RPI algorithm accurately predicts COVID-19 patient outcomes

Coronavirus

Rensselaer Polytechnic Institute

TROY, N.Y. (WTEN) – With communities experiencing a wave of COVID-19 infections, clinicians need effective tools that will enable them to aggressively and accurately treat patients based on their specific disease presentation, health history, and medical risks. In research recently published in “Medical Image Analysis,” engineers at Rensselaer Polytechnic Institute (RPI) demonstrated a new algorithm that successfully predicts if a COVID-19 patient would need ICU intervention.

The research team, led by Pingkun Yan, an assistant professor of biomedical engineering at RPI, developed this method by combining chest computed tomography (CT) images that assess the severity of a patient’s lung infection with non-imaging data, such as demographic information, vital signs, and laboratory blood test results. By combining these data points, the algorithm is able to predict patient outcomes, specifically whether or not a patient will need ICU intervention.

The algorithm was tested on datasets collected from 295 patients from three different hospitals — one in the United States, one in Iran, and one in Italy. Researchers were able to compare the algorithm’s predictions to what kind of treatment a patient actually ended up needing.

This development is the result of research supported by a recent National Institutes of Health grant, which was awarded to provide solutions during this worldwide pandemic. As the team continues its work, Yan said, researchers will integrate their new algorithm with another that Yan had previously developed to assess a patient’s risk of cardiovascular disease using chest CT scans. 

“We know that a key factor in COVID mortality is whether a patient has underlying conditions and heart disease is a significant comorbidity,” Yan said. “How much this contributes to their disease progress is, right now, fairly subjective. So, we have to have a quantification of their heart condition and then determine how we factor that into this prediction.”

Yan is joined at RPI by Ge Wang, a chair professor of biomedical engineering and member of Center for Biotechnology and Interdisciplinary Studies, as well as graduate students Hanqing Chao, Xi Fang, and Jiajin Zhang. The RPI team is working in collaboration with Massachusetts General Hospital. When this work is complete, the team hopes to translate its algorithm into a method that doctors at Massachusetts General can use to assess their patients.

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