Qualitative and quantitative aspects of information processing in first psychosis: latent class analyses in patients, at-risk subjects, and controls

Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

Deficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically different disease subgroups. We applied machine learning algorithms on measures of electrophysiology and cognition to identify potential subgroups of schizophrenia. Next, we explored subgroup differences regarding treatment response. Sixty-six antipsychotic-naive first-episode schizophrenia patients and sixty-five healthy controls underwent extensive electrophysiological and neurocognitive test batteries. Patients were assessed on the Positive and Negative Syndrome Scale (PANSS) before and after 6 weeks of monotherapy with the relatively selective D₂ receptor antagonist, amisulpride (280.3±159 mg per day). A reduced principal component space based on 19 electrophysiological variables and 26 cognitive variables was used as input for a Gaussian mixture model to identify subgroups of patients. With support vector machines, we explored the relation between PANSS subscores and the identified subgroups. We identified two statistically distinct subgroups of patients. We found no significant baseline psychopathological differences between these subgroups, but the effect of treatment in the groups was predicted with an accuracy of 74.3% (P=0.003). In conclusion, electrophysiology and cognition data may be used to classify subgroups of schizophrenia patients. The two distinct subgroups, which we identified, were psychopathologically inseparable before treatment, yet their response to dopaminergic blockade was predicted with significant accuracy. This proof of principle encourages further endeavors to apply data-driven, multivariate and multimodal models to facilitate progress from symptom-based psychiatry toward individualized treatment regimens.

Vulnerability, Stress, and Support in the Disease Trajectory from Prodrome to Diagnosed Schizophrenia: Diathesis-Stress-Support Model

Schizophrenia is a debilitating psychiatric disorder seen across the world. The goal of current research is to provide a more comprehensive understanding of prodrome, the initial period before the disease manifests as schizophrenia. Unfortunately, there is little information to comprehensively understand the indicators that later lead to schizophrenia. This has resulted in a lack of scientific knowledge to aid in early identification and intervention by psychiatric nurses or school nurses. Such information could inform nursing assessment and care plan development. The purpose of this paper is to construct a conceptual model for describing the important factors relating to the schizophrenia prodrome process in human developmental manner by critically synthesizing empirical evidence and reviewing the strengths and limitations of current conceptual models.