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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

Published online by Cambridge University Press:  27 February 2013

L. R. Valmaggia ,
D. Stahl ,
A. R. Yung ,
B. Nelson ,
P. Fusar-Poli ,
P. D. McGorry  and
P. K. McGuire
L. R. Valmaggia*
Affiliation:
King's College London, Institute of Psychiatry, London, UK Outreach and Support in South London Service (OASIS), South London and Maudsley NHS Trust, London, UK
D. Stahl
Affiliation:
King's College London, Institute of Psychiatry, London, UK
A. R. Yung
Affiliation:
Orygen Youth Health, Department of Psychiatry, University of Melbourne, Australia Institute of Brain, Behaviour & Mental Health, University of Manchester, UK
B. Nelson
Affiliation:
Orygen Youth Health, Department of Psychiatry, University of Melbourne, Australia
P. Fusar-Poli
Affiliation:
King's College London, Institute of Psychiatry, London, UK Outreach and Support in South London Service (OASIS), South London and Maudsley NHS Trust, London, UK
P. D. McGorry
Affiliation:
Orygen Youth Health, Department of Psychiatry, University of Melbourne, Australia
P. K. McGuire
Affiliation:
King's College London, Institute of Psychiatry, London, UK Outreach and Support in South London Service (OASIS), South London and Maudsley NHS Trust, London, UK
*
*Address for correspondence: Dr L. R. Valmaggia, King's College London, King's Health Partners, Institute of Psychiatry (PO77), De Crespigny Park, London SE5 8AF, UK. (Email: lucia.valmaggia@kcl.ac.uk)
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Abstract

Background

Many 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.

Method

Data 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.

Results

LCCA 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.

Conclusions

The 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.

Keywords

At-risk mental stateearly detectionearly interventionnegative symptomspsychosis
Type
Original Articles
Information
Psychological Medicine , Volume 43 , Issue 11 , November 2013 , pp. 2311 - 2325
Copyright
Copyright © Cambridge University Press 2013 

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