In:
Psychological Medicine, Cambridge University Press (CUP), Vol. 53, No. 12 ( 2023-09), p. 5717-5728
Abstract:
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 FDR 〈 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.
Type of Medium:
Online Resource
ISSN:
0033-2917
,
1469-8978
DOI:
10.1017/S003329172200294X
Language:
English
Publisher:
Cambridge University Press (CUP)
Publication Date:
2023
detail.hit.zdb_id:
1470300-2
detail.hit.zdb_id:
217420-0
SSG:
5,2
Permalink