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

Hyeonyong Hae, Soo-Jin Kang, Won-Jang Kim, So-Yeon Choi, June-Goo Lee, Youngoh Bae, Hyungjoo Cho, Dong Hyun Yang, Joon-Won Kang, Tae-Hwan Lim, Cheol Hyun Lee, Do-Yoon Kang, Pil Hyung Lee, Jung-Min Ahn, Duk-Woo Park, Seung-Whan Lee, Young-Hak Kim, Cheol Whan Lee, Seong-Wook Park and Seung-Jung Park

PLOS Medicine, 2018, vol. 15, issue 11, 1-19

Abstract: 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?:

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pmed00:1002693

DOI: 10.1371/journal.pmed.1002693

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