A computational model now in development could give a Michigan hospital and its care providers a leg up on COVID-19 by predicting which patients are likely to quickly deteriorate upon admission. Once implemented, the model could help the hospital anticipate fast-changing patient needs while keeping care providers safe.
Called M-CURES and developed by a team of computer science, industrial operations and engineering and health care researchers at the University of Michigan College of Engineering, Precision Health and Michigan Medicine, the model uses a machine learning algorithm to crunch more than 200 health and demographic variables of individual COVID-19 patients. The researchers have found that some of the most predictive variables include age, underlying health conditions and current medications. The model then outputs a numerical score, updated every four hours, that predicts the patient’s likelihood of requiring ICU-level care. Preliminary validation of M-CURES has shown it to be effective in predicting the progression of the disease.
“M-CURES could help the hospital get better answers to questions like who is likely to need ICU care and how many ICU beds it will need within a given time frame,” said Jenna Wiens, an associate professor of computer science at engineering and co-director of Precision Health at U-M. “It could also help the families of severely ill COVID patients by giving them more time to evaluate treatment options.”
Michael Sjoding, an assistant professor at Michigan Medicine who is working with Wiens on M-CURES, says M-CURES could also be an important way to manage the complexities of treating a highly contagious disease. Donning protective gear and carefully monitoring the number of care providers in a room takes up valuable time in an emergency, and he says M-CURES could help care providers better anticipate patient needs and prevent emergencies before they start.