JHU researchers to predict heart damage in COVID-19 victims through machine learning

Johns Hopkins researchers received $195,000 Rapid Response Research grant from National Science Foundation to use machine learning for identifying COVID-19 patients who are at risk of adverse cardiac events.

Hyderabad: With machine learning, Johns Hopkins researchers are trying to identify which COVID-19 patients are at risk of adverse cardiac events such as heart failure, sustained abnormal heartbeats, heart attacks, cardiogenic shock and death.

The team recently received $195,000 Rapid Response Research grant from the National Science Foundation.

Researchers said that the increasing evidence of COVID-19’s negative impacts on the cardiovascular system highlights a great need for identifying COVID-19 patients at risk for heart problems.

The Murray B. Sachs Professor in the Department of Biomedical Engineering of the Johns Hopkins University Schools of Engineering and Medicine and the project’s principal investigator Natalia Trayanova said: “This project will provide clinicians with early warning signs and ensure that resources are allocated to patients with the greatest need.”

The Johns Hopkins Health System (JHHS) will collect data from more than 300 COVID-19 patients admitted with ECG, cardiac-specific laboratory tests, continuously-obtained vital signs like heart rate and oxygen saturation, and imaging data such as CT scans and echocardiography. Through this data, they will train the algorithm.

The algorithm will be tested with data from COVID-19 patients having heart injury at JHHS or other nearby hospitals.

The researchers want to create a predictive risk score that can determine up to 24 hours ahead of time which patients are at risk of developing adverse cardiac events.

“As a clinician, major knowledge gaps exist in the ideal approach to risk stratify COVID-19 patients for new heart problems that are common and may be life-threatening. These patients have varying clinical presentations and a very unpredictable hospital course,” says Allison G. Hays, Associate Professor of Medicine in the Johns Hopkins University School of Medicine’s Division of Cardiology and the project’s clinical collaborator.

This research will help to know how COVID-19 related heart injuries could lead to sudden cardiac death and heart dysfunction.

This can be made widely available for implementation for any interested health care institution after the research team creates and tests their algorithm.

“By predicting who’s at risk for developing the worst outcomes, health care professionals will be able to undertake the best routes of therapy or primary prevention and save lives,” said Trayanova.

The clinicians could get help to determine which biomarkers are most predictive of adverse clinical outcome with this project.