Models to predict relapse in psychosis: A systematic review

Abstract

BackgroundThere 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.MethodWe 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.ResultsThere 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.ConclusionsDue 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.

A critical appraisal of the clinical applicability and risk of bias of the predictive models for mortality and recurrence in patients with oropharyngeal cancer: Systematic review

“…To our knowledge, this is the first systematic review of prognostic models in patients with oropharyngeal cancer, which makes it difficult to compare our results. We must therefore base this comparison on reviews of other conditions that have used PROBAST as a tool to assess the quality of prediction models (risk of bias and applicability) . These reviews show results very similar to ours in terms of risk of bias, with a high proportion having high risk due to methodological and statistical analysis issues.…”

Identification of Risk Factors to Predict the Occurrences of Relapses in Individuals with Schizophrenia Spectrum Disorder in Iran

“…While the pattern of results might be discouraging, we also note that it matches with what we know so far from statistical models predicting relapses in psychosis. Thus, Sullivan et al [ 5 ] concluded in their systematic review that due to the lack of high quality evidence, it was impossible to formulate recommendations in regards to possible predictors. Similarly, it appears that evidence from neuroimaging has not generated robust predictors [ 6 ].…”

“…Despite progress in describing the brain morphology of individuals with SSD, distinctive features identified in imaging studies do not appear to be associated with either the incidence or prevalence of relapses [ 3 , 5 , 6 ]. On this point, in their exploratory quantitative meta-analysis of structural neuroimaging studies, Chee et al [ 7 ] concluded that neuroanatomical indices that are predictive of relapses in individuals with SSS have yet to be identified.…”

Are we getting any better at staying better? The long view on relapse and recovery in first episode nonaffective psychosis and schizophrenia

“…To the best of the authors’ knowledge there currently are no known demographic or clinical variables that reliably predict relapse. 33…”