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Predicting first-episode psychosis patients who will never relapse over 10 years

2018; Cambridge University Press; Volume: 49; Issue: 13 Linguagem: Inglês

10.1017/s0033291718003070

1469-8978

Christy Lai Ming Hui, William G. Honer, Edwin Lee, WC Chang, Sherry Kit Wa Chan, Emily SM Chen, Edwin P.F. Pang, Simon S. Y. Lui, Dicky W. S. Chung, W.S. Yeung, Roger MK Ng, William TL Lo, Peter B. Jones, Pak C. Sham, Eric Chen,

Mental Health and Psychiatry

Abstract Background Although relapse in psychosis is common, a small proportion of patients will not relapse in the long term. We examined the proportion and predictors of patients who never relapsed in the 10 years following complete resolution of positive symptoms from their first psychotic episode. Method Patients who previously enrolled in a 12-month randomized controlled trial on medication discontinuation and relapse following first-episode psychosis (FEP) were followed up after 10 years. Relapse of positive symptoms was operationalized as a change from a Clinical Global Impression scale positive score of <3 for at least 3 consecutive months to a score of ⩾3 (mild or more severe). Baseline predictors included basic demographics, premorbid functioning, symptoms, functioning, and neurocognitive functioning. Results Out of 178 first-episode patients, 37 (21%) never relapsed during the 10-year period. Univariate predictors ( p ⩽ 0.1) of patients who never relapsed included a duration of untreated psychosis (DUP) ⩽30 days, diagnosed with non-schizophrenia spectrum disorders, having less severe negative symptoms, and performing better in logical memory immediate recall and verbal fluency tests. A multivariate logistic regression analysis further suggested that the absence of any relapsing episodes was significantly related to better short-term verbal memory, shorter DUP, and non-schizophrenia spectrum disorders. Conclusions Treatment delay and neurocognitive function are potentially modifiable predictors of good long-term prognosis in FEP. These predictors are informative as they can be incorporated into an optimum risk prediction model in the future, which would help with clinical decision making regarding maintenance treatment in FEP.