investigate larger samples of APS and BLIPS through collaboration across large-scale international consortia.

Influence of cannabis use on incidence of psychosis in people at clinical high risk.

AIMS: Evidence for case-control studies suggests that cannabis use is a risk factor for the development of psychosis. However, there have been limited prospective studies and the direction of this association remains controversial. The primary aim of the present study was to examine the association between cannabis use and the incidence of psychotic disorders in people at clinical high risk of psychosis. Secondary aims were to assess associations between cannabis use and the persistence of psychotic symptoms, and with functional outcome. METHODS: Current and previous cannabis use were assessed in individuals at clinical high risk of psychosis (n = 334) and healthy controls (n = 67), using a modified version of the Cannabis Experience Questionnaire. Participants were assessed at baseline and followed up for 2 years. Transition to psychosis and persistence of psychotic symptoms were assessed using the Comprehensive Assessment of At-Risk Mental States criteria. Level of functioning at follow up was assessed using the Global Assessment of Functioning disability scale. RESULTS: During follow up, 16.2% of the clinical high-risk sample developed psychosis. Of those who did not become psychotic, 51.4% had persistent symptoms and 48.6% were in remission. There was no significant association between any measure of cannabis use at baseline and either transition to psychosis, the persistence of symptoms, or functional outcome. CONCLUSIONS: These findings contrast with epidemiological data that suggest that cannabis use increases the risk of psychotic disorder.

Does cannabidiol make cannabis safer? A randomised, double-blind, cross-over trial of cannabis with four different CBD:THC ratios.

As countries adopt more permissive cannabis policies, it is increasingly important to identify strategies that can reduce the harmful effects of cannabis use. This study aimed to determine if increasing the CBD content of cannabis can reduce its harmful effects. Forty-six healthy, infrequent cannabis users participated in a double-blind, within-subject, randomised trial of cannabis preparations varying in CBD content. There was an initial baseline visit followed by four drug administration visits, in which participants inhaled vaporised cannabis containing 10 mg THC and either 0 mg (0:1 CBD:THC), 10 mg (1:1), 20 mg (2:1), or 30 mg (3:1) CBD, in a randomised, counter-balanced order. The primary outcome was change in delayed verbal recall on the Hopkins Verbal Learning Task. Secondary outcomes included change in severity of psychotic symptoms (e.g., Positive and Negative Syndrome Scale [PANSS] positive subscale), plus further cognitive, subjective, pleasurable, pharmacological and physiological effects. Serial plasma concentrations of THC and CBD were measured. THC (0:1) was associated with impaired delayed verbal recall (t(45) = 3.399, d = 0.50, p = 0.001) and induced positive psychotic symptoms on the PANSS (t(45) = -4.709, d = 0.69, p = 2.41 × 10-5). These effects were not significantly modulated by any dose of CBD. Furthermore, there was no evidence of CBD modulating the effects of THC on other cognitive, psychotic, subjective, pleasurable, and physiological measures. There was a dose-response relationship between CBD dose and plasma CBD concentration, with no effect on plasma THC concentrations. At CBD:THC ratios most common in medicinal and recreational cannabis products, we found no evidence that CBD protects against the acute adverse effects of cannabis. This should be considered in health policy and safety decisions about medicinal and recreational cannabis.

The role of the electroencephalogram (EEG) in determining the aetiology of catatonia: a systematic review and meta-analysis of diagnostic test accuracy.

BACKGROUND: Catatonia is a psychomotor syndrome that has a wide range of aetiologies. Determining whether catatonia is due to a medical or psychiatric cause is important for directing treatment but is clinically challenging. We aimed to ascertain the performance of the electroencephalogram (EEG) in determining whether catatonia has a medical or psychiatric cause, conventionally defined. METHODS: In this systematic review and meta-analysis of diagnostic test accuracy (PROSPERO CRD42021239027), Medline, EMBASE, PsycInfo, and AMED were searched from inception to May 11, 2022 for articles published in peer-reviewed journals that reported EEG findings in catatonia of a medical or psychiatric origin and were reported in English, French, or Italian. Eligible study types were clinical trials, cohort studies, case-control studies, cross-sectional studies, case series, and case reports. The reference standard was the final clinical diagnosis. Data extraction was conducted using individual patient-level data, where available, by two authors. We prespecified two types of studies to overcome the limitations anticipated in the data: larger studies (n ≥ 5), which were suitable for formal meta-analytic methods but generally lacked detailed information about participants, and smaller studies (n 

Impact of mental disorders on clinical outcomes of physical diseases: an umbrella review assessing population attributable fraction and generalized impact fraction.

Empirical evidence indicates a significant bidirectional association between mental disorders and physical diseases, but the prospective impact of men-tal disorders on clinical outcomes of physical diseases has not been comprehensively outlined. In this PRISMA- and COSMOS-E-compliant umbrella review, we searched PubMed, PsycINFO, Embase, and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports, up to March 15, 2022, to identify systematic reviews with meta-analysis that examined the prospective association between any mental disorder and clinical outcomes of physical diseases. Primary outcomes were disease-specific mortality and all-cause mortality. Secondary outcomes were disease-specific incidence, functioning and/or disability, symptom severity, quality of life, recurrence or progression, major cardiac events, and treatment-related outcomes. Additional inclusion criteria were further applied to primary studies. Random effect models were employed, along with I2 statistic, 95% prediction intervals, small-study effects test, excess significance bias test, and risk of bias (ROBIS) assessment. Associations were classified into five credibility classes of evidence (I to IV and non-significant) according to established criteria, complemented by sensitivity and subgroup analyses to examine the robustness of the main analysis. Statistical analysis was performed using a new package for conducting umbrella reviews (https://metaumbrella.org). Population attributable fraction (PAF) and generalized impact fraction (GIF) were then calculated for class I-III associations. Forty-seven systematic reviews with meta-analysis, encompassing 251 non-overlapping primary studies and reporting 74 associations, were included (68% were at low risk of bias at the ROBIS assessment). Altogether, 43 primary outcomes (disease-specific mortality: n=17; all-cause mortality: n=26) and 31 secondary outcomes were investigated. Although 72% of associations were statistically significant (p<0.05), only two showed convincing (class I) evidence: that between depressive disorders and all-cause mortality in patients with heart failure (hazard ratio, HR=1.44, 95% CI: 1.26-1.65), and that between schizophrenia and cardiovascular mortality in patients with cardiovascular diseases (risk ratio, RR=1.54, 95% CI: 1.36-1.75). Six associations showed highly suggestive (class II) evidence: those between depressive disorders and all-cause mortality in patients with diabetes mellitus (HR=2.84, 95% CI: 2.00-4.03) and with kidney failure (HR=1.41, 95% CI: 1.31-1.51); that between depressive disorders and major cardiac events in patients with myocardial infarction (odds ratio, OR=1.52, 95% CI: 1.36-1.70); that between depressive disorders and dementia in patients with diabetes mellitus (HR=2.11, 95% CI: 1.77-2.52); that between alcohol use disorder and decompensated liver cirrhosis in patients with hepatitis C (RR=3.15, 95% CI: 2.87-3.46); and that between schizophrenia and cancer mortality in patients with cancer (standardized mean ratio, SMR=1.74, 95% CI: 1.41-2.15). Sensitivity/subgroup analyses confirmed these results. The largest PAFs were 30.56% (95% CI: 27.67-33.49) for alcohol use disorder and decompensated liver cirrhosis in patients with hepatitis C, 26.81% (95% CI: 16.61-37.67) for depressive disorders and all-cause mortality in patients with diabetes mellitus, 13.68% (95% CI: 9.87-17.58) for depressive disorders and major cardiac events in patients with myocardial infarction, 11.99% (95% CI: 8.29-15.84) for schizophrenia and cardiovascular mortality in patients with cardiovascular diseases, and 11.59% (95% CI: 9.09-14.14) for depressive disorders and all-cause mortality in patients with kidney failure. The GIFs confirmed the preventive capacity of these associations. This umbrella review demonstrates that mental disorders increase the risk of a poor clinical outcome in several physical diseases. Prevention targeting mental disorders - particularly alcohol use disorders, depressive disorders, and schizophrenia - can reduce the incidence of adverse clinical outcomes in people with physical diseases. These findings can inform clinical practice and trans-speciality preventive approaches cutting across psychiatric and somatic medicine.

Associations of remote mental healthcare with clinical outcomes: a natural language processing enriched electronic health record data study protocol.

INTRODUCTION: People often experience significant difficulties in receiving mental healthcare due to insufficient resources, stigma and lack of access to care. Remote care technology has the potential to overcome these barriers by reducing travel time and increasing frequency of contact with patients. However, the safe delivery of remote mental healthcare requires evidence on which aspects of care are suitable for remote delivery and which are better served by in-person care. We aim to investigate clinical and demographic associations with remote mental healthcare in a large electronic health record (EHR) dataset and the degree to which remote care is associated with differences in clinical outcomes using natural language processing (NLP) derived EHR data. METHODS AND ANALYSIS: Deidentified EHR data, derived from the South London and Maudsley (SLaM) National Health Service Foundation Trust Biomedical Research Centre (BRC) Case Register, will be extracted using the Clinical Record Interactive Search tool for all patients receiving mental healthcare between 1 January 2019 and 31 March 2022. First, data on a retrospective, longitudinal cohort of around 80 000 patients will be analysed using descriptive statistics to investigate clinical and demographic associations with remote mental healthcare and multivariable Cox regression to compare clinical outcomes of remote versus in-person assessments. Second, NLP models that have been previously developed to extract mental health symptom data will be applied to around 5 million documents to analyse the variation in content of remote versus in-person assessments. ETHICS AND DISSEMINATION: The SLaM BRC Case Register and Clinical Record Interactive Search (CRIS) tool have received ethical approval as a deidentified dataset (including NLP-derived data from unstructured free text documents) for secondary mental health research from Oxfordshire REC C (Ref: 18/SC/0372). The study has received approval from the SLaM CRIS Oversight Committee. Study findings will be disseminated through peer-reviewed, open access journal articles and service user and carer advisory groups.

Physical Health in Clinical High Risk for Psychosis Individuals: A Cross-Sectional Study.

BACKGROUND: The clinical high risk for psychosis (CHR-P) phase represents an opportunity for prevention and early intervention in young adults, which also could focus on improving physical health trajectories. METHODS: We conducted a RECORD-compliant clinical register-based cohort study. The primary outcome was to describe the physical health of assessed CHR-P individuals, obtained via Electronic Health Records at the South London and Maudsley (SLaM) NHS Foundation Trust, UK (January 2013-October 2020). RESULTS: The final database included 194 CHR-P subjects (46% female). Mean age was 23.70 ± 5.12 years. Percentage of tobacco smokers was 41% (significantly higher than in the age-matched general population [24%]). We found that 49% of subjects who consumed alcohol had an AUDIT-C (Alcohol Use Disorder Identification Test) score above 5 (hazardous drinking), with an average score of 4.94 (significantly higher than in the general population [2.75]). Investigating diet revealed low fiber intake in most subjects and high saturated fat intake in 10% of the individuals. We found that 47% of CHR-P subjects met the UK recommended physical activity guidelines (significantly lower than in the general population [66%]). Physical parameters (e.g., weight, heart rate, blood pressure) were not significantly different from the general population. CONCLUSIONS: This evidence corroborates the need for monitoring physical health parameters in CHR-P subjects, to implement tailored interventions that target daily habits.

Electronic Health Records to Detect Psychosis Risk

Improving outcomes in psychosis is reliant on efficient early detection of individuals at risk; however, current strategies are suboptimal. Electronic health records (EHRs) contain detailed individual-level data that are routinely collected as part of clinical care, providing a unique opportunity for personalised prognostication. This chapter explores current detection strategies, their limitations and how prognostic models leveraging EHR data have attempted to address them. Research in primary care has highlighted potential symptom-based predictors for future prognostic models and shown that frequency of consultations increases closer to psychosis onset. A transdiagnostic risk calculator in secondary mental health care has displayed adequate prognostic accuracy in different settings in the UK and US. It is the only prognostic model in psychiatry to be implemented in real-world clinical practice, showing good evidence of feasibility. Dynamic prognostic models may be better able to model the time course of psychosis risk compared to static models.

Acceptability of cannabidiol in patients with psychosis.

BACKGROUND: Cannabidiol (CBD) is a promising novel candidate treatment for psychosis. It has a more benign side effect profile than antipsychotic medications, and being treated with CBD is not perceived as being stigmatising. These observations suggest that patients with psychosis would find CBD to be a relatively acceptable treatment. OBJECTIVE: This study tested the above hypothesis by assessing the views of a sample of patients. METHODS: Patients with a psychotic disorder were invited to complete a survey exploring their expectations about the efficacy and side effects of CBD. RESULTS: Seventy patients completed the survey. The majority (86%) were willing to try CBD as a treatment. Most patients believed that CBD would improve their psychotic symptoms (69%) and that it would have fewer side effects than their current medication (64%; mainly antipsychotics). A minority of patients (10%) were concerned that CBD might exacerbate their psychotic symptoms. This, however, appeared to reflect confusion between the effects of CBD and those of cannabis. CONCLUSION: Most patients with psychosis regard CBD as an acceptable treatment. Although CBD has not yet been approved as a treatment for psychosis, many patients are aware of it through the presence of CBD in cannabis and in health supplements. When added to the emerging evidence of its efficacy and the low risk of side effects, the high acceptability of CBD underlines its therapeutic potential.

The importance of external validation to advance precision psychiatry

Ethical considerations for precision psychiatry: A roadmap for research and clinical practice

Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.