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JACC Cardiovasc Interv. 2014 Jan;7(1):72-8. doi: 10.1016/j.jcin.2013.05.024. Epub 2013 Dec 11.

A novel noninvasive technology for treatment planning using virtual coronary stenting and computed tomography-derived computed fractional flow reserve.

Author information

1
Department of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
2
Department of Medicine, Inje University Ilsan Paik Hospital, Goyang, Republic of Korea.
3
Department of Medicine, Seoul National University Hospital, Seoul, Republic of Korea. Electronic address: bkkoo@snu.ac.kr.
4
Department of Medicine, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California.
5
Department of Medicine, Pauls Stradins Clinical University Hospital, Riga, Latvia.

Abstract

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).

KEYWORDS:

FFR; FFR(CT); LAD; TIMI; Thrombolysis In Myocardial Infarction; cCTA; computational fluid dynamics; computed fractional flow reserve from coronary computed tomographic angiography; coronary computed tomographic angiography; fractional flow reserve; left anterior descending

PMID:
24332418
DOI:
10.1016/j.jcin.2013.05.024
[Indexed for MEDLINE]
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