AI Might Spot At-Risk COVID-19 Patients

Working with researchers at two hospitals in Wenzhou, China, the NYU team devised a computer model based on what co-author Anasse Bari described as the kind of “predictive analytics” used to forecast stock market activity and voting patterns. Bari is a clinical assistant professor of computer science at NYU’s Courant Institute.

They fed it relevant patient information, such as results of lung scans and blood tests, muscle ache and fever patterns, immune responses, age and gender.

To their surprise, researchers found that the factors most clinicians would likely focus on — such as lung status, age and gender — were not helpful in predicting outcomes.

So what was?

The most accurate predictors were slight elevations in a liver enzyme called alanine aminotransferase (ALT); deep muscle aches; and higher levels of hemoglobin, the protein that facilitates blood transport of oxygen throughout the body.

“That’s the value of this approach,” Coffee said, “to look for what we, as clinicians, might not notice.”

While the program needs to be validated on larger populations, she said it would be easy to roll out if future testing finds similar accuracy.

The tool could prove “very useful,” said Dr. Maria Luisa Alcaide, a fellow with the Infectious Diseases Society of America who reviewed the findings.

“What’s happening with COVID-19 is that cases have significantly increased to the point where some hospitals’ ICUs are overwhelmed,” she noted. “And for reasons that are not well-understood, not everyone who gets very sick fits the profile of an older person with underlying conditions.”

The better doctors are able to predict those who will, the more carefully they could track them and their care, said Alcaide, who is also an associate professor of infectious diseases at the University of Miami.

But the method has been tested only in a very small sample of patients, and hundreds of thousands are now infected, she pointed out.

“It’s unlikely that this small sample is representative of all COVID-19 patients,” Alcaide said. Some of these predictors may turn out to be important. But we just don’t know yet. Other markers may turn out to be more important. So this really needs to be validated with more patients.”

The findings are reported online in the March 30 issue of the journal Computers, Materials & Continua.