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

IntroductionIndividuals at Ultra High Risk (UHR) for psychosis typically present with attenuated psychotic symptoms. However it is difficult to predict which individuals will later develop frank psychosis when their mental state is rated in terms of individual symptoms.The objective of the study was to examine the phenomenological structure of the UHR mental state and identify symptom profiles that predict later transition to psychosis.MethodPsychopathological data from a large sample of UHR subjects were analysed using latent class cluster analysis.A total of 318 individuals with a UHR for psychosis. Data were collected from two specialised community mental health services for people at UHR for psychosis: OASIS in London and PACE, in Melbourne.ResultsLatent class cluster analysis produced 4 classes: Class 1 - Mild was characterized by lower scores on all the CAARMS items. Subjects in Class 2 - Moderate scored moderately on all CAARMS items and was more likely to be in employment. Those in Class 3 - Moderate-Severe scored moderately-severe on negative symptoms, social isolation and impaired role functioning. Class 4 - Severe was the smallest group and was associated with the most impairment: subjects in this class scored highest on all items of the CAARMS, had the lowest GAF score and were more likely to be unemployed. This group was also characterized by the highest transition rate (41%).ConclusionsDifferent constellations of symptomatology are associates with varying levels of risk to of transition to psychosis.

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Latent class cluster analysis identified hidden headache phenotypes in COVID-19: impact of pulmonary infiltration and IL-6

Neurological Sciences ◽

10.1007/s10072-020-04978-2 ◽

2021 ◽

Author(s):

Ömer Karadaş ◽

Bilgin Öztürk ◽

Ali Rıza Sonkaya ◽

Bahar Taşdelen ◽

Aynur Özge ◽

...

Keyword(s):

Cluster Analysis ◽

Latent Class ◽

Latent Class Cluster Analysis ◽

Pulmonary Infiltration

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Typology of online lotteries and scratch games gamblers' behaviours: A multilevel latent class cluster analysis applied to player account‐based gambling data

International Journal of Methods in Psychiatric Research ◽

10.1002/mpr.1746 ◽

2018 ◽

Vol 27 (4) ◽

Cited By ~ 1

Author(s):

Bastien Perrot ◽

Jean‐Benoit Hardouin ◽

Marie Grall‐Bronnec ◽

Gaëlle Challet‐Bouju

Keyword(s):

Cluster Analysis ◽

Latent Class ◽

Latent Class Cluster Analysis

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Latent Class Cluster Analysis

Encyclopedia of Quality of Life and Well-Being Research ◽

10.1007/978-94-007-0753-5_102221 ◽

2014 ◽

pp. 3508-3508 ◽

Cited By ~ 1

Keyword(s):

Cluster Analysis ◽

Latent Class ◽

Latent Class Cluster Analysis

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Negative psychotic symptoms and impaired role functioning predict transition outcomes in the at-risk mental state: a latent class cluster analysis study

Psychological Medicine ◽

10.1017/s0033291713000251 ◽

2013 ◽

Vol 43 (11) ◽

pp. 2311-2325 ◽

Cited By ~ 71

Author(s):

L. R. Valmaggia ◽

D. Stahl ◽

A. R. Yung ◽

B. Nelson ◽

P. Fusar-Poli ◽

...

Keyword(s):

At Risk ◽

Cluster Analysis ◽

Mental State ◽

Latent Class ◽

Mental States ◽

Psychotic Symptoms ◽

Role Functioning ◽

Latent Class Cluster Analysis ◽

At Risk Mental State

BackgroundMany research groups have attempted to predict which individuals with an at-risk mental state (ARMS) for psychosis will later develop a psychotic disorder. However, it is difficult to predict the course and outcome based on individual symptoms scores.MethodData from 318 ARMS individuals from two specialized services for ARMS subjects were analysed using latent class cluster analysis (LCCA). The score on the Comprehensive Assessment of At-Risk Mental States (CAARMS) was used to explore the number, size and symptom profiles of latent classes.ResultsLCCA produced four high-risk classes, censored after 2 years of follow-up: class 1 (mild) had the lowest transition risk (4.9%). Subjects in this group had the lowest scores on all the CAARMS items, they were younger, more likely to be students and had the highest Global Assessment of Functioning (GAF) score. Subjects in class 2 (moderate) had a transition risk of 10.9%, scored moderately on all CAARMS items and were more likely to be in employment. Those in class 3 (moderate–severe) had a transition risk of 11.4% and scored moderately severe on the CAARMS. Subjects in class 4 (severe) had the highest transition risk (41.2%), they scored highest on the CAARMS, had the lowest GAF score and were more likely to be unemployed. Overall, class 4 was best distinguished from the other classes on the alogia, avolition/apathy, anhedonia, social isolation and impaired role functioning.ConclusionsThe different classes of symptoms were associated with significant differences in the risk of transition at 2 years of follow-up. Symptomatic clustering predicts prognosis better than individual symptoms.

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Clinical subtypes that predict conversion to psychosis: A canonical correlation analysis study from the ShangHai At Risk for Psychosis program

Australian & New Zealand Journal of Psychiatry ◽

10.1177/0004867419872248 ◽

2019 ◽

Vol 54 (5) ◽

pp. 482-495 ◽

Cited By ~ 2

Author(s):

TianHong Zhang ◽

XiaoChen Tang ◽

HuiJun Li ◽

Kristen A Woodberry ◽

Emily R Kline ◽

...

Keyword(s):

At Risk ◽

Cluster Analysis ◽

High Risk ◽

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Clinical Symptoms ◽

Structured Interview ◽

Two Dimensions ◽

Clinical High Risk

Objective: Since only 30% or fewer of individuals at clinical high risk convert to psychosis within 2 years, efforts are underway to refine risk identification strategies to increase their predictive power. The clinical high risk is a heterogeneous syndrome presenting with highly variable clinical symptoms and cognitive dysfunctions. This study investigated whether subtypes defined by baseline clinical and cognitive features improve the prediction of psychosis. Method: Four hundred clinical high-risk subjects from the ongoing ShangHai At Risk for Psychosis program were enrolled in a prospective cohort study. Canonical correlation analysis was applied to 289 clinical high-risk subjects with completed Structured Interview for Prodromal Syndromes and cognitive battery tests at baseline, and at least 1-year follow-up. Canonical variates were generated by canonical correlation analysis and then used for hierarchical cluster analysis to produce subtypes. Kaplan–Meier survival curves were constructed from the three subtypes to test their utility further in predicting psychosis. Results: Canonical correlation analysis determined two linear combinations: (1) negative symptom and functional deterioration-related cognitive features, and (2) Positive symptoms and emotional disorganization-related cognitive features. Cluster analysis revealed three subtypes defined by distinct and relatively homogeneous patterns along two dimensions, comprising 14.2% (subtype 1, n = 41), 37.4% (subtype 2, n = 108) and 48.4% (subtype 3, n = 140) of the sample, and each with distinctive features of clinical and cognitive performance. Those with subtype 1, which is characterized by extensive negative symptoms and cognitive deficits, appear to have the highest risk for psychosis. The conversion risk for subtypes 1–3 are 39.0%, 11.1% and 18.6%, respectively. Conclusion: Our results define important subtypes within clinical high-risk syndromes that highlight clinical symptoms and cognitive features that transcend current diagnostic boundaries. The three different subtypes reflect significant differences in clinical and cognitive characteristics as well as in the risk of conversion to psychosis.

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