Increasing evidence of COVID-19’s negative impacts on the cardiovascular system highlights a great need for identifying those patients at risk of heart problems. However, no such predictive capabilities currently exist.
Now, with a $195,000 Rapid Response Research grant from the National Science Foundation, a team of Johns Hopkins researchers is using machine learning to identify which patients with COVID-19 are at risk of adverse cardiac events such as heart failure, sustained abnormal heartbeats, heart attacks, cardiogenic shock and death.
“This project will provide clinicians with early warning signs and ensure that resources are allocated to patients with the greatest need,” says biomedical engineer Natalia Trayanova, the project’s principal investigator.
The first phase of the one-year project, which in May received institutional review board approval for Suburban Hospital and Sibley Memorial Hospital within the Johns Hopkins Health System, will collect the following data from more than 300 COVID-19 patients admitted to the health system: ECG; cardiac-specific laboratory tests; continuously obtained vital signs, like heart rate and oxygen saturation; and imaging data, such as CT scans and echocardiography. These data will be used to train the algorithm.
The researchers will then test the algorithm with data from patients with COVID-19 with heart injury at Johns Hopkins, 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 become 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 cardiologist Allison Hays, 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.”