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

purposePredicting 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

methodsA multicentre national cohort analysis of prospectively collected clinical data from the UK Rehabilitation Outcomes (UKROC) database 2010-2018. Patients (

resultsTriangulation through these techniques consistently identified characteristics associated with emergence from PDOC.

conclusionsThis initial exploration demonstrates the potential opportunities to enhance prediction of outcome using machine learning techniques to explore routinely collected clinical data. Implications for rehabilitationPredicting 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.