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Prevalence and Vascular Distribution of Multiterritorial Atherosclerosis Among Community-Dwelling Adults in Southeast China

Key Points Question What are the prevalence, vascular distribution, and burden of atherosclerotic plaque and stenosis across multiple vascular territories in community-dwelling populations in China? Findings In this cross-sectional study of 3067 adults in southeastern China, atherosclerotic plaque and stenosis were highly prevalent, with many individuals having atherosclerosis in multiple vascular territories. Meaning These findings suggest that atherosclerosis screening and intensification of primary cardiovascular prevention might be needed for older adults in China.


eMethods 1. MRI Parameters
Subjects were scanned on a 3.0T MRI scanner (Ingenia; Philips, Best, the Netherlands). The multi-contrast MRI protocol includes structural brain MRI, functional brain MRI and vascular MRI. Structural and functional brain MRI includes three dimensional T1-weighted magnetization-prepared rapid acquisition gradient-echo (T1w MPRAGE), two dimensional T2weighted (T2w), two-dimensional fluid-attenuated inversion recovery (FLAIR), diffusion weighted imaging (DWI), susceptibility-weighted imaging (SWI), three-dimensional time-offlight magnetic resonance angiography (TOF MRA), resting-state fMRI and diffusion tensor imaging (DTI). Vascular MRI for intracranial and carotidal arteries includes 3D isotropic highresolution black-blood T1w vessel wall imaging (VWI) and Simultaneous Non-contrast Angiography and intraPlaque haemorrhage (SNAP) imaging. The detailed scanner parameters are listed as following:  Intracranial artery plaque and stenosis were evaluated by both 3D-TOF MRA and 3D isotropic  high-resolution black-blood T1w vessel wall imaging (3D VISTA T1 weighted sequence, 0.6 mm × 0.6mm × 0.6 mm isotropic). We used Warfarin-Aspirin Symptomatic Intracranial Disease (WASID) method to evaluate the plaque and stenosis in intracranial artery. 2 The diameter of the artery was evaluated by vessel wall imaging. Percent stenosis = [(1-(diameter of stenosis / diameter of normal))] ×100%, where diameter of stenosis = the diameter of the artery at the site of the most severe degree of stenosis, and diameter of normal = the diameter of the proximal normal artery. If the proximal artery was diseased (e.g., middle cerebral artery origin stenosis), the diameter of the distal portion of the artery at its widest, parallel, non-tortuous normal segment was substituted (second choice).

Extracranial artery evaluation：
Extracranial artery plaque and stenosis were evaluated 3D isotropic high-resolution black-blood T1w vessel wall imaging (3D VISTA T1 weighted sequence, 0.8 mm × 0.8mm × 0.8 mm isotropic). We used the North American Symptomatic Carotid Endarterectomy Trial (NASCET) method to evaluate the plaque and stenosis in extracranial artery. 3 The diameter of the artery was evaluated by vessel wall imaging. Percent stenosis = [(1-(diameter of stenosis / diameter of normal))] ×100%, where diameter of stenosis = the diameter of the artery at the site of the most severe degree of stenosis, and diameter of normal = the diameter of the distal portion normal of the artery.

eMethods 3. Thoracoabdominal CTA Scan
Thoracoabdominal CTA for coronary, subclavian, aorta, renal and iliofemoral arteries was performed at baseline using one dual-source CT scanner (SOMATOM Force, Siemens, Germany) by trained investigators, based on a standardized protocol. In preparation for CTA imaging, renal function and potential contraindications were assessed to exclude participants for whom administration of contrast media could pose a risk. Each participant needed to rest before CTA examination to control the heart rate. Contrast medium iodoxanol (320 mg I/mL; Visipaque, GE Healthcare) was administered to perform CTA examination. A Siemens CARE Dose 4D automatic exposure control system was used to optimize the radiation dose in the examination process. The median total dose-length product (DLP) was 988 mGy-cm in this study.

eMethods 4. Evaluation of Plaque and Stenosis in Coronary, Subclavian, Aorta, Renal and Iliofemoral Arteries
CTA data were collected in DICOM format on discs and then reconstructed and analyzed by two raters (Z.ZQ and Z.ZX) at a cardiac image-viewing workstation in the Core Imaging Laboratory of Keya Medical Technology (Shenzhen, China). Operators and readers were blinded to the participants' information. Analysis of CTA image was conducted following 3 main steps: firstly, imaging processing software developed at Core Imaging Laboratory of Keya Medical Technology (Shenzhen, China) was used to automatically reconstruct 3D anatomical geometry of the input CTA imaging (aorta, coronary arteries, renal arteries, etc.). The software performs a set of imaging process algorithms that include both well-validated deep learning methods and traditional imaging processing technique. A multi-task deep learning network was used to extract the vessels from CTA images. This multi-task network simultaneously predicts the voxel-wise segmentation for the vessel and the distance maps of the segmentation. Meanwhile, the branch endpoints were also detected in order to generate the centerline. The segmentation mask was further refined based on the centerline generated from the distance map and the branch endpoints. 4,5 Once the segmentation and the centerline were extracted, the cross section of vessel perpendicular to the centerline normal direction at each centerline point was extracted and the cross-sectional area was calculated based on the lumen mask. Next, experienced imaging analyst at the core lab reviewed the reconstructed geometry with the CTA image, and conducted geometry modification if necessary to ensure accurate 3D anatomical geometry. Finally, the atherosclerotic plaque and stenosis was analyzed by the two readers with both visual inspection from 3D anatomical geometry and CTA imaging, along with quantitative results provided by the software (such as stenosis degree). After CTA image processing by the software and experienced imaging analyst, quantitative results for plaque and stenosis can also be calculated and characterized by the software. With these information, reader could determine the final result based on the 3D geometry, quantitative results, and CTA image. Usually, CTA image has a transversal resolution of 0.4 mm, which would be the minimal diameter that the software could possibly handle. In the analysis procedure, the readers assessed the coronary arteries down to 1.5 mm of diameter according to the Standards of appropriate utilization and diagnostic reporting on coronary CT angiography-coronary artery disease-reporting and data system in China.
The software uses a set of trained algorithms specifically developed for each vascular territory. For instance, there is a trained and well-validated model for coronary arteries, while another model would be selected to process aorta. The model selection is done automatically as a pre-step by the software.
Images with poor quality or technical failures were excluded from the analyzed sample. Deep learning-based algorithms emerged as promising approaches for solving image analysis problems and have been successfully implemented for metal artifact reduction in CT imaging. 6 The software implements a deep-learning based algorithm to correct calcium blooming artifact. The algorithm uses DSA imaging as ground-truth, and trained the model to correlate CTA calcium reconstruction with ground-truth lumen from DSA. This correction was found to improve the accuracy of CTA lumen geometry reconstruction to be closer to the ground-truth DSA.
The presence of atherosclerotic plaque was defined as structures of at least 1-mm 2 area within or adjacent to the artery lumen and clearly distinguishable from the vessel lumen. If no narrowing was identified, the plaque was considered to have no detectable stenosis. For each territory, areabased degree of narrowing was recorded for the most stenotic plaque according to the Society of Cardiovascular Computed Tomography criteria. 7 Percent stenosis = [(1-(MLA / NLA))] ×100%, where MLA = the lumen area of the artery at the site of the most severe degree of stenosis, and NLA = the lumen area of the proximal normal artery. If the proximal artery was diseased (e.g., renal artery origin stenosis), the lumen area of the distal portion of the artery at its widest, parallel, non-tortuous normal segment was substituted (second choice). Non-atherosclerotic stenosis, such as narrowing caused by myocardial bridging and physiological stenosis, were not included in this analysis. Each plaque was also characterized as calcified or noncalcified. The interobserver agreement for the detection of plaque and stenosis was evaluated by replicating the read of a random sample of 120 participants. Good reproducibility was found for the presence of plaque and artery stenosis in all territories (Cohen κ= 0.96 and 0.92 for coronary artery, Cohen κ= 0.96 and 0.64 for subclavian artery, Cohen κ= 1.00 and 1.00 for aortas, Cohen κ= 1.00 and 0.85 for renal artery, Cohen κ= 1.00 and 0.89 for iliofemoral artery).
For coronary arteries, participants with ≥50% stenosis were further classified as having 1-, 2or 3-vessel disease and whether they had left main or proximal left anterior descending disease. 8 The following anatomical assumptions were made to define the three main coronary vessels: Left anterior descending artery (LAD): left main, left descending artery, diagonal artery and septal branch. Left circumflex artery (CXA): left circumflex artery and obtuse margin. Right coronary artery (RCA): right coronary artery, posterior lateral branches and right posterior descending artery. One-vessel, two-vessel or three-vessel disease was then defined by a ≥50% stenosis in one, two or three of the above vessels.