Neurological assessment of an ICU patient’s level of consciousness is an important but time-consuming task that may take up to an hour. Now, researchers have developed an algorithm that can accurately track patients’ level of consciousness based on simple physiological markers that are already routinely monitored in hospital settings.
The work, published in Neurocritical Care, may eventually yield a way to reduce the strain on medical staff, and could also provide vital new data to guide clinical decisions and enable the development of new treatments.
“Consciousness isn’t a light switch that’s either on or off – it’s more like a dimmer switch, with degrees of consciousness that change over the course of the day,” said Associate Prof Samantha Kleinberg at Stevens Institute of Technology. “If you only check patients once per day, you just get one data point. With our algorithm, you could track consciousness continuously, giving you a far clearer picture.”
To develop their algorithm, A/Prof Kleiberg’s team gathered a variety of data, simple heart rate monitors up to sophisticated devices that measure brain temperature, and used them to forecast the results of a clinician’s assessment of a patient’s level of consciousness. Yet, even using just the simplest physiological data, the algorithm proved as accurate as a trained clinical examiner, and only slightly less accurate than more sophisticated tests such as MRI.
“That’s hugely important, because it means this tool could potentially be deployed in virtually any hospital setting – not just neurological ICUs where they have more sophisticated technology,” A/Prof Kleinberg explained. The algorithm could be installed as a simple software module on existing bedside patient-monitoring systems, she noted, making it relatively cheap and easy to roll out at scale.
Besides giving doctors better clinical information, and patients’ families a clearer idea of their loved ones’ prognosis, continuous monitoring could help to drive new research and ultimately improve patient outcomes.
“Consciousness is incredibly hard to study, and part of the reason is that there simply isn’t much data to work with,” said A/Prof Kleinberg. “Having round-the-clock data showing how patients’ consciousness changes could one day make it possible to treat these patients far more effectively.”
More work will be needed before the team’s algorithm can be rolled out in clinical settings. The team’s algorithm was trained based on data collected immediately prior to a clinician’s assessment, and further development will be needed to show that it can accurately track consciousness around the clock. Additional data will also be required to train the algorithm for use in other clinical settings such as paediatric ICUs.
A/Prof Kleinberg also hopes to improve the algorithm’s accuracy by cross-referencing different kinds of physiological data, and studying the way they coincide or lag one another over time. Some such relationships are known to correlate with consciousness, potentially making it possible to validate the algorithm’s consciousness ratings during periods when assessments by human clinicians aren’t available.
Source: Stevens Institute of Technology
