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Schizophrenia, a term introduced by Swiss psychiatrist Eugen Bleuler in 1911, describes a severe psychological disorder marked by profound disruptions in attention, thought processes, language, emotion, and interpersonal relationships. The core feature of schizophrenia is psychosis — a state characterized by a fundamental detachment from reality. This disconnection manifests through distorted logic, impaired perception, and atypical behavior, severely affecting the lives of those...
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Area of Science:

  • Psychiatry and Clinical Psychology
  • Computational Neuroscience
  • Medical Data Science

Background:

  • The clinical course following a first episode of psychosis (FEP) is highly variable.
  • Personalized treatment strategies require understanding and predicting longitudinal symptom trajectories in FEP patients.

Purpose of the Study:

  • To identify distinct patient subgroups based on longitudinal positive and negative symptoms after FEP.
  • To predict the cluster membership of FEP patients using baseline clinical data.

Main Methods:

  • K-means clustering was applied to longitudinal symptom data from 411 FEP patients.
  • Ridge logistic regression was used to predict cluster membership based on baseline characteristics.

Main Results:

  • Three distinct clusters emerged: Low Symptoms (LS), Low Positive/Persistent Negative (LPPN), and Persistent Positive and Negative Symptoms (PPNS).
  • Cluster membership was predicted with an AUC of 0.74.
  • Predictors for LS included lower apathy and affective flattening; predictors for PPNS included hallucination severity and positive thought disorder.

Conclusions:

  • Distinct symptom trajectories exist following FEP, characterized by different symptom profiles and antipsychotic dose requirements.
  • Baseline clinical features can predict these trajectories, paving the way for personalized interventions in FEP.
  • Understanding these heterogeneous pathways is crucial for tailoring treatments to individual patient needs.