Latent Profile Analysis and Conversion to Psychosis: Characterizing Subgroups to Enhance Risk Prediction.

Published

Journal Article

BACKGROUND: Groups at clinical high risk (CHR) of developing psychosis are heterogeneous, composed of individuals with different clusters of symptoms. It is likely that there exist subgroups, each associated with different symptom constellations and probabilities of conversion. METHOD: Present study used latent profile analysis (LPA) to ascertain subgroups in a combined sample of CHR (n = 171) and help-seeking controls (HSCs; n = 100; PREDICT study). Indicators in the LPA model included baseline Scale of Prodromal Symptoms (SOPS), Calgary Depression Scale for Schizophrenia (CDSS), and neurocognitive performance as measured by multiple instruments, including category instances (CAT). Subgroups were further characterized using covariates measuring demographic and clinical features. RESULTS: Three classes emerged: class 1 (mild, transition rate 5.6%), lowest SOPS and depression scores, intact neurocognitive performance; class 2 (paranoid-affective, transition rate 14.2%), highest suspiciousness, mild negative symptoms, moderate depression; and class 3 (negative-neurocognitive, transition rate 29.3%), highest negative symptoms, neurocognitive impairment, social cognitive impairment. Classes 2 and 3 evidenced poor social functioning. CONCLUSIONS: Results support a subgroup approach to research, assessment, and treatment of help-seeking individuals. Class 3 may be an early risk stage of developing schizophrenia.

Full Text

Duke Authors

Cited Authors

  • Healey, KM; Penn, DL; Perkins, D; Woods, SW; Keefe, RSE; Addington, J

Published Date

  • February 15, 2018

Published In

Volume / Issue

  • 44 / 2

Start / End Page

  • 286 - 296

PubMed ID

  • 29036587

Pubmed Central ID

  • 29036587

Electronic International Standard Serial Number (EISSN)

  • 1745-1701

Digital Object Identifier (DOI)

  • 10.1093/schbul/sbx080

Language

  • eng

Conference Location

  • United States