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. 2019 Mar 7;14(3):e0212846.
doi: 10.1371/journal.pone.0212846. eCollection 2019.

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

Samuel P Leighton  1 , Rajeev Krishnadas  2   3 , Kelly Chung  1 , Alison Blair  3 , Susie Brown  3 , Suzy Clark  3 , Kathryn Sowerbutts  3 , Matthias Schwannauer  4 , Jonathan Cavanagh  1 , Andrew I Gumley  1
Affiliations

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

Samuel P Leighton et al. PLoS One. .

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.

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Conflict of interest statement

Andrew Gumley has been commissioned by NHS Education for Scotland to provide training for NHS staff in early intervention for psychosis, psychosocial interventions for psychosis and CBT. Jonathan Cavanagh is part of the Wellcome Trust-funded consortium on the Neuroimmunology of Mood and Alzheimer’s. In addition to the academic partners, this consortium includes industrial collaborators: GSK, Lundbeck, Pfizer and Janssen. GSK, Lundbeck, Pfizer and Janssen are commercial funders; this does not alter our adherence to PLOS ONE policies on sharing data and materials. The other authors declare no conflict of interest involving the work under consideration for publication.

Figures

Fig 1
Fig 1
ROC curves for model of one-year EET status (A), point remission (B), and period remission (C). ROC = Receiver Operating Characteristic; EET = Employment, Education or Training.
Fig 2
Fig 2. One-year EET status model’s predictors and respective odds ratios.
Features selected by elastic net regularisation and respective odds ratios for one-year EET status model. EET = Employment, Education or Training; PANSS = Positive And Negative Symptoms Scale.
Fig 3
Fig 3. One-year point remission model’s predictors and respective odds ratios.
Features selected by elastic net regularisation and respective odds ratios for one-year point remission model. PANSS = Positive And Negative Symptoms Scale.
Fig 4
Fig 4. One-year period remission model’s predictors and respective odds ratios.
Features selected by elastic net regularisation and respective odds ratios for one-year period remission model. PANSS = Positive And Negative Symptoms Scale.
See this image and copyright information in PMC

References

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