Latent Class Cluster Analysis of Symptom Ratings Identifies Distinct Subgroups Within the Clinical High Risk for Psychosis Syndrome

Schizophr Res. 2018 Jul;197:522-530. doi: 10.1016/j.schres.2017.12.001. Epub 2017 Dec 24.

Abstract

The clinical-high-risk for psychosis (CHR-P) syndrome is heterogeneous in terms of clinical presentation and outcomes. Identifying more homogenous subtypes of the syndrome may help clarify its etiology and improve the prediction of psychotic illness. This study applied latent class cluster analysis (LCCA) to symptom ratings from the North American Prodrome Longitudinal Studies 1 and 2 (NAPLS 1 and 2). These analyses produced evidence for three to five subgroups within the CHR-P syndrome. Differences in negative and disorganized symptoms distinguished among the subgroups. Subgroup membership was found to predict conversion to psychosis. The authors contrast the methods employed within this study with previous attempts to identify more homogenous subgroups of CHR-P individuals and discuss how these results could be tested in future samples of CHR-P individuals.

Keywords: Disorganization symptoms; Finite mixture models; Heterogeneity; Prodrome; Schizophrenia.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Disease Progression*
  • Female
  • Humans
  • Latent Class Analysis
  • Longitudinal Studies
  • Male
  • North America
  • Prodromal Symptoms*
  • Psychotic Disorders / classification*
  • Psychotic Disorders / physiopathology*
  • Risk
  • Young Adult