Predicting one-year outcome in first episode psychosis using machine learning

Leighton, S. P. , Krishnadas, R., Chung, K., Blair, A., Brown, S., Clark, S., Sowerbutts, K., Schwannauer, M., Cavanagh, J. and Gumley, A. (2019) Predicting one-year outcome in first episode psychosis using machine learning. PLoS ONE, 14(3), e0212846. (doi:10.1371/journal.pone.0212846) (PMID:30845268) (PMCID:PMC6405084)

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Abstract

Background: Early illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier intervention corresponds to significantly better symptomatic and functional outcomes. Our study objective is to use routinely collected baseline demographic and clinical characteristics to predict employment, education or training (EET) status, and symptom remission in patients with first episode psychosis (FEP) at one-year. Methods and findings: 83 FEP patients were recruited from National Health Service (NHS) Glasgow between 2011 and 2014 to a 24-month prospective cohort study with regular assessment of demographic and psychometric measures. An external independent cohort of 79 FEP patients were recruited from NHS Glasgow and Edinburgh during a 12-month study between 2006 and 2009. Elastic net regularised logistic regression models were built to predict binary EET status, period and point remission outcomes at one-year on 83 Glasgow patients (training dataset). Models were externally validated on an independent dataset of 79 patients from Glasgow and Edinburgh (validation dataset). Only baseline predictors shared across both cohorts were made available for model training and validation. After excluding participants with missing outcomes, models were built on the training dataset for EET status, period and point remission outcomes and externally validated on the validation dataset. Models predicted EET status, period and point remission with receiver operating curve (ROC) area under the curve (AUC) performances of 0.876 (95%CI: 0.864, 0.887), 0.630 (95%CI: 0.612, 0.647) and 0.652 (95%CI: 0.635, 0.670) respectively. Positive predictors of EET included baseline EET and living with spouse/children. Negative predictors included higher PANSS suspiciousness, hostility and delusions scores. Positive predictors for symptom remission included living with spouse/children, and affective symptoms on the Positive and Negative Syndrome Scale (PANSS). Negative predictors of remission included passive social withdrawal symptoms on PANSS. A key limitation of this study is the small sample size (n) relative to the number of predictors (p), whereby p approaches n. The use of elastic net regularised regression rather than ordinary least squares regression helped circumvent this difficulty. Further, we did not have information for biological and additional social variables, such as nicotine dependence, which observational studies have linked to outcomes in psychosis. Conclusions and relevance: Using advanced statistical machine learning techniques, we provide the first externally validated evidence, in a temporally and geographically independent cohort, for the ability to predict one-year EET status and symptom remission in individual FEP patients.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Leighton, Dr Samuel and Cavanagh, Professor Jonathan and Gumley, Professor Andrew and Chung, Miss Kelly and Krishnadas, Dr Rajeev
Creator Roles:
Leighton, S. P.Formal analysis, Methodology, Writing – original draft
Krishnadas, R.Conceptualization, Formal analysis, Methodology, Supervision, Validation, Writing – original draft
Chung, K.Data curation, Project administration
Cavanagh, J.Writing – review and editing
Gumley, A. I.Conceptualization, Data curation, Funding acquisition, Writing – review and editing
Authors: Leighton, S. P., Krishnadas, R., Chung, K., Blair, A., Brown, S., Clark, S., Sowerbutts, K., Schwannauer, M., Cavanagh, J., and Gumley, A.
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Mental Health and Wellbeing
College of Medical Veterinary and Life Sciences > Institute of Neuroscience and Psychology
College of Social Sciences > School of Education
Journal Name:PLoS ONE
Publisher:Public Library of Science
ISSN:1932-6203
ISSN (Online):1932-6203
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in PLoS ONE 14(3):e0212846
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
517391Functional MRI markers associated with initial and sustained response to CBTJonathan CavanaghNHS Greater Glasgow and Clyde (NHSGGC)SB10328IHW - MENTAL HEALTH & WELLBEING
657471Consortium of Neuroimmunology of Mood Disorders and Alzheimer's DiseaseJonathan CavanaghWellcome Trust (WELLCOTR)104025/Z/14/ZIHW - MENTAL HEALTH & WELLBEING

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