Rebecca Lee1, Samuel P Leighton2, Lucretia Thomas3, Georgios V Gkoutos4, Stephen J Wood5, Sarah-Jane H Fenton1, Fani Deligianni6, Jonathan Cavanagh7, Pavan K Mallikarjun1. 1. Institute for Mental Health, University of Birmingham, UK. 2. Institute of Health and Wellbeing, University of Glasgow, UK. 3. Birmingham Medical School, University of Birmingham, UK. 4. Institute of Cancer and Genomic Sciences, University of Birmingham, UK. 5. Orygen Youth Health Research Centre, National Centre of Excellence in Youth Mental Health, Australia; School of Psychological Sciences, University of Melbourne, Australia; and School of Psychology, University of Birmingham,UK. 6. School of Computing Science, University of Glasgow, UK. 7. Institute of Infection, Immunity and Inflammation, University of Glasgow, UK.
Abstract
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.
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.
Authors: A Demjaha; J M Lappin; D Stahl; M X Patel; J H MacCabe; O D Howes; M Heslin; U A Reininghaus; K Donoghue; B Lomas; M Charalambides; A Onyejiaka; P Fearon; P Jones; G Doody; C Morgan; P Dazzan; R M Murray Journal: Psychol Med Date: 2017-04-11 Impact factor: 7.723
Authors: Sagnik Bhattacharyya; Tabea Schoeler; Rashmi Patel; Marta di Forti; Robin M Murray; Philip McGuire Journal: Schizophr Res Date: 2020-10-14 Impact factor: 4.939
Authors: Samuel P Leighton; Rachel Upthegrove; Rajeev Krishnadas; Michael E Benros; Matthew R Broome; Georgios V Gkoutos; Peter F Liddle; Swaran P Singh; Linda Everard; Peter B Jones; David Fowler; Vimal Sharma; Nicholas Freemantle; Rune H B Christensen; Nikolai Albert; Merete Nordentoft; Matthias Schwannauer; Jonathan Cavanagh; Andrew I Gumley; Max Birchwood; Pavan K Mallikarjun Journal: Lancet Digit Health Date: 2019-09-12
Authors: Karel G M Moons; Robert F Wolff; Richard D Riley; Penny F Whiting; Marie Westwood; Gary S Collins; Johannes B Reitsma; Jos Kleijnen; Sue Mallett Journal: Ann Intern Med Date: 2019-01-01 Impact factor: 25.391
Authors: Robert F Wolff; Karel G M Moons; Richard D Riley; Penny F Whiting; Marie Westwood; Gary S Collins; Johannes B Reitsma; Jos Kleijnen; Sue Mallett Journal: Ann Intern Med Date: 2019-01-01 Impact factor: 25.391