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: | Cavanagh, Professor Jonathan and Chung, Miss Kelly and Leighton, Dr Samuel and Gumley, Professor Andrew 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 > School of Health & Wellbeing > Mental Health and Wellbeing College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience College of Social Sciences > School of Education |
| Research Centre: | College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Immunobiology |
| 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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