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PLoS One. 2017 Sep 21;12(9):e0183998. doi: 10.1371/journal.pone.0183998. eCollection 2017.

Models to predict relapse in psychosis: A systematic review.

Author information

NIHR CLAHRC West, United Hospitals Bristol NHS Foundation Trust, Bristol, United Kingdom.
School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom.
Otsuka Pharmaceutical Europe Ltd, trading as Otsuka Health Solutions, Wexham Springs, Slough, United Kingdom.
Avon & Wiltshire Mental Health NHS Trust, Jenner House, Chippenham, Wilts, United Kingdom.



There is little evidence on the accuracy of psychosis relapse prediction models. Our objective was to undertake a systematic review of relapse prediction models in psychosis.


We conducted a literature search including studies that developed and/or validated psychosis relapse prediction models, with or without external model validation. Models had to target people with psychosis and predict relapse. The key databases searched were; Embase, Medline, Medline In-Process Citations & Daily Update, PsychINFO, BIOSIS Citation Index, CINAHL, and Science Citation Index, from inception to September 2016. Prediction modelling studies were assessed for risk of bias and applicability using the PROBAST tool.


There were two eligible studies, which included 33,088 participants. One developed a model using prodromal symptoms and illness-related variables, which explained 14% of relapse variance but was at high risk of bias. The second developed a model using administrative data which was moderately discriminative (C = 0.631) and associated with relapse (OR 1.11 95% CI 1.10, 1.12) and achieved moderately discriminative capacity when validated (C = 0.630). The risk of bias was low.


Due to a lack of high quality evidence it is not possible to make any specific recommendations about the predictors that should be included in a prognostic model for relapse. For instance, it is unclear whether prodromal symptoms are useful for predicting relapse. The use of routine data to develop prediction models may be a more promising approach, although we could not empirically compare the two included studies.

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