An in silico marker for coronary artery disease (CAD) has been found by researchers at the Icahn School of Medicine at Mount Sinai in New York using machine learning and clinical data from electronic health records.
The findings, published online this week in The Lancet, aim to provide non-invasive, more targeted diagnosis and better disease management of CAD, the most common type of heart disease and a leading cause of death worldwide.
The Mount Sinai researchers say their study is the first known to map characteristics of CAD on a spectrum. Previous similar studies have focused only on whether a patient was a case (has disease) or control (does not have disease). This approach could underestimate cases, leading to inappropriate management, and poorer clinical outcomes, say the investigators.
There has been steady increase in the use of AI for many applications, including heart disease detection. In December of this year, a group at the Massachusetts General and Brigham and Women’s Hospital described how they used deep learning to develop a population-based cardiovascular disease risk screening approach using existing chest X-ray images.
In this retrospective study, the researchers trained the machine learning model, named in silico score for coronary artery disease (ISCAD) to accurately measure CAD on a spectrum using more than 80,000 electronic health records from two large health system-based biobanks, the BioMe Biobank at the Mount Sinai Health System and the UK Biobank.
“Our model delineates coronary artery disease patient populations on a disease spectrum; this could provide more insights into disease progression and how those affected will respond to treatment. Having the ability to reveal distinct gradations of disease risk, atherosclerosis, and survival, for example, which may otherwise be missed with a conventional binary framework, is critical,” said Ron Do, PhD, senior study author and the Charles Bronfman Professor in Personalized Medicine at the Icahn School.
The model, which the researchers termed a “digital marker,” incorporated hundreds of different clinical features from the electronic health record, including vital signs, laboratory test results, medications, symptoms, and diagnoses, and compared it to both an existing clinical score for CAD, which uses only a small number of predetermined features, and a genetic score for CAD.
The 95,935 participants included participants of African, Hispanic/Latino, Asian, and European ethnicities, as well as a large share of women. Most clinical and machine learning studies on CAD have focused on white European ethnicity.
The investigators found the model accurately tracked the degree of narrowing of coronary arteries (coronary stenosis), mortality, and complications such as heart attack.
“Machine learning models like this could also benefit the health care industry at large by designing clinical trials based on appropriate patient stratification. It may also lead to more efficient data-driven individualized therapeutic strategies,” says lead author Iain S. Forrest, PhD, a postdoctoral fellow in Do’s lab.
He added that, “Despite this progress, it is important to remember that physician and procedure-based diagnosis and management of coronary artery disease are not replaced by artificial intelligence, but rather potentially supported by ISCAD as another powerful tool in the clinician’s toolbox.”
Next, the investigators envision conducting a prospective large-scale study to further validate the clinical utility and actionability of ISCAD, including in other populations. They also plan to assess a more portable version of the model that can be used universally across health systems.
