Delirium in COVID-19 and post-liver transplant patients: an observational study.

OBJECTIVE: Delirium is a major complication in hospitalised patients. This study aimed to compare the mortality trends of patients with delirium according to the underlying physical condition. METHODS: Hospitalised patients diagnosed with delirium by the Modena Consultation-Liaison Psychiatry Service (Italy) during 2020 were enrolled. Three groups were identified: COVID; after orthotopic liver transplant (OLT); other conditions. The full medical records were screened to retrieve socio-demographic and clinical data. INTERMED score and Delirium Rating Scale were used to retrospectively rate bio-social-complexity and delirium severity. Early (20 days) and end of study (31st January 2021) mortality were ascertained for each subject. RESULTS: A total of 103 patients were enrolled. Patients hospitalised for COVID showed higher INTERMED scores (two-tailed t-test, p = 0.019) and higher 20-day mortality (HR = 3.68, p = 0.014). When considering a 1-year follow-up, the main predictor of mortality was patients' age in all three subgroups (HR = 1.06; p = 0.003). CONCLUSION: Our results suggest that patients hospitalised for COVID-19 with delirium showed higher bio-psycho-social complexity and higher short-term mortality, regardless of the severity of delirium. OLT patients showed lower mortality and bio-psycho-social complexity, despite being still considered as 'complex', according to the INTERMED score. Future research should focus on understanding the underlying mechanisms in the relationship between delirium and mortality.Key pointsPatients hospitalised for COVID-19 with delirium were found at risk of higher short-term mortality and higher bio-psycho-social complexity.OLT patients showed lower overall mortality and lower bio-psycho-social complexity than the other two groups, despite being still in the 'complex' range according to the INTERMED score.Future research should assess the areas of impact of delirium in patients affected by COVID-19, considering short- and long-term outcomes.

Microaggression toward LGBTIQ people and implications for mental health: A systematic review.

BACKGROUND: Research suggests that microaggressions detrimentally impact the mental health of members of marginalized social groups. AIMS: The aim of this systematic review was to assess the exposure to microaggressions and related implications on mental health of Lesbian, Gay, Bisexual, Transgender, Intersex, and Queer (LGBTIQ) people. METHOD: Medline, Scopus, PsycINFO, CINAHL, and EMBASE were searched until January 2023. Studies reporting data on the exposure to microaggressions toward LGBTIQ people were identified. Meta-analyses of rates of exposure to microaggression and of the association between microaggressions and mental health outcomes were based on odds ratio (OR) and standardized mean difference (SMD) with 95% confidence intervals (95% CI), estimated through inverse variance models with random effects. RESULTS: The review process led to the selection of 17 studies, involving a total of 9036 LGBTIQ people, of which 6827 identifying as cisgenders, and 492 as heterosexuals, were included in the quantitative synthesis. Overall, LGBTIQ people showed an increased risk of microaggression (SMD: 0.89; 95% CI [0.28, 1.50]), with Transgender people having the highest risk (OR: 10.0; 95% CI [3.08, 32.4]). Microaggression resulted associated with risk of depression (SMD: 0.21; 95% CI [0.05, 0.37]), anxiety (SMD: 0.29; 95% CI [0.17, 0.40]), suicide attempts (OR: 1.13; 95% CI [1.08, 1.18]), alcohol abuse (OR: 1.32; 95% CI [1.13, 1.54]), but not to suicidal ideation (OR: 1.56; 95% CI [0.64, 3.81]) and cannabis abuse (OR: 1.44; 95% CI [0.82, 2.55]). The quality of the evidence was limited by the small number of studies. CONCLUSIONS: LGBTIQ people are at higher risk of microaggressions compared with their cisgender/heterosexual peers, which may lead to mental health consequences. This evidence may contribute to public awareness of LGBTIQ mental health needs and suggest supportive strategies as well as preventive interventions (e.g., supportive programs and destigmatizing efforts) as parts of tailored health-care planning aimed to reduce psychiatric morbidity in this population.

Investigating the impact of poverty on mental illness in the UK Biobank using Mendelian randomization.

It is unclear whether poverty and mental illness are causally related. Using UK Biobank and Psychiatric Genomic Consortium data, we examined evidence of causal links between poverty and nine mental illnesses (attention deficit and hyperactivity disorder (ADHD), anorexia nervosa, anxiety disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, post-traumatic stress disorder and schizophrenia). We applied genomic structural equation modelling to derive a poverty common factor from household income, occupational income and social deprivation. Then, using Mendelian randomization, we found evidence that schizophrenia and ADHD causally contribute to poverty, while poverty contributes to major depressive disorder and schizophrenia but decreases the risk of anorexia nervosa. Poverty may also contribute to ADHD, albeit with uncertainty due to unbalanced pleiotropy. The effects of poverty were reduced by approximately 30% when we adjusted for cognitive ability. Further investigations of the bidirectional relationships between poverty and mental illness are warranted, as they may inform efforts to improve mental health for all.

Post-traumatic stress disorder among LGBTQ people: a systematic review and meta-analysis.

AIMS: Lesbian, gay, bisexual, transgender and queer people (LGBTQ) are at increased risk of traumatization. This systematic review aimed to summarize data regarding the risk of post-traumatic stress disorder (PTSD) for LGBTQ people and their subgroups. METHODS: Medline, Scopus, PsycINFO and EMBASE were searched until September 2022. Studies reporting a comparative estimation of PTSD among LGBTQ population and the general population (i.e., heterosexual/cisgender), without restrictions on participants' age and setting for the enrolment, were identified. Meta-analyses were based on odds ratio (OR and 95% confidence intervals [CI]), estimated through inverse variance models with random effects. RESULTS: The review process led to the selection of 27 studies, involving a total of 31,903 LGBTQ people and 273,842 controls, which were included in the quantitative synthesis. Overall, LGBTQ people showed an increased risk of PTSD (OR: 2.20 [95% CI: 1.85; 2.60]), although there was evidence of marked heterogeneity in the estimate (I2 = 91%). Among LGBTQ subgroups, transgender people showed the highest risk of PTSD (OR: 2.52 [95% CI: 2.22; 2.87]) followed by bisexual people (OR: 2.44 [95% CI: 1.05; 5.66]), although these comparisons are limited by the lack of data for other sexual and gender minorities, such as intersex people. Interestingly, the risk of PTSD for bisexual people was confirmed also considering lesbian and gay as control group (OR: 1.44 [95% CI: 1.07; 1.93]). The quality of the evidence was low. CONCLUSIONS: LGBTQ people are at higher risk of PTSD compared with their cisgender/heterosexual peers. This evidence may contribute to the public awareness on LGBTQ mental health needs and suggest supportive strategies as well as preventive interventions (e.g., supportive programs, counselling, and destigmatizing efforts) as parts of a tailored health-care planning aimed to reduce psychiatric morbidity in this at-risk population.

Machine-Learning for Prescription Patterns: Random Forest in the Prediction of Dose and Number of Antipsychotics Prescribed to People with Schizophrenia.

Objective: We aimed to predict antipsychotic prescription patterns for people with schizophrenia using machine learning (ML) algorithms. Methods: In a cross-sectional design, a sample of community mental health service users (SUs; n = 368) with a primary diagnosis of schizophrenia was randomly selected. Socio-demographic and clinical features, including the number, total dose, and route of administration of the antipsychotic treatment were recorded. Information about the number and the length of psychiatric hospitalization was retrieved. Ordinary Least Square (OLS) regression and ML algorithms (i.e., random forest [RF], supported vector machine, K-nearest neighborhood, and Naïve Bayes) were used to estimate the predictors of total antipsychotic dosage and prescription of antipsychotic polytherapy (APP). Results: The strongest predictor of the total dose was APP. The number of Community Mental Health Centers (CMHC) contacts was the most important predictor of APP and, with APP omitted, of dosage. Treatment with anticholinergics predicted APP, emphasizing the strong correlation between APP and higher antipsychotic dose. RF performed better than OLS regression and the other ML algorithms in predicting both antipsychotic dose (root square mean error = 0.70, R2 = 0.31) and APP (area under the receiving operator curve = 0.66, true positive rate = 0.41, and true negative rate = 0.78). Conclusion: APP is associated with the prescription of higher total doses of antipsychotics. Frequent attenders at CMHCs, and SUs recently hospitalized are often treated with APP and higher doses of antipsychotics. Future prospective studies incorporating standardized clinical assessments for both psychopathological severity and treatment efficacy are needed to confirm these findings.

Childhood maltreatment mediates the effect of the genetic background on psychosis risk in young adults.

Childhood maltreatment (CM) and genetic vulnerability are both risk factors for psychosis, but the relations between them are not fully understood. Guided by the recent identification of genetic risk to CM, this study investigates the hypothesis that genetic risk to schizophrenia also increases the risk of CM and thus impacts psychosis risk. The relationship between schizophrenia polygenetic risk, CM, and psychotic-like experiences (PLE) was investigated in participants from the Utrecht Cannabis Cohort (N = 1262) and replicated in the independent IMAGEN cohort (N = 1740). Schizophrenia polygenic risk score (SZ-PRS) were calculated from the most recent GWAS. The relationship between CM, PRS, and PLE was first investigated using multivariate linear regression. Next, mediation of CM in the pathway linking SZ-PRS and PLE was examined by structural equation modeling, while adjusting for a set of potential mediators including cannabis use, smoking, and neuroticism. In agreement with previous studies, PLE were strongly associated with SZ-PRS (B = 0.190, p = 0.009) and CM (B = 0.575, p < 0.001). Novel was that CM was also significantly associated with SZ-PRS (B = 0.171, p = 0.001), and substantially mediated the effects of SZ-PRS on PLE (proportion mediated = 29.9%, p = 0.001). In the replication cohort, the analyses yielded similar results, confirming equally strong mediation by CM (proportion mediated = 34.7%, p = 0.009). Our results suggest that CM acts as a mediator in the causal pathway linking SZ-PRS and psychosis risk. These findings open new perspectives on the relations between genetic and environmental risks and warrant further studies into potential interventions to reduce psychosis risk in vulnerable people.

The structure of post-traumatic stress symptoms in survivors of war: confirmatory factor analyses of the Impact of Event Scale--revised.

The study aimed at establishing the factor structure of the Impact of Event Scale-Revised (IES-R) in survivors of war. A total sample of 4167 participants with potentially traumatic experiences during the war in Ex-Yugoslavia was split into three samples: two independent samples of people who stayed in the area of conflict and one sample of refugees to Western European countries. Alternative models with three, four, and five factors of post-traumatic symptoms were tested in one sample. The other samples were used for cross-validation. Results indicated that the model of best fit had five factors, i.e., intrusion, avoidance, hyperarousal, numbing, and sleep disturbance. Model superiority was cross-validated in the two other samples. These findings suggest a five-factor model of post-traumatic stress symptoms in war survivors with numbing and sleep disturbance as separate factors in addition to intrusion, avoidance and hyperarousal.