Artificial Intelligence Predicts Coronary Artery Disease Mortality

A novel artificial intelligence score allows for accurate forecasting of death within ten years for patients with suspected or known coronary artery disease.

Unlike traditional methods based on clinical data, the new scores also include imaging information on the heart, measured by stress cardiovascular magnetic resonance (CMR). “Stress” refers to researchers giving patients drugs to mimic the effect of exercise on the heart while in the magnetic resonance imaging scanner.

“This is the first study to show that machine learning with clinical parameters plus stress CMR can very accurately predict the risk of death,” said study author Theo Pezel, MD, of the Johns Hopkins Hospital, said in a press release.

“The findings indicate that patients with chest pain, dyspnoea, or risk factors for cardiovascular disease should undergo a stress CMR exam and have their score calculated. This would enable us to provide more intense follow-up and advice on exercise, diet, and so on to those in greatest need.”

Risk stratification is commonly used in patients with, or at high risk of, cardiovascular disease to develop methods aimed to prevent heart attack, stroke, and sudden cardiac death. Conventional calculators implement limited clinical information, including age, sex, smoking status, blood pressure, and cholesterol.

The study analyzed the accuracy of machine learning using stress CMR and clinical data to predict 10-year all-cause mortality in patients with suspected or known coronary artery disease and compared its performance to existing scores.

“For clinicians, some information we collect from patients may not seem relevant for risk stratification. But machine learning can analyze a large number of variables simultaneously and may find associations we did not know existed, thereby improving risk prediction,” Pezel explained.

The stress featured 31,752 patients referred for stress CMR between 2008 and 2018 because of chest pain, shortness of breath on exertion, or high risk of cardiovascular disease. High risk was defined as having at least two risk factors, such as hypertension, diabetes, dyslipidemia, and current smoking.

The average age of participants was 64 years and 66 percent were male. Patients were followed up for a median of six years for all-cause death. During the follow-up period, 8.4 percent of patients died.

“Machine learning was conducted in two steps. First, it was used to select which of the clinical and CMR parameters could predict death, and which could not,” the press release stated.

“Second, machine learning was used to build an algorithm based on the important parameters identified in step one, allocating different emphasis to each to create the best prediction. Patients were then given a score of 0 (low risk) to 10 (high risk) for the likelihood of death within 10 years.”

The machine learning score determined which patients would be alive or dead with 76 percent accuracy, meaning around three out of four patients were correctly predicted.

“Stress CMR is a safe technique that does not use radiation. Our findings suggest that combining this imaging information with clinical data in an algorithm produced by artificial intelligence might be a useful tool to help prevent cardiovascular disease and sudden cardiac death in patients with cardiovascular symptoms or risk factors,” Pezel said.