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Schizophr Res. 2017 Dec 23. pii: S0920-9964(17)30753-3. doi: 10.1016/j.schres.2017.12.001. [Epub ahead of print]

Latent class cluster analysis of symptom ratings identifies distinct subgroups within the clinical high risk for psychosis syndrome.

Author information

1
Department of Psychology, Emory University, Atlanta, GA, United States. Electronic address: arthur.ryan@va.gov.
2
Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada.
3
Semel Institute for Neuroscience and Human Behavior & Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.
4
Department of Psychiatry, UCSD, San Diego, CA, United States.
5
Department of Psychiatry, Zucker Hillside Hospital, Long Island, NY, United States.
6
Department of Psychiatry, UCSF, San Francisco, CA, United States; SFVA Medical Center, San Francisco, CA, United States.
7
Department of Psychiatry, Yale University, New Haven, CT, United States.
8
Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States.
9
Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, MA, United States; Massachusetts General Hospital, Boston, MA, United States.
10
Department of Psychiatry, Yale University, New Haven, CT, United States; Department of Psychology, Yale University, New Haven, CT, United States.
11
Department of Psychology, Emory University, Atlanta, GA, United States; Department of Psychiatry, Emory University, Atlanta, GA, United States.

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

PMID:
29279247
PMCID:
PMC6015526
[Available on 2019-06-23]
DOI:
10.1016/j.schres.2017.12.001

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