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Computed Tomography Fractional Flow Reserve Can Identify Culprit Lesions in Aortoiliac Occlusive Disease Using
Erin P Ward1, Daniele Shiavazzi2, Divya Sood1
1Department of Vascular Surgery, University of California, San Diego, San Diego, CA.
Insights
Computed tomography (CT) FFR accurately identifies aortoiliac lesions, offering a minimally invasive alternative to traditional angiography for diagnosing significant pressure drops in aortoiliac occlusive disease (AIOD). This novel approach shows high sensitivity and specificity.
Area of Science:
- Vascular Medicine
- Interventional Cardiology
- Medical Imaging
Background:
- Angiography is the standard for diagnosing aortoiliac lesions.
- Fractional flow reserve (FFR) is established in coronary artery disease.
- Advancements in computational fluid dynamics enable non-invasive FFR estimation.
Purpose of the Study:
- To adapt computational fluid dynamics for FFR estimation in aortoiliac occlusive disease (AIOD).
- To validate CT-derived FFR against conventional angiography measurements.
Main Methods:
- Retrospective analysis of 7 patients with AIOD and claudication.
- Conventional angiography with pullback pressure measurements.
- CT angiography (CTA) data used to create computational fluid dynamics models via SimVascular software.
- Windkessel outlet boundary conditions optimized to match physiological pressures.
Main Results:
- CT FFR successfully identified significant aortoiliac lesions.
- High sensitivity and specificity (AUC=1) for CT FFR compared to measured FFR.
- Average difference between measured and CT FFR was 0.136.
Conclusions:
- CT FFR accurately detects flow-limiting stenosis in AIOD.
- CT FFR presents a potential minimally invasive diagnostic tool for AIOD.
- This method may reduce the need for invasive angiography.
Background:
Currently, the gold standard diagnostic examination for significant aortoiliac lesions is angiography. Fractional flow reserve (FFR) has a growing body of literature in coronary artery disease as a minimally invasive diagnostic procedure. Improvements in numerical hemodynamics have allowed for an accurate and minimally invasive approach to estimating FFR, utilizing cross-sectional imaging. We aim to demonstrate a similar approach to aortoiliac occlusive disease (AIOD).
Methods:
A retrospective review evaluated 7 patients with claudication and cross-sectional imaging showing AIOD. FFR was subsequently measured during conventional angiogram with pull-back pressures in a retrograde fashion. To estimate computed tomography (CT) FFR, CT angiography (CTA) image data were analyzed using the SimVascular software suite to create a computational fluid dynamics model of the aortoiliac system. Inlet flow conditions were derived based on cardiac output, while 3-element Windkessel outlet boundary conditions were optimized to match the expected systolic and diastolic pressures, with outlet resistance distributed based on Murray's law. The data were evaluated with a Student's t-test and receiver operating characteristic curve.
Results:
All patients had evidence of AIOD on CT and FFR was successfully measured during angiography. The modeled data were found to have high sensitivity and specificity between the measured and CT FFR (P = 0.986, area under the curve = 1). The average difference between the measured and calculated FFRs was 0.136, with a range from 0.03 to 0.30.
Conclusions:
CT FFR successfully identified aortoiliac lesions with significant pressure drops that were identified with angiographically measured FFR. CT FFR has the potential to provide a minimally invasive approach to identify flow-limiting stenosis for AIOD.
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