A narrative review of intervention in first-episode affective psychoses

White matter alterations in affective and non-affective early psychosis: A diffusion MRI study

BACKGROUND

The early years after the onset of psychotic disorders, known as "early psychosis" (EP) are critical to determining the path of psychosis trajectory. We used a Diffusion Magnetic Resonance Imaging (DMRI) connectometry approach to assess the microstructural changes of white matter (WM) associated with EP.

METHODS

We used the Human Connectome Project in Early Psychosis (HCP-EP) dataset to collect DMRI data from patients with EP. The imaging data were processed in the Montreal Neuroimaging Initiative space and transformed into quantitative anisotropy (QA). The QA value was translated into the WM connectivity of each tract and used in the subsequent analysis.

RESULTS

121 patients with EP (94 non-affective/27 affective) and 56 healthy controls were recruited. EP was associated with increased QA in the body and tapetum of corpus callosum (CC) and decreased QA in the bilateral cerebellum, and middle cerebellar peduncle. Compared to non-affective psychosis, affective psychosis showed increased QA in the bilateral cerebellum and vermis and decreased QA in the forceps minor, body of CC, right cingulum, and bilateral inferior fronto-occipital fasciculus. Furthermore, QA changes in several WM tracts were correlated with positive and negative symptom scale scores.

LIMITATIONS

DMRI intrinsic limitations, limited sample size, and neurobiological effects of psychotropic treatment.

CONCLUSIONS

EP is associated with alterations in WM connectivity primarily in the CC and cerebellar regions. Also, affective and non-affective psychosis have distinct alterations in WM connectivity. These results can be used for the early diagnosis and differentiation of psychotic disorders.

The co-occurrence of manic and depressive dimensions in early psychosis: a latent transition analysis

BACKGROUND

Frequently associated with early psychosis, depressive and manic dimensions may play an important role in its course and outcome. While manic and depressive symptoms can alternate and co-occur, most of the studies in early intervention investigated these symptoms independently. The aim of this study was therefore to explore the co-occurrence of manic and depressive dimensions, their evolution and impact on outcomes.

METHODS

We prospectively studied first-episode psychosis patients (N = 313) within an early intervention program over 3 years. Based on latent transition analysis, we identified sub-groups of patients with different mood profiles considering both manic and depressive dimensions, and studied their outcomes.

RESULTS

Our results revealed six different mood profiles at program entry and after 1.5 years follow-up (absence of mood disturbance, co-occurrence, mild depressive, severe depressive, manic and hypomanic), and four after 3 years (absence of mood disturbance, co-occurrence, mild depressive and hypomanic). Patients with absence of mood disturbance at discharge had better outcomes. All patients with co-occurring symptoms at program entry remained symptomatic at discharge. Patients with mild depressive symptoms were less likely to return to premorbid functional level at discharge than the other subgroups. Patients displaying a depressive component had poorer quality of physical and psychological health at discharge.

CONCLUSIONS

Our results confirm the major role played by mood dimensions in early psychosis, and show that profiles with co-occurring manic and depressive dimensions are at risk of poorer outcome. An accurate assessment and treatment of these dimensions in people with early psychosis is crucial.

A systematic review and meta-analysis of delayed help-seeking, delayed diagnosis and duration of untreated illness in bipolar disorders

OBJECTIVES

To examine the time delay between the age at onset of symptoms or episodes of bipolar disorders (BD) and the age at diagnosis of and/or receipt of clinical practice guideline recommended interventions for BD.

METHODS

Systematic search of five databases to identify publications from January 2000 to July 2022 that reported one or more of the following reliable and valid estimates of latency: delay in help seeking (DHS), delay in diagnosis (DD) and duration of untreated BD (DUB). Eligible studies were included in random effects meta-analyses and multivariate meta-regression was used to assess factors associated with each latency construct.

RESULTS

Screening of 1074 publications identified 59 eligible studies (reported in 66 publications) of >40,000 individuals that estimated DHS (8 studies), DD (20 studies) and/or DUB (45 studies). The median DHS, DD and DUB were 3.5 (IQR: 2.8, 8.48), 6.7 (IQR: 5.6, 8.9) and 5.9 years (IQR: 1.1, 8.2), respectively. Key factors associated with shorter DD included older age and residing outside North America; shorter DUB was associated with psychotic or manic onset and access to early intervention services.

CONCLUSIONS

Greater consensus on definitions of latency constructs and better-quality targeted research is required regarding DHS, DD and DUB. This review suggests that, while the peak age at onset of BD is 15-25, diagnosis and guideline recommended interventions (e.g., mood stabilizers) are likely to be delayed until age 25-35 years except for a minority of individuals with access to early intervention services.

Differences of affective and non-affective psychoses in early intervention services from Latin America

BACKGROUND

Psychosis presentation can be affected by genetic and environmental factors. Differentiating between affective and non-affective psychosis (A-FEP and NA-FEP, respectively) may influence treatment decisions and clinical outcomes. The objective of this paper is to examine differences between patients with A-FEP or NA-FEP in a Latin American sample.

METHODS

Patients from two cohorts of patients with a FEP recruited from Brazil and Chile. Subjects included were aged between 15 and 30 years, with an A-FEP or NA-FEP (schizophrenia-spectrum disorders) according to DSM-IV-TR. Sociodemographic data, duration of untreated psychosis and psychotic/mood symptoms were assessed. Generalized estimating equation models were used to assess clinical changes between baseline-follow-up according to diagnosis status.

RESULTS

A total of 265 subjects were included. Most of the subjects were male (70.9 %), mean age was 21.36 years. A-FEP and NA-FEP groups were similar in almost all sociodemographic variables, but A-FEP patients had a higher probability of being female. At baseline, the A-FEP group had more manic symptoms and a steeper reduction in manic symptoms scores during the follow- up. The NA-FEP group had more negative symptoms at baseline and a higher improvement during follow-up. All domains of The Positive and Negative Syndrome Scale improved for both groups. No difference for DUP and depression z-scores at baseline and follow-up.

LIMITATIONS

The sample was recruited at tertiary hospitals, which may bias the sample towards more severe cases.

CONCLUSIONS

This is the largest cohort comparing A-FEP and NA-FEP in Latin America. We found that features in FEP patients could be used to improve diagnosis and support treatment decisions.

Evaluating physical urban features in several mental illnesses using electronic health record data

Objectives

Understanding the potential impact of physical characteristics of the urban environment on clinical outcomes on several mental illnesses.

Materials and Methods

Physical features of the urban environment were examined as predictors for affective and non-affective several mental illnesses (SMI), the number and length of psychiatric hospital admissions, and the number of short and long-acting injectable antipsychotic prescriptions. In addition, the urban features with the greatest weight in the predicted model were determined. The data included 28 urban features and 6 clinical variables obtained from 30,210 people with SMI receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) using the Clinical Record Interactive Search (CRIS) tool. Five machine learning regression models were evaluated for the highest prediction accuracy followed by the Self-Organising Map (SOM) to represent the results visually.

Results

The prevalence of SMI, number and duration of psychiatric hospital admission, and antipsychotic prescribing were greater in urban areas. However, machine learning analysis was unable to accurately predict clinical outcomes using urban environmental data.

Discussion

The urban environment is associated with an increased prevalence of SMI. However, urban features alone cannot explain the variation observed in psychotic disorder prevalence or clinical outcomes measured through psychiatric hospitalisation or exposure to antipsychotic treatments.

Conclusion

Urban areas are associated with a greater prevalence of SMI but clinical outcomes are likely to depend on a combination of urban and individual patient-level factors. Future mental healthcare service planning should focus on providing appropriate resources to people with SMI in urban environments.