Machine-learning model predicts adverse cardiac outcomes By Erik L. Ridley, AuntMinnie staff writer
A machine-learning algorithm for analysis of coronary CT angiography (CCTA) exams was able to predict major adverse cardiac events at a higher level of accuracy than other traditional risk scores and risk factors, according to a presentation on Tuesday morning at the virtual RSNA 2020 meeting. In a retrospective study, researchers led by Dr. Christian Tesche of the Medical University of South Carolina found that their machinelearning model could improve risk stratification for major cardiac adverse events compared with conventional risk scores and clinical information. It also outperformed conventional regression analysis. “Machine learning may improve the integration of patient information to improve risk stratification,” Tesche said. CCTA-based risk scores mostly reflect the coronary plaque burden by the location, extent, and severity of coronary artery disease (CAD), according to Tesche. Previous studies have shown that these risk scores have prognostic value, yielding superior outcome predictions to traditional clinical risk scores. As machine learning can yield improved time efficiency and diagnostic accuracy for optimizing predictions based on various input features, the researchers sought to use
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the technology to evaluate the long-term prognostic value of CCTA-derived plaque measures and clinical parameters on major adverse cardiac events, Tesche said.
“RUSBoost is particularly well-suited for imbalanced datasets which are typically observed in the context of [major adverse cardiac event] prediction.” – DR. CHRISTIAN TESCHE
The researchers retrospectively analyzed a dataset of 361 patients who were suspected to have CAD and who received a CCTA exam. Next, they recorded the occurrence of major adverse cardiac events more than 90 days after the CCTA study. These events included cardiac death as well as unstable angina leading to coronary revascularization with more than six weeks between CCTA and revascularization procedure. Of the 361 patients, 31 (8.6%) had a major adverse cardiac event over the median follow-up period of 5.4 years. The authors then assessed several CCTAderived plaque measures, including lowattenuation plaque, napkin-ring sign, spotty calcifications, remodeling index, segment stenosis score, and segment involvement score. In addition, they obtained cardiovascular risk factors and the Framingham risk score from medical records.
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