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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.
- Source :
- Psychological Medicine; Sep2023, Vol. 53 Issue 12, p5717-5728, 12p
- Publication Year :
- 2023
-
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 (p <subscript>FDR</subscript> < 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00332917
- Volume :
- 53
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Psychological Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 171833750
- Full Text :
- https://doi.org/10.1017/S003329172200294X