AI-powered risk model evaluates long-term risk of coronary artery disease
Researchers have used machine learning to develop an advanced risk model for coronary artery disease (CAD), presenting their findings in Nature Medicine.[1]
The group considered approximately 2,000 factors that may or may not influence a person’s long-term heart health; the list included demographic details, lifestyle choices, medications, genetics and much more. They then explored years of data from the U.K. Biobank database, training an artificial intelligence (AI) model to identify factors that increase an individual’s odds of receiving a diagnosis of CAD later in life.
Once that list of roughly 2,000 predictive features was narrowed down to just 53, it was time to put it to the test. Overall, the group found that their risk model achieved an area under the ROC curve (AUC) of 0.84. When tested on a completely independent patient population, meanwhile, the AUC was 0.81 for predicting an individual’s 10-year risk of CAD. This was seen as an improvement when compared to the clinical scores presently being used by care teams to evaluate their patients.
“I think more precise and personalized risk prediction could motivate patients to engage in early prevention,” senior author Ali Torkamani, PhD, professor and director of genomics and genome informatics at the Scripps Research Translational Institute, said in a statement. “Our model first predicts the risk that a person will develop CAD, and then it provides information to allow personalized intervention.”
“Compared to traditional clinical tools, the new model improved risk classification for approximately one in four individuals — helping to better identify those truly at risk while avoiding unnecessary concern for those who are not,” added first author Shang-Fu “Shaun” Chen, a former doctoral student who worked with Torkamani.
The team behind this advanced risk model hope it can help identify more young and female patients who may face an increased risk of developing CAD. By finding these individuals early, clinicians can work to get out in front of the disease by adapting as necessary.
“We think the most important thing is for patients to be aware of their individual risks so that they can receive the appropriate treatments and make lifestyle changes,” Chen added.
The next step for this research is a long-term clinical test exploring the effectiveness of using this new risk model to improve patient care.
Click here to read the full analysis in Nature Medicine.
Michael has more than 18 years of experience as a professional writer and editor. He has written at length about cardiology, radiology, artificial intelligence and other key healthcare topics.