Alexandra Hunt ^1* Heather Law ^2,3 Rebekah Carney ^2,3 Rachel Mulholland ² Allan Flores ¹ Catrin Tudur Smith ¹ Filippo Varese ^2,3 Sophie Parker ^2,3 Alison Yung ⁴ Laura Bonnett ¹

• ¹ Department of Health Data Science, Institute of Population Health, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, North West England, United Kingdom
• ² Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
• ³ Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, England, United Kingdom
• ⁴ Centre for Innovation in Mental and Physical Health and Clinical Treatment, Faculty of Health, Deakin Univeristy, Geelong, Victoria, Australia

To review studies developing or validating a prediction model for transition to psychosis in individuals meeting At Risk Mental State (ARMS) criteria focussing on predictors that can be obtained as part of standard clinical practice. Prediction of transition is crucial to facilitating identification of patients who would benefit from cognitive behavioural therapy, and conversely those that would benefit from less costly and less intensive regular mental state monitoring. The review aims to determine whether prediction models rated as low risk of bias exist and, if not, what further research is needed within the field.Bibliographic databases (PsycINFO, Medline, EMBASE, CINAHL) were searched using index terms relating to the clinical field and prognosis from 1994, the initial year of the first prospective study using ARMS criteria, to July 2024. Screening of titles, abstracts and subsequently full texts was conducted by two reviewers independently using predefined criteria. Study quality was assessed using the prediction model risk of bias assessment tool (PROBAST).Studies in any setting were included.The primary outcome for the review was the identification of prediction models considering transition risk and a summary of their risk of bias.Forty-eight unique prediction models considering risk of transition to psychosis were identified. Variables found to be consistently important when predicting transition were age, gender, global functioning score, trait vulnerability and unusual thought content. PROBAST criteria categorised four unique prediction models as having an overall low risk bias. Other studies were insufficiently powered for the number of candidate predictors, or lacking enough information to draw a conclusion regarding risk of bias.Two of the forty-eight identified prediction models were developed using current best practice statistical methodology, validated their model in independent data and presented low risk of bias overall, in line with the PROBAST guidelines. Any new prediction model built to evaluate the risk of transition to psychosis in people meeting ARMS criteria should be informed by the latest statistical methodology and adhere to the TRIPOD reporting guidelines to ensure that clinical practice is informed by the best possible evidence. External validation of such models should be carefully planned particularly considering generalisation across different countries.