App determines COVID-19 disease severity using artificial intelligence, biomarkers

Date:
June 3, 2020
Source:
New York University
Summary:
A new mobile app can help clinicians determine which patients
with the novel coronavirus (COVID-19) are likely to have severe
cases. Created by researchers at NYU College of Dentistry, the app
uses artificial intelligence (AI) to assess risk factors and key
biomarkers from blood tests, producing a COVID-19 'severity score.'


FULL STORY ==========================================================================
A new mobile app can help clinicians determine which patients with the
novel coronavirus (COVID-19) are likely to have severe cases. Created
by researchers at NYU College of Dentistry, the app uses artificial intelligence (AI) to assess risk factors and key biomarkers from blood
tests, producing a COVID-19 "severity score."

========================================================================== Current diagnostic tests for COVID-19 detect viral RNA to determine
whether someone does or does not have the virus -- but they do not
provide clues as to how sick a COVID-positive patient may become.

"Identifying and monitoring those at risk for severe cases could help
hospitals prioritize care and allocate resources like ICU beds and
ventilators. Likewise, knowing who is at low risk for complications
could help reduce hospital admissions while these patients are safely
managed at home," said John T.

McDevitt, PhD, professor of biomaterials at NYU College of Dentistry,
who led the research.

"We want doctors to have both the information they need and the
infrastructure required to save lives. COVID-19 has challenged both
of these key areas." Creating a Severity Score Using data from 160 hospitalized COVID-19 patients in Wuhan, China, the researchers identified
four biomarkers measured in blood tests that were significantly elevated
in patients who died versus those who recovered: C- reactive protein
(CRP), myoglobin (MYO), procalcitonin (PCT), and cardiac troponin I
(cTnI). These biomarkers can signal complications that are relevant to COVID-19, including acute inflammation, lower respiratory tract infection,
and poor cardiovascular health.



==========================================================================
The researchers then built a model using the biomarkers as well as age and
sex, two established risk factors. They trained the model using a machine learning algorithm, a type of AI, to define the patterns of COVID-19
disease and predict its severity. When a patient's biomarkers and risk
factors are entered into the model, it produces a numerical COVID-19
severity score ranging from 0 (mild or moderate) to 100 (critical).

The model was validated using data from 12 hospitalized COVID-19 patients
from Shenzhen, China, which confirmed that the model's severity scores
were significantly higher for the patients that died versus those who
were discharged. These findings are published in Lab on a Chip, a journal
of the Royal Society of Chemistry.

As New York City emerged as the epicenter of the pandemic, the researchers further validated the model using data from more than 1,000 New York
City COVID-19 patients. To make the tool available and convenient for clinicians, they developed a mobile app that can be used at point-of-care
to quickly calculate a patient's severity score.

A Clinical Decision Support Tool The app has been retrospectively
evaluated in the Family Health Centers at NYU Langone in Brooklyn,
which serve more than 102,000 patients each year as one of the nation's
largest Federally Qualified Health Center networks.



========================================================================== "Real time clinical decision support tools for COVID-19 can be extremely helpful, particularly in the outpatient setting, to help guide monitoring
and treatment plans for those at greatest risk," said Isaac P. Dapkins,
MD, chief medical officer for the Family Health Centers at NYU Langone
and a co-author on the Lab on a Chip study.

After optimizing the clinical utility of the app at the Family Health
Centers at NYU Langone in May, the researchers aim to roll it out
nationwide in the coming weeks. It is possible that the COVID-19
severity score could be integrated with electronic health records,
thereby providing clinicians with actionable information at an early
stage for those diagnosed with COVID-19.

"We hope this tool can help identify those at high risk for adverse
outcomes and reduce the health disparities present with COVID-19,"
said Larry K.

McReynolds, executive director for the Family Health Centers at NYU
Langone.

Building on Innovations in Testing The COVID-19 severity score leverages a model McDevitt previously developed to predict outcomes for patients with cardiac disease. Cardiac health is one of several priorities of McDevitt's
lab, which creates point-of-care diagnostic systems that can be programmed
to test for oral cancer, cardiac disease, and now COVID-19 biomarkers.

The diagnostic system uses small, non-invasive samples -- such as
swabs of saliva or drops of blood from a fingertip -- which are added
to credit card- sized cartridges armed with bio-nano-chips pioneered
by McDevitt. The cartridge is inserted into a portable analyzer that simultaneously tests for a range of biomarkers, with results available
in less than half an hour.

Because this technology is currently used for research and informational purposes only, the COVID-19 app can be used with existing laboratory
tests and requires oversight by an authorized clinician. However, over
the next few months, McDevitt's laboratory, in partnership with SensoDx,
a company spun out of his lab, plans to develop and scale the ability to
test a drop of blood for COVID-19 severity biomarkers -- similar to how
a person with diabetes tests their blood sugar -- and produce a severity
score on the spot.

"With COVID-19, point-of-care testing, coupled with a decision support
system, could improve how clinicians triage patients -- and potentially
improve their outcomes, particularly for those who need more immediate
and aggressive care," said McDevitt.

In addition to McDevitt's research group at NYU College of Dentistry,
the study involved collaborators from NYU Grossman School of Medicine,
NYU Tandon School of Engineering, Zhongnan Hospital of Wuhan University,
and Latham BioPharm Group. The app was developed by McDevitt's laboratory
and OraLiva, a company founded by McDevitt, and is available for both
Apple and Android devices. The app is designated for use by authorized clinicians and is not intended for general use by patients.

Funding for the research was provided by the National Institute of Dental
and Craniofacial Research (3U01DE017793-02S1 and 5U01DE017793-2).


========================================================================== Story Source: Materials provided by New_York_University. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Michael P. McRae, Glennon W. Simmons, Nicolaos J. Christodoulides,
Zhibing Lu, Stella K. Kang, David Fenyo, Timothy Alcorn, Isaac P.

Dapkins, Iman Sharif, Deniz Vurmaz, Sayli S. Modak,
Kritika Srinivasan, Shruti Warhadpande, Ravi Shrivastav, John
T. McDevitt. Clinical decision support tool and rapid point-of-care
platform for determining disease severity in patients with
COVID-19. Lab on a Chip, 2020; DOI: 10.1039/ d0lc00373e ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200603132529.htm https://www.sciencedaily.com/releases/2020/06/200603132529.htm

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