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Psychosis prediction in Secondary Mental Health Services. a Broad, Comprehensive Approach to the “at Risk Mental State” Syndrome

Published online by Cambridge University Press:  16 December 2016

M. Francesconi ,
A. Minichino ,
R.E. Carrión ,
R. Delle Chiaie ,
A. Bevilacqua ,
M. Parisi ,
S. Rullo ,
F. Saverio Bersani ,
M. Biondi  and
K. Cadenhead
M. Francesconi
Affiliation:
Department of Neurology and Psychiatry, Sapienza University of Rome, Italy Department of Psychiatry, UCSD, La Jolla, CA, United States
A. Minichino*
Affiliation:
Department of Neurology and Psychiatry, Sapienza University of Rome, Italy Department of Psychiatry, UCSD, La Jolla, CA, United States
R.E. Carrión
Affiliation:
Division of Psychiatry, Zucker Hillside Hospital, Long Island, NY, United States
R. Delle Chiaie
Affiliation:
Department of Neurology and Psychiatry, Sapienza University of Rome, Italy
A. Bevilacqua
Affiliation:
Research Center in Neurobiology, Daniel Bovet (CRiN), Rome, Italy Department of Psychology, Section of Neuroscience, Sapienza University of Rome, Italy
M. Parisi
Affiliation:
Villa Armonia Nuova, Rome, Italy
S. Rullo
Affiliation:
Casa di Cura Villa Letizia, Rome, Italy
F. Saverio Bersani
Affiliation:
Department of Neurology and Psychiatry, Sapienza University of Rome, Italy
M. Biondi
Affiliation:
Department of Neurology and Psychiatry, Sapienza University of Rome, Italy
K. Cadenhead
Affiliation:
Department of Psychiatry, UCSD, La Jolla, CA, United States
*
* Corresponding author at: Viale dell’Universita’, 30, 00185 Rome, Italy. Tel.: +39 3389561007/+1 4152440441. E-mail address:[email protected] (A. Minichino).
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Abstract

Background

Accuracy of risk algorithms for psychosis prediction in “at risk mental state” (ARMS) samples may differ according to the recruitment setting. Standardized criteria used to detect ARMS individuals may lack specificity if the recruitment setting is a secondary mental health service. The authors tested a modified strategy to predict psychosis conversion in this setting by using a systematic selection of trait-markers of the psychosis prodrome in a sample with a heterogeneous ARMS status.

Methods

138 non-psychotic outpatients (aged 17–31) were consecutively recruited in secondary mental health services and followed-up for up to 3 years (mean follow-up time, 2.2 years; SD = 0.9). Baseline ARMS status, clinical, demographic, cognitive, and neurological soft signs measures were collected. Cox regression was used to derive a risk index.

Results

48% individuals met ARMS criteria (ARMS-Positive, ARMS+). Conversion rate to psychosis was 21% for the overall sample, 34% for ARMS+, and 9% for ARMS-Negative (ARMS−). The final predictor model with a positive predictive validity of 80% consisted of four variables: Disorder of Thought Content, visuospatial/constructional deficits, sensory-integration, and theory-of-mind abnormalities. Removing Disorder of Thought Content from the model only slightly modified the predictive accuracy (−6.2%), but increased the sensitivity (+9.5%).

Conclusions

These results suggest that in a secondary mental health setting the use of trait-markers of the psychosis prodrome may predict psychosis conversion with great accuracy despite the heterogeneity of the ARMS status. The use of the proposed predictive algorithm may enable a selective recruitment, potentially reducing duration of untreated psychosis and improving prognostic outcomes.

Keywords

Clinical high riskUltra high riskTheory of mindNeurocognitionNeurological soft signs
Type
Original article
Information
European Psychiatry , Volume 40 , February 2017 , pp. 96 - 104
Copyright
Copyright © Elsevier Masson SAS 2017

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Footnotes

1

These two authors contributed equally to this work.

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