Patient subgroups of schizophrenia based on the Positive and Negative Syndrome Scale: composition and transition between acute and subsided disease states

Schizophrenia heterogeneity revisited: Clinical, cognitive, and psychosocial correlates of statistically-derived negative symptoms subgroups

Traditional efforts to delineate the clinical heterogeneity of schizophrenia have been unsuccessful because of the absence of a valid, stable, and meaningful subtyping scheme. A clinically-informed nosology supported by multivariate statistical classification methods may provide a better approach for classifying schizophrenia. The goals of the current study were to 1) use multivariate classification methods to validate a clinical subtyping scheme based on the profile of negative symptoms; and 2) following validation to contrast the statistically-derived subgroups to ascertain distinguishing demographic, clinical, cognitive, and functional characteristics. In the current study, 706 people with schizophrenia completed measures of positive and negative symptoms, premorbid adjustment, cognition, and psychosocial functioning. Latent class analysis served to identify the number of negative symptom subgroups in schizophrenia. Next, statistical classification methods-Bayes Theorem and the Base Rate Classification Technique-were used to assign participants into the identified subgroups. Subgroups were compared on external validation variables not used in the classification process via logistic regression and discriminant function analysis. Latent class analysis supported a three-class model of schizophrenia that included deficit, persistent, and transient negative symptom subgroups. Posthoc comparisons showed that demographic characteristics, positive symptoms, premorbid adjustment, and cognitive profiles can distinguish the schizophrenia subgroups with moderate accuracy. The deficit subgroup had the greatest impairments in psychosocial functioning and quality of life variables. Findings suggest that schizophrenia encapsulates qualitatively distinct negative symptom subgroups that differ in their demographic, symptomatic, neuropsychological, and functional profiles. Schizophrenia heterogeneity reflects a combination of non-arbitrary subgroups and severity-based differences in negative symptoms.

Allocation Variable-Based Probabilistic Algorithm to Deal with Label Switching Problem in Bayesian Mixture Models

The label switching problem occurs as a result of the nonidentifiability of posterior distribution over various permutations of component labels when using Bayesian approach to estimate parameters in mixture models. In the cases where the number of components is fixed and known, we propose a relabelling algorithm, an allocation variable-based (denoted by AVP) probabilistic relabelling approach, to deal with label switching problem. We establish a model for the posterior distribution of allocation variables with label switching phenomenon. The AVP algorithm stochastically relabel the posterior samples according to the posterior probabilities of the established model. Some existing deterministic and other probabilistic algorithms are compared with AVP algorithm in simulation studies, and the success of the proposed approach is demonstrated in simulation studies and a real dataset.

Subtyping schizophrenia: A comparison of positive/negative and system-specific approaches

BACKGROUND

Schizophrenia is a heterogeneous disorder. Over the years, different approaches have been proposed to approach this heterogeneity by categorizing symptom patterns. The study aimed to compare positive/negative and system-specific approaches to subtyping.

METHODS

We used the Positive and Negative Syndrome Scale (PANSS) and Bern Psychopathology Scale (BPS), which consists of subscales for three domains (language, affect and motor behavior) that are hypothesized to be related to specific brain circuits, to assess cross-sectional psychopathological characteristics in a sample of 100 inpatients with schizophrenia spectrum disorders. We then categorized participants into positive/negative and system-specific subgroups to allow comparisons of the two approaches.

RESULTS

The analyses revealed correlations between the PANSS positive subscore and the BPS affective subscore (r=.446, p<.001) and between the PANSS negative subscore and the BPS motor behavior subscore (r=.227, p=.023). As regards the positive and negative subtype, more participants were classified as positive in the language-dominant subtype (30.3%) and affect-dominant subtype (30.3%), whereas more were classified as negative in the motor behavior-dominant subtype (44.4%). However, most patients met the criteria for the mixed subtype.

CONCLUSIONS

The results suggest that the positive/negative and system-specific approaches can be regarded as complementary. Future studies should examine both approaches in a longitudinal assessment of psychopathological symptoms and link them with qualitative-phenomenological approaches.

Bayesian inferences of latent class models with an unknown number of classes

This paper focuses on analyzing data collected in situations where investigators use multiple discrete indicators as surrogates, for example, a set of questionnaires. A very flexible latent class model is used for analysis. We propose a Bayesian framework to perform the joint estimation of the number of latent classes and model parameters. The proposed approach applies the reversible jump Markov chain Monte Carlo to analyze finite mixtures of multivariate multinomial distributions. In the paper, we also develop a procedure for the unique labeling of the classes. We have carried out a detailed sensitivity analysis for various hyperparameter specifications, which leads us to make standard default recommendations for the choice of priors. The usefulness of the proposed method is demonstrated through computer simulations and a study on subtypes of schizophrenia using the Positive and Negative Syndrome Scale (PANSS).

Attempted suicide in a Xhosa schizophrenia and schizoaffective disorder population

Suicide risk behavior is a significant contributor to the mortality and morbidity of schizophrenia. We previously reported affected sibship status in a Xhosa schizophrenia or schizoaffective disorder sample to be protective in nature; given the counterintuitive nature of this finding, we expanded the sample size to seek further clarification. Subjects were assessed with the Diagnostic Interview for Genetic Studies and then stratified into two groups: with (n = 137) or without (n = 837) a previous suicide attempt. The presence of lifetime bizarre behavior (OR 1.5; 95% CI 1.12-1.87) or cannabis use or abuse (OR 1.2; 95% CI 10.01-1.47) was a significant predictor of suicide attempts, while a higher global alogia score (OR 0.84; 95% CI 0.74-0.96) was a protective factor. Our data seem to support that in this population, encouraging family members to report bizarre behavior and implementing dual diagnosis interventions for cannabis use or abuse could be an appropriate starting point toward developing a targeted suicide prevention program for further research.