Sustained symptomatic remission in schizophrenia: Course and predictors from a two-year prospective study
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Published on: December 9, 2015
Bernardo Carpiniello1, Federica Pinna1, Mirko Manchia2
1Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy; Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy.
Background:
Although remission is a priority target in psychosis, reported rates show a marked variation across studies and instability over time. Such variability, partly due to methodology, emphasizes the need to define the optimal assessment procedure, as well as to identify reliable predictors. This study aims to: 1. longitudinally compare remission status according to different criteria; 2. identify predictors of duration and stability.
Methods:
112 patients with schizophrenia or schizoaffective disorder underwent comprehensive clinical evaluations, with 24-month follow-up. Remission was assessed using three criteria: Remission in Schizophrenia Working Group (RSWG) vs Positive and Negative Syndrome Scale (PANSS) positive and negative scales (PANSS-PN) vs total score (PANSS-T). Kaplan-Meier survival analysis was used for longitudinal comparison, regression models to identify predictors of duration and stability.
Results:
At enrolment 50% of patients were in remission according to RSWG, while only 23.2% reached the other criteria. PANSS-T cumulative remission rates showed the greatest stability. Stable remission according to RSWG criteria was predicted by negative symptoms, while no significant predictors emerged for PANSS-T. Remission duration was predicted by negative, positive and cognitive symptoms and treatment dosage for RSWG criteria, while for PANSS-T the predictors were cognitive symptoms and duration of illness.
Conclusion:
Results are in line with previous literature on remission rates and further support the role of basal clinical predictors. In addition, this study shows that more stringent criteria are more stable over time, suggesting their predictive value and the relevance of their use to optimize evaluations also in clinical settings.
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