Algorithm Installed on Bedside Patient Monitor Tracks Consciousness of ICU Patients

These tests are the only way to accurately gauge a patient’s prognosis, or to spot vital warning signs that a patient’s health is declining - but with each test taking as long as an hour to complete, they place an enormous burden on clinical teams. 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 by the team of researchers at Stevens Institute of Technology (Hoboken, NJ, USA) is still in its early stages but promises to significantly ease the strain on medical staff, and could also provide vital new data to guide clinical decisions and enable the development of new treatments. To develop the algorithm, the researchers collected data from a range of ICU sensors - from simple heart rate monitors up to sophisticated devices that measure brain temperature - and used it to forecast the results of a clinician’s assessment of a patient’s level of consciousness. The results were startling: using only the simplest physiological data, the algorithm proved as accurate as a trained clinical examiner, and only slightly less accurate than tests conducted with expensive imaging equipment such as fMRI machines.

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 pediatric ICUs. The algorithm could be installed as a simple software module on existing bedside patient-monitoring systems, 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.

The researchers also hope 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.

“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 Samantha Kleinberg, an associate professor in Stevens’ department of Computer Science. “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.”

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Stevens Institute of Technology