Research team creates statistical model to predict COVID-19 resistance
Proof-of-concept study shows promise for machine-learning system that
uses electronic health data to make its predictions

Date:
February 22, 2023
Source:
Johns Hopkins Medicine
Summary:
Researchers have created and preliminarily tested what they believe
may be one of the first models for predicting who has the highest
probability of being resistant to COVID-19 in spite of exposure
to SARS-CoV-2, the virus that causes it.


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FULL STORY ========================================================================== Researchers from Johns Hopkins Medicine and The Johns Hopkins University
have created and preliminarily tested what they believe may be one of
the first models for predicting who has the highest probability of being resistant to COVID-19 in spite of exposure to SARS-CoV-2, the virus that
causes it.


==========================================================================
The study is reported online today in the journal PLOS ONE.

"If we can identify which people are naturally able to avoid infection
by SARS- CoV-2, we may be able to learn -- in addition to societal
and behavioral factors -- which genetic and environmental differences
influence their defense against the virus," says lead study author
Karen (Kai-Wen) Yang, a biomedical engineering graduate student in the Translational Informatics Research and Innovation Lab at The Johns Hopkins University. "That insight could lead to new preventive measures and more
highly targeted treatments." For its study, the research team set out
to determine if a machine-learning statistical model could use health characteristics stored in electronic health records -- providing patient
data such as comorbidities (other medical conditions) and prescribed medications -- as a means to pinpoint people with a natural ability
to avoid SARS-CoV-2 infection. Those persons, says Yang, could then be
studied to better understand the factors enabling their resistance.

A machine-learning model is a computer program or system that uses
mathematical algorithms to find statistical patterns, and then apply the patterns moving forward. This gives such systems the ability to imitate
human thinking and reasoning, and similar to the brain, learn over time.

"Using a machine-learning system to recognize complex patterns in large
numbers of people with COVID-19 enabled another team of Johns Hopkins
Medicine researchers in 2021 to predict the course of an individual
patient's case and determine the likelihood that it would become severe,"
says co-senior study author Stuart Ray, M.D., vice chair of medicine for
data integrity and analytics, and professor of medicine at the Johns
Hopkins University School of Medicine. "Based on their success, our
team wondered if the same approach also might be applied to predicting
who could be exposed to SARS-CoV-2 in close quarters and still not
get infected." To demonstrate the model's ability to predict COVID-19 resistance, the researchers first acquired data from a clinical registry
called the Johns Hopkins COVID-19 Precision Medicine Analytics Platform Registry (JH-CROWN). The registry contains information for patients
seen within the Johns Hopkins Health System who have been suspected of,
or confirmed as, having a SARS-CoV- 2 infection.

For their resistance study, the researchers only included individuals who received a COVID-19 test between June 10, 2020, and Dec. 15, 2020, and
who reported "potential exposure to the virus" as the reason for testing.

The ending date was the point at which large-scale COVID-19 vaccination
efforts started in the United States. Choosing this date, the researchers
say, enabled them to avoid the effects on their findings of vaccines
preventing infection rather than natural resistance.

The 8,536 study participants who reported exposure as their reason
for getting COVID tested were divided into two groups: those who did
not share a residence (called a "household" in this study) with any
COVID-19 patients or their residence had 10 or more patients; and those
who shared a residence with 10 or fewer people, with at least one being
a COVID-19 patient. The first group, with 8,476 of the participants,
was designated as the Training and Testing Set, while the second group,
called the Household Index (HHI) Set, had 60 members, and was used as
a separate testing set.

Keeping the household number to 10 or fewer, the researchers say,
excluded people living in apartment complexes, dormitories and other higher-density, multi-unit living areas where exposure to a particular
person positive for SARS-CoV-2 would be less intense.

To identify patterns and cluster participants so that those naturally
resistant to SARS-CoV-2 stand out, both study sets were analyzed using the Maximal- frequent All-confident pattern Selection Pattern-based Clustering (MASPC) algorithm. MASPC is specifically designed for electronic health
record data analysis that combines patient demographic information (age,
sex and race), the International Statistical Classification of Diseases
and Related Health Problems (ICD) medical diagnostic codes relevant to
each case, outpatient medication orders and the number of comorbidities
(other diseases) present.

"We hypothesized that MASPC would enable us to cluster patients
with similar patterns in their data to define them as resistant and non-resistant to SARS- CoV-2, and with the hope that the algorithm would
learn with each analysis how to improve the accuracy and reliability of
future assignments," says Ray. "This initial study using JH-CROWN data
was conducted to give life to that hypothesis, a proof-of-concept trial
of our statistical model to show that resistance to COVID-19 might be predictable based a patient's clinical and demographic profile." "In the Training and Testing Set, we identified 56 patterns of ICD codes split
into two groups: associated with resistance or not associated," Yang says.

"Statistical analyses of how well these patterns differentiated between resistance and non-resistance yielded five patterns that did the best
job for our small and localized [Baltimore-Washington, D.C., metroplex]
study population to define who was most likely exposed to SARS-CoV-2."
"Looking for these patterns in HHI Set -- the individuals most likely
to have been exposed to SARS-CoV-2 in close quarters -- and then
statistically analyzing the results, our model's best performance was
0.61," says Ray. "Since a score of 0.5 shows only chance association
between the prediction and reality, and 1 is 100% association, this shows
the model has promise as a tool for identifying people with COVID-19
resistance who can be further studied," says Ray.

Limitations to the study, says Ray, include potential bias from
self-reporting of COVID-19 exposure by participants, the small number
of participants in the HHI group, the possibility that participants
tested for SARS-CoV-2 using home kits or at facilities outside the
Johns Hopkins system (and therefore, the tests were not recorded in the JH-CROWN database), and the short timeframe of the study itself. He adds
that future trails using national patient data are needed to validate
the model's ability.

Along with Yang and Ray, the members of the study team from Johns Hopkins Medicine and Johns Hopkins University are graduate and undergraduate
students Yijia Chen, Jacob Desman, Kevin Gorman, Chloe' Paris, Ilia
Rattsev, Tony Wei and Rebecca Yoo; and faculty co-senior authors Joseph Greenstein and Casey Overby Taylor.

The study authors report no conflicts of interest.

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


========================================================================== Journal Reference:
1. Kai-Wen K. Yang, Chloe' F. Paris, Kevin T. Gorman, Ilia Rattsev,
Rebecca
H. Yoo, Yijia Chen, Jacob M. Desman, Tony Y. Wei, Joseph
L. Greenstein, Casey Overby Taylor, Stuart C. Ray. Factors
associated with resistance to SARS-CoV-2 infection discovered using
large-scale medical record data and machine learning. PLOS ONE,
2023; 18 (2): e0278466 DOI: 10.1371/ journal.pone.0278466 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/02/230222210542.htm

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