Identifying strategies to improve PANSS based dimensional models in schizophrenia: Accounting for multilevel structure, Bayesian model and clinical staging.

BACKGROUND: Dimensional approaches can decompose a construct in a set of continuous variables, improving the characterization of complex phenotypes, such as schizophrenia. However, the five-factor model of the Positive and Negative Syndrome Scale (PANSS), the most used instrument in schizophrenia research, yielded poor fits in most confirmatory factor analysis (CFA) studies, raising concerns about its applications. Thus, we aimed to identify dimensional PANSS CFA models with good psychometric properties by comparing the traditional CFA with three methodological approaches: Bayesian CFA, multilevel modeling, and Multiple Indicators Multiple Causes (MIMIC) modeling. METHODS: Clinical data of 700 schizophrenia patients from four centers were analyzed. We first performed a traditional CFA. Next, we tested the three techniques: 1) a Bayesian CFA; 2) a multilevel analysis using the centers as level; and 3) a MIMIC modeling to evaluate the impact of clinical staging on PANSS factors and items. RESULTS: CFA and Bayesian CFA produced poor fit models. However, when adding a multilevel structure to the CFA model, a good fit model emerged. MIMIC modeling yielded significant differences in the factor structure between the clinical stages of schizophrenia. Sex, age, age of onset, and duration of illness did not significantly affect the model fit. CONCLUSION: Our comparison of different CFA methods highlights the need for multilevel structure to achieve a good fit model and the potential utility of staging models (rather than the duration of illness) to deal with clinical heterogeneity in schizophrenia. Large prospective samples with biological data should help to understand the interplay between psychometrics concerns and neurobiology research.

What is the Best Latent Structure of Negative Symptoms in Schizophrenia? A Systematic Review.

BACKGROUND: Determining the best latent structure of negative symptoms in schizophrenia could benefit assessment tools, neurobiological research, and targeted interventions. However, no review systematically evaluated studies that assessed and validated latent models of negative symptoms. OBJECTIVE: To identify and evaluate existing latent structure models in the literature of negative symptoms and to determine the best model. METHOD: Systematic search of MEDLINE, EMBASE, and Scopus on July 19, 2020, for confirmatory factor analysis models of negative symptoms in patients with schizophrenia. The available evidence was assessed through 2 sets of criteria: (1) study design quality-based on negative symptoms assessment and modeling strategy and (2) psychometric quality and model fit-based on fit indices and factor definition quality. RESULTS: In total, 22 studies (n = 17 086) from 9 countries were included. Studies differed greatly regarding symptom scales, setting, and sample size (range = 86-6889). Dimensional models included 2-6 factors (median = 4). Twelve studies evaluated competing models and adopted appropriate instruments to assess the latent structure of negative symptoms. The 5-factor and hierarchical models outperformed unitary, 2-factor, and 3-factor models on all direct comparisons, and most of the analyses derived from the Brief Negative Symptom Scale. Considering the quality criteria proposed, 5-factor and hierarchical models achieved excellent fit in just one study. CONCLUSIONS: Our review points out that the 5-factor and hierarchical models represent the best latent structure of negative symptoms, but the immaturity of the relevant current literature may affect the robustness of this conclusion. Future studies should address current limitations regarding psychometric properties and also address biological and clinical validity to refine available models.

Schneider's first-rank symptoms as predictors of remission in antipsychotic-naive first-episode psychosis

Objective: German psychiatrist Kurt Schneider proposed the concept of first-rank symptoms (FRS) of schizophrenia in 1959. However, their relevance for diagnosis and prediction of treatment response are still unclear. Most studies have investigated FRS in chronic or medicated patients. The present study sought to evaluate whether FRS predict remission, response, or improvement in functionality in antipsychotic-naive first-episode psychosis. Methods: Follow-up study of 100 patients at first episode of psychosis (FEP), with no previous treatment, assessed at baseline and after 2 months of treatment. The participants were evaluated with the standardized Positive and Negative Syndrome Scale (PANSS) and Global Assessment of Functioning (GAF) and for presence of FRS. Results: Logistic regression analysis showed that, in this sample, up to three individual FRS predicted remission: voices arguing, voices commenting on one's actions, and thought broadcasting. Conclusion: Specific FRS may predict remission after treatment in FEP patients. This finding could give new importance to Kurt Schneider's classic work by contributing to future updates of diagnostic protocols and improving estimation of prognosis.

A symptom combination predicting treatment-resistant schizophrenia - A strategy for real-world clinical practice.

Early identification of symptoms that can predict treatment-resistant schizophrenia (TRS) could help clinicians to avoid delays in clozapine therapy. This study aims to investigate symptom patterns that could predict TRS using a discovery/replication study design. First, we followed a cohort of inpatients with schizophrenia (n = 164) in which the most discriminative items at baseline of the Positive and Negative Syndrome Scale (PANSS) were determined using logistic regression with TRS status as an outcome. Using Receiver Operating Characteristic (ROC) curves, we tested the prediction performance of multiple combinations of the identified items. The same items' combination was tested in an independent replication sample of (n = 207) outpatients with schizophrenia. In the discovery sample, the best combination to predict TRS at the discharge was the sum of three baseline PANSS items - conceptual disorganization (P2), difficulty in abstract thinking (N5), and unusual thought content (G9). The P2 + N5 + G9 model yielded an area under the curve (AUC) of 0.881, a sensitivity of 77.8%, and a specificity of 83.3%. In the outpatient sample, the model P2 + N5 + G9 predictive accuracy for TRS was only in the range of "acceptable" with an AUC of 0.756 and sensitivity of 72.3% and a specificity of 74.4%. Overall, the P2 + N5 + G9 model corresponds to the construct of formal thought disorder composed of disorganized thinking, concrete thinking, and bizarre-idiosyncratic thinking. Pronounced levels of these symptoms are easily identifiable in clinical practice and may be a feasible strategy in TRS. Replicating in first-episode cohorts is desirable to understand the likely clinical utility.

Schneider's first-rank symptoms as predictors of remission in antipsychotic-naive first-episode psychosis.

OBJECTIVE: German psychiatrist Kurt Schneider proposed the concept of first-rank symptoms (FRS) of schizophrenia in 1959. However, their relevance for diagnosis and prediction of treatment response are still unclear. Most studies have investigated FRS in chronic or medicated patients. The present study sought to evaluate whether FRS predict remission, response, or improvement in functionality in antipsychotic-naive first-episode psychosis. METHODS: Follow-up study of 100 patients at first episode of psychosis (FEP), with no previous treatment, assessed at baseline and after 2 months of treatment. The participants were evaluated with the standardized Positive and Negative Syndrome Scale (PANSS) and Global Assessment of Functioning (GAF) and for presence of FRS. RESULTS: Logistic regression analysis showed that, in this sample, up to three individual FRS predicted remission: voices arguing, voices commenting on one's actions, and thought broadcasting. CONCLUSION: Specific FRS may predict remission after treatment in FEP patients. This finding could give new importance to Kurt Schneider's classic work by contributing to future updates of diagnostic protocols and improving estimation of prognosis.

Cannabis acute use impacts symptoms and functionality in a cohort of antipsychotic naïve First Episode of Psychosis individuals.

INTRODUCTION: Cannabis use increases the risk of developing psychosis, and subjects with psychosis are more likely to use cannabis. However, studies on the influence of cannabis on psychotic dimensions, response to treatment, and functional outcomes showed conflicting results. Such heterogeneity may be due the inclusion of patients who were already under treatment, and lack of specificity in evaluations. We investigated whether cannabis use yields distinct symptom profiles and functionality in a cohort of antipsychotic-naïve patients at first episode of psychosis (FEP). METHODS: This research is part of a prospective cohort study performed in Sao Paulo, Brazil. The baseline assessment was completed by 175 individuals, and 99 of them were reassessed in a ten-week follow up. We investigated the relationship between cannabis exposure variables (acute use, lifetime use and age at first use) and outcomes: symptom dimensions and functioning. RESULTS: Individuals who reported acute use of cannabis had higher excitement symptoms at baseline, higher excitement and positive response rates, but no significant differences at follow-up. Additionally, more days of cannabis use in the last month predicted worse functionality and clinical impression at baseline but not at follow-up. DISCUSSION: The acute use of cannabis influenced the clinical presentation at our FEP baseline assessment, but did not to influence symptoms or functional outcomes at 10-week follow-up. Additionally, acute cannabis users had a better response for excitement and positive symptoms. Higher excitement symptoms at presentation of FEP should raise concerns of possible acute use of cannabis. Longer follow-up times may elucidate whether the effects on functionality would be more evident later in disease development.

Factor structure of the Positive and Negative Syndrome Scale (PANSS) in Brazil: convergent validation of the Brazilian version

Objectives: The Positive and Negative Syndrome Scale (PANSS) was developed to assess the symptoms of schizophrenia dimensionally. Although it is widely used in clinical trials in Brazil, it is not fully validated. The aim of this study is to assess the factor structure of the Brazilian PANSS and generate validation data for its current version. Methods: A total of 292 patients diagnosed with schizophrenia were enrolled. Results: Principal component analysis suggested a forced five-factor final model that accounted for 58.44% of the total variance, composed of negative, disorganization/cognition, excitement, positive, and depression/anxiety. Conclusion: The Brazilian PANSS has a similar factor structure and internal consistency compared to its other country versions.

Factor structure of the Positive and Negative Syndrome Scale (PANSS) in Brazil: convergent validation of the Brazilian version.

OBJECTIVES: The Positive and Negative Syndrome Scale (PANSS) was developed to assess the symptoms of schizophrenia dimensionally. Although it is widely used in clinical trials in Brazil, it is not fully validated. The aim of this study is to assess the factor structure of the Brazilian PANSS and generate validation data for its current version. METHODS: A total of 292 patients diagnosed with schizophrenia were enrolled. RESULTS: Principal component analysis suggested a forced five-factor final model that accounted for 58.44% of the total variance, composed of negative, disorganization/cognition, excitement, positive, and depression/anxiety. CONCLUSION: The Brazilian PANSS has a similar factor structure and internal consistency compared to versions in several other languages.