Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation

Background: Invasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%–65%) for the prediction of FFR 53% (66%, AUC = 0.71, 95% confidence intervals 0.65–0.78). The external validation showed 84% accuracy (AUC = 0.89, 95% confidence intervals 0.83–0.95). The retrospective design, single ethnicity, and the lack of clinical outcomes may limit this prediction model’s generalized application. Conclusion: We found that angiography-based ML is useful to predict subtended myocardial territories and ischemia-producing lesions by mitigating the visual–functional mismatch between angiographic and FFR. Assessment of clinical utility requires further validation in a large, prospective cohort study. Soo-Jin Kang and colleagues present a machine learning–based model for estimating the risk of ischemia resulting from a coronary stenosis. If prospectively validated, the tool may reduce the invasive nature of this diagnosis.Why was this study done?: What did the researchers do and find?: What do these findings mean?: