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A new algorithm can accurately track patients鈥� 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 鈥� from simple heart rate monitors up to sophisticated devices that measure brain temperature 鈥� and used it to forecast the results of a clinician鈥檚 assessment of a patient鈥檚 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.

鈥淐onsciousness isn鈥檛 a light switch that鈥檚 either on or off 鈥� it鈥檚 more like a dimmer switch, with degrees of consciousness that change over the course of the day,鈥� said Samantha Kleinberg, an associate professor in Stevens鈥� department of Computer Science.

鈥淚f 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.鈥�

This tool could potentially be deployed in virtually any hospital setting 鈥� 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鈥� families a clearer idea of their loved ones鈥� prognoses, continuous monitoring could help to drive new research and ultimately improve patient outcomes.

More work will be needed before the team鈥檚 algorithm can be rolled out in clinical settings. The team鈥檚 algorithm was trained based on data collected immediately prior to a clinician鈥檚 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鈥檚 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鈥檚 consciousness ratings during periods when assessments by human clinicians aren鈥檛 available.

Findings of the study have been published in .

Image credit: iStock.com/andresr