Quantification Of Plaque Burden Research Articles

A Video-based Automated Tracking and Analysis System of Plaque Burden in Carotid Artery Using Deep Learning: A Comparison with Senior Sonographers.

The incidence of stroke is rising, and it is the second major cause of mortality and the third leading cause of disability around the globe. The goal of this study was to rapidly and accurately identify carotid plaques and automatically quantify plaque burden using our automated tracking and segmentation US-video system. We collected 88 common carotid artery transection videos (11048 frames) with a history of atherosclerosis or risk factors for atherosclerosis, which were randomly divided into training, test, and validation sets using a 6:3:1 ratio. We first trained different segmentation models to segment the carotid intima and adventitia, and calculate the maximum plaque burden automatically. Finally, we statistically analyzed the plaque burden calculated automatically by the best model and the results of manual labeling by senior sonographers. Of the three Artificial Intelligence (AI) models, the Robust Video Matting (RVM) segmentation model's carotid intima and adventitia Dice Coefficients (DC) were the highest, reaching 0.93 and 0.95, respectively. Moreover, the RVM model has shown the strongest correlation coefficient (0.61±0.28) with senior sonographers, and the diagnostic effectiveness between the RVM model and experts was comparable with paired-t test and Bland-Altman analysis [P= 0.632 and ICC 0.01 (95% CI: -0.24~0.27), respectively]. Our findings have indicated that the RVM model can be used in ultrasound carotid video. The RVM model can automatically segment and quantify atherosclerotic plaque burden at the same diagnostic level as senior sonographers. The application of AI to carotid videos offers more precise and effective methods to evaluate carotid atherosclerosis in clinical practice.

Validation and Reproducibility of Total Plaque Thickness in Carotid and Femoral Arteries Using Ultrasound

Plaque burden quantification by ultrasound improves cardiovascular (CV) risk prediction. However, measuring total plaque volume (TPV) with 3-D ultrasound, the current gold standard, is time consuming. In the present study we investigated the reproducibility of weighted total plaque thickness (wTPT) measured by 2-D ultrasound and its correlation with TPV. Participants in an ongoing study of subclinical atherosclerosis and CV risk with no known atherosclerotic CV disease but who were found to have one or more plaques in carotid or femoral arteries by 2-D ultrasound were included. A total of 34 women and 26 men (mean age: 59.4 y, standard deviation: 8.7) underwent primary 2-D and 3-D ultrasound examinations. Participants then underwent a 2-D ultrasound examination by another radiologist blinded to the first radiologist's findings. Finally, all participants underwent a follow-up 2-D ultrasound by the first radiologist. Comparison of wTPT measurements between the 2-D studies revealed no significant difference (mean difference: 0.29 mm, 95% confidence interval [CI]: -0.48 to 1.17). Inter-observer and intra-observer analyses revealed intraclass correlation coefficients of 0.97 (95% CI: 0.96-0.98) and 1.0 (95% CI: 0.99-1.00), respectively. wTPT correlated with TPV (Spearman's ρ=0.98, 95% CI: 0.96-0.99). Elapsed time for assessing wTPT was less than that for TPV (mean difference: 36.1 min, 95% CI: 26.0-46.3). wTPT had high reproducibility and correlation with TPV while requiring substantially less time. Future studies addressing the role of wTPT in predicting CV disease are needed.

Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization.

Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (ΔDL-NIRS-IVUS: -37.8 ± 89.0 vs. ΔConv-NIRS-IVUS: 243.3 ± 183.7 mm3, variance ratio: 4.262, P < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, P < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, P < 0.001) and quantified minimum lumen area (ΔDL-NIRS-IVUS: -0.35 ± 1.81 vs. ΔConv-NIRS-IVUS: 1.37 ± 2.32 mm2, variance ratio: 1.634, P < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, P = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, P < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s. The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644).

Plaque burden estimated from optical coherence tomography with deep learning: In vivo validation using co-registered intravascular ultrasound.

The objective of the present study was to compare plaque burden (PB) calculated from optical coherence tomography (OCT) using deep learning (DL) with PB derived from co-registered intravascular ultrasound (IVUS). A DL algorithm was developed for automated plaque characterization and PB quantification from OCT images. However, the performance of this algorithm for PB quantification has not been validated. Five-year follow-up OCT and IVUS images from 15 patients implanted with bioresorbable vascular scaffold (BVS) at baseline were analyzed. Precise co-registration for 72 anatomical slices was achieved utilizing unique BVS radiopaque markers. PB derived from OCT DL and IVUS were compared. OCT cross-sections were divided into four subgroups with different media visibility level. The impact of media visibility on the numerical difference between OCT-derived and IVUS-derived PB was investigated. The stent sizes selected by OCT DL and IVUS were compared. Sixty-four paired OCT and IVUS cross-sections were compared. OCT DL showed good concordance with IVUS for PB assessment (ICC = 0.81, difference = -3.53 ± 6.17%, p < 0.001). The numerical difference between OCT DL-derived PB and IVUS-derived PB was not substantially impacted by missing segments of media visualization (p = 0.21). OCT DL showed a diagnostic accuracy of 92% in identifying PB > 65%. The stent sizes selected by OCT DL were smaller compared to the ones selected by IVUS (difference = 0.30 ± 0.34 mm, p < 0.001). The DL algorithm provides a feasible and reliable method for automated PB estimation from OCT, irrespective of media visibility. OCT DL showed good diagnostic accuracy in identifying PB > 65%, revealing its potential to complement conventional OCT imaging.

Computed Tomography Coronary Angiogram; updates Regarding Evidence, Clinical Applications, and Emerging Technologies: A Literature of Review

Assessing of the coronary artery disease (CAD) especially coronary stenosis by coronary computed tomography angiography (CCTA) has shown a classic shift in the last decades. Recent advancements in cardiac CT have improved the quality of image and decreased exposure of patients to radiation. CCTA is a noninvasive technique for visualizing the anatomy of CAD. CCTA, in combination with newer techniques like plaque characterization, physiologic and functional evaluation, leads to accurate diagnostic and prognostic evaluation of any low to intermediate subjects for prevention.&#x0D; Evidence strongly recommends the use of CCTA all over the stages of CAD, early from detecting of minute subclinical diseases up to the estimation of acute chest pain. Furthermore, CCTA can be used to noninvasively quantify plaque burden and identify high-risk plaque, which can help with diagnosis, prognosis, and treatment. This is especially important in the evaluation of CAD in immune-driven conditions with a higher prevalence of cardiovascular disease.&#x0D; Furthermore, key findings from large cohort trials have contributed to a better understanding of cardiovascular disease risk as a function of overall coronary plaque burden and the morphological appearance of individual plaques. With the introduction of CT-derived fractional flow reserve, an anatomical and functional test will be established within a single modality. Recent research has been published that looks at the short-term impact of CT-derived fractional flow reserve on downstream care and clinical outcomes. Furthermore, machine learning is a concept that is increasingly being applied to diagnostic medicine. Emerging CCTA applications based on hemodynamic indices and plaque characterization may provide personalized risk assessment, influence disease detection, and guide therapy further.&#x0D; This review provides an update on the evidence, clinical applications, and emerging technologies related to CCTA. We also discuss how CCTA could be used to characterize coronary atherosclerosis, stratify asymptomatic subjects' prognosis, and guide medical therapy.

Systematic phenotyping and characterization of the 5xFAD mouse model of Alzheimer\u2019s disease

Mouse models of human diseases are invaluable tools for studying pathogenic mechanisms and testing interventions and therapeutics. For disorders such as Alzheimer’s disease in which numerous models are being generated, a challenging first step is to identify the most appropriate model and age to effectively evaluate new therapeutic approaches. Here we conducted a detailed phenotypic characterization of the 5xFAD model on a congenic C57BL/6 J strain background, across its lifespan – including a seldomly analyzed 18-month old time point to provide temporally correlated phenotyping of this model and a template for characterization of new models of LOAD as they are generated. This comprehensive analysis included quantification of plaque burden, Aβ biochemical levels, and neuropathology, neurophysiological measurements and behavioral and cognitive assessments, and evaluation of microglia, astrocytes, and neurons. Analysis of transcriptional changes was conducted using bulk-tissue generated RNA-seq data from microdissected cortices and hippocampi as a function of aging, which can be explored at the MODEL-AD Explorer and AD Knowledge Portal. This deep-phenotyping pipeline identified novel aspects of age-related pathology in the 5xFAD model.

Usefulness of carotid and femoral plaque burden quantification by 3-dimensional vascular ultrasound for cardiovascular risk assessment in the cardiovascular disease prevention unit

Abstract Introduction Subclinical atherosclerosis improves cardiovascular risk assessment and is considered a risk modifier in individuals at borderline-to-intermediate estimated risk by clinical risk scores. Purpose We seek to propose a new decision algorithm for statin allocation based on the quantification of carotid and/or femoral atherosclerosis burden by novel 3-dimensional vascular ultrasound (3DVUS) and to explore its potential additive value when used in combination with conventional risk evaluation. Methods We conducted an observational study in all outpatients without previous history of cardiovascular events assessed in the Cardiovascular Disease Prevention Unit during 2017–2020. Cardiovascular risk was assessed with the ACC/AHA 10-year atherosclerotic cardiovascular disease (ASCVD) risk algorithm to establish the indication for initiating statin therapy following current clinical guidelines. All patients underwent 3DVUS evaluation for carotid plaque burden (CPB) calculated as the sum of all plaque volumes present in bilateral carotid arteries. Femoral 3DVUS evaluation was included later in the study protocol. Global plaque burden (GPB) was calculated in patients with both carotid and femoral 3DVUS as the sum of all plaque volumes present in bilateral carotid and femoral arteries. Carotid and global plaque burden was classified as high, moderate and low if a patient presented a percentile (p) &amp;gt;75, p50–75 and p&amp;lt;50, respectively, based on the age and sex-adjusted reference values reported by PESA study (1). We determined the percentage of patients reclassified for considering (p&amp;gt;75) or discouraging (p&amp;lt;50) statin therapy over clinical recommendation. Results One hundred sixty-three patients (age 51±8 years; 72,4% men; 10y-ASCVD 5,9%±4,8%) were included, being the most frequent reason for referral the presence of metabolic syndrome (37%), followed by intermediate or indeterminate estimated CV risk (33%), one markedly elevated single risk factor (15%) and family history of early CVD (13%). 10y-ASCVD score classified 80 (49%) patients as low-risk without recommendation for initiating statins, 18 (11%) as high-risk indicating statin therapy and 65 (40%) patients that needed clinical-patient risk discussion. Among patients under risk discussion, the CPB percentile re-stratified 51 (78%) of them, recommending statins in 26 (40%) and discouraging statins in 25 (38%). In addition, CPB re-stratified 24 (30%) low-risk patients to recommend statins. A sub-group of 114 patients additionally underwent femoral 3DVUS, and calculated GPB led to similar results of patient re-stratification (Figure). Conclusions Quantification of 3D atherosclerosis burden and its percentile would re-stratify a significant number of patients with intermediate risk, becoming a potentially useful tool for clinical decision making. In addition, it possibly improves the detection of low-risk patients who would benefit from statin therapy. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Dr. Lόpez-Melgar has received a grant from the Spanish Society of Cardiology “Proyecto de investigaciόn traslacional en Cardiología 2020” Patient re-stratification with CPB/GPB

Cardiovascular risk stratification by coronary computed tomography angiography imaging: current state-of-the-art.

Current cardiovascular risk stratification by use of clinical risk score systems or plasma biomarkers is good but less than satisfactory in identifying patients at residual risk for coronary events. Recent clinical evidence puts now further emphasis on the role of coronary anatomy assessment by coronary computed tomography angiography (CCTA) for the management of patients with stable ischaemic heart disease. Available computed tomography (CT) technology allows the quantification of plaque burden, identification of high-risk plaques, or the functional assessment of coronary lesions for ischaemia detection and revascularization for refractory angina symptoms. The current CT armamentum is also further enhanced by perivascular Fat Attenuation Index (FAI), a non-invasive metric of coronary inflammation, which allows for the first time the direct quantification of the residual vascular inflammatory burden. Machine learning and radiomic features' extraction and spectral CT for tissue characterization are also expected to maximize the diagnostic and prognostic yield of CCTA. The combination of anatomical, functional, and biological information on coronary circulation by CCTA offers a unique toolkit for the risk stratification of patients, and patient selection for targeted aggressive prevention strategies. We hereby provide a review of the current state-of-the art in the field and discuss how integrating the full capacities of CCTA into clinical care pathways opens new opportunities for the tailored management of coronary artery disease.

Artificial intelligence and optical coherence tomography for the automatic characterisation of human atherosclerotic plaques.

Intravascular optical coherence tomography (IVOCT) enables detailed plaque characterisation in vivo, but visual assessment is time-consuming and subjective. This study aimed to develop and validate an automatic framework for IVOCT plaque characterisation using artificial intelligence (AI). IVOCT pullbacks from five international centres were analysed in a core lab, annotating basic plaque components, inflammatory markers and other structures. A deep convolutional network with encoding-decoding architecture and pseudo-3D input was developed and trained using hybrid loss. The proposed network was integrated into commercial software to be externally validated on additional IVOCT pullbacks from three international core labs, taking the consensus among core labs as reference. Annotated images from 509 pullbacks (391 patients) were divided into 10,517 and 1,156 cross-sections for the training and testing data sets, respectively. The Dice coefficient of the model was 0.906 for fibrous plaque, 0.848 for calcium and 0.772 for lipid in the testing data set. Excellent agreement in plaque burden quantification was observed between the model and manual measurements (R2=0.98). In the external validation, the software correctly identified 518 out of 598 plaque regions from 300 IVOCT cross-sections, with a diagnostic accuracy of 97.6% (95% CI: 93.4-99.3%) in fibrous plaque, 90.5% (95% CI: 85.2-94.1%) in lipid and 88.5% (95% CI: 82.4-92.7%) in calcium. The median time required for analysis was 21.4 (18.6-25.0) seconds per pullback. A novel AI framework for automatic plaque characterisation in IVOCT was developed, providing excellent diagnostic accuracy in both internal and external validation. This model might reduce subjectivity in image interpretation and facilitate IVOCT quantification of plaque composition, with potential applications in research and IVOCT-guided PCI.

Beta-2-glycoprotein-I IgA antibodies predict coronary plaque progression in rheumatoid arthritis

ObjectivesTo evaluate whether anti-Beta-2-Glycoprotein-I (anti-β2GPI) IgA antibodies associate with progression of coronary atherosclerosis and cardiovascular disease (CVD) events in rheumatoid arthritis (RA). MethodsOne hundred-fifty patients underwent plaque evaluation (total, non-calcified, mixed and calcified) with coronary computed tomography angiography; 101 were re-imaged within 6.9±0.3 years to assess progression. The Framingham-D'Agostino score assessed cardiovascular risk. Coronary artery calcium (CAC) and segment involvement score quantified plaque burden. ResultsAnti-β2GPI IgA were seen in 45 (30%) patients. Despite no link to baseline plaque burden, anti-β2GPI IgA associated with segment involvement score increase (adjusted-RR=1.64 [95%CI 1.02–2.63]), CAC change (adjusted-β=0.33 [95%CI 0.002–0.656]) and developing new extensive or obstructive plaque at follow-up (adjusted-OR=4.24 [95%CI 1.30–13.87]). Adding anti-β2GPI IgA to logistic regression models with conventional risk factors predicting plaque progression outcomes increased Area under the receiver-operator curve and improved Net Reclassification and Integrated Discrimination Improvement indices (all P<0.05). In per-segment analyses, anti-β2GPI IgA predicted mixed plaque formation (adjusted-OR=3.20 [95%CI 1.01–10.09]) and lower likelihood of transition of mixed to calcified plaque (adjusted-OR=0.19 [95%CI 0.04–0.96]). Anti-β2GPI IgA moderated the effect of C-reactive protein on CAC change such that C-reactive protein associated with CAC change (β=0.26 [95%CI 0.14–0.38]) and CVD risk (adjusted-HR=1.89 [95%CI 1.02–3.51]) only in anti-β2GPI IgA positive patients. ConclusionAnti-β2GPI IgA addition to clinical risk models improved prediction accuracy of CAC, plaque progression and transition to extensive/obstructive disease. They associated with new high-risk mixed plaques and delayed healing to calcified lesions. Anti-β2GPI IgA further modified the effect of inflammation on plaque progression and CVD events.

Deep learning reveals 3D atherosclerotic plaque distribution and composition

Complications of atherosclerosis are the leading cause of morbidity and mortality worldwide. Various genetically modified mouse models are used to investigate disease trajectory with classical histology, currently the preferred methodology to elucidate plaque composition. Here, we show the strength of light-sheet fluorescence microscopy combined with deep learning image analysis for characterising and quantifying plaque burden and composition in whole aorta specimens. 3D imaging is a non-destructive method that requires minimal ex vivo handling and can be up-scaled to large sample sizes. Combined with deep learning, atherosclerotic plaque in mice can be identified without any ex vivo staining due to the autofluorescent nature of the tissue. The aorta and its branches can subsequently be segmented to determine how anatomical position affects plaque composition and progression. Here, we find the highest plaque accumulation in the aortic arch and brachiocephalic artery. Simultaneously, aortas can be stained for markers of interest (for example the pan immune cell marker CD45) and quantified. In ApoE−/− mice we observe that levels of CD45 reach a plateau after which increases in plaque volume no longer correlate to immune cell infiltration. All underlying code is made publicly available to ease adaption of the method.

Usefulness of preoperative coronary computed tomography angiography in high risk non-cardiovascular surgery old patients with unknown or suspected coronary artery disease

BackgroundCumulative evidence has shown that the non-invasive modality of coronary computed tomography angiography (CCTA) has evolved as an alternative to invasive coronary angiography, which can be used to quantify plaque burden and stenosis and identify vulnerable plaque, assisting in diagnosis, prognosis and treatment. With the increasing elderly population, many patients scheduled for non-cardiovascular surgery may have concomitant coronary artery disease (CAD). The aim of this study was to investigate the usefulness of preoperative CCTA to rule out or detect significant CAD in this cohort of patients and the impact of CCTA results to clinical decision-making.Methods841 older patients (age 69.5 ± 5.8 years, 74.6% males) with high risk non-cardiovascular surgery including 771 patients with unknown CAD and 70 patients with suspected CAD who underwent preoperative CCTA were retrospectively enrolled. Multivariate logistic regression analysis was performed to determine predictors of significant CAD and the event of cancelling scheduled surgery in patients with significant CAD.Results677 (80.5%) patients had non-significant CAD and 164 (19.5%) patients had significant CAD. Single-, 2-, and 3- vessel disease was found in 103 (12.2%), 45 (5.4%) and 16 (1.9%) patients, respectively. Multivariate analysis demonstrated that positive ECG analysis and Agatston score were independently associated with significant CAD, and the optimal cutoff of Agatston score was 195.9. The event of cancelling scheduled surgery was increased consistently according to the severity of stenosis and number of obstructive major coronary artery. Multivariate analysis showed that the degree of stenosis was the only independent predictor for cancelling scheduled surgery. In addition, medication using at perioperative period increased consistently according to the severity of stenosis.ConclusionsIn older patients referred for high risk non-cardiovascular surgery, preoperative CCTA was useful to rule out or detect significant CAD and subsequently influence patient disposal. However, it might be unnecessary for patients with negative ECG and low Agatston score.Trial registration Retrospectively registered.

Dual-probe molecular MRI for the in vivo characterization of atherosclerosis in a mouse model: Simultaneous assessment of plaque inflammation and extracellular-matrix remodeling

Molecular MRI is a promising in-vivo modality to detect and quantify morphological and molecular vessel-wall changes in atherosclerosis. The combination of different molecular biomarkers may improve the risk stratification of patients. This study aimed to investigate the feasibility of simultaneous visualization and quantification of plaque-burden and inflammatory activity by dual-probe molecular MRI in a mouse-model of progressive atherosclerosis and in response-to-therapy. Homozygous apolipoprotein E knockout mice (ApoE−/−) were fed a high-fat-diet (HFD) for up to four-months prior to MRI of the brachiocephalic-artery. To assess response-to-therapy, a statin was administered for the same duration. MR imaging was performed before and after administration of an elastin-specific gadolinium-based and a macrophage-specific iron-oxide-based probe. Following in-vivo MRI, samples were analyzed using histology, immunohistochemistry, inductively-coupled-mass-spectrometry and laser-inductively-coupled-mass-spectrometry. In atherosclerotic-plaques, intraplaque expression of elastic-fibers and inflammatory activity were not directly linked. While the elastin-specific probe demonstrated the highest accumulation in advanced atherosclerotic-plaques after four-months of HFD, the iron-oxide-based probe showed highest accumulation in early atherosclerotic-plaques after two-months of HFD. In-vivo measurements for the elastin and iron-oxide-probe were in good agreement with ex-vivo histopathology (Elastica-van-Giesson stain: y = 298.2 + 5.8, R2 = 0.83, p < 0.05; Perls‘ Prussian-blue-stain: y = 834.1 + 0.67, R2 = 0.88, p < 0.05). Contrast-to-noise-ratio (CNR) measurements of the elastin probe were in good agreement with ICP-MS (y = 0.11x-11.3, R² = 0.73, p < 0.05). Late stage atherosclerotic-plaques displayed the strongest increase in both CNR and gadolinium concentration (p < 0.05). The gadolinium probe did not affect the visualization of the iron-oxide-probe and vice versa. This study demonstrates the feasibility of simultaneous assessment of plaque-burden and inflammatory activity by dual-probe molecular MRI of progressive atherosclerosis. The in-vivo detection and quantification of different MR biomarkers in a single scan could be useful to improve characterization of atherosclerotic-lesions.

Serial Coronary Plaque Assessment Using Computed Tomography Coronary Angiography

Serial Coronary Plaque Assessment Using Computed Tomography Coronary Angiography

Highlights From the Circulation Family of Journals.

Highlights From the Circulation Family of Journals.

Quantification of Coronary Atherosclerosis in the Assessment of Coronary Artery Disease.

Diagnosis of coronary artery disease and management strategies have relied solely on the presence of diameter stenosis ≥50%. We assessed whether direct quantification of plaque burden (PB) and plaque characteristics assessed by coronary computed tomography angiography could provide additional value in terms of predicting rapid plaque progression. From a 13-center, 7-country prospective observational registry, 1345 patients (60.4±9.4 years old; 57.1% male) who underwent repeated coronary computed tomography angiography >2 years apart were enrolled. For conventional angiographic analysis, the presence of stenosis ≥50%, number of vessel involved, segment involvement score, and the presence of high-risk plaque feature were determined. For quantitative analyses, PB and annual change in PB (△PB/y) in the entire coronary tree were assessed. Clinical outcomes (cardiac death, nonfatal myocardial infarction, and coronary revascularization) were recorded. Rapid progressors, defined as a patient with ≥median value of △PB/y (0.33%/y), were older, more frequently male, and had more clinical risk factors than nonrapid progressors (all P<0.05). After risk adjustment, addition of baseline PB improved prediction of rapid progression to each angiographic assessment of coronary artery disease, and the presence of high-risk plaque further improved the predictive performance (all P<0.001). For prediction of adverse outcomes, adding both baseline PB and △PB/y showed best predictive performance (C statistics, 0.763; P<0.001). Direct quantification of atherosclerotic PB in addition to conventional angiographic assessment of coronary artery disease might be beneficial for improving risk stratification of coronary artery disease. URL: https://www.clinicaltrials.gov. Unique identifier: NCT02803411.

Abstract P049: Serum Uric Acid Levels Are Associated With Non-Calcified Coronary Plaque Independent of Traditional Cardiovascular Risk Factors in Patients With Psoriasis

Introduction: Serum uric acid (sUA), a known inflammosome-inducer, is associated with prospective risk of coronary artery disease in a dose-dependent fashion. Psoriasis (PSO), a chronic inflammatory disease associated with elevated burden of systemic inflammation and subclinical coronary artery disease, provides a reliable human model to study how sUA may relate to non-calcified coronary plaque burden (NCB) measured by computed coronary tomography angiography (CCTA). Hypothesis: We hypothesized that sUA would directly associate with NCB beyond traditional cardiovascular (CV) risk factors. Methods: 103 consecutive PSO patients and 47 healthy volunteers (HV) underwent CCTA (320 detector row, Toshiba) for coronary plaque burden quantification using QAngio (Medis). PSO severity was assessed by Psoriasis Area Severity Score (PASI) and divided into severe PSO (PASI&gt;10) and mild-moderate PSO (PASI&lt;10). All patients had fasting blood draws for the measurement of sUA at a certified clinical lab. Results: PSO patients were older than HV and had a higher CV risk by Framingham risk score (FRS) (Table 1). We observed a significant trend towards increase in sUA among severe PSO, mild-moderate PSO, and HV groups (mean 6.4, 5.9, 5.4 respectively, p=0.02 for trend). A positive association was observed between sUA and NCB, which was stronger in severe PSO after adjustment for traditional CV risk, alcohol, statins, and systemic/biologic PSO treatment (Severe PSO: β=0.27, p&lt;0.001; Mild-moderate PSO: β=0.18, p=0.03), not significant in HV (β=0.18, p=0.12). Conclusions: sUA is independently associated with NCB in states of chronic inflammation such as PSO, and as such, may potentially serve as a biomarker for subclinical coronary atherosclerosis. However, larger prospective studies of CV outcomes in chronic inflammatory diseases are needed to confirm these results.

Subclinical Atherosclerosis Burden by 3D Ultrasound in Mid-Life: The PESA Study

BackgroundDetection of subclinical atherosclerosis improves risk prediction beyond cardiovascular risk factors (CVRFs) and risk scores, but quantification of plaque burden may improve it further. Novel 3-dimensional vascular ultrasound (3DVUS) provides accurate volumetric quantification of plaque burden. ObjectivesThe authors evaluated associations between 3DVUS-based plaque burden and CVRFs and explored potential added value over simple plaque detection. MethodsThe authors included 3,860 (92.2%) PESA (Progression of Early Subclinical Atherosclerosis) study participants (age 45.8 ± 4.3 years; 63% men). Bilateral carotid and femoral territories were explored by 3DVUS to determine the number of plaques and territories affected, and to quantify global plaque burden defined as the sum of all plaque volumes. Linear regression and proportional odds models were used to evaluate associations of plaque burden with CVRFs and estimated 10-year cardiovascular risk. ResultsPlaque burden was higher in men (63.4 mm3 [interquartile range (IQR): 23.8 to 144.8 mm3] vs. 25.7 mm3 [IQR: 11.5 to 61.6 mm3] in women; p < 0.001), in the femoral territory (64 mm3 [IQR: 27.6 to 140.5 mm3] vs. 23.1 mm3 [IQR: 9.9 to 48.7 mm3] in the carotid territory; p < 0.001), and with increasing age (p < 0.001). Age, sex, smoking, and dyslipidemia were more strongly associated with femoral than with carotid disease burden, whereas hypertension and diabetes showed no territorial differences. Plaque burden was directly associated with estimated cardiovascular risk independently of the number of plaques or territories affected (p < 0.01). Conclusions3DVUS quantifies higher plaque burden in men, in the femoral territory, and with increasing age during midlife. Plaque burden correlates strongly with CVRFs, especially at the femoral level, and reflects estimated cardiovascular risk more closely than plaque detection alone. (Progression of Early Subclinical Atherosclerosis [PESA] Study; NCT01410318)

Current status of carotid ultrasound in atherosclerosis.

Cardiovascular disease (CVD) primarily caused by atherosclerosis is a major cause of death and disability in developed countries. Sonographic carotid intima-media thickness (CIMT) is widely studied as a surrogate marker for detecting subclinical atherosclerosis for risk prediction and disease progress to guide medical intervention. However, there is no standardized CIMT measurement methodology in clinical studies resulting in inconsistent findings, thereby undermining the clinical value of CIMT. Increasing evidences show that CIMT alone has weak predictive value for CVD while CIMT including plaque presence consistently improves the predictive power. Quantification of plaque burden further enhances the predictive power beyond plaque presence. Sonographic carotid plaque characteristics have been found to be predictive of cerebral ischaemic events. With advances in ultrasound technology, enhanced assessment of carotid plaques is feasible to detect high-risk/vulnerable plaques, and provide risk assessment for ischemic stroke beyond measurement of luminal stenosis.

Quantifying Plaque Burden and Morphology Using Coronary Computed Tomography Angiography to Predict Coronary Physiology

> Now this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning. > > —Winston Churchill Coronary computed tomography angiography (CTA) is a powerful noninvasive technique that can be used to visualize the presence, extent, and severity of both noncalcified and calcified plaque. When compared with other noninvasive imaging approaches, coronary CTA has the highest sensitivity to detect anatomic stenosis and, consequently, the highest negative predictive value to exclude obstructive coronary artery disease (CAD).1 However, once a coronary stenosis is identified, a limitation of CTA—which is equally problematic for invasive coronary angiography—is that the physiological consequences associated with those stenoses cannot be determined with any degree of accuracy.2,3 On the other hand, the presence and severity of ischemia are essential for understanding the pathophysiology of patient’s symptoms and determining the potential role of coronary revascularization. Consequently, multiple techniques have been introduced to harness physiological data from CTA and infer whether a stenosis is hemodynamically significant. Some investigators have applied computational fluid dynamic modeling to estimate coronary pressure gradients and calculation of the fractional flow reserve across stenosis.4 Others have relied on the use of iodinated contrast at rest and during pharmacological stress to assess myocardial perfusion (computed tomography [CT] perfusion)5 in a similar manner to radionuclide scintigraphy and magnetic resonance imaging–based myocardial perfusion techniques. Recent clinical trials have demonstrated that in carefully selected patients, these techniques can provide useful information to understand the physiological significance of stenoses.6–9 So, it is clear that the paradigm of isolated stenosis quantification with angiography (noninvasive or invasive) has provided insufficient and, sometimes, misleading information for diagnosis and management of CAD. However, there is limited data addressing the question of whether a more complete quantification of …