Development of a point of care system for automated coma prognosis: a prospective cohort study protocol

John F Connolly, James P Reilly, Alison Fox-Robichaud, Patrick Britz, Stefanie Blain-Moraes, Ranil Sonnadara, Cindy Hamielec, Adianes Herrera-Díaz, Rober Boshra, John F Connolly, James P Reilly, Alison Fox-Robichaud, Patrick Britz, Stefanie Blain-Moraes, Ranil Sonnadara, Cindy Hamielec, Adianes Herrera-Díaz, Rober Boshra

Abstract

Introduction: Coma is a deep state of unconsciousness that can be caused by a variety of clinical conditions. Traditional tests for coma outcome prediction are based mainly on a set of clinical observations. Recently, certain event-related potentials (ERPs), which are transient electroencephalogram (EEG) responses to auditory, visual or tactile stimuli, have been introduced as useful predictors of a positive coma outcome (ie, emergence). However, such tests require the skills of clinical neurophysiologists, who are not commonly available in many clinical settings. Additionally, none of the current standard clinical approaches have sufficient predictive accuracies to provide definitive prognoses.

Objective: The objective of this study is to develop improved machine learning procedures based on EEG/ERP for determining emergence from coma.

Methods and analysis: Data will be collected from 50 participants in coma. EEG/ERP data will be recorded for 24 consecutive hours at a maximum of five time points spanning 30 days from the date of recruitment to track participants' progression. The study employs paradigms designed to elicit brainstem potentials, middle-latency responses, N100, mismatch negativity, P300 and N400. In the case of patient emergence, data are recorded on that occasion to form an additional basis for comparison. A relevant data set will be developed from the testing of 20 healthy controls, each spanning a 15-hour recording period in order to formulate a baseline. Collected data will be used to develop an automated procedure for analysis and detection of various ERP components that are salient to prognosis. Salient features extracted from the ERP and resting-state EEG will be identified and combined to give an accurate indicator of prognosis.

Ethics and dissemination: This study is approved by the Hamilton Integrated Research Ethics Board (project number 4840). Results will be disseminated through peer-reviewed journal articles and presentations at scientific conferences.

Trial registration number: NCT03826407.

Keywords: neurological injury; neurophysiology.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Protocol design starting at recruitment and consenting and including the sequence of a maximum of five 24-hour electroencephalogram sessions. BAEPs, brainstem auditory evoked potentials; GCS, Glasgow Coma Scale; GCS-P, Glasgow Coma Scale-Pupils; MLAEPs, middle latency auditory evoked potentials; MMN, mismatch negativity; RS, resting-state.

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