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Clinical and psychological factors associated with resilience in patients with schizophrenia: data from the Italian network for research on psychoses using machine learning

Published online by Cambridge University Press:  11 October 2022

Linda A. Antonucci ,
Giulio Pergola ,
Antonio Rampino ,
Paola Rocca ,
Alessandro Rossi ,
Mario Amore ,
Eugenio Aguglia ,
Antonello Bellomo ,
Valeria Bianchini  and
Claudio Brasso
...Show all authors
Linda A. Antonucci
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
Giulio Pergola
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
Antonio Rampino
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
Paola Rocca
Affiliation:
Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
Alessandro Rossi
Affiliation:
Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
Mario Amore
Affiliation:
Section of Psychiatry, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Genoa, Italy
Eugenio Aguglia
Affiliation:
Department of Clinical and Molecular Biomedicine, Psychiatry Unit, University of Catania, Catania, Italy
Antonello Bellomo
Affiliation:
Psychiatry Unit, Department of Medical Sciences, University of Foggia, Foggia, Italy
Valeria Bianchini
Affiliation:
Unit of Psychiatry, Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
Claudio Brasso
Affiliation:
Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
Paola Bucci
Affiliation:
Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy
Bernardo Carpiniello
Affiliation:
Section of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, Cagliari, Italy
Liliana Dell'Osso
Affiliation:
Section of Psychiatry, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
Fabio di Fabio
Affiliation:
Department of Neurology and Psychiatry, “La Sapienza” University of Rome, Rome, Italy
Massimo di Giannantonio
Affiliation:
Department of Neuroscience and Imaging, “G. D'Annunzio” University, Chieti, Italy
Andrea Fagiolini
Affiliation:
Department of Molecular Medicine and Clinical Department of Mental Health, University of Siena, Siena, Italy
Giulia Maria Giordano
Affiliation:
Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy
Matteo Marcatilli
Affiliation:
Department of Psychiatry, University of Milan, Milan, Italy
Carlo Marchesi
Affiliation:
Department of Neuroscience, Psychiatry Unit, University of Parma, Parma, Italy
Paolo Meneguzzo
Affiliation:
Psychiatric Clinic, Department of Neurosciences, University of Padua, Padua, Italy
Palmiero Monteleone
Affiliation:
Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana” Section of Neuroscience, University of Salerno, Salerno, Italy
Maurizio Pompili
Affiliation:
Department of Neurosciences, Mental Health and Sensory Organs, S. Andrea Hospital, “La Sapienza” University of Rome, Rome, Italy
Rodolfo Rossi
Affiliation:
Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
Alberto Siracusano
Affiliation:
Department of Systems Medicine, Psychiatry and Clinical Psychology Unit, “Tor Vergata” University of Rome, Rome, Italy
Antonio Vita
Affiliation:
Psychiatric Unit, School of Medicine, University of Brescia, Brescia, Italy Department of Mental Health, Spedali Civili Hospital, Brescia, Italy
Patrizia Zeppegno
Affiliation:
Department of Translational Medicine, Psychiatric Unit, University of Eastern Piedmont, Novara, Italy
Silvana Galderisi
Affiliation:
Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy
Alessandro Bertolino*
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
Mario Maj
Affiliation:
Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy
Italian Network for Research on Psychoses
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
*
Author for correspondence: Alessandro Bertolino, E-mail: alessandro.bertolino@uniba.it
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Abstract

Background

Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR).

Methods

SCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients.

Results

The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (pFDR < 0.05).

Conclusions

We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia.

Keywords

Italian network for research on psychosesmachine learningpersonalized interventionsresilienceschizophrenia
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 12 , September 2023 , pp. 5717 - 5728
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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Footnotes

*

The members of the Italian Network for Research on Psychoses involved in this study are listed in the Appendix.

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