Development and validation of multivariable prediction models of remission, recovery and quality of life outcomes in people with first episode psychosis: a machine learning approach

Research output: Contribution to journalArticlepeer-review

Authors

  • Samuel Leighton
  • Rajeev Krishnadas
  • Michael Benros
  • Peter Liddle
  • Swaran Singh
  • Linda Everard
  • Peter Jones
  • David Fowler
  • Vimal Sharma
  • Nicholas Freemantle
  • Rune Christensen
  • Nikolai Albert
  • Merete Nordentoft
  • Matthias Schwannnauer
  • Jonathan Cavanagh
  • Andrew Gumley
  • Max Birchwood

External organisations

  • University of Glasgow
  • Copenhagen University Hospital Gentofte
  • Birmingham and Solihull Mental Health Foundation Trust
  • University of Cambridge
  • School of Psychology and Sackler Centre for Consciousness Science, University of Sussex, UK.
  • University of Chester
  • University College London
  • University of Copenhagen
  • University of Edinburgh
  • University of Nottingham
  • Warwick Medical School

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.

Details

Original languageEnglish
Pages (from-to)e261-e270
Number of pages10
JournalThe Lancet Digital Health
Volume1
Issue number6
Early online date12 Sep 2019
Publication statusPublished - Oct 2019

Keywords

  • Machine Learning, First episode psychosis, Outcome prediction