Objective: Existing staging models have not been fully validated. Thus, after classifying patients with schizophrenia according to the staging model proposed by McGorry et al. (2010), we explored the validity of this staging model and its stability after one-year of follow-up.
Method: Using unsupervised machine-learning algorithm, we classified 770 outpatients into 5 clinical stages, the highest being the most severe. Analyses of (co)variance were performed to compare each stage in regard to socio-demographics factors, clinical characteristics, co-morbidities, ongoing treatment and neuropsychological profiles.
Results: The precision of clinical staging can be improved by sub-dividing intermediate stages (II and III). Clinical validators of class IV include the presence of concomitant major depressive episode (42.6% in stage IV versus 3.4% in stage IIa), more severe cognitive profile, lower adherence to medication and prescription of >3 psychotropic medications. Follow-up at one-year showed good stability of each stage.
Conclusion: Clinical staging in schizophrenia could be improved by adding clinical elements such as mood symptoms and cognition to severity, relapses and global functioning. In terms of therapeutic strategies, attention needs to be paid on the factors associated with the more stages of schizophrenia such as treatment of comorbid depression, reduction of the number of concomitant psychotropic medications, improvement of treatment adherence, and prescription of cognitive remediation.
Keywords: Clinical staging; Cognition; Comorbidity; Prognosis; Schizophrenia.
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