skip to main content

Cunningham comments on predictive algorithm for COVID-19 mortality


Joseph Park, Illinois ECE

Brian T Cunningham
Brian T Cunningham

Illinois ECE Professor Brian T Cunningham, Donald Biggar Willett Professor in Engineering, was recently featured in an article from Chemistry World, providing his personal insight on their new technology from a team of American and Chinese researchers. In the study, the researchers developed a severity scoring system for COVID-19 by analyzing the biomarkers present in a blood sample. 

Considering other risk factors such as age and gender, the researchers used a statistical learning algorithm to predict the likelihood of a COVID-19 patient dying from the disease. This is the first tool of its kind to predict the mortality of individual COVID-19 patients and does not require laboratory work.

The test employs a "single-use microfluidic cartridge placed within a biosensor platform, which generates immunofluorescent signals that correlate to antigen concentrations." Biomarkers were chosen based on linkage to poor outcomes in patients. Biomolecules including D-dimer, C-reactive protein, and procalcitonin were dramatically increased in patients who died compared to patients who recovered. Using AI and outcomes of COVID-19 patients in Wuhan, the model continues to learn. 

According to Chemistry World, Cunningham stated that the model could "never be fully predictive, even when it is used to evaluate a group of patients with similar characteristics." However, he did acknowledge that this tool could potentially identify high-risk patients, allowing for further intensive care.

The research team is now working to release a free app to help medical centers around the world. Cunningham is affiliated with the Beckman Institute and HMNTL.

Read more from Chemistry World here