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In Psychological medicine ; h5-index 82.0

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.

Antonucci Linda A, Pergola Giulio, Rampino Antonio, Rocca Paola, Rossi Alessandro, Amore Mario, Aguglia Eugenio, Bellomo Antonello, Bianchini Valeria, Brasso Claudio, Bucci Paola, Carpiniello Bernardo, Dell’Osso Liliana, di Fabio Fabio, di Giannantonio Massimo, Fagiolini Andrea, Giordano Giulia Maria, Marcatilli Matteo, Marchesi Carlo, Meneguzzo Paolo, Monteleone Palmiero, Pompili Maurizio, Rossi Rodolfo, Siracusano Alberto, Vita Antonio, Zeppegno Patrizia, Galderisi Silvana, Bertolino Alessandro, Maj Mario


Italian network for research on psychoses, machine learning, personalized interventions, resilience, schizophrenia