Researchers develop model to predict likelihood of testing positive for COVID-19, disease outcomes
Prediction model reveals new characteristics that may affect risk

Date:
June 15, 2020
Source:
Cleveland Clinic
Summary:
A new risk prediction model for healthcare providers can forecast
an individual patient's likelihood of testing positive for COVID-19
as well as their outcomes from the disease.



FULL STORY ========================================================================== Cleveland Clinic researchers have developed the world's first risk
prediction model for healthcare providers to forecast an individual
patient's likelihood of testing positive for COVID-19 as well as their
outcomes from the disease.


========================================================================== According a new study published in CHEST, the risk prediction
model (called a nomogram) shows the relevance of age, race, gender, socioeconomic status, vaccination history and current medications in
COVID-19 risk. The risk calculator is a new tool for healthcare providers
to aid them in predicting patient risk and tailoring decision-making
about care. It provides a more scientific approach to testing which is important for the healthcare community which has faced increased demand
for testing and limited resources.

"The ability to accurately predict whether or not a patient is likely
to test positive for COVID-19, as well as potential outcomes including
disease severity and hospitalization, will be paramount in effectively
managing our resources and triaging care," said Lara Jehi, M.D., Cleveland Clinic's Chief Research Information Officer and corresponding author
on the study. "As we continue to battle this pandemic and prepare
for a potential second wave, understanding a person's risk is the
first step in potential care and treatment planning." The nomogram,
which has been deployed as a freely available online risk calculator
at https://riskcalc.org/COVID19/, was developed using data from nearly
12,000 patients enrolled in Cleveland Clinic's COVID-19 Registry, which includes all individuals tested at Cleveland Clinic for the disease,
not just those that test positive.

Data scientists, including co-author on the study Michael Kattan, Ph.D.,
Chair of Lerner Research Institute's Department of Quantitative Health Sciences, used statistical algorithms to transform data from registry
patients' electronic medical records into the first-of-its-kind nomogram.

This study revealed several novel insights into disease risk, including:
* Patients who have received the pneumococcal polysaccharide vaccine
(PPSV23) and flu vaccine are less likely to test positive for
COVID-19 than those who have not received the vaccinations.

* Patients actively taking melatonin (over-the-counter sleep aid),
carvedilol (high blood pressure and heart failure treatment)
or paroxetine (anti-depressant) are less likely to test positive
than patients not taking the drugs.

* Patients of low socioeconomic status (as measured in this study
by zip
code) are more likely to test positive than patients of greater
economic means.

* Patients of Asian descent are less likely than Caucasian patients
to test
positive.

"Our findings corroborated several risk factors already reported in
existing literature -- including that being male and of advancing age both increase the likelihood of testing positive for COVID-19 -- but we also
put forth some new associations," said Dr. Jehi. "Further validation and research are needed into these initial insights but these correlations
are extremely intriguing." In a previous network medicine study led
by Lerner Research Institute scientists, 16 drugs (including melatonin, carvedilol and paroxetine) and three drug combinations were identified
as candidates for repurposing as potential COVID-19 treatments. While
these findings suggest an association between taking these medications
and reduced risk of testing positive for COVID-19, additional studies
are needed to assess how these drugs may affect disease progression.

"The data suggest some interesting correlations but do not confer cause
and effect," said Kattan. "For example, our data do not prove that
melatonin reduces your risk of testing positive for COVID-19. There
may be something else about patients who take melatonin that is indeed responsible for their apparent reduced risk, and we don't know what that
is. Consumers should not change anything about their behavior based on
our findings." The nomogram, developed using data from patients tested
at Cleveland Clinic for COVID-19 before April 2, 2020, showed good
performance and reliability when used in a different geographic region (Florida) and over time (patients tested after April 2, 2020). This
suggests that the patterns and predictors identified in the model are consistent across regions and communities and can be potentially adopted
for clinical practice in healthcare systems across the country.

"This nomogram will bring precision medicine to the COVID-19 pandemic,
helping to enable researchers and physicians to predict an individual's
risk of testing positive," said Kattan. "Additionally, while testing
solutions continue to be needed, it is so important to make sure we
are responsibly and optimally dispatching our resources NOT- including
clinical personnel, personal protective equipment and hospital beds. Our
risk prediction model stands to greatly assist hospital systems in
this planning." The COVID-19 research registry, which now has data
from more than 23,000 patients, is being used to inform a variety
of studies. Researchers from across the Cleveland Clinic enterprise
are using the dynamic registry data in more than 140 COVID-19-related
research projects in areas such as cancer, pediatrics and intensive care.


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


========================================================================== Journal Reference:
1. Lara Jehi, Xinge Ji, Alex Milinovich, Serpil Erzurum, Brian Rubin,
Steve
Gordon, James Young, Michael W. Kattan. Individualizing risk
prediction for positive COVID-19 testing: results from 11,672
patients.. Chest, 2020; DOI: 10.1016/j.chest.2020.05.580 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200615140852.htm

--- up 20 weeks, 6 days, 2 hours, 34 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1337:3/111)