Machine Learning Tool Gives Early Warning of Cardiac Issues or Blood Clots in COVID Patients

The machine-learning algorithm was built with more than 100 clinical data points, demographic information and laboratory results obtained from the JH-CROWN registry that Johns Hopkins established to collect COVID data from every patient in the hospital system. The scientists also added other variables reported by doctors on Twitter and from other pre-print papers.

The team did not anticipate that electrocardiogram data would play a critical role in the prediction of blood clotting. But once it was added, ECG data became one of the most accurate indicators for the condition. The next step for the researchers is to develop the best method for setting up the technology in hospitals to aid with the care of COVID-19 patients.

“It’s an early warning system to predict in real time these two outcomes in hospitalized COVID patients,” said senior author Natalia Trayanova, the Murray B. Sachs professor of Biomedical Engineering and a professor of medicine. “The continuously updating predictor can help hospitals allocate the appropriate resources and proper interventions to attain the best outcomes for patients.”

“The COVID-HEART predictor tool could help in the rapid triage of COVID-19 patients in the clinical setting especially when resources are limited,” said Allison Hays, associate professor of medicine in the Johns Hopkins University School of Medicine and the project’s main clinical collaborator. “This may have implications for the treatment and closer monitoring of Covid-19 patients to help prevent these poor outcomes.”

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