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

• 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
• Journal Title: Schizophrenia Research
• Volume: Volume 197
• Publisher: Elsevier: 12 months | 2018-07-01, Pages 522-530
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.schres.2017.12.001
• Subjects: Psychology, Clinical
• Keywords: schizophrenia; prodrome; finite mixture models; disorganized symptoms; heterogeneity

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.