Samuel P Leighton1, Rachel Upthegrove2, Rajeev Krishnadas3, Michael E Benros4, Matthew R Broome2, Georgios V Gkoutos5, Peter F Liddle6, Swaran P Singh7, Linda Everard8, Peter B Jones9, David Fowler10, Vimal Sharma11, Nicholas Freemantle12, Rune H B Christensen4, Nikolai Albert4, Merete Nordentoft13, Matthias Schwannauer14, Jonathan Cavanagh1, Andrew I Gumley1, Max Birchwood7, Pavan K Mallikarjun15. 1. Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK. 2. Institute for Mental Health, University of Birmingham, Birmingham, UK. 3. Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK. 4. Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark. 5. Institute of Cancer and Genomics, University of Birmingham, Birmingham, UK; Institute of Translational Medicine, University of Birmingham, Birmingham, UK; Health Data Research UK Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK. 6. Institute of Mental Health, University of Nottingham, Nottingham, UK. 7. Mental Health and Wellbeing, Warwick Medical School, University of Warwick, Coventry, UK. 8. The Barberry, Birmingham, UK. 9. Wolfson College, University of Cambridge, Cambridge, UK. 10. School of Psychology, University of Sussex, Brighton, UK. 11. Department of Health and Social Care, University of Chester, Chester, UK. 12. Comprehensive Trials Unit, University College London, London, UK. 13. Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. 14. School of Health in Social Science, Clinical Psychology, University of Edinburgh, Edinburgh, UK. 15. Institute for Mental Health, University of Birmingham, Birmingham, UK. Electronic address: p.mallikarjun@bham.ac.uk.
Abstract
BACKGROUND: Outcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the outcome for individual patients in the first clinical contact. In this study, we aimed to build multivariable prediction models of 1-year remission and recovery outcomes using baseline clinical variables in people with first-episode psychosis. METHODS: In this machine learning approach, we applied supervised machine learning, using regularised regression and nested leave-one-site-out cross-validation, to baseline clinical data from the English Evaluating the Development and Impact of Early Intervention Services (EDEN) study (n=1027), to develop and internally validate prediction models at 1-year follow-up. We assessed four binary outcomes that were recorded at 1 year: symptom remission, social recovery, vocational recovery, and quality of life (QoL). We externally validated the prediction models by selecting from the top predictor variables identified in the internal validation models the variables shared with the external validation datasets comprised of two Scottish longitudinal cohort studies (n=162) and the OPUS trial, a randomised controlled trial of specialised assertive intervention versus standard treatment (n=578). FINDINGS: The performance of prediction models was robust for the four 1-year outcomes of symptom remission (area under the receiver operating characteristic curve [AUC] 0·703, 95% CI 0·664-0·742), social recovery (0·731, 0·697-0·765), vocational recovery (0·736, 0·702-0·771), and QoL (0·704, 0·667-0·742; p<0·0001 for all outcomes), on internal validation. We externally validated the outcomes of symptom remission (AUC 0·680, 95% CI 0·587-0·773), vocational recovery (0·867, 0·805-0·930), and QoL (0·679, 0·522-0·836) in the Scottish datasets, and symptom remission (0·616, 0·553-0·679), social recovery (0·573, 0·504-0·643), vocational recovery (0·660, 0·610-0·710), and QoL (0·556, 0·481-0·631) in the OPUS dataset. INTERPRETATION: In our machine learning analysis, we showed that prediction models can reliably and prospectively identify poor remission and recovery outcomes at 1 year for patients with first-episode psychosis using baseline clinical variables at first clinical contact. FUNDING: Lundbeck Foundation.
BACKGROUND: Outcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the outcome for individual patients in the first clinical contact. In this study, we aimed to build multivariable prediction models of 1-year remission and recovery outcomes using baseline clinical variables in people with first-episode psychosis. METHODS: In this machine learning approach, we applied supervised machine learning, using regularised regression and nested leave-one-site-out cross-validation, to baseline clinical data from the English Evaluating the Development and Impact of Early Intervention Services (EDEN) study (n=1027), to develop and internally validate prediction models at 1-year follow-up. We assessed four binary outcomes that were recorded at 1 year: symptom remission, social recovery, vocational recovery, and quality of life (QoL). We externally validated the prediction models by selecting from the top predictor variables identified in the internal validation models the variables shared with the external validation datasets comprised of two Scottish longitudinal cohort studies (n=162) and the OPUS trial, a randomised controlled trial of specialised assertive intervention versus standard treatment (n=578). FINDINGS: The performance of prediction models was robust for the four 1-year outcomes of symptom remission (area under the receiver operating characteristic curve [AUC] 0·703, 95% CI 0·664-0·742), social recovery (0·731, 0·697-0·765), vocational recovery (0·736, 0·702-0·771), and QoL (0·704, 0·667-0·742; p<0·0001 for all outcomes), on internal validation. We externally validated the outcomes of symptom remission (AUC 0·680, 95% CI 0·587-0·773), vocational recovery (0·867, 0·805-0·930), and QoL (0·679, 0·522-0·836) in the Scottish datasets, and symptom remission (0·616, 0·553-0·679), social recovery (0·573, 0·504-0·643), vocational recovery (0·660, 0·610-0·710), and QoL (0·556, 0·481-0·631) in the OPUS dataset. INTERPRETATION: In our machine learning analysis, we showed that prediction models can reliably and prospectively identify poor remission and recovery outcomes at 1 year for patients with first-episode psychosis using baseline clinical variables at first clinical contact. FUNDING: Lundbeck Foundation.
Authors: Rebecca Lee; Samuel P Leighton; Lucretia Thomas; Georgios V Gkoutos; Stephen J Wood; Sarah-Jane H Fenton; Fani Deligianni; Jonathan Cavanagh; Pavan K Mallikarjun Journal: Br J Psychiatry Date: 2022-01-24 Impact factor: 10.671
Authors: Dominic Oliver; Giulia Spada; Amir Englund; Edward Chesney; Joaquim Radua; Abraham Reichenberg; Rudolf Uher; Philip McGuire; Paolo Fusar-Poli Journal: Schizophr Res Date: 2020-04-24 Impact factor: 4.939