Prediction models in first-episode psychosis: systematic review and critical appraisal

Lee, R., Leighton, S. P. , Thomas, L., Gkoutos, G. V., Wood, S. J., Fenton, S.-J. H., Deligianni, F. , Cavanagh, J. and Mallikarjun, P. K. (2022) Prediction models in first-episode psychosis: systematic review and critical appraisal. British Journal of Psychiatry, 220(SI4), pp. 179-191. (doi: 10.1192/bjp.2021.219) (PMID:35067242)

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Background: People presenting with first-episode psychosis (FEP) have heterogenous outcomes. More than 40% fail to achieve symptomatic remission. Accurate prediction of individual outcome in FEP could facilitate early intervention to change the clinical trajectory and improve prognosis. Aims: We aim to systematically review evidence for prediction models developed for predicting poor outcome in FEP. Method: A protocol for this study was published on the International Prospective Register of Systematic Reviews, registration number CRD42019156897. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance, we systematically searched six databases from inception to 28 January 2021. We used the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Prediction Model Risk of Bias Assessment Tool to extract and appraise the outcome prediction models. We considered study characteristics, methodology and model performance. Results: Thirteen studies reporting 31 prediction models across a range of clinical outcomes met criteria for inclusion. Eleven studies used logistic regression with clinical and sociodemographic predictor variables. Just two studies were found to be at low risk of bias. Methodological limitations identified included a lack of appropriate validation, small sample sizes, poor handling of missing data and inadequate reporting of calibration and discrimination measures. To date, no model has been applied to clinical practice. Conclusions: Future prediction studies in psychosis should prioritise methodological rigour and external validation in larger samples. The potential for prediction modelling in FEP is yet to be realised.

Item Type:Articles
Additional Information:RL is funded by an Institute for Mental Health Priestley Scholarship, University of Birmingham. SPL is funded by a Clinical Academic Fellowship from the Chief Scientist Office, Scotland (CAF/19/04). SJW is funded by the Medical Research Council, UK (MR/K013599).
Glasgow Author(s) Enlighten ID:Leighton, Dr Samuel and Cavanagh, Professor Jonathan and Deligianni, Dr Fani
Authors: Lee, R., Leighton, S. P., Thomas, L., Gkoutos, G. V., Wood, S. J., Fenton, S.-J. H., Deligianni, F., Cavanagh, J., and Mallikarjun, P. K.
College/School:College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > General Practice and Primary Care
College of Science and Engineering > School of Computing Science
Research Centre:College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Immunobiology
Journal Name:British Journal of Psychiatry
Publisher:Cambridge University Press
ISSN (Online):1472-1465
Published Online:24 January 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in British Journal of Psychiatry 220(SI4): 179-191
Publisher Policy:Reproduced under a Creative Commons licence

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