Clinical staging for youth at-risk for serious mental illness: A longitudinal perspective

PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia

BACKGROUND

One of the challenges of psychiatry is the staging of patients, especially those with severe mental disorders. Therefore, we aim to develop an empirical staging model for schizophrenia.

METHODS

Data were obtained from 212 stable outpatients with schizophrenia: demographic, clinical, psychometric (PANSS, CAINS, CDSS, OSQ, CGI-S, PSP, MATRICS), inflammatory peripheral blood markers (C-reactive protein, interleukins-1RA and 6, and platelet/lymphocyte [PLR], neutrophil/lymphocyte [NLR], and monocyte/lymphocyte [MLR] ratios). We used machine learning techniques to develop the model (genetic algorithms, support vector machines) and applied a fitness function to measure the model's accuracy (% agreement between patient classification of our model and the CGI-S).

RESULTS

Our model includes 12 variables from 5 dimensions: 1) psychopathology: positive, negative, depressive, general psychopathology symptoms; 2) clinical features: number of hospitalizations; 3) cognition: processing speed, visual learning, social cognition; 4) biomarkers: PLR, NLR, MLR; and 5) functioning: PSP total score. Accuracy was 62% (SD = 5.3), and sensitivity values were appropriate for mild, moderate, and marked severity (from 0.62106 to 0.6728).

DISCUSSION

We present a multidimensional, accessible, and easy-to-apply model that goes beyond simply categorizing patients according to CGI-S score. It provides clinicians with a multifaceted patient profile that facilitates the design of personalized intervention plans.

Transdiagnostic risk identification: A validation study of the Clinical High At Risk Mental State (CHARMS) criteria

A set of clinical criteria, the Clinical High At-Risk Mental State (CHARMS) criteria, have been developed to identify symptomatic young people who are at-risk of disorder progression. The current study aimed to validate the CHARMS criteria by testing whether they prospectively identify individuals at-risk of progressing from attenuated symptomatology to a first episode of serious mental disorder, namely first episode psychosis, first episode mania, severe major depression, and borderline personality disorder. 121 young people completed clinical evaluations at baseline, 6- and 12-month follow-up. The Kaplan-Meier method was used to assess transition rates. Cox regression and LASSO were used to examine baseline clinical predictors of transition. Linear mixed effects modelling was used to examine symptom severity. 28 % of CHARMS+ individuals transitioned to a Stage 2 disorder by 12-month follow-up. The CHARMS+ group had more severe symptoms at follow-up than the CHARMS- group. 96 % of Stage 2 transitions were initially to severe depression. Meeting criteria for multiple CHARMS subgroups was associated with higher transition risk: meeting one at-risk group = 24 %; meeting two at-risk groups = 17 %, meeting three at-risk groups = 55 %, meeting four at-risk groups = 50 %. The strongest baseline predictor of transition was severity of depressive symptoms. The CHARMS criteria identified a group of individuals at-risk of imminent onset of severe mental disorder, particularly severe depression. Larger scale studies and longer follow-up periods are required to validate and extend these findings.

Brain connectomes in youth at risk for serious mental illness: an exploratory analysis

BACKGROUND

Identifying early biomarkers of serious mental illness (SMI)-such as changes in brain structure and function-can aid in early diagnosis and treatment. Whole brain structural and functional connectomes were investigated in youth at risk for SMI.

METHODS

Participants were classified as healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9) based on clinical assessments. Imaging data was collected from two sites. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers derived from constrained spherical deconvolution of the diffusion tensor imaging (DTI) scans, and from the correlations between brain regions measured with resting state functional magnetic resonance imaging (fMRI) data.

RESULTS

Linear mixed effects analysis and analysis of covariance revealed no significant differences between groups in global or nodal metrics after correction for multiple comparisons. A follow up machine learning analysis broadly supported the findings. Several non-overlapping frontal and temporal network differences were identified in the structural and functional connectomes before corrections.

CONCLUSIONS

Results suggest significant brain connectome changes in youth at transdiagnostic risk may not be evident before illness onset.

Is it possible to stage schizophrenia? A systematic review

INTRODUCTION

A staging model is a clinical tool used to define the development of a disease over time. In schizophrenia, authors have proposed different theoretical staging models of increasing complexity. Therefore, the aims of our study were to provide an updated and critical view of the proposed clinical staging models for schizophrenia and to review the empirical data that support them.

METHODS

Systematic literature review following PRISMA guidelines. From the PubMed database and backward reference search, a total of 141 records were retrieved, but only 20 were selected according to the inclusion criteria: (a) available in English; (b) participants with schizophrenia ≥ 18 years; and (c) theoretical and empirical research studies intended to develop, validate, and/or improve staging models of schizophrenia.

RESULTS

Different clinical staging models for schizophrenia were identified, information about the proposed stages was tabulated and presented in the Results section (Tables 1, 2). Most of which include neuroimaging, functioning, and psychopathology, but only two models add objective biomarkers and none include patient point of view. However, few models have been psychometrically tested or used small samples and thus have been validated only partially. In addition, five studies proposed therapeutic interventions according to the stage of the disorder from a theoretical point of view.

DISCUSSION

In conclusion, it is possible to stage schizophrenia, but the models developed have several limitations. Empirical validation and inclusion of more specific biomarkers and measures of other life areas affected by schizophrenia could help in the development of more valid models.

Cerebello-limbic functional connectivity patterns in youth at clinical high risk for psychosis

Youth at clinical high risk (CHR) for psychosis can present not only with characteristic attenuated psychotic symptoms but also may have other comorbid conditions, including anxiety and depression. These undifferentiated mood symptoms can overlap with the clinical presentation of youth with Distress syndromes. Increased resting-state functional connectivity within cerebello-thalamo-cortical (CTC) pathways has been proposed as a trait-specific biomarker for CHR. However, it is unclear whether this functional neural signature remains specific when compared to a different risk group: youth with Distress syndromes. The purpose of the present work was to describe CTC alterations that distinguish between CHR and Distressed individuals. Using machine learning algorithms, we analyzed CTC connectivity features of CHR (n = 51), Distressed (n = 41), and healthy control (n = 36) participants. We found four cerebellar (lobes VII and left Crus II anterior/posterior) and two basal ganglia (right putamen and right thalamus) nodes containing a set of specific connectivity features that distinguished between CHR, Distressed and healthy control groups. Hyperconnectivity between medial lobule VIIb, somatomotor network and middle temporal gyrus was associated with CHR status and more severe symptoms. Detailed atlas parcellation suggested that CHR individuals may have dysfunction mainly within the associative (cognitive) pathways, particularly, between those brain areas responsible for the multi-sensory signal integration.