Artificial intelligence identifies individuals at risk for heart disease complications
System mines Electronic Health Records (EHRs) to assess combined effects
of various risk factors

Date:
January 24, 2022
Source:
University of Utah Health
Summary:
For the first time, University of Utah Health scientists have shown
that artificial intelligence could lead to better ways to predict
the onset and course of cardiovascular disease. The researchers,
working in conjunction with physicians from Intermountain Primary
Children's Hospital, developed unique computational tools to
precisely measure the synergistic effects of existing medical
conditions on the heart and blood vessels.



FULL STORY ==========================================================================
For the first time, University of Utah Health scientists have shown that artificial intelligence could lead to better ways to predict the onset and course of cardiovascular disease. The researchers, working in conjunction
with physicians from Intermountain Primary Children's Hospital, developed unique computational tools to precisely measure the synergistic effects
of existing medical conditions on the heart and blood vessels.


==========================================================================
The researchers say this comprehensive approach could help physicians
foresee, prevent, or treat serious heart problems, perhaps even before
a patient is aware of the underlying condition.

Although the study only focused on cardiovascular disease, the researchers believe it could have far broader implications. In fact, they suggest
that these findings could eventually lead to a new era of personalized, preventive medicine. Doctors would proactively contact patients to alert
them to potential ailments and what can be done to alleviate the problem.

"We can turn to AI to help refine the risk for virtually every medical diagnosis," says Martin Tristani-Firouzi, M.D. the study's corresponding
author and a pediatric cardiologist at U of U Health and Intermountain
Primary Children's Hospital, and scientist at the Nora Eccles Harrison Cardiovascular Research and Training Institute. "The risk of cancer, the
risk of thyroid surgery, the risk of diabetes -- any medical term you can imagine." The study appears in the online journal PLOS Digital Health.

Current methods for calculating the combined effects of various risk
factors - - such as demographics and medical history -- on cardiovascular disease are often imprecise and subjective, according to Mark Yandell,
Ph.D., senior author of the study, a professor of human genetics,
H.A. and Edna Benning Presidential Endowed Chair at U of U Health,
and co-founder of Backdrop Health. As a result, these methods fail to
identify certain interactions that could have profound effects on the
health of the heart and blood vessels.



==========================================================================
To more accurately measure how these interactions, also known
as comorbidities, influence health, Tristani-Firouzi, Yandell, and
colleagues from U of U Health and Intermountain Primary Children's
Hospital, used machine learning software to sort through more than 1.6
million electronic health records (EHRs) after names and other identifying information were deleted.

These electronic records, which document everything that happens to a
patient, including lab tests, diagnoses, medication usage, and medical procedures, helped the researchers identify the comorbidities most
likely to aggravate a particular medical condition such as cardiovascular disease.

In their current study, the researchers used a form of artificial
intelligence called probabilistic graphical networks (PGM) to calculate
how any combination of these comorbidities could influence the risks
associated with heart transplants, congenital heart disease, or sinoatrial
node dysfunction (SND, a disruption or failure of the heart's natural pacemaker).

Among adults, the researchers found that:
* Individuals who had a prior diagnosis of cardiomyopathy (disease
of the
heart muscle) were at 86 times higher risk of needing a heart
transplant than those who didn't.

* Those who had viral myocarditis had about a 60 times higher risk of
requiring a heart transplant.

* Usage of milrinone, a vasodilating drug used to treat heart failure,
pushed the transplant risk 175 times higher. This was the strongest
individual predictor of heart transplant.

In some instances, the combined risk was even greater. For instance,
among patients who had cardiomyopathy and were taking milrinone, the
risk of needing a heart transplant was 405 times higher than it was for
those whose hearts were healthier.



========================================================================== Comorbidities had a significantly different influence on the transplant
risk among children, according to Tristani-Firouzi. Overall, the risk
of pediatric heart transplant ranged from 17 to 102 times higher than
children who didn't have pre-existing heart conditions, depending on
the underlying diagnosis.

The researchers also examined influences that a mother's health during pregnancy had on her children. Women who had high blood pressure during pregnancy were about twice as likely to give birth to infants who had congenital heart and circulatory problems. Children with Down syndrome
had about three times greater risk of having a heart anomaly.

Infants who had Fontan surgery, a procedure that corrects a congenital
blood flow defect in the heart, were about 20 times more likely to
develop SND heart rate dysfunction than those who didn't need the surgery.

The researchers also detected important demographic differences. For
instance, a Hispanic patient with atrial fibrillation (rapid heartbeat)
had twice the risk of SND compared with Blacks and Whites, who had
similar medical histories.

Josh Bonkowsky, M.D. Ph.D., Director of the Primary Children's Center
for Personalized Medicine, who is not an author on the study, believes
this research could lead to development of a practical clinical tool
for patient care.

"This novel technology demonstrates that we can estimate the risk for
medical complications with precision and can even determine medicines
that are better for individual patients."Bonkowsky says.

Moving forward, Tristani-Firouzi and Yandell hope their research will
also help physicians untangle the growing web of disorienting medical information enveloping them every day.

"No matter how aware you are, there's no way to keep all of the knowledge
that you need in your head as a medical professional in this day and
age to treat patients in the best way possible," Yandell says. "The computational machines we are developing will help physicians make
the best possible patient care decisions, using all of the pertinent information available in our electronic age. These machines are vital
to the future of medicine." In addition to Drs. Tristani-Firouzi and
Yandell, University of Utah Health scientists contributing to this
research were S. Wesolowski, G. Lemmon, E.J.

Hernandez, A. Henrie, T.A. Miller, D. Wyhrauch, M.D. Puchalski,
B.E. Bray, R.U.

Shah, V.G. Deshmukh, R. Delaney, H.J. Yost, and K. Eilbeck.

The study was supported by the AHA Children's Strategically Focused
Research Network, the Nora Eccles Treadwell Foundation, and the National
Heart, Lung and Blood Institute.

Competing interests: Yandell, Deshmukh and Lemmon own shares in Backdrop Health; there are no financial ties regarding this research.

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


========================================================================== Journal Reference:
1. Sergiusz Wesołowski, Gordon Lemmon, Edgar J. Hernandez,
Alex Henrie,
Thomas A. Miller, Derek Weyhrauch, Michael D. Puchalski, Bruce
E. Bray, Rashmee U. Shah, Vikrant G. Deshmukh, Rebecca Delaney,
H. Joseph Yost, Karen Eilbeck, Martin Tristani-Firouzi, Mark
Yandell. An explainable artificial intelligence approach for
predicting cardiovascular outcomes using electronic health
records. PLOS Digital Health, 2022; 1 (1): e0000004 DOI:
10.1371/journal.pdig.0000004 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220124115058.htm

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