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

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.

Latent profile analysis of psychosis liability in a community‐derived sample of adolescents: Links with mental health difficulties, suicidal ideation, bipolar‐like experiences and psychotic‐like experiences

“…It is relevant to note that schizotypy, as a multidimensional and complex construct, allow us to identify these subgroups and to take into account the multiple expression of the psychosis phenotype (positive, negative and disorganization) rather than only the positive dimension of this phenotype (Pignon et al, ). Previous studies have examined the latent structure across all psychotic spectrum such as psychotic symptoms (Kendler et al, ; Pignon et al, ), schizotypal personality disorder (Fossati et al, ), subclinical psychosis symptoms (Ryan et al, ; Valmaggia et al, ), PLEs (Ahmed et al, ; Cella et al, ; Gale et al, ; Shevlin et al, ) and schizotypal traits (Cella et al, ; Denovan et al, ; Fonseca‐Pedrero, Gooding, Ortuño‐Sierra, & Paino, ; Fonseca‐Pedrero, Ortuno‐Sierra, et al, ; Hori et al, ; Tabak & Weisman de Mamani, ; Wang et al, ). Although there is heterogeneity in the measures used and the nature of the samples, previous studies conducted in adolescent population are clearly convergent with the results found in this study.…”

“…It is relevant to note that schizotypy, as a multidimensional and complex construct, allow us to identify these subgroups and to take into account the multiple expression of the psychosis phenotype (positive, negative and disorganization) rather than only the positive dimension of this phenotype (Pignon et al, 2018). Previous studies have examined the latent structure across all psychotic spectrum such as psychotic symptoms (Kendler et al, 1998;Pignon et al, 2018), schizotypal personality disorder (Fossati et al, 2001), subclinical psychosis symptoms (Ryan et al, 2017;Valmaggia et al, 2011), PLEs (Ahmed et al, 2012;Cella et al, 2011;Gale et al, 2011;Shevlin et al, 2007) Another important issue involves the clinical implications of these homogeneous groups of psychosis liability. First, the literature on schizotypy suggest that anhedonia is the most consistently found predictor of conversion to psychosis Flückiger et al, 2016;Radua et al, 2018); thus it could be hypothesized that, in a longitudinal study, the "Social Disorganization schizotypy" group would entail higher conversion rates than the other groups.…”

“…Previous studies have examined the latent structure across the psychosis phenotype such as psychotic symptoms (Kendler, Karkowski, & Walsh, ; Pignon et al, ), schizotypal personality disorder (Fossati et al, ), subclinical psychosis symptoms (Ryan et al, ; Valmaggia, Stahl, Yung, Nelson, & McGorry, ), PLEs (Ahmed, Buckley, & Mabe, ; Cella, Sisti, Rocchi, & Preti, ; Gale, Wells, McGee, & Oakley Browne, ; Shevlin, Murphy, Dorahy, & Adamson, ) and schizotypal traits (Cella et al, ; Denovan, Dagnall, Drinkwater, & Parker, ; Fonseca‐Pedrero, Gooding, Ortuño‐Sierra, & Paino, ; Fonseca‐Pedrero, Ortuño‐Sierra, de Álbeniz, Muñiz, & Cohen, ; Hori et al, ; Tabak & Weisman de Mamani, ; Wang et al, ). In particular, adolescence is a development stage where these kind of analyses, prior to the development of the first psychotic symptoms, may be relevant with the aim to identify the true liability subgroups and to implement early detection and intervention strategies.…”

Subtyping attenuated psychotic symptoms: A cluster analytic approach

“…In a similar study, Ryan et al. () found three clusters of at‐risk individuals: (1) perceptual abnormalities (mainly characterized by the presence of hallucinations, distortions, illusions, and low levels of negative symptoms), (2) disorganized speech (with mainly negative and disorganization symptoms), and (3) a cluster distinguished by impairment in personal hygiene. Furthermore, the disorganized speech cluster showed the highest conversion rate to psychosis.…”

Latent Psychotic Symptom Profiles Amongst People Who Use Methamphetamine: What Do They Tell Us About Existing Diagnostic Categories?

“…Latent class analysis classifies population heterogeneity into categorical groups of homogeneous individuals which may have implications for classification (i.e., diagnosis), prognosis (i.e., longitudinal course), and treatment (i.e., propensity to respond to different treatments) ( 17 ). Latent class analysis has been increasingly applied in psychiatry to identify subgroups of patients or clinical markers that may have clinical utility but which are obscured by more traditional methods of analysis, such as pairwise group comparisons that presuppose diagnostic structures and their relationship to clinical characteristics ( 18 – 20 ). In latent class analysis, the presence of a group of people who are homogenous in their symptom profile should present as a single latent class.…”