Machine learning used to generate risk score based on clinical data and measurements from stress cardiovascular magnetic resonance<\/p>\n<\/h3>\n
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THURSDAY, Dec. 16, 2021 (HealthDay News) — A machine learning (ML) score based on clinical data and stress cardiovascular magnetic resonance (CMR) measurements can predict 10-year all-cause mortality in patients with known or suspected coronary artery disease (CAD), according to a study presented at EuroEcho 2021, a scientific congress of the European Society of Cardiology, held from Dec. 9 to 11 in Berlin.<\/p>\n
Theo Pezel, M.D., from the Johns Hopkins Hospital in Baltimore, and colleagues examined the feasibility and accuracy of ML using stress CMR and clinical data to predict 10-year all-cause mortality in patients with known or suspected CAD. All consecutive patients referred for stress CMR between 2008 and 2018 were included, with a median follow-up of 6.0 years. Overall, 23 clinical and 11 stress CMR parameters were examined.<\/p>\n
The researchers found that 8.4 percent of the 31,752 consecutive patients died with 206,453 patient-years of follow-up. Compared with the clinical-stress CMR-10 (C-CMR-10) score, European Society of Cardiology (ESC)-score, QRISK3-score, Framingham Risk Score (FRS), and stress CMR data alone, the ML score exhibited a higher area under the curve for prediction of 10-year all-cause mortality (0.76 for ML versus 0.68 for C-CMR-10, 0.66 for ESC, 0.64 for QRISK3, 0.63 for FRS, 0.66 for extent of inducible ischemia, and 0.65 for extent of late gadolinium enhancement).<\/p>\n
“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 in a statement.<\/p>\n
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