It has become increasingly clear that there’s a big difference from patient to patient when it comes to the effects of the coronavirus. A new tool developed by researchers at the NYU Grossman School of Medicine and NYU Courant Institute of Mathematical Sciences, in partnership with Wenzhou Central Hospital and Cangnan People’s Hospital, both in Wenzhou, China, is intended to help clinicians determine the difference as early in the disease’s onset as possible.

According to a study recently published in Computers, Materials & Continua, using AI the researchers accurately predicted which patients newly diagnosed with COVID-19 would go on to develop severe respiratory disease, the treatment for which can require oxygen and prolonged ventilation.

“Our goal was to design and deploy a decision-support tool using AI capabilities—mostly predictive analytics—to flag future clinical coronavirus severity,” said co-author Anasse Bari, PhD, a clinical assistant professor in computer science at NYU Courant Institute of Mathematical Science, in a statement. “We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds, and who can safely go home, when hospital resources are stretched thin.”

Acute respiratory distress syndrome (ARDS), fluid buildup in the lungs that can be fatal in the elderly, is a key feature in COVID-19 patients, and the team wanted to determine whether AI techniques could help accurately predict which patients with the virus would go on to develop ARDS.

Researchers collected laboratory, demographic, and radiological findings from 53 patients who tested positive for COVID-19 at two Chinese hospitals, with an average age of 43 years. The team then used the data to train AI models designed to get smarter with the more data they consider.

The researchers found that characteristics thought to be hallmarks of COVID-19, such as specific patterns in lung images, fever, and strong immune responses, were not useful in predicting which patients with initial mild symptoms would go on to develop severe lung disease.

Instead, the AI tool determined that changes in three features, including levels of the liver enzyme alanine aminotransferase (ALT), reported myalgia, and hemoglobin levels, were most accurately predictive of subsequent, severe disease. With these factors, the team was able to predict risk of ARDS with up to 80 percent accuracy.

“While work remains to further validate our model, it holds promise as another tool to predict the patients most vulnerable to the virus, but only in support of physicians’ hard-won clinical experience in treating viral infections,” said corresponding study author Megan Coffee, MD, PhD, clinical assistant professor in the Department of Medicine and member of the Division of Infectious Diseases and Immunology at NYU Langone.