Models to predict relapse in psychosis: A systematic review

Sarah Sullivan, Kate Northstone, Caroline Gadd, Julian Walker, Ruta Margelyte, Alison C Richards, Penny Whiting

Research output: Contribution to journalArticle (Academic Journal)

3 Citations (Scopus)
227 Downloads (Pure)

Abstract

Background
There is little evidence on the accuracy of psychosis relapse prediction models.

Aim
To conduct a systematic review of psychosis relapse prediction models
.
Method
We conducted a literature search including studies that developed and/or validated psychosis relapse prediction models. Study quality was assessed and narrative synthesis was used to combine studies.

Results
There were two eligible studies. 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.

Conclusions
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.
Original languageEnglish
Article numbere0183998
Number of pages12
JournalPLoS ONE
Volume12
Issue number9
DOIs
Publication statusPublished - 21 Sep 2017

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