AI model predicts death, complications for patients undergoing angioplasty, stents

It showed high levels of accuracy at predicting death, major bleeding events and the need for blood transfusion

5:00 AM

Author | Noah Fromson

doctor heart floating stethoscope
Getty Images

Listen to this article on Health Lab's podcast.

When a person has one or more blocked arteries, providers may choose to conduct a minimally invasive procedure known as percutaneous coronary intervention, or PCI. 

By inflating a balloon and potentially placing a stent, blood can flow more freely from the heart. 

Despite carrying less risk than open surgery, stenting and balloon angioplasty can result in complications like bleeding and kidney injury. 

Researchers at Michigan Medicine developed an AI-driven algorithm that accurately predicts death and complications after PCI — which could emerge as a tool for clinicians as they determine treatment for blocked heart arteries.

The results are published in European Heart Journal.

“The risks for patients undergoing percutaneous coronary intervention vary greatly depending on the individual patient, and both patients and clinicians have historically both over and underestimated the harms associated with PCI,” said lead David E. Hamilton, M.D., a cardiology-critical care fellow at Michigan Medicine. 

“Precise risk prediction is critical to treatment selection and the shared decision-making process. Our tool can recognize a wide array of outcomes after PCI and can be used by care providers and patients together to decide the best course of treatment.”

While other risk stratification tools have been created to identify risk after PCI, researcher say, many have modest accuracy and were made without involving a key party: patients. 

The Michigan Medicine team collected data from all adult patients who underwent PCI between April 2018 and the end of 2021 using the Blue Cross Blue Shield of Michigan Cardiovascular Consortium, or BMC2, registry. 

The consortium is comprised of hospitals across the state of Michigan that use data they collect to inform quality projects, and improve care and patient outcomes.

Researchers used that data — including more than 20 pre-procedural characteristics, such as age, blood pressure and total cholesterol — to create a risk prediction model with the machine learning software “XGBoost”.

The AI-driven model showed high levels of accuracy at predicting death, major bleeding events and the need for blood transfusion. It outperformed other models that used the same pre-procedural characteristics.

“We combined the predictive model with patient feedback from the PCI Patient Advisory Council to transform machine learning into this patient-centered, individualized risk prediction tool,” said senior author Hitinder Gurm, MBBS, interim chief medical officer at U-M Health.  

“In the age of widespread smartphones and electronic medical records, this computerized risk score could be integrated into electronic health systems  and made easy to use at the bedside. It would not only help relay complex information to the provider quickly, but it could also be used to enhance patient education on the risks related to PCI.”

The innovative technology has been harnessed into a computer and phone application to allow for free and widespread use.

Jeremy Albright Ph.D., Milan Seth, Devraj Sukul M.Sc., M.D., all of Michigan Medicine, Ian Painter Ph.D., of Washington State Department of Health and Foundations for Health Care Quality, Charles Maynard Ph.D., of Foundations for Health Care Quality and University of Washington, and Ravi S. Hira M.D., of University of Washington and Pulse Heart Institute and Multicare Health System. 

Support for BMC2 is provided by Blue Cross and Blue Shield of Michigan and Blue Care Network as part of the BCBSM Value Partnerships program. Although Blue Cross Blue Shield of Michigan and BMC2 work collaboratively, the opinions, beliefs and viewpoints expressed by the author do not necessarily reflect the opinions, beliefs, and viewpoints of BCBSM or any of its employees.

Paper cited: “Merging Machine Learning and Patient Preference: Patient-Centered Tool for Predicting Risk of Percutaneous Coronary Intervention,” European Heart Journal. DOI: 10.1093/eurheartj/ehad836

Sign up for Health Lab newsletters today. Get medical tips from top experts and learn about new scientific discoveries every week by subscribing to Health Lab’s two newsletters, Health & Wellness and Research & Innovation.

Sign up for the Health Lab Podcast: Add us on SpotifyApple Podcasts or wherever you get you listen to your favorite shows.


More Articles About: Interventional cardiology Heart Attack Treatment Heart Attack Risk Factors Cardiovascular: Diagnostics & Procedures Cardiovascular: Diseases & Conditions Cardiovascular: Treatment & Surgery Emerging Technologies Hospitals & Centers
Health Lab word mark overlaying blue cells
Health Lab

Explore a variety of health care news & stories by visiting the Health Lab home page for more articles.

Media Contact Public Relations

Department of Communication at Michigan Medicine

[email protected]

734-764-2220

Related
kidneys blue yellow
Health Lab
Algorithm predicts females have higher risk for kidney damage after aneurysm repair
For an abdominal aortic aneurysm, female patients have a higher risk for kidney damage after endovascular repair, a Michigan Medicine study finds.
Stay Informed

Want top health & research news weekly? Sign up for Health Lab’s newsletters today!

Subscribe
Featured News & Stories blood pressure cuff on mans arm with white coat doctor taking it
Health Lab
Blood pressure high for years? Beware of stroke risk
A study led by Michigan Medicine narrows in on the cumulative effects of years of high systolic blood pressure — the top number on the blood pressure reading and how hard the heart pumps blood to the arteries — finding that a higher average reading during adulthood is linked with a greater risk for the two most common types of stroke.
cell phone with brain on screen in blue with blue background
Health Lab
Mental health apps may help those waiting for care, study finds
People with depression, anxiety and even suicidal thoughts can wait weeks for a mental health appointment, but a new study shows mobile apps and activity trackers might help during the wait.
woman laying down and sheet over going into surgery
Health Lab
Older women more likely to receive heart surgery, die at low quality hospitals
Women over the age of 65 who require complex heart surgery are more likely than men to receive care at low quality hospitals — where they also die in greater numbers following the procedure, a Michigan Medicine study finds.
Dart flying toward target precision medicine
News Release
From ‘trial and error’ to targeted precision: $17.9M grant accelerates U-M mental health research
A new major grant aims to bring the same precision to mental health care for depression, anxiety and other psychiatric conditions that already exists for cancer and heart disease.
Woman Patient Preparing Surgery Anesthesia
Health Lab
Female heart patients less likely to have additional problems fixed during surgery
Two studies led by Michigan Medicine find that female patients who undergo heart surgery are less likely to have secondary ailments corrected during a procedure — despite guidelines that indicate they should.
woman smiling with man in michigan gear selfie
Health Lab
Getting ahead of aortic disease
Patient bypasses a life threatening aortic aneurysm with the help of Michigan Medicine's genetic counseling and a streamlined cardiac referral program.