Prediction of emergence from prolonged disorders of consciousness from measures within the UK rehabilitation outcomes collaborative database: a multicentre analysis using machine learning

TY - JOUR

T1 - Prediction of emergence from prolonged disorders of consciousness from measures within the UK rehabilitation outcomes collaborative database

T2 - a multicentre analysis using machine learning

AU - Siegert, Richard J.

AU - Narayanan, Ajit

AU - Turner-Stokes, Lynne

N1 - Publisher Copyright: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

PY - 2022/8/27

Y1 - 2022/8/27

N2 - Purpose: Predicting emergence from prolonged disorders of consciousness (PDOC) is important for planning care and treatment. We used machine learning to examine which variables from routine clinical data on admission to specialist rehabilitation units best predict emergence by discharge. Materials and methods: A multicentre national cohort analysis of prospectively collected clinical data from the UK Rehabilitation Outcomes (UKROC) database 2010–2018. Patients (n = 1170) were operationally defined as “still in PDOC” or “emerged” by their total UK Functional Assessment Measure (FIM + FAM) discharge score. Variables included: Age, aetiology, length of stay, time since onset, and all items of the Neurological Impairment Scale, Rehabilitation Complexity Scale, Northwick Park Dependency Scale, and the Patient Categorisation Tool. After filtering, prediction of emergence was explored using four techniques: binary logistic regression, linear discriminant analysis, artificial neural networks, and rule induction. Results: Triangulation through these techniques consistently identified characteristics associated with emergence from PDOC. More severe motor impairment, complex disability, medical and behavioural instability, and anoxic aetiology were predictive of non-emergence, whereas those with less severe motor impairment, agitated behaviour and complex disability were predictive of emergence. Conclusions: This initial exploration demonstrates the potential opportunities to enhance prediction of outcome using machine learning techniques to explore routinely collected clinical data.Implications for rehabilitation Predicting emergence from prolonged disorders of consciousness is important for planning care and treatment. Few evidence-based criteria exist for aiding clinical decision-making and existing criteria are mostly based upon acute admission data. Whilst acknowledging the limitations of using proxy data for diagnosis of emergence, this study suggests that key items from the UKROC dataset, routinely collected on admission to specialist rehabilitation some months post injury, may help to predict those patients who are more (or less) likely to regain consciousness. Machine learning can help to enhance our understanding of the best predictors of outcome and thus assist with clinical decision-making in PDOC.

AB - Purpose: Predicting emergence from prolonged disorders of consciousness (PDOC) is important for planning care and treatment. We used machine learning to examine which variables from routine clinical data on admission to specialist rehabilitation units best predict emergence by discharge. Materials and methods: A multicentre national cohort analysis of prospectively collected clinical data from the UK Rehabilitation Outcomes (UKROC) database 2010–2018. Patients (n = 1170) were operationally defined as “still in PDOC” or “emerged” by their total UK Functional Assessment Measure (FIM + FAM) discharge score. Variables included: Age, aetiology, length of stay, time since onset, and all items of the Neurological Impairment Scale, Rehabilitation Complexity Scale, Northwick Park Dependency Scale, and the Patient Categorisation Tool. After filtering, prediction of emergence was explored using four techniques: binary logistic regression, linear discriminant analysis, artificial neural networks, and rule induction. Results: Triangulation through these techniques consistently identified characteristics associated with emergence from PDOC. More severe motor impairment, complex disability, medical and behavioural instability, and anoxic aetiology were predictive of non-emergence, whereas those with less severe motor impairment, agitated behaviour and complex disability were predictive of emergence. Conclusions: This initial exploration demonstrates the potential opportunities to enhance prediction of outcome using machine learning techniques to explore routinely collected clinical data.Implications for rehabilitation Predicting emergence from prolonged disorders of consciousness is important for planning care and treatment. Few evidence-based criteria exist for aiding clinical decision-making and existing criteria are mostly based upon acute admission data. Whilst acknowledging the limitations of using proxy data for diagnosis of emergence, this study suggests that key items from the UKROC dataset, routinely collected on admission to specialist rehabilitation some months post injury, may help to predict those patients who are more (or less) likely to regain consciousness. Machine learning can help to enhance our understanding of the best predictors of outcome and thus assist with clinical decision-making in PDOC.

KW - artificial neural networks

KW - logistic regression

KW - machine learning

KW - outcomes

KW - PDOC

KW - prediction

KW - Prolonged disorders of consciousness

KW - vegetative or minimally conscious states

UR - http://www.scopus.com/inward/record.url?scp=85137028442&partnerID=8YFLogxK

U2 - 10.1080/09638288.2022.2114017

DO - 10.1080/09638288.2022.2114017

M3 - Article

AN - SCOPUS:85137028442

SN - 0963-8288

JO - Disability and Rehabilitation

JF - Disability and Rehabilitation

ER -