Rapid exclusion of COVID-19 infection using AI, EKG technology

Date:
June 15, 2021
Source:
Mayo Clinic
Summary:
Artificial intelligence (AI) may offer a way to accurately determine
that a person is not infected with COVID-19. An international
retrospective study finds 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.



FULL STORY ========================================================================== Artificial intelligence (AI) may offer a way to accurately determine that
a person is not infected with COVID-19. An international retrospective
study finds 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%.

The findings are published in Mayo Clinic Proceedings.

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.

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

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 lessons from this global working group showed what is feasible,
and the need pushed members in industry and academia to partner in
solving the complex questions of how to gather and transfer data from
multiple centers with their own EKG systems, electronic health records
and variable access to their own data," says Suraj Kapa, M.D., a cardiac electrophysiologist at Mayo Clinic.

"The relationships and data processing frameworks refined through
this collaboration can support the development and validation of new
algorithms in the future." 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%.

Zachi Attia, Ph.D., a Mayo Clinic engineer in the Department of
Cardiovascular Medicine, explains that prevalence is a variable in the calculation of positive and negative predictive values. Specifically,
as the prevalence decreases, the negative predictive value
increases. Dr. Attia is co-first author of the study with Dr. Kapa.

"Accuracy is one of the biggest hurdles in determining the value of any
test for COVID-19," says Dr. Attia. "Not only do we need to know the sensitivity and specificity of the test, but also the prevalence of the disease. Adding the extra control EKG data was critical to demonstrating
how a variable prevalence of the disease -- as we have encountered with
regions having widely different rates of disease at different stages of
the pandemic -- would impact how the test would perform." "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," notes Dr. Friedman. "Studies
are underway now to address that question." About this study This
study was designed and conceived by Mayo Clinic investigators, and the
work was made possible in part by a philanthropic gift from the Lerer
Family Charitable Foundation Inc., and by the voluntary support from participating physicians and hospitals around the world who contributed in
an effort to combat the COVID-19 pandemic. Technical support was donated
by GE Healthcare, Philips and Epiphany Healthcare for the transfer of
EKG data.

========================================================================== Story Source: Materials provided by Mayo_Clinic. Original written by
Terri Malloy. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Attia ZI, Kapa S, Dugan J, et al. Rapid exclusion of COVID
infection with
the artificial intelligence ECG. Mayo Clinic Proceedings, 2021;
DOI: 10.1016/j.mayocp.2021.05.027 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/06/210615132221.htm

--- up 5 weeks, 4 days, 22 hours, 45 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1337:3/111)