New technique could help predict if patients in coma will recover and regain consciousness, claims study
Signs of 'hidden consciousness' detected with EEG just days after brain injury can reveal which patients are likely to recover
While there is no perfect way to accurately predict the chance of recovery for brain-injured patients who are unconscious or in a coma or vegetative state, a new technique demonstrated by researchers shows it is possible to say who among them will wake up and get better.
Just days after brain injury, close analysis of EEG (electroencephalography) data reveals that nearly one in seven brain-injured patients in the intensive care unit (ICU) shows evidence of "hidden consciousness". Researchers found that one in seven of these patients show distinct patterns of brain activity in response to commands, implying that they could understand the commands, but could not perform the movement. Patients with such signs of hidden consciousness are more likely to recover, according to neurologists at Columbia University and NewYork-Presbyterian, who conducted the analysis.
One of the most challenging problems for healthcare providers in the ICU involves predicting recovery for patients who are unconscious after a brain injury. The study is therefore significant as it can help overcome this challenge. It suggests that the EEG — a tool that is readily available at the patient’s bedside in the ICU in almost any hospital across the world to monitor critically ill patients at risk for seizures — has the potential to completely change how doctors manage patients with acute brain injury. It could be a relatively easy and fast way to identify signs of hidden consciousness in unresponsive patients who have recently experienced a brain injury.
The results are also critical for families as the decision to withdraw life-sustaining therapies from patients who appear to have little chance to recover are frequently made within the first weeks following brain injury. If families have better information, it can help them make better treatment choices or aid in making critical decisions, suggest the findings.
Machine Learning Technique used to analyze EEG data
The paper says that clinicians use neurological examinations, imaging and electrophysiological studies, and laboratory values to estimate the likelihood of recovery for people who are unresponsive in the days and weeks after sustaining a brain injury. However, these predictions are frequently inaccurate, suggesting that hospitals and doctors could be overlooking some patients who stand a better chance of recovery. Studies also indicate that some unresponsive patients may have hidden cognitive abilities and that delayed recovery is more common than previously thought, states the paper.
Accordingly, the team studied 104 adult patients in an intensive care unit, who had recently sustained a sudden brain injury due to bleeding, trauma, or oxygen deprivation. Though none of the patients were paralyzed, they were unable to talk and did not respond to commands to move. Every day, in addition to standard neurological exams, the patients were asked multiple times to open and close their hands or stop opening and closing their hands. "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," says the paper.
The team obtained a total of 240 EEG recordings from the patients, of which 126 (52%) were acquired while patients were comatose, 54 (22%) while patients were in the vegetative state, and 60 (25%) while patients were in the minimally conscious state. The researchers used a machine learning technique to analyze standard EEG data collected from these unresponsive patients to look for patient-specific brain activity, indicating that they could understand instructions to move their hands.
"A complex algorithm was used to analyze EEG data from these sessions to look for signs that a patient detected a difference between the commands. A reproducibly different pattern of activity between the commands suggested that a patient was able to understand the command but could not perform the movement," says the paper. The research team subsequently followed up with the patients who were discharged from the hospital a year later.
Results found brain activity in 15% of still unresponsive patients
The analysis shows that within four days of the injury, 15% (16 out of 104) of the still-unresponsive patients had brain activity patterns on at least one EEG recording, suggesting hidden consciousness. Among the patients with these patterns, 50% improved and were able to follow verbal commands before being discharged from the hospital versus 26% of those without such brain activity. "The condition in 8 of these 16 patients (50%) and 23 of 88 patients (26%) without brain activation improved such that they were able to follow commands before discharge," says the paper.
A year later, states the study, 44% of patients (7 out of 16) with brain activity patterns were able to function independently for up to eight hours daily, compared with only 14% of those without such signals. Approximately one-third of patients in each group — those with early EEG evidence of hidden consciousness and those without — died.
The research team says since recovery after a severe brain injury is a complex process, it is important to begin monitoring with EEG as early as possible and to assess at several time points. They say bigger studies, involving more patients, are needed to confirm the technique. Further, since the study did not determine whether the algorithm works best for a particular type of brain injury, future studies, especially in patients with a single cause of brain injury, are needed to determine the utility of EEG monitoring in predicting patient outcomes, say the researchers.
The results of the study were published on June 26 in the New England Journal of Medicine.