Coronary Artery Disease Clinical Trial
Official title:
Evaluation of the Efficacy of Computed Tomographic Coronary Angiography in Assessing Coronary Artery Morphology and Physiology
Computed tomographic coronary angiography (CTCA) has been recently introduced to non-invasively evaluate coronary artery pathology. Histology and intravascular ultrasound imaging studies have demonstrated that CTCA enables identification of plaque characteristics associated with increased vulnerability (i.e., plaque burden and composition) and allows assessment of vessel physiology (i.e., local haemodynamic forces), and reports have shown that CTCA can predict atherosclerotic evolution and detect lesions that will progress and cause cardiovascular events. Despite the wealth of data provided, CTCA has still a limited role in the study of atherosclerosis. Prior to unlocking the full potential of CTCA and enable its broad use, further work is needed to develop user-friendly processing tools that will allow fast and accurate analysis of CTCA, and examine in detail the accuracy of modern CTCA imaging in assessing plaque pathology. In this application, the investigators aim 1) to develop a CTCA analysis system that will enable fast segmentation, reliable coronary reconstruction and blood flow simulation in a user-friendly environment and 2) validate the efficacy of state-of-the-art CTCA for assessment of coronary plaque morphology and physiology against intravascular plaque imaging using hybrid near infrared spectroscopy-intravascular ultrasound.
STUDY DESIGN
1. Patent recruitment Seventy patients with typical angina symptoms who had elective
coronary angiography showing at least one complex (i.e., bifurcation lesion, long
lesion, calcified lesion) obstructive lesion (>70% diameter stenosis on coronary
angiography, or a fractional flow reserve <0.80) that is considered suitable for
percutaneous coronary intervention (PCI) under IVUS guidance will be included in the
study. Exclusion criteria are: 1) age >75 years, 2) ACS within <3 months, 2) eGFR
<60ml/min/1.73m², 3) previous coronary artery bypass surgery, 3) decompensated heart
failure, or left ventricular ejection fraction ≤30%, 4) intravenous contrast allergy or
inability to receive treatment with aspirin, heparin, or thienopyridines, 5) anticipated
life expectancy <1 year, 6) history of heart transplantation, 7) patient that requires
surgical revascularization, and 8) extensive coronary artery disease (i.e., multiple
chronic total occlusions) or tortuous coronary anatomy that does not allow assessment of
the coronary arteries with NIRS-IVUS imaging. The recruited patients will provide
informed consent and undergo CTCA imaging using a dedicated 3rd generation CT
dual-source scanner (Siemens Force). The first 4 months of the study effort will be made
to optimise image acquisition protocols so as the obtained CTCA data to be suitable for
automated segmentation. Efficient imaging matrixes will be used to improve in-plane
spatial resolution and sharper reconstruction kernels and iterative reconstruction
algorithms will be implemented to enhance image segmentation.
Two weeks after CTCA imaging the patients will undergo planned PCI. During PCI effort
will be made to study all the 3 epicardial coronary arteries - including the stenotic
lesion - and some of their major side branches (i.e., large diagonals, obtuse marginals,
the posterior descending artery or the left ventricular branch of the right coronary
artery) with the combined NIRS-IVUS catheter. Following PCI the participants will be
discharged on optimal medical treatment.
2. Segmentation of the CTCA imaging data and reconstruction of coronary artery anatomy
Imaging data will be anonymised and analysed blinded to clinical details by an expert
operator using dedicated workstation. Anatomical landmarks (i.e., side branches) will be
identified in the CTCA and NIRS-IVUS imaging data and will be used to define segments of
interest.
Analysis of the CTCA data will be performed using dedicated software that enables
automated extraction of the luminal centreline, semi-automated detection of the lumen
and outer vessel wall borders, and quantification of the plaque burden and incorporates
a plaque characterisation algorithm that allows automated characterisation of the
composition of the plaque. The plaque characterisation algorithm takes into account
predefined fixed intensity cut-off values of the Hounsfield units and an adaptive
approach that allows modification of these cut-off values according to image
attenuation. Currently the segmentation process takes on average 3h per patient. In this
project the investigators aim to optimise CTCA image acquisition and segmentation
algorithms so as this process to become automated and reduce the time for CTCA
segmentation to <1 hour.
3. Segmentation of the NIRS-IVUS imaging data and reconstruction of coronary artery anatomy
The NIRS-IVUS data portraying the segments of interest will be analysed by an expert
operator, blinded to the clinical details and CTCA imaging data, with a 3-month interval
from the analysis of the CTCA data using a software that enables detection of the lumen
and outer vessel wall borders, quantification of plaque burden and annotation of the
calcific tissue component in IVUS. The output of the analysis of the NIRS imaging data
is the chemogram which is a colour coded map of the distribution of the lipid component
along the vessel wall (yellow indicates increased probability and red low probability of
lipid tissue). A metric of the lipid burden is the lipid core burden index (LCBI) which
is computed as the fraction of the yellow pixels that correspond to lipid component
divided by 1000. In addition, for each 2mm segments the block chemogram is generated
that provides a summary of the chemogram for this segment and displays the probability
of the presence of lipid tissue in a 2mm block of the coronary artery. The block
chemogram has been validated against histology and it has been shown that it enables
accurate detection of lipid-rich plaques.
The segmented NIRS-IVUS data will be used to reconstruct the coronary anatomy using an
established and well-validated methodology. Side branches with a diameter >1.5mm will be
reconstructed from the angiographic data and fused with the main vessel geometry
reconstructed from the NIRS-IVUS, since it has been shown that side branches affect ESS
distribution.
4. Blood flow simulation Identical boundary conditions will be applied to both IVUS-based
and CTCA-based models. Blood will be considered to be a laminar and incompressible
Newtonian fluid with a dynamic viscosity of 0.0035 Pa•s and a density of 1,050 kg/m3. A
steady flow profile will be imposed at the inflow of the lumen as this reduces
computation time and there is evidence that there is no significant difference in the
estimated ESS when a steady or a pulsatile flow profile is used. Murray's theory of
constant ESS will be used to derive boundary conditions in the main and side branches.
The arterial wall will be considered to be rigid and no-slip conditions will be applied
at the luminal surface. Flow velocity will be estimated from the angiographic data by
measuring the number of frames required for the contrast agent to pass from the inlet to
the outlet of the reconstructed segment, the volume of the segment at baseline, and the
cine frame rate.
5. Analysis of the NIRS-IVUS and CTCA imaging data It is anticipated that NIRS-IVUS imaging
will be performed on average in 2.5 vessels per patient; from these 40 randomly selected
vessels will be used to train the algorithms for CTCA segmentation and plaque
characterisation (training dataset) and the remaining for validation purposes
(validation dataset).
In the training set, the segments of interest reconstructed from the CTCA and NIRS-IVUS data
will be divided in 2mm segments and corresponding 2mm segments will be identified in the CTCA
and NIRS-IVUS models. For each 2mm segment the following metrics will be estimated in the
NIRS-IVUS models: mean lumen area, mean outer vessel wall area, mean plaque area, mean plaque
burden (defined as: 100 x plaque area/vessel area), mean calcific area, the LCBI and the
predominant ESS. In addition each segment will be classified as lipid-rich or non-lipid rich
according to the block chemogram.
Similarly, in the CTCA models the mean lumen area, outer vessel wall area, plaque area,
plaque burden, calcific area and the mean predominant ESS will be estimated for every 2mm
segment and compared with the estimations of NIRS-IVUS. Several approaches will be tested to
optimise the segmentation of the vessel wall borders and the best will be adopted. Segments
with increased calcific burden and blooming artifacts will be identified and in case of
significant differences between CTCA and NIRS-IVUS annotations, machine learning techniques,
that take advantage of the information provided by NIRS-IVUS, will be implemented to optimise
CTCA segmentation. The adaptive Hounsfield unit cut-offs that best identify lipid and
calcific tissue will be defined. Spread-out vessel plots portraying the distribution of the
lipid tissue in the CTCA models will be created and in these the LCBICT will be estimated for
each 2mm segment and compared with the output of NIRS. Area under the curve (AUC) analysis
will be used to identify the best CT-derived plaque burden, LCBI and ESS cut-off values that
correspond to the NIRS-IVUS cutoff values that indicate high-risk plaques (plaque burden:
67%, LCBI: 178 and ESS: 1Pa). The block chemogram in NIRS-IVUS will be used to identify the
2mm LCBICT cut-off that enables accurate classification of the 2mm segments in as lipid or
non-lipid rich. The accuracy of these cut-offs will be tested in the validation dataset.
In addition, in the validation dataset the NIRS-IVUS data will be used to identify coronary
lesions - defined as segments with a plaque burden >40% in 3 consecutive frames. For each
lesion its remodelling index will be estimated and used to classify them as lesions with a
positive or negative remodelling. The NIRS-IVUS data will be used to characterise their
phenotype and classify them as: pathological intimal thickening/fibrotic plaques,
fibro-calcific plaques, fibroatheromas (FA), and calcified fibroatheromas. The NIRS-IVUS
lesion classification will be used as reference standard in order to assess the accuracy of
CTCA in characterising lesion phenotype.
STATISTICAL ANALYSIS - POWER CALCULATION The primary endpoint of the study is the ability of
CTCA in detecting FA. In a study of Garcia-Garcia that included 129 patients undergoing singe
vessel IVUS imaging, 1.7 lesions were identified per patient. In the study of Puri et al.,
45% of the lesions were FA on histology. In that study NIRS combined with IVUS enabled
detection of FA with an excellent accuracy (c-index: 0.80). We anticipate that we will be
able to perform NIRS-IVUS imaging in 2.5 coronary arteries per patient and that CTCA imaging
quality will be optimal in 93% of the studied patients. If we recruit 70 patients we
anticipate to successfully study with NIRS-IVUS and CTCA 162 vessels of which 120 (203
lesions - 92 FA) will be used as a validation dataset. This dataset is anticipated to give an
80% power to demonstrate using the 5% significance level, that the sensitivity of CTCA in
identifying FA is not different from NIRS-IVUS (AUC of CTCA range: 0.89-0.71), assuming a
true sensitivity of 0.80 for NIRS-IVUS.
Secondary endpoints of the study are the accuracy of CTCA to identify: a) lipid-rich segments
(using the block chemogram of NIRS-IVUS as gold standard), and b) segments exposed to low ESS
(<1Pa, using the ESS estimated in the NIRS-IVUS models as reference standard).
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