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Social cognition in people with schizophrenia: a cluster-analytic approach

Published online by Cambridge University Press:  20 September 2016

P. Rocca
Affiliation:
Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
S. Galderisi
Affiliation:
Department of Psychiatry, University of Naples SUN, Naples, Italy
A. Rossi
Affiliation:
Department of Biotechnological and Applied Clinical Sciences, Section of Psychiatry, University of L'Aquila, L'Aquila, Italy
A. Bertolino
Affiliation:
Department of Neurological and Psychiatric Sciences, University of Bari, Bari, Italy
P. Rucci
Affiliation:
Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
D. Gibertoni
Affiliation:
Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
C. Montemagni
Affiliation:
Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
M. Sigaudo
Affiliation:
Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
A. Mucci
Affiliation:
Department of Psychiatry, University of Naples SUN, Naples, Italy
P. Bucci
Affiliation:
Department of Psychiatry, University of Naples SUN, Naples, Italy
T. Acciavatti
Affiliation:
Department of Neuroscience and Imaging, Chair of Psychiatry, G. d'Annunzio University, Chieti, Italy
E. Aguglia
Affiliation:
Department of Clinical and Molecular Biomedicine, Psychiatry Unit, University of Catania, Catania, Italy
M. Amore
Affiliation:
Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, Section of Psychiatry, University of Genoa, Genoa, Italy
A. Bellomo
Affiliation:
Department of Medical Sciences, Psychiatry Unit, University of Foggia, Foggia, Italy
D. De Ronchi
Affiliation:
Department of Biomedical and Neuromotor Sciences, Section of Psychiatry, University of Bologna, Bologna, Italy
L. Dell'Osso
Affiliation:
Department of Clinical and Experimental Medicine, Section of Psychiatry, University of Pisa, Pisa, Italy
F. Di Fabio
Affiliation:
Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy
P. Girardi
Affiliation:
Department of Neurosciences, Mental Health and Sensory Organs, S. Andrea Hospital, Sapienza University of Rome, Rome, Italy
A. Goracci
Affiliation:
Department of Molecular Medicine and Clinical Department of Mental Health, University of Siena, Siena, Italy
C. Marchesi
Affiliation:
Department of Neuroscience, Psychiatry Unit, University of Parma, Parma, Italy
P. Monteleone
Affiliation:
Department of Medicine and Surgery, Chair of Psychiatry, University of Salerno, Salerno, Italy
C. Niolu
Affiliation:
Department of Systems Medicine, Chair of Psychiatry, Tor Vergata University of Rome, Rome, Italy
F. Pinna
Affiliation:
Department of Public Health, Clinical and Molecular Medicine, Section of Psychiatry, University of Cagliari, Cagliari, Italy
R. Roncone
Affiliation:
Department of Life, Health and Environmental Sciences, Unit of Psychiatry, University of L'Aquila, L'Aquila, Italy
E. Sacchetti
Affiliation:
Psychiatric Unit, School of Medicine, University of Brescia, Brescia, Italy Department of Mental Health, Spedali Civili Hospital, Brescia, Italy
P. Santonastaso
Affiliation:
Psychiatric Clinic, Department of Neurosciences, University of Padua, Padua, Italy
P. Zeppegno
Affiliation:
Department of Translational Medicine, Psychiatric Unit, University of Eastern Piedmont, Novara, Italy
M. Maj
Affiliation:
Department of Psychiatry, University of Naples SUN, Naples, Italy
Corresponding
E-mail address:

Abstract

Background

The study aimed to subtype patients with schizophrenia on the basis of social cognition (SC), and to identify cut-offs that best discriminate among subtypes in 809 out-patients recruited in the context of the Italian Network for Research on Psychoses.

Method

A two-step cluster analysis of The Awareness of Social Inference Test (TASIT), the Facial Emotion Identification Test and Mayer–Salovey–Caruso Emotional Intelligence Test scores was performed. Classification and regression tree analysis was used to identify the cut-offs of variables that best discriminated among clusters.

Results

We identified three clusters, characterized by unimpaired (42%), impaired (50.4%) and very impaired (7.5%) SC. Three theory-of-mind domains were more important for the cluster definition as compared with emotion perception and emotional intelligence. Patients more able to understand simple sarcasm (⩾14 for TASIT-SS) were very likely to belong to the unimpaired SC cluster. Compared with patients in the impaired SC cluster, those in the very impaired SC cluster performed significantly worse in lie scenes (TASIT-LI <10), but not in simple sarcasm. Moreover, functioning, neurocognition, disorganization and SC had a linear relationship across the three clusters, while positive symptoms were significantly lower in patients with unimpaired SC as compared with patients with impaired and very impaired SC. On the other hand, negative symptoms were highest in patients with impaired levels of SC.

Conclusions

If replicated, the identification of such subtypes in clinical practice may help in tailoring rehabilitation efforts to the person's strengths to gain more benefit to the person.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2016 

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