Johns Hopkins Researchers to Use Machine Learning to Predict Heart Damage in COVID-19 Victims

Johns Hopkins researchers recently received a $195,000 Rapid Response Research grant from the National Science Foundation to, using machine learning, 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.

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 researchers say. However, no such predictive capabilities currently exist.

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

The first phase of the one-year project, which just received IRB approval for Suburban Hospital and Sibley Memorial Hospital within the Johns Hopkins Health System (JHHS), will collect the following data from more than 300 COVID-19 patients admitted to JHHS: ECG, cardiac-specific laboratory tests, continuously-obtained vital signs like heart rate and oxygen saturation, and imaging data such as CT scans and echocardiography. This data will be used to train the algorithm.

The researchers will then test the algorithm with data from COVID-19 patients with heart injury at JHHS, other nearby hospitals and perhaps some in New York City. The hope is 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.

For new patients, the model will perform a baseline prediction that is updated each time new health data becomes available.

As far as the researchers are aware, their approach will be the first to predict COVID-19-related cardiovascular outcomes.

“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 project aims to help clinicians quickly risk stratify patients using real time clinical data, with the goal of widely disseminating this knowledge to help medical practitioners around the world in their approach to treating and monitoring patients suffering from COVID-19.”

Similar studies exist, but only for predictions of general COVID-19 mortality or a patient’s need for ICU care. Furthermore, this approach is significantly more advanced, as it will analyze multiple sources of data and will produce a risk score that is updated as new data is acquired.

This project will shed more light on how COVID-19-related heart injury could result in heart dysfunction and sudden cardiac death, which is critical in the fight against COVID-19. The project will also help clinicians determine which biomarkers are most predictive of adverse clinical outcome.

Once the research team creates and tests their algorithm, they will make it widely available to any interested health care institution to implement.

“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,” says Trayanova.

Trayanova, whose work focuses on bringing engineering approaches to the clinical realm, is hopeful that this project will augment the role of engineering in helping patients live longer and lead healthier lives.

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