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

• Type of Material: Article
• Authors: Arthur T. Ryan, Emory University; Jean Addington, University of Calgary; Carrie E. Bearden, University of California Los Angeles; Kristin S. Cadenhead, University of California San Diego; Barbara A. Cornblatt, Zucker Hillside Hospital; Daniel H. Mathalon, University of California San Francisco; Thomas H. McGlashan, Yale University; Diana O. Perkins, University of North Carolina; Larry J. Seidman, Harvard Medical School; Ming T. Tsuang, University of California San Diego; Scott W. Woods, Yale University; Tyrone D. Cannon, Yale University; Elaine Walker, Emory University
• Language: English
• Date: 2018-07-01
• Publisher: Elsevier: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017
• Final Published Version (URL): http://dx.doi.org/10.1016/j.schres.2017.12.001
• Title of Journal or Parent Work: Schizophrenia Research
• ISSN: 0920-9964
• Volume: 197
• Start Page: 522
• End Page: 530
• Grant/Funding Information: AR is supported by the Department of Veterans Affairs Office of Academic Affiliations Advanced Fellowship Program in Mental Illness Research and Treatment. His work was supported with resources and the use of facilities at the VA Capitol Health Care Network (VISN 5) MIRECC and the University of Maryland School of Medicine Department of Psychiatry.; This work was supported by a collaborative U01 award from the National Institute of Mental Health at the National Institutes of Health (MH081902 to TDC; MH081857 to BAC; MH081988 to EW; MH081928 to LJS; MH082004 to DP; MH082022 to KC; MH081984 to JA; MH082022 to SWW) and NIMH P50 MH066286 and Staglin Music Festival for Mental Health (CEB), NIMH P50 MH080272, and the Commonwealth of Massachusetts (SCDMH82101008006) to LJS.
• Keywords: prodrome; schizophrenia; finite mixture models; disorganized symptoms; heterogeneity
• Research Categories: Psychology, Clinical

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.