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KMID : 0311120200610020137
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Yonsei Medical Journal
2020 Volume.61 No. 2 p.137 ~ p.144
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Diagnostic Accuracy of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing System
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Park Hyung-Bok
Jang Yeong-Gul
Arsanjani Reza
Nguyen Minh Tuan
Lee Sang-Eun
Jeon Byung-Hwan
Jung Sung-Hee
Hong Young-Taek
Ha Seong-Min
Kim Se-Keun
Lee Sang-Wook
Chang Hyuk-Jae
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Abstract
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Purpose: To evaluate the diagnostic accuracy of a novel on-site virtual fractional flow reserve (vFFR) derived from coronary computed tomography angiography (CTA).
Materials and Methods: We analyzed 100 vessels from 57 patients who had undergone CTA followed by invasive FFR during coronary angiography. Coronary lumen segmentation and three-dimensional reconstruction were conducted using a completely automated algorithm, and parallel computing based vFFR prediction was performed. Lesion-specific ischemia based on FFR was defined as significant at ≤0.8, as well as ≤0.75, and obstructive CTA stenosis was defined that ≥50%. The diagnostic performance of vFFR was compared to invasive FFR at both ≤0.8 and ≤0.75.
Results: The average computation time was 12 minutes per patient. The correlation coefficient (r) between vFFR and invasive FFR was 0.75 [95% confidence interval (CI) 0.65 to 0.83], and Bland-Altman analysis showed a mean bias of 0.005 (95% CI ?0.011 to 0.021) with 95% limits of agreement of ?0.16 to 0.17 between vFFR and FFR. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 78.0%, 87.1%, 72.5%, 58.7%, and 92.6%, respectively, using the FFR cutoff of 0.80. They were 87.0%, 95.0%, 80.0%, 54.3%, and 98.5%, respectively, with the FFR cutoff of 0.75. The area under the receiver-operating characteristics curve of vFFR versus obstructive CTA stenosis was 0.88 versus 0.61 for the FFR cutoff of 0.80, respectively; it was 0.94 versus 0.62 for the FFR cutoff of 0.75.
Conclusion: Our novel, fully automated, on-site vFFR technology showed excellent diagnostic performance for the detection of lesion-specific ischemia.
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KEYWORD
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Fractional flow reserve, myocardial, computed tomography angiography, patient-specific computational modeling
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