Developing prediction models for symptom severity around the time of discharge from a tertiary-care program for treatment-resistant psychosis

Antipsychotics are the only therapeutic class indicated in the symptomatic management of psychotic disorders. However, individuals diagnosed with schizophrenia or schizoaffective disorder may not always benefit from these first-line agents. This refractoriness to conventional treatment can be difficult to address in most clinical settings. Therefore, a referral to a tertiary-care program that is better able to deliver specialized care in excess of the needs of most individuals may be necessary. The average outcome following a period of treatment at these programs tends to be one of improvement. Nonetheless, accurate prognostication of individual-level responses may be useful in identifying those who are unlikely to improve despite receiving specialized care. Thus, the main objective of this study was to predict symptom severity around the time of discharge from the Refractory Psychosis Program in British Columbia, Canada using only clinicodemographic information and prescription drug data available at the time of admission. To this end, a different boosted beta regression model was trained to predict the total score on each of the five factors of the Positive and Negative Syndrome Scale (PANSS) using a data set composed of 320 hospital admissions. Internal validation of these prediction models was then accomplished by nested cross-validation. Insofar as it is possible to make comparisons of model performance across different outcomes, the correlation between predictions and observations tended to be higher for the negative and disorganized factors than the positive, excited, and depressed factors on internal validation. Past scores had the greatest effect on the prediction of future scores across all 5 factors. The results of this study serve as a proof of concept for the prediction of symptom severity using this specific approach

White Matter Deficits Assessed by Diffusion Tensor Imaging and Cognitive Dysfunction in Psychostimulant Users With Comorbid Human Immunodeficiency Virus Infection

Background Psychostimulant drug use is commonly associated with drug-related infection, including the human immunodeficiency virus (HIV). Both psychostimulant use and HIV infection are known to damage brain white matter and impair cognition. To date, no study has examined white matter integrity using magnetic resonance imaging (MRI) diffusion tensor imaging (DTI) in chronic psychostimulant users with comorbid HIV infection, and determined the relationship of white matter integrity to cognitive function. Methods Twenty-one subjects (mean age 37.5 ± 9.0 years) with a history of heavy psychostimulant use and HIV infection (8.7 ± 4.3 years) and 22 matched controls were scanned on a 3T MRI. Fractional anisotropy (FA) values were calculated with DTI software. Four regions of interest were manually segmented, including the genu of the corpus callosum, left and right anterior limbs of the internal capsule, and the anterior commissure. Subjects also completed a neurocognitive battery and questionnaires about physical and mental health. Results The psychostimulant using, HIV positive group displayed decreased white matter integrity, with significantly lower FA values for all white matter tracts (p < 0.05). This group also exhibited decreased cognitive performance on tasks that assessed cognitive set-shifting, fine motor speed and verbal memory. FA values for the white matter tracts correlated with cognitive performance on many of the neurocognitive tests. Conclusions White matter integrity was thus impaired in subjects with psychostimulant use and comorbid HIV infection, which predicted worsened cognitive performance on a range of tests. Further study on this medical comorbidity is required

COVID-19 Vaccines and the Virus: Impact on Drug Metabolism and Pharmacokinetics

This article reports on an American Society of Pharmacology and Therapeutics, Division of Drug Metabolism and Disposition symposium held at Experimental Biology on April 2nd, 2022, in Philadelphia. As of July 2022, over 500 million people have been infected with SARS-CoV-2 (the virus causing COVID-19) and over 12,000,000,000 vaccine doses have been administered. Clinically significant interactions between viral infections and hepatic drug metabolism were first recognized over 40 years ago during a cluster of pediatric theophylline toxicity cases attributed to reduced hepatic drug metabolism amidst an influenza B outbreak. Today, a substantive body of research supports that the activated innate immune response generally decreases hepatic cytochrome P450 (CYP) activity. The interactions extend to drug transporters and other organs and have the potential to impact drug absorption, distribution, metabolism, and excretion (ADME). Based on this knowledge, altered ADME is predicted with SARS-CoV-2 infection or vaccination. The report begins with a clinical case exploring the possibility of SARS-CoV-2 vaccination increasing clozapine levels. This is followed by discussions of how SARS-CoV-2 infection or vaccines alter the metabolism and disposition of complex drugs, such as nanoparticles and biologics and small molecule therapies. The review concludes with a discussion of the effects of viral infections on placental amino acid transport and their potential to impact fetal development. The session improved our understanding of the impact of emerging viral infections and vaccine technologies on drug metabolism and disposition, which will help mitigate drug toxicity and improve drug and vaccine safety and effectiveness. Significance Statement Altered pharmacokinetics of small molecule and complex molecule drugs and fetal brain distribution of amino acids following SARS-CoV-2 infection or immunization are possible. The proposed mechanisms involve decreased liver CYP metabolism of small molecules, enhanced innate immune system metabolism of complex molecules and altered placental and fetal blood-brain barrier amino acid transport, respectively. Future research is needed to understand the effects of these interactions on adverse drug responses, drug and vaccine safety and effectiveness and fetal neurodevelopment

Amygdala Nuclei Volumes Are Selectively Associated With Social Network Size in Homeless and Precariously Housed Persons

Objective: The amygdala is a brain region comprised of a group of functionally distinct nuclei that play a central role in social behavior. In homeless and precariously housed individuals, high rates of multimorbidity, and structural aspects of the environment may dysregulate social functioning. This study examined the neurobiological substrates of social connection in homeless and precariously housed persons by examining associations between amygdala nuclei volumes and social network size. Methods: Sixty participants (mean age 43.6 years; 73.3% male) were enrolled from an ongoing study of homeless and precariously housed adults in Vancouver, Canada. Social network size was assessed using the Arizona Social Support Interview Schedule. Amygdala nuclei volumes were extracted from anatomic T1-weighted MRI data. The central and basolateral amygdala nuclei were selected as they are implicated in anxiety-related and social behaviors. The hippocampus was included as a control brain region. Multivariable regression analysis investigated the relationship between amygdala nuclei volumes and social network size. Results: After controlling for age, sex, and total brain volume, individuals with the larger amygdala and central nucleus volumes had a larger network size. This association was not observed for the basolateral amygdala complex, though subsequent analysis found the basal and accessory basal nuclei of the basolateral amygdala were significantly associated with social network size. No association was found for the lateral amygdala nucleus or hippocampus. Conclusions: These findings suggest that select amygdala nuclei may be differentially involved in the social connections of persons with multimorbid illness and social marginalization

Predicting first-episode psychosis patients who will never relapse over 10 years.

BACKGROUND: Although relapse in psychosis is common, a small proportion of patients will not relapse in the long term. We examined the proportion and predictors of patients who never relapsed in the 10 years following complete resolution of positive symptoms from their first psychotic episode. METHOD: Patients who previously enrolled in a 12-month randomized controlled trial on medication discontinuation and relapse following first-episode psychosis (FEP) were followed up after 10 years. Relapse of positive symptoms was operationalized as a change from a Clinical Global Impression scale positive score of <3 for at least 3 consecutive months to a score of ⩾3 (mild or more severe). Baseline predictors included basic demographics, premorbid functioning, symptoms, functioning, and neurocognitive functioning. RESULTS: Out of 178 first-episode patients, 37 (21%) never relapsed during the 10-year period. Univariate predictors (p ⩽ 0.1) of patients who never relapsed included a duration of untreated psychosis (DUP) ⩽30 days, diagnosed with non-schizophrenia spectrum disorders, having less severe negative symptoms, and performing better in logical memory immediate recall and verbal fluency tests. A multivariate logistic regression analysis further suggested that the absence of any relapsing episodes was significantly related to better short-term verbal memory, shorter DUP, and non-schizophrenia spectrum disorders. CONCLUSIONS: Treatment delay and neurocognitive function are potentially modifiable predictors of good long-term prognosis in FEP. These predictors are informative as they can be incorporated into an optimum risk prediction model in the future, which would help with clinical decision making regarding maintenance treatment in FEP

Identification of genes associated with dissociation of cognitive performance and neuropathological burden: Multistep analysis of genetic, epigenetic, and transcriptional data

Introduction: The molecular underpinnings of the dissociation of cognitive performance and neuropathological burden are poorly understood, and there are currently no known genetic or epigenetic determinants of the dissociation. Methods and findings: “Residual cognition” was quantified by regressing out the effects of cerebral pathologies and demographic characteristics on global cognitive performance proximate to death. To identify genes influencing residual cognition, we leveraged neuropathological, genetic, epigenetic, and transcriptional data available for deceased participants of the Religious Orders Study (n = 492) and the Rush Memory and Aging Project (n = 487). Given that our sample size was underpowered to detect genome-wide significance, we applied a multistep approach to identify genes influencing residual cognition, based on our prior observation that independent genetic and epigenetic risk factors can converge on the same locus. In the first step (n = 979), we performed a genome-wide association study with a predefined suggestive p < 10−5, and nine independent loci met this threshold in eight distinct chromosomal regions. Three of the six genes within 100 kb of the lead SNP are expressed in the dorsolateral prefrontal cortex (DLPFC): UNC5C, ENC1, and TMEM106B. In the second step, in the subset of participants with DLPFC DNA methylation data (n = 648), we found that residual cognition was related to differential DNA methylation of UNC5C and ENC1 (false discovery rate < 0.05). In the third step, in the subset of participants with DLPFC RNA sequencing data (n = 469), brain transcription levels of UNC5C and ENC1 were evaluated for their association with residual cognition: RNA levels of both UNC5C (estimated effect = −0.40, 95% CI −0.69 to −0.10, p = 0.0089) and ENC1 (estimated effect = 0.0064, 95% CI 0.0033 to 0.0096, p = 5.7 × 10−5) were associated with residual cognition. In secondary analyses, we explored the mechanism of these associations and found that ENC1 may be related to the previously documented effect of depression on cognitive decline, while UNC5C may alter the composition of presynaptic terminals. Of note, the TMEM106B allele identified in the first step as being associated with better residual cognition is in strong linkage disequilibrium with rs1990622A (r2 = 0.66), a previously identified protective allele for TDP-43 proteinopathy. Limitations include the small sample size for the genetic analysis, which was underpowered to detect genome-wide significance, the evaluation being limited to a single cortical region for epigenetic and transcriptomic data, and the use of categorical measures for certain non-amyloid-plaque, non-neurofibrillary-tangle neuropathologies. Conclusions: Through a multistep analysis of cognitive, neuropathological, genomic, epigenomic, and transcriptomic data, we identified ENC1 and UNC5C as genes with convergent genetic, epigenetic, and transcriptomic evidence supporting a potential role in the dissociation of cognition and neuropathology in an aging population, and we expanded our understanding of the TMEM106B haplotype that is protective against TDP-43 proteinopathy

Maintenance treatment with quetiapine versus discontinuation after one year of treatment in patients with remitted first episode psychosis: randomised controlled trial

Objective To study rates of relapse in remitted patients with first episode psychosis who either continued or discontinued antipsychotic drugs after at least one year of maintenance treatment

Component Processes of Decision Making in a Community Sample of Precariously Housed Persons: Associations With Learning and Memory, and Health-Risk Behaviors

The Iowa Gambling Task (IGT) is a widely used measure of decision making, but its value in signifying behaviors associated with adverse, “real-world” consequences has not been consistently demonstrated in persons who are precariously housed or homeless. Studies evaluating the ecological validity of the IGT have primarily relied on traditional IGT scores. However, computational modeling derives underlying component processes of the IGT, which capture specific facets of decision making that may be more closely related to engagement in behaviors associated with negative consequences. This study employed the Prospect Valence Learning (PVL) model to decompose IGT performance into component processes in 294 precariously housed community residents with substance use disorders. Results revealed a predominant focus on gains and a lack of sensitivity to losses in these vulnerable community residents. Hypothesized associations were not detected between component processes and self-reported health-risk behaviors. These findings provide insight into the processes underlying decision making in a vulnerable substance-using population and highlight the challenge of linking specific decision making processes to “real-world” behaviors

Traumatic Brain Injury in Precariously Housed Persons: Incidence and Risks

Background Homeless and precarious housed persons are particularly prone to traumatic brain injuries (TBIs), but existent incidence rates are hampered by poor case acquisition. We rigorously documented TBIs in precariously housed persons transitioning in and out of homelessness. Methods Between December 2016 and May 2018, 326 precariously housed participants enrolled in a longitudinal study in Vancouver, Canada were assessed monthly for TBI occurrences after education on sequelae. Over one participant-year, 2433 TBI screenings were acquired for 326 person-years and variables associated with odds of incident TBI were evaluated. Findings One hundred participants acquired 175 TBIs, yielding an observed incidence proportion of 30¢7% and event proportion of 53¢7%. Of the injured, 61% reported one TBI and 39% reported multiple injuries. Acute intoxication was present for more than half of the TBI events assessed. Additionally, 9¢7% of TBI events occurred in the context of a drug overdose. Common injury mechanisms were falls (45¢1%), assaults (25¢1%), and hitting one’s head on an object (13¢1%). In this community-based but non-randomly recruited sample, exploratory analyses identified factors associated with odds of an incident TBI over one year of follow-up, including: schizophrenia disorders (odds ratio (OR) = 0¢43, 95% confidence interval (CI) 0¢19, 0¢94), role functioning (OR = 0¢69, 95% CI 0¢52, 0¢91), opioid dependence (OR = 2¢17, 95% CI 1¢27, 3¢72) and those reporting past TBIs (OR = 1¢99, 95% CI 1¢13, 3¢52). Interpretation Given the ubiquity of TBIs revealed in this precariously housed sample, we identify an underappreciated and urgent healthcare priority. Several factors modified the odds of incident TBI, which can facilitate investigations into targeted prevention efforts

Effectiveness of pharmacological treatments for severe agitation in real-world emergency settings: protocol of individual-participant-data network meta-analysis.

BACKGROUND Severe psychomotor agitation and aggression often require immediate pharmacological intervention, but clear evidence-based recommendations for choosing among the multiple options are lacking. To address this gap, we plan a systematic review and individual-participant-data network meta-analysis to investigate their comparative effectiveness in real-world emergency settings with increased precision. METHODS We will include randomized controlled trials investigating intramuscular or intravenous pharmacological interventions, as monotherapy or in combination, in adults with severe psychomotor agitation irrespective of the underlying diagnosis and requiring rapid tranquilization in general or psychiatric emergency settings. We will exclude studies before 2002, those focusing on specific reasons for agitation and placebo-controlled trials to avoid concerns related to the transitivity assumption and potential selection biases. We will search for eligible studies in BIOSIS, CENTRAL, CINAHL Plus, Embase, LILACS, MEDLINE via Ovid, PubMed, ProQuest, PsycINFO, ClinicalTrials.gov, and WHO-ICTRP. Individual-participant data will be requested from the study authors and harmonized into a uniform format, and aggregated data will also be extracted from the studies. At least two independent reviewers will conduct the study selection, data extraction, risk-of-bias assessment using RoB 2, and applicability evaluation using the RITES tool. The primary outcome will be the number of patients achieving adequate sedation within 30 min after treatment, with secondary outcomes including the need for additional interventions and adverse events, using odds ratios as the effect size. If enough individual-participant data will be collected, we will synthesize them in a network meta-regression model within a Bayesian framework, incorporating study- and participant-level characteristics to explore potential sources of heterogeneity. In cases where individual-participant data are unavailable, potential data availability bias will be explored, and models allowing for the inclusion of studies reporting only aggregated data will be considered. We will assess the confidence in the evidence using the Confidence in Network Meta-Analysis (CINeMA) approach. DISCUSSION This individual-participant-data network meta-analysis aims to provide a fine-tuned synthesis of the evidence on the comparative effectiveness of pharmacological interventions for severe psychomotor agitation in real-world emergency settings. The findings from this study can greatly be provided clearer evidence-based guidance on the most effective treatments. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42023402365