Johns Hopkins University https://www.jhu.edu researchers recently received a $195,000 “Rapid Response Research” grant from the National Science Foundation https://www.nsf.gov. The grant funding will enable researchers to use machine learning, to identify which COVID-19 patients are at risk for adverse cardiac events such as heart failure, sustained abnormal heartbeats, heart attacks, cardiogenic shock, and death.
There has been increasing evidence of COVID-19’s negative impact on the cardiovascular system highlighting the need for identifying COVID-19 patients at risk for heart problems, but so far, there are no predictive capabilities that currently exist.
The first phase of the one year project has just received IRB approval for Suburban Hospital and Sibley Memorial Hospital within the Johns Hopkins HealtSystem (JHHS), to collect data from more than 300 COVID-19 patients within the JHHS system.
The data collected will include ECG, cardiac-specific laboratory tests, continuously obtained vital signs like heart rate and oxygen saturation, plus imaging data to include CT scans and echocardiography.
Similar studies exist, but only for predictions for general COVID-19 mortality or a patient’s need for ICU care. This new approach is significantly more advanced as it will have the ability to analyze multiple sources of data and produce a risk score that is updated as new data is acquired
According to Allison G. Hays, MD, Associate Professor of Medicine in JHU’s School of Medicine’s Division of Cardiology and the Clinical Collaborator on the project, “The project will help clinicians quickly risk stratify patients using real time clinical data with the goal to widely disseminate the knowledge to help medical practitioners develop their approach to treating and monitoring patients with COVID-19.”