Author + information
- Received February 15, 2013
- Revision received May 20, 2013
- Accepted May 24, 2013
- Published online January 1, 2014.
- Kyung-Hee Kim, MD∗,
- Joon-Hyung Doh, MD†,
- Bon-Kwon Koo, MD∗∗ (, )
- James K. Min, MD‡,
- Andrejs Erglis, MD§,
- Han-Mo Yang, MD∗,
- Kyung-Woo Park, MD∗,
- Hae-Young Lee, MD∗,
- Hyun-Jae Kang, MD∗,
- Yong-Jin Kim, MD∗,
- Sung Yun Lee, MD† and
- Hyo-Soo Kim, MD∗
- ∗Department of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- †Department of Medicine, Inje University Ilsan Paik Hospital, Goyang, Republic of Korea
- ‡Department of Medicine, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
- §Department of Medicine, Pauls Stradins Clinical University Hospital, Riga, Latvia
- ↵∗Reprint requests and correspondence:
Dr. Bon-Kwon Koo, Division of Cardiology, Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea.
Objectives This study sought to determine whether computational modeling can be used to predict the functional outcome of coronary stenting by virtual stenting of ischemia-causing stenoses identified on the pre-treatment model.
Background Computed tomography (CT)-derived fractional flow reserve (FFR) is a novel noninvasive technology that can provide computed (FFRct) using standard coronary CT angiography protocols.
Methods We prospectively enrolled 44 patients (48 lesions) who had coronary CT angiography before angiography and stenting, and invasively measured FFR before and after stenting. FFRct was computed in blinded fashion using coronary CT angiography and computational fluid dynamics before and after virtual coronary stenting. Virtual stenting was performed by modification of the computational model to restore the area of the target lesion according to the proximal and distal reference areas.
Results Before intervention, invasive FFR was 0.70 ± 0.14 and noninvasive FFRct was 0.70 ± 0.15. FFR after stenting and FFRct after virtual stenting were 0.90 ± 0.05 and 0.88 ± 0.05, respectively (R = 0.55, p < 0.001). The mean difference between FFRct and FFR was 0.006 for pre-intervention (95% limit of agreement: –0.27 to 0.28) and 0.024 for post-intervention (95% limit of agreement: –0.08 to 0.13). Diagnostic accuracy of FFRct to predict ischemia (FFR ≤0.8) prior to stenting was 77% (sensitivity: 85.3%, specificity: 57.1%, positive predictive value: 83%, and negative predictive value: 62%) and after stenting was 96% (sensitivity: 100%, specificity: 96% positive predictive value: 50%, and negative predictive value: 100%).
Conclusions Virtual coronary stenting of CT-derived computational models is feasible, and this novel noninvasive technology may be useful in predicting functional outcome after coronary stenting. (Virtual Coronary Intervention and Noninvasive Fractional Flow Reserve [FFR]; NCT01478100)
This study was supported by grants from the Innovative Research Institute for Cell Therapy (A062260); the Korea Healthcare Technology Research and Development Project, Ministry of Health and Welfare, Republic of Korea (A102065, A070001); and the Seoul National University Hospital Research Fund (03-2010-0270). The fractional flow reserve from coronary computed tomographic angiography analysis was performed by HeartFlow, Inc. Dr. Min has received research support from GE Healthcare and Philips Healthcare; has served on the medical advisory boards of GE Healthcare, Arineta, AstraZeneca, and Bristol-Myers Squibb; has served on the Speakers' Bureau of GE Healthcare; has received consulting fees from AstraZeneca, Bristol-Myers Squibb, and HeartFlow Inc.; and has equity interest in TC3 and MDDX. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Drs. K.-H. Kim and Doh contributed equally to this paper.
- Received February 15, 2013.
- Revision received May 20, 2013.
- Accepted May 24, 2013.
- American College of Cardiology Foundation