AI-Enhanced EKG Can Be Used as Rapid, Reliable COVID-19 Screening Test to Rule out Infection

Artificial intelligence (AI) may offer a way to accurately determine that a person is not infected with COVID-19.

An international retrospective study led by researchers at Mayo Clinic (Rochester, MN, USA) has found that infection with SARS-CoV-2, the virus that causes COVID-19, creates subtle electrical changes in the heart. An AI-enhanced EKG can detect these changes and potentially be used as a rapid, reliable COVID-19 screening test to rule out COVID-19 infection.

The AI-enhanced EKG was able to detect COVID-19 infection in the test with a positive predictive value - people infected - of 37% and a negative predictive value - people not infected - of 91%. When additional normal control subjects were added to reflect a 5% prevalence of COVID-19 - similar to a real-world population - the negative predictive value jumped to 99.2%.

COVID-19 has a 10- to 14-day incubation period, which is long compared to other common viruses. Many people do not show symptoms of infection, and they could unknowingly put others at risk. Also, the turnaround time and clinical resources needed for current testing methods are substantial, and access can be a problem. The realization of a global health crisis brought together stakeholders around the world to develop a tool that could address the need to rapidly, noninvasively and cost-effectively rule out the presence of acute COVID-19 infection. The study, which included data from racially diverse populations, was conducted through a global volunteer consortium spanning four continents and 14 countries.

The researchers selected patients with EKG data from around the time their COVID-19 diagnosis was confirmed by a genetic test for the SARS-Co-V-2 virus. These data were control-matched with similar EKG data from patients who were not infected with COVID-19. Researchers used more than 26,000 of the EKGs to train the AI and nearly 4,000 others to validate its readings. Finally, the AI was tested on 7,870 EKGs not previously used. In each of these sets, the prevalence of COVID-19 was around 33%. To accurately reflect a real-world population, more than 50,000 additional normal EKGs were then added to reach a 5% prevalence rate of COVID-19. This raised the negative predictive value of the AI from 91% to 99.2%.

"If validated prospectively using smartphone electrodes, this will make it even simpler to diagnose COVID infection, highlighting what might be done with international collaborations," said Paul Friedman, M.D., chair of Mayo Clinic's Department of Cardiovascular Medicine in Rochester and senior author of the study.

"This study demonstrates the presence of a biological signal in the EKG consistent with COVID-19 infection, but it included many ill patients. While it is a hopeful signal, we must prospectively test this in asymptomatic people using smartphone-based electrodes to confirm that it can be practically used in the fight against the pandemic," added Dr. Friedman. "Studies are underway now to address that question."

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