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

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.

Automated summary

This study used machine learning techniques to predict the recovery of patients with prolonged disorders of consciousness using routine clinical data. The findings suggest that severe motor impairment, complex disability, medical and behavioral instability, and anoxic etiology are associated with non-recovery, while less severe motor impairment, agitated behavior, and complex disability are associated with recovery.