4.6 Article

A Novel Noninvasive Technology for Treatment Planning Using Virtual Coronary Stenting andComputed Tomography-Derived Computed Fractional Flow Reserve

Journal

JACC-CARDIOVASCULAR INTERVENTIONS
Volume 7, Issue 1, Pages 72-78

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.jcin.2013.05.024

Keywords

coronary computed tomographic angiography; computational fluid dynamics; fractional flow reserve

Funding

  1. Innovative Research Institute for Cell Therapy [A062260]
  2. Korea Healthcare Technology Research and Development Project, Ministry of Health and Welfare, Republic of Korea [A102065, A070001]
  3. Seoul National University Hospital Research Fund [03-2010-0270]
  4. GE Healthcare
  5. Philips Healthcare
  6. AstraZeneca
  7. Bristol-Myers Squibb
  8. HeartFlow Inc.

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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 (C) 2014 by the American College of Cardiology Foundation

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