Patients

From July 2014 through September 2017, we prospectively screened all patients who were admitted with acute brain injury to the neuroscience ICU of our hospital; screening was performed within 3 days after admission. Patients were screened for the absence of the ability to follow spoken commands — for example, “stick out your tongue” or “show me two fingers with your right hand” (details are provided in the Supplementary Appendix, available with the full text of this article at NEJM.org). In keeping with our routine practice and in accordance with guidelines regarding EEG monitoring of patients in the ICU,9 unresponsive patients either were monitored by continuous EEG or were anticipated to be connected to monitoring within 12 hours after screening, unless imminent death was expected. We enrolled all patients who were in a coma, vegetative state, or minimally conscious state–minus (defined as unresponsiveness with preserved visual fixation, visual pursuit, or localization to noxious stimuli); who had an acute brain injury of any type; and who were undergoing or were expected to undergo imminent continuous EEG monitoring. The presence of the minimally conscious state–minus was determined with the use of the Coma Recovery Scale–Revised (CRS-R,10 a six-dimension, 23-point scale of hierarchically arranged items [with no cutoff score used for enrollment]), which we assessed among patients who were not receiving deep sedation or neuromuscular blockade. The exclusion criteria were an age of less than 18 years, a preexisting disorder of consciousness before the onset of the acute brain injury that resulted in the current admission, pregnancy, deafness before the acute brain injury, clinical recovery of the ability to follow commands before enrollment, patients or families who did not want to participate in the study, or logistic reasons (details are provided in the Supplementary Appendix).

Patients, families, and treating physicians were unaware of the results of the EEG recordings, and these results were not made available to treating clinicians in relation to decisions regarding the withdrawal of care. Demographic data and data on complications that occurred during the hospital stay and on outcomes were prospectively collected. In addition, we recorded EEGs from 10 healthy volunteers with a mean age of 31 years, using the same EEG protocol as in the patients (see the Supplementary Appendix).

Study Oversight

The study was approved for patients and healthy volunteers by the local institutional review board. Written informed consent was obtained from the patients’ surrogates and from the healthy volunteers; patients who recovered consciousness were given the opportunity to withdraw from the study.

The first and last authors are responsible for the study design and drafting of the manuscript. There was no industry involvement in or support for the study. The authors vouch for the accuracy and completeness of the data and for the fidelity of the trial to the protocol, available at NEJM.org. The results are reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting observational studies.11

Study Procedures

Daily neurologic examinations, including a clinical assessment of the ability or inability of the patient to follow spoken commands (“stick out your tongue,” “show me two fingers with your right hand,” and “wiggle your toes”), were performed during morning rounds, and the results were recorded.12 Each EEG assessment was preceded by a clinical examination that included the CRS-R10 in order to categorize the clinical state of consciousness at the time of the recording (details are provided in the Supplementary Appendix).

Functional outcome was assessed with the Glasgow Outcome Scale–Extended (GOS-E; levels range from 0 to 8, with higher levels indicating better outcomes), with data obtained in a structured telephone interview at 12 months after the injury.13,14 Both the patient and the interviewer who performed the outcome assessments were unaware of the results of the above-noted examinations during routine rounds and were unaware of the EEG categorization. Outcomes were dichotomized at a GOS-E level of 4, a level that signifies the ability to be left up to 8 hours during the day without assistance.

At the time of the EEG and clinical assessments, all patients were evaluated to ensure the absence of the following complications: seizures, hyperglycemia (serum glucose level, >11.1 mmol per liter [>200 mg per deciliter]), hyponatremia or hypernatremia (serum sodium level <133 and >150 mmol per liter, respectively), and renal or fulminant liver failure. For the daily neurologic assessment, sedated patients underwent interruption or reduction of sedation if it was deemed safe by the attending physician during rounds.15 Both the behavioral and the EEG assessments were performed during interruption of sedation whenever possible. To account for cases in which stopping sedation was unsafe, during the study we developed a post hoc method to explore the effect of sedative and analgesic medications on EEG responses by collecting information on the doses of administered medications at the time of the assessments as well as the cumulative doses received within the two preceding elimination half-lives of each agent.3 Sedation was categorized as “minimal” for discontinuous (e.g., single-push) administration and as “low” or “moderate” according to the cumulative doses administered through continuous drip during the two previous half-lives (details are provided in Table S3 in the Supplementary Appendix).

Motor Command Protocol

Figure 1. Figure 1. Motor Command Protocol and Data Processing. Each block in the motor command protocol consisted of eight trials alternating between the instructions “keep opening and closing your right (left) hand” and “stop opening and closing your right (left) hand” (Panel A). The 10 seconds of electroencephalographic (EEG) recording after the instructions were given were extracted and segmented in five epochs, each 2 seconds long, for further analysis (Panel B). This procedure resulted in 480 epochs in patients (5 epochs×2 instructions×8 trials×6 blocks) and 240 epochs in controls (5 epochs×2 instructions×8 trials×3 blocks). Power spectral density (PSD) analysis was applied to the obtained EEG matrix in four frequency bands (δ [1 to 3 Hz], θ [4 to 7 Hz], α [8 to 13 Hz], and β [14 to 30 Hz]) (Panel C). The resulting features were used to train and test a support vector machine (SVM). The classification performance of the SVM for a given recording was assessed as the area under the receiver-operating-characteristic curve (AUC).

Spoken command instructions during EEG recording alternated between “keep opening and closing your right hand” and “stop opening and closing your right hand.”16 A total of six blocks each with eight consecutive trials of “keep opening …” and “stop opening …” commands were recorded (Figure 1A). For each patient, we recorded three blocks in which the patient was asked to move the right hand and three blocks in which the patient was asked to move the left hand (recordings of the right-hand and left-hand blocks were alternated; see the Supplementary Appendix). The total duration of the motor command session was approximately 25 minutes.

EEG Acquisition and Processing

Digital bedside EEG monitoring was performed with a standard 21-electrode montage.9 EEG recording quality (e.g., lead maintenance and movement artifact) was determined by bedside visual observation at the time of recording in addition to twice-daily lead maintenance by EEG technicians (see the Supplementary Appendix). For each EEG recording, power in predefined frequency ranges was calculated3,12,17 and used to train a machine-learning algorithm (support vector machine [SVM] with a linear kernel) to distinguish between the EEG responses that followed the commands “keep opening …” and “stop opening ….”

Statistical Analysis

The performance of the machine-learning algorithm for each EEG recording was estimated as the area under the receiver-operating-characteristic curve (AUC). To evaluate the significance of the AUC, a one-tailed permutation test was performed (training and evaluation of the classifier 500 times after random shuffling of the “keep opening …” and “stop opening …” commands18,19). Recordings were considered to show evidence of brain activation that was temporally concordant with spoken commands if the AUC was significantly greater than 0.5 (corresponding to the level that would be expected by chance), after application of the Benjamini–Hochberg false-discovery-rate method in cases of multiple recordings in a given patient.20,21 All EEG analyses were performed with the use of open-source packages, including MNE-Python (www.martinos.org/mne/stable/index.html)22 and Scikit-learn (http://scikit-learn.org/stable/index.html).23

Categorical variables were expressed as numbers and percentages and were compared with the use of Fisher’s exact or ordinal chi-square tests, as appropriate. Continuous variables were expressed as medians and interquartile ranges or as means and standard deviations, as appropriate, and were compared with the use of Wilcoxon signed-rank tests. All tests (other than the permutation test applied on the SVM output) were two-sided. Statistical analyses were performed with R statistical software, version 3.4.1 (R Project for Statistical Computing).24