Solution can track, predict ICU patients' consciousness

A new algorithm can accurately track patients’ level of consciousness based on simple physiological markers that are already routinely monitored in hospital settings. Though still in its early stages, the work by researchers at Stevens Institute of Technology could help 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 their algorithm, Kleinberg and her PhD student Louis A Gomez partnered with Jan Claassen, director of Critical Care Neurology at Columbia University, to collect 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.

“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.”

This tool could potentially be deployed in virtually any hospital setting — not just neurological ICUs where they have more sophisticated technology, Kleinberg said.

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’ prognoses, continuous monitoring could help to drive new research and ultimately improve patient outcomes.

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.

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.

Findings of the study have been published in Neurocritical Care.

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