AI-Based Stress CMR Evaluated to Predict Mortality Risk in Patients With Suspected or Known Heart Disease

December 11, 2021—The European Society of Cardiology (ESC) announced findings demonstrating that a novel artificial intelligence (AI) score provides a more accurate forecast of the likelihood of patients with suspected or known coronary artery disease dying within 10 years than established scores used by health professionals worldwide.

The ESC press release noted that unlike traditional methods based on clinical data, the new score also includes imaging information on the heart, measured by stress cardiovascular MR (CMR). For stress, patients are given a drug to mimic the effect of exercise on the heart while in the MRI 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,” commented study investigator Theo Pezel, MD, in the ESC press release. “The findings indicate that patients with chest pain, dyspnea, 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.”

Dr. Pezel, who is with Johns Hopkins Hospital in Baltimore, Maryland, further explained, “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.”

The study findings were presented at the EuroEcho 2021 scientific congress, which was held online December 9-11. EuroEcho is the flagship congress of the European Association of Cardiovascular Imaging, which is a branch of the ESC.

According to the ESC press release, risk stratification is commonly used in patients with, or at high risk of, cardiovascular disease to tailor management aimed at preventing heart attack, stroke, and sudden cardiac death. Conventional calculators use a limited amount of clinical information, such as age, sex, smoking status, blood pressure, and cholesterol.

This study examined 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.

As summarized by ESC, the study included 31,752 patients who were referred for stress CMR between 2008 and 2018 to a center in Paris because of chest pain, shortness of breath on exertion, or high risk of cardiovascular disease but no symptoms. High risk was defined as having at least two risk factors, such as hypertension, diabetes, dyslipidemia, and current smoking.

The patients’ average age was 64 years, and 66% were men. Information was collected on 23 clinical and 11 CMR parameters. Patients were followed-up for a median of 6 years for all-cause death, which was obtained from the national death registry in France. During the follow-up period, 2,679 (8.4%) patients died.

The ESC press release explained that 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. 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 was able to predict which patients would be alive or dead with 76% accuracy (in statistical terms, the area under the curve was 0.76). “This means that in approximately three out of four patients, the score made the correct prediction,” noted Dr. Pezel.

Using the same data, the investigators calculated the 10-year risk of all-cause death using established scores (Systematic Coronary Risk Evaluation [SCORE], QRISK3, and Framingham Risk Score [FRS]) and a previously derived score incorporating clinical and CMR data (clinical-stressCMR [C-CMR-10])²—none of which used machine learning.

The machine learning score had a significantly higher area under the curve for the prediction of 10-year all-cause mortality compared with the other scores: SCORE = 0.66, QRISK3 = 0.64, FRS = 0.63, and C-CMR-10 = 0.68.

In the ESC press release, Dr. Pezel concluded, “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 AI might be a useful tool to help prevent cardiovascular disease and sudden cardiac death in patients with cardiovascular symptoms or risk factors.”