We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Impact of coronary calcium score and lesion characteristics on the diagnostic performance of machine-learning-based computed tomography-derived fractional flow reserve.
- Authors
Koo, Hyun Jung; Kang, Joon-Won; Kang, Soo-Jin; Kweon, Jihoon; Lee, June-Goo; Ahn, Jung-Min; Park, Duk-Woo; Lee, Seung Whan; Lee, Cheol Whan; Park, Seong-Wook; Park, Seung-Jung; Kim, Young-Hak; Yang, Dong Hyun
- Abstract
Aims To evaluate the impact of coronary artery calcium (CAC) score, minimal lumen area (MLA), and length of coronary artery stenosis on the diagnostic performance of the machine-learning-based computed tomography-derived fractional flow reserve (ML-FFR). Methods and results In 471 patients with coronary artery disease, computed tomography angiography (CTA) and invasive coronary angiography were performed with fractional flow reserve (FFR) in 557 lesions at a single centre. Diagnostic performances of ML-FFR, computational fluid dynamics-based CT-FFR (CFD-FFR), MLA, quantitative coronary angiography (QCA), and visual stenosis grading were evaluated using invasive FFR as a reference standard. Diagnostic performances were analysed according to lesion characteristics including the MLA, length of stenosis, CAC score, and stenosis degree. ML-FFR was obtained by automated feature selection and model building from quantitative CTA. A total of 272 lesions showed significant ischaemia, defined by invasive FFR ≤0.80. There was a significant correlation between CFD-FFR and ML-FFR (r = 0.99, P < 0.001). ML-FFR showed moderate sensitivity and specificity in the per-patient analysis. Diagnostic performances of CFD-FFR and ML-FFR did not decline in patients with high CAC scores (CAC > 400). Sensitivities of CFD-FFR and ML-FFR showed a downward trend along with the increase in lesion length and decrease in MLA. The area under the curve (AUC) of ML-FFR (0.73) was higher than those of QCA and visual grading (AUC = 0.65 for both, P < 0.001) and comparable to those of MLA (AUC = 0.71, P = 0.21) and CFD-FFR (AUC = 0.73, P = 0.86). Conclusion ML-FFR showed comparable results to MLA and CFD-FFR for the prediction of lesion-specific ischaemia. Specificities and accuracies of CFD-FFR and ML-FFR decreased with smaller MLA and long lesion length.
- Subjects
CORONARY artery stenosis; BLOOD vessels; MACHINE learning; HEALTH outcome assessment; CORONARY circulation; CORONARY angiography; CORONARY artery disease; DESCRIPTIVE statistics; CALCIUM; COMPUTED tomography; RECEIVER operating characteristic curves; STATISTICAL correlation
- Publication
European Heart Journal - Cardiovascular Imaging, 2021, Vol 22, Issue 9, p998
- ISSN
2047-2404
- Publication type
Article
- DOI
10.1093/ehjci/jeab062