Low-density Non-calcified Plaque Research Articles

Lipoprotein(a) and Long-Term Plaque Progression, Low-Density Plaque, and Pericoronary Inflammation

Lipoprotein(a) (Lp[a]) is a causal risk factor for cardiovascular disease; however, long-term effects on coronary atherosclerotic plaque phenotype, high-risk plaque formation, and pericoronary adipose tissue inflammation remain unknown. To investigate the association of Lp(a) levels with long-term coronary artery plaque progression, high-risk plaque, and pericoronary adipose tissue inflammation. This single-center prospective cohort study included 299 patients with suspected coronary artery disease (CAD) who underwent per-protocol repeated coronary computed tomography angiography (CCTA) imaging with an interscan interval of 10 years. Thirty-two patients were excluded because of coronary artery bypass grafting, resulting in a study population of 267 patients. Data for this study were collected from October 2008 to October 2022 and analyzed from March 2023 to March 2024. The median scan interval was 10.2 years. Lp(a) was measured at follow-up using an isoform-insensitive assay. CCTA scans were analyzed with a previously validated artificial intelligence-based algorithm (atherosclerosis imaging-quantitative computed tomography). The association between Lp(a) and change in percent plaque volumes was investigated in linear mixed-effects models adjusted for clinical risk factors. Secondary outcomes were presence of low-density plaque and presence of increased pericoronary adipose tissue attenuation at baseline and follow-up CCTA imaging. The 267 included patients had a mean age of 57.1 (SD, 7.3) years and 153 were male (57%). Patients with Lp(a) levels of 125 nmol/L or higher had twice as high percent atheroma volume (6.9% vs 3.0%; P = .01) compared with patients with Lp(a) levels less than 125 nmol/L. Adjusted for other risk factors, every doubling of Lp(a) resulted in an additional 0.32% (95% CI, 0.04-0.60) increment in percent atheroma volume during the 10 years of follow-up. Every doubling of Lp(a) resulted in an odds ratio of 1.23 (95% CI, 1.00-1.51) and 1.21 (95% CI, 1.01-1.45) for the presence of low-density plaque at baseline and follow-up, respectively. Patients with higher Lp(a) levels had increased pericoronary adipose tissue attenuation around both the right circumflex artery and left anterior descending at baseline and follow-up. In this long-term prospective serial CCTA imaging study, higher Lp(a) levels were associated with increased progression of coronary plaque burden and increased presence of low-density noncalcified plaque and pericoronary adipose tissue inflammation. These data suggest an impact of elevated Lp(a) levels on coronary atherogenesis of high-risk, inflammatory, rupture-prone plaques over the long term.

Computed Tomography Angiography Characteristics of Thin-Cap Fibroatheroma in Patients With Diabetes.

It was recently reported that thin-cap fibroatheroma (TCFA) detected by optical coherence tomography was an independent predictor of future cardiac events in patients with diabetes. However, the clinical usefulness of this finding is limited by the invasive nature of optical coherence tomography. Computed tomography angiography (CTA) characteristics of TCFA have not been systematically studied. The aim of this study was to investigate CTA characteristics of TCFA in patients with diabetes. Patients with diabetes who underwent preintervention CTA and optical coherence tomography were included. Qualitative and quantitative analyses were performed for plaques on CTA. TCFA was assessed by optical coherence tomography. Among 366 plaques in 145 patients with diabetes, 111 plaques had TCFA. The prevalence of positive remodeling (74.8% versus 50.6%, P<0.001), low attenuation plaque (63.1% versus 33.7%, P<0.001), napkin-ring sign (32.4% versus 11.0%, P<0.001), and spotty calcification (55.0% versus 34.9%, P<0.001) was significantly higher in TCFA than in non-TCFA. Low-density noncalcified plaque volume (25.4 versus 15.7 mm3, P<0.001) and remodeling index (1.30 versus 1.20, P=0.002) were higher in TCFA than in non-TCFA. The presence of napkin-ring sign, spotty calcification, high low-density noncalcified plaque volume, and high remodeling index were independent predictors of TCFA. When all 4 predictors were present, the probability of TCFA increased to 82.4%. The combined qualitative and quantitative plaque analysis of CTA may be helpful in identifying TCFA in patients with diabetes. URL: https://www.clinicaltrials.gov; Unique identifier: NCT04523194.

Evaluation of peri-plaque pericoronary adipose tissue attenuation in coronary atherosclerosis using a dual-layer spectral detector CT.

This study aimed to evaluate the differences between pericoronary adipose tissue (PCAT) attenuation at different measured locations in evaluating coronary atherosclerosis using spectral computed tomography (CT) and to explore valuable imaging indicators. A total of 330 patients with suspicious coronary atherosclerosis were enrolled and underwent coronary CT angiography with dual-layer spectral detector CT (SDCT). Proximal and peri-plaque fat attenuation index (FAI) of stenosis coronary arteries were measured using both conventional images (CIs) and virtual monoenergetic images (VMIs) ranging from 40 keV to 100 keV. The slopes of the spectral attenuation curve (λ) of proximal and peri-plaque PCAT at three different monoenergetic intervals were calculated. Additionally, peri-plaque FAI on CI and virtual non-contrast images, and effective atomic number were measured manually. A total of 231 coronary arteries with plaques and lumen stenosis were finally enrolled. Peri-plaque FAICI and FAIVMI were significantly higher in severe stenosis than in mild and moderate stenosis (p < 0.05), while peri-plaque λ, proximal FAI, and proximal λ were not statistically different. Proximal FAI, peri-plaque FAI, and peri-plaque λ were significantly higher in low-density non-calcified plaque (LD-NCP) and non-calcified plaque (NCP) than in calcified plaque (p < 0.01). Peri-plaque FAI was the highest in the LD-NCP group, while proximal FAI was the highest in the NCP group. In severe stenosis and in LD-NCP, peri-plaque FAI was significantly higher than proximal FAI (p < 0.05). The manually measured parameters related to peri-plaque PCAT attenuation had a positive correlation with the results of peri-plaque FAI measured automatically. Peri-plaque PCAT has more value in assessing coronary atherosclerosis than proximal PCAT. Peri-plaque PCAT attenuation is expected to be used as a standard biomarker for evaluating plaque vulnerability and hemodynamic characteristics.

Reproducibility of artificial intelligence-enabled plaque measurements between systolic and diastolic phases from coronary computed tomography angiography.

Current coronary CT angiography (CTA) guidelines suggest both end-systolic and mid-diastolic phases of the cardiac cycle can be used for CTA image acquisition. However, whether differences in the phase of the cardiac cycle influence coronary plaque measurements is not known. We aim to explore the potential impact of cardiac phases on quantitative plaque assessment. We enrolled 39 consecutive patients (23 male, age 66.2 ± 11.5 years) who underwent CTA with dual-source CT with visually evident coronary atherosclerosis and with good image quality. End-systolic and mid- to late-diastolic phase images were reconstructed from the same CTA scan. Quantitative plaque and stenosis were analyzed in both systolic and diastolic images using artificial intelligence (AI)-enabled plaque analysis software (Autoplaque). Overall, 186 lesions from 39 patients were analyzed. There were excellent agreement and correlation between systolic and diastolic images for all plaque volume measurements (Lin's concordance coefficient ranging from 0.97 to 0.99; R ranging from 0.96 to 0.98). There were no substantial intrascan differences per patient between systolic and diastolic phases (p > 0.05 for all) for total (1017.1 ± 712.9 mm3 vs. 1014.7 ± 696.2 mm3), non-calcified (861.5 ± 553.7 mm3 vs. 856.5 ± 528.7 mm3), calcified (155.7 ± 229.3 mm3 vs. 158.2 ± 232.4 mm3), and low-density non-calcified plaque volume (151.4 ± 106.1 mm3 vs. 151.5 ± 101.5 mm3) and diameter stenosis (42.5 ± 18.4% vs 41.3 ± 15.1%). Excellentagreement and no substantial differences were observed in AI-enabled quantitative plaque measurements on CTA in systolic and diastolic images. Following further validation, standardized plaque measurements can be performed from CTA in systolic or diastolic cardiac phase. Quantitative plaque assessment using artificial intelligence-enabled plaque analysis software can provide standardized plaque quantification, regardless of cardiac phase. • The impact of different cardiac phases on coronary plaque measurements is unknown. • Plaque analysis using artificial intelligence-enabled software on systolic and diastolic CT angiography images shows excellent agreement. • Quantitative coronary artery plaque assessment can be performed regardless of cardiac phase.

Abstract 18209: The Association Between Lp(a) and Low-Density Plaque Volume is Not Modified by CRP

BACKGROUND: Inconsistent data have suggested an interaction between Lp(a) and hs-CRP levels, putting patients with concomitant elevation at the greatest risk for atherosclerotic cardiovascular events. To date, the association between Lp(a), systemic inflammation as measured by hs-CRP, and coronary plaque phenotype has not been unraveled. METHODS: Among consecutive asymptomatic patients evaluated for primary prevention of coronary artery disease, we explored the associations between Lp(a), hs-CRP and coronary atherosclerosis using atherosclerosis imaging-quantitative coronary CT angiography (AI-QCT; Cleerly Labs, Cleerly Inc., Denver,CO). The associations between Lp(a), hs-CRP and the AI-QCT outcomes defined as percent atheroma volume (PAV) as well as percent low-density non-calcified plaque volume (LD-NCPV) were assessed using multivariable linear regression adjusted for clinical risk factors. RESULTS: The mean age of the 373 included patients was 56.2 ± 8.9 years, 71.6% were male, and 54.2% was on statin therapy. Mean±SD LDL-C was 106 ± 41 mg/dl, median (IQR) Lp(a) was 31 (11, 89) nmol/L, while median hs-CRP levels were 0.8 (0.4, 1.8) mg/L. In the multivariable-adjusted analysis, Lp(a) levels were significantly associated with an increase in PAV (0.367 increase in PAV per 100 nmol/l increase in Lp(a), p=0.026) and percent LD-NCPV (0.018 increase in PAV per 100 nmol/l increase in Lp(a), p=0.031). However, there was no effect of adding hs-CRP to the models (p=0.446), and we observed no direct relationship between hs-CRP and PAV (p=0.446) or percent LD-NCPV (0.871). Table 1. CONCLUSIONS: Lp(a) is associated with low-density non-calcified plaque and total coronary plaque burden. The current study found no impact of hs-CRP on these associations, nor were hs-CRP levels associated with plaque burden.

Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference

Background and aimsArtificial intelligence quantitative CT (AI-QCT) determines coronary plaque morphology with high efficiency and accuracy. Yet, its performance to quantify lipid-rich plaque remains unclear. This study investigated the performance of AI-QCT for the detection of low-density noncalcified plaque (LD-NCP) using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). MethodsThe INVICTUS Registry is a multi-center registry enrolling patients undergoing clinically indicated coronary CT angiography and IVUS, NIRS-IVUS, or optical coherence tomography. We assessed the performance of various Hounsfield unit (HU) and volume thresholds of LD-NCP using maxLCBI4mm ≥ 400 as the reference standard and the correlation of the vessel area, lumen area, plaque burden, and lesion length between AI-QCT and IVUS. ResultsThis study included 133 atherosclerotic plaques from 47 patients who underwent coronary CT angiography and NIRS-IVUS The area under the curve of LD-NCP<30HU was 0.97 (95% confidence interval [CI]: 0.93–1.00] with an optimal volume threshold of 2.30 mm3. Accuracy, sensitivity, and specificity were 94% (95% CI: 88–96%], 93% (95% CI: 76–98%), and 94% (95% CI: 88–98%), respectively, using <30 HU and 2.3 mm3, versus 42%, 100%, and 27% using <30 HU and >0 mm3 volume of LD-NCP (p < 0.001 for accuracy and specificity). AI-QCT strongly correlated with IVUS measurements; vessel area (r2 = 0.87), lumen area (r2 = 0.87), plaque burden (r2 = 0.78) and lesion length (r2 = 0.88), respectively. ConclusionsAI-QCT demonstrated excellent diagnostic performance in detecting significant LD-NCP using maxLCBI4mm ≥ 400 as the reference standard. Additionally, vessel area, lumen area, plaque burden, and lesion length derived from AI-QCT strongly correlated with respective IVUS measurements.

Impact of visceral fat on coronary artery disease as defined by quantitative computed tomography angiography.

Obesity is associated with all-cause mortality and cardiovascular disease (CVD). Visceral fat (VF) is an important CVD risk metric given its independent correlation with myocardial infarction and stroke. This study aims to clarify the relationship between the presence and severity of VF with the presence and severity of coronary artery plaque. In 145 consecutive asymptomatic patients, atherosclerosis imaging-quantitative computed tomography was performed for total plaque volume (TPV) and percentage atheroma volume, as well as the volume of noncalcified plaque (NCP), calcified plaque, and low-density NCP (LD-NCP), diameter stenosis, and vascular remodeling. This study also included VF analysis and subcutaneous fat analysis, recording of outer waist circumference, and percentage body fat analysis. The mean age of the patients was 56.1 [SD 8.5] years, and 84.0% were male. Measures of visceral adiposity (mean [SD, Q1-Q3 thresholds]) included estimated body fat, 28.7% (9.0%, 24.1%-33.0%); VF, 169.8 cm2 (92.3, 102.0-219.0 cm2 ); and subcutaneous fat, 223.6 mm2 (114.2, 142.5-288.0 mm2 ). The Spearman correlation coefficients of VF and plaque volume included TPV 0.22 (p = 0.0074), calcified plaque 0.12 (p = 0.62), NCP 0.25 (p = 0.0023), and LD-NCP 0.37 (p < 0.0001). There was a progression of the median coronary plaque volume for each quartile of VF including TPV (Q1: 19.8, Q2: 48.1, Q3: 86.4, and Q4: 136.6 mm3 [p = 0.0098]), NCP (Q1: 15.7, Q2: 35.4, Q3: 86.4, and Q4: 136.6 mm3 [p = 0.0032]), and LD-NCP (Q1: 0.6, Q2: 0.81, Q3: 2.0, and Q4: 5.0 mm3 [p < 0.0001]). These findings demonstrate progression with regard to VF and TPV, NCP volume, and LD-NCP volume. Notably, there was a progression of VF and amount of LD-NCP, which is known to be high risk for future cardiovascular events. A consistent progression may indicate the future utility of VF in CVD risk stratification.

Relation of Gender to Atherosclerotic Plaque Characteristics by Differing Angiographic Stenosis Severity

It is unknown whether gender influences the atherosclerotic plaque characteristics (APCs) of lesions of varying angiographic stenosis severity. This study evaluated the imaging data of 303 symptomatic patients from the derivation arm of the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial, all of whom underwent coronary computed tomographic angiography and clinically indicated nonemergent invasive coronary angiography upon study enrollment. Index tests were interpreted by 2 blinded core laboratories, one of which performed quantitative coronary computed tomographic angiography using an artificial intelligence application to characterize and quantify APCs, including percent atheroma volume (PAV), low-density noncalcified plaque (LD-NCP), noncalcified plaque (NCP), calcified plaque (CP), lesion length, positive arterial remodeling, and high-risk plaque (a combination of LD-NCP and positive remodeling ≥1.10); the other classified lesions as obstructive (≥50% diameter stenosis) or nonobstructive (<50% diameter stenosis) based on quantitative invasive coronary angiography. The relation between APCs and angiographic stenosis was further examined by gender. The mean age of the study cohort was 64.4±10.2years (29.0% female). In patients with obstructive disease, men had more LD-NCP PAV (0.5±0.4 vs 0.3±0.8, p=0.03) and women had more CP PAV (11.7±1.6 vs 8.0±0.8, p=0.04). Obstructive lesions had more NCP PAV compared with their nonobstructive lesions in both genders, however, obstructive lesions in women also demonstrated greater LD-NCP PAV (0.4±0.5 vs 1.0±1.8, p=0.03), and CP PAV (17.4±16.5 vs 25.9±18.7, p=0.03) than nonobstructive lesions. Comparing the composition of obstructive lesions by gender, women had more CP PAV (26.3±3.4 vs 15.8±1.5, p=0.005) whereas men had more NCP PAV (33.0±1.6 vs 26.7±2.5, p=0.04). Men had more LD-NCP PAV in nonobstructive lesions compared with women (1.2±0.2 vs 0.6±0.2, p=0.02). In conclusion, there are gender-specific differences in plaque composition based on stenosis severity.

Low-density Non-calcified Plaque Burden Derived From Artificial Intelligence-guided CCTA Quantification Is Associated With 1-year Cardiovascular Outcomes

Low-density Non-calcified Plaque Burden Derived From Artificial Intelligence-guided CCTA Quantification Is Associated With 1-year Cardiovascular Outcomes

Latent Coronary Plaque Morphology From Computed Tomography Angiography, Molecular Disease Activity on Positron Emission Tomography, and Clinical Outcomes.

Assessments of coronary disease activity with 18F-sodium fluoride positron emission tomography and radiomics-based precision coronary plaque phenotyping derived from coronary computed tomography angiography may enhance risk stratification in patients with coronary artery disease. We sought to investigate whether the prognostic information provided by these 2 approaches is complementary in the prediction of myocardial infarction. Patients with known coronary artery disease underwent coronary 18F-sodium fluoride positron emission tomography and coronary computed tomography angiography on a hybrid positron emission tomography/computed tomography scanner. Coronary 18F-NaF uptake was determined by the coronary microcalcification activity. We performed quantitative plaque analysis of coronary computed tomography angiography datasets and extracted 1103 radiomic features for each plaque. Using weighted correlation network analysis, we derived latent morphological features of coronary lesions which were aggregated to patient-level radiomics nomograms to predict myocardial infarction. Among 260 patients with established coronary artery disease (age, 65±9 years; 83% men), 179 (69%) participants showed increased coronary 18F-NaF activity (coronary microcalcification activity>0). Over 53 (40-59) months of follow-up, 18 patients had a myocardial infarction. Using weighted correlation network analysis, we derived 15 distinct eigen radiomic features representing latent morphological coronary plaque patterns in an unsupervised fashion. Following adjustments for calcified, noncalcified, and low-density noncalcified plaque volumes and 18F-NaF coronary microcalcification activity, 4 radiomic features remained independent predictors of myocardial infarction (hazard ratio, 1.46 [95% CI, 1.03-2.08]; P=0.03; hazard ratio, 1.62 [95% CI, 1.04-2.54]; P=0.02; hazard ratio, 1.49 [95% CI, 1.07-2.06]; P=0.01; and hazard ratio, 1.50 (95% CI, 1.05-2.13); P=0.02). In patients with established coronary artery disease, latent coronary plaque morphological features, quantitative plaque volumes, and disease activity on 18F-sodium fluoride positron emission tomography are additive predictors of myocardial infarction.

Identification of vulnerable non-culprit lesions by coronary computed tomography angiography in patients with chronic coronary syndrome and diabetes mellitus.

Among patients with diabetes mellitus (DM) and chronic coronary syndrome (CCS), non-culprit lesions (NCLs) are responsible for a substantial number of future major adverse cardiovascular events (MACEs). Thus, we aimed to establish the natural history relationship between adverse plaque characteristics (APCs) of NCLs non-invasively identified by coronary computed tomography angiography (CCTA) and subsequent MACEs in these patients. Between January 2016 and January 2019, 523 patients with DM and CCS were included in the present study after CCTA and successful percutaneous coronary intervention (PCI). All patients were followed up for MACEs (the composite of cardiac death, myocardial infarction, and unplanned coronary revascularization) until January 2022, and the independent clinical event committee classified MACEs as indeterminate, culprit lesion (CL), and NCL-related. The primary outcome was MACEs arising from untreated NCLs during the follow-up. The association between plaque characteristics detected by CCTA and primary outcomes was determined by Marginal Cox proportional hazard regression. Overall, 1,248 NCLs of the 523 patients were analyzed and followed up for a median of 47 months. The cumulative rates of indeterminate, CL, and NCL-related MACEs were 2.3%, 14.5%, and 20.5%, respectively. On multivariate analysis, NCLs associated with recurrent MACEs were more likely to be characterized by a plaque burden >70% [hazard ratio (HR), 4.35, 95% confidence interval (CI): 2.92-6.44], a low-density non-calcified plaque (LDNCP) volume >30 mm3 (HR: 3.40, 95% CI: 2.07-5.56), a minimal luminal area (MLA) <4 mm2 (HR: 2.30, 95% CI: 1.57-3.36), or a combination of three APCs (HR: 13.69, 95% CI: 9.34-20.12, p < 0.0001) than those not associated with recurrent MACEs. Sensitivity analysis regarding all indeterminate MACEs as NCL-related ones demonstrated similar results. In DM patients who presented with CCS and underwent PCI, half of the MACEs occurring during the follow-up were attributable to recurrence at the site of NCLs. NCLs responsible for unanticipated MACEs were frequently characterized by a large plaque burden and LDNCP volume, a small MLA, or a combination of these APCs, as determined by CCTA.

The value of quantitative plaque analysis based on coronary computed tomography angiography in predicting the percutaneous coronary intervention outcome of chronic total occlusion lesions.

Due to the uncertainty of the success of percutaneous coronary intervention (PCI) and the complexity of selecting suitable treatment cases, the interventional outcome of coronary chronic total occlusion (CTO) remains challenging. The purpose of this study was to evaluate the role of quantitative plaque analysis based on coronary computed tomography angiography (CCTA) in predicting the CTO-PCI outcome. We retrospectively included 78 patients with CTO (80 lesions) confirmed by invasive coronary angiography from July 2016 to December 2018. All patients underwent PCI treatment according to standard practice. A total of 47 lesions in 47 patients were successfully treated with PCI. PCI failed in the remaining 33 lesions in 31 patients. The following conventional CCTA morphologic parameters were evaluated and compared between the PCI-success and PCI-failure groups: stump morphology; occlusion length, tortuous course; CTO lesion calcium; bridging collateral vessel; retrograde collateral vessel; the appearance of the occluded distal segment; and quantitative CTO plaque characteristics, including total plaque volume, calcified plaque (CP) volume, noncalcified plaque (NCP) volume, low-density noncalcified plaque (LDNCP) volume, and plaque length. Univariate and multivariate logistic regression analyses were performed to determine independent parameters predictive of CTO-PCI outcomes. The predictive performances were assessed using receiver operating characteristic curve analysis. The blunt stump was the only independent CCTA morphologic parameter to predict the outcome of CTO-PCI [odds ratio (OR): 10.807; P<0.001]. NCP volume (OR: 1.018; P<0.001), CP volume (OR: 1.026; P=0.049), and plaque length (OR: 1.058; P=0.037) were independent quantitative CTO plaque characteristics predictive of CTO-PCI outcomes. The plaque-based model combining NCP volume with CP volume and plaque length had a higher area under the curve (AUC =0.96) than did the morphology-based model that included blunt stump (AUC 0.68) in predicting the outcomes of CTO-PCI (P<0.001). The CCTA-based plaque characteristics, including NCP volume, CP volume, and plaque length, outperformed morphologic parameters in predicting the CTO-PCI outcomes.

Diabetes, Atherosclerosis, and Stenosis by AI.

This study evaluates the relationship between atherosclerotic plaque characteristics (APCs) and angiographic stenosis severity in patients with and without diabetes. Whether APCs differ based on lesion severity and diabetes status is unknown. We retrospectively evaluated 303 subjects from the Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia (CREDENCE) trial referred for invasive coronary angiography with coronary computed tomographic angiography (CCTA) and classified lesions as obstructive (≥50% stenosed) or nonobstructive using blinded core laboratory analysis of quantitative coronary angiography. CCTA quantified APCs, including plaque volume (PV), calcified plaque (CP), noncalcified plaque (NCP), low-density NCP (LD-NCP), lesion length, positive remodeling (PR), high-risk plaque (HRP), and percentage of atheroma volume (PAV; PV normalized for vessel volume). The relationship between APCs, stenosis severity, and diabetes status was assessed. Among the 303 patients, 95 (31.4%) had diabetes. There were 117 lesions in the cohort with diabetes, 58.1% of which were obstructive. Patients with diabetes had greater plaque burden (P = 0.004). Patients with diabetes and nonobstructive disease had greater PV (P = 0.02), PAV (P = 0.02), NCP (P = 0.03), PAV NCP (P = 0.02), diseased vessels (P = 0.03), and maximum stenosis (P = 0.02) than patients without diabetes with nonobstructive disease. APCs were similar between patients with diabetes with nonobstructive disease and patients without diabetes with obstructive disease. Diabetes status did not affect HRP or PR. Patients with diabetes had similar APCs in obstructive and nonobstructive lesions. Patients with diabetes and nonobstructive stenosis had an association to similar APCs as patients without diabetes who had obstructive stenosis. Among patients with nonobstructive disease, patients with diabetes had more total PV and NCP.

Sex differences in computed tomography angiography-derived coronary plaque burden in relation to invasive fractional flow reserve

BackgroundDistinct sex-related differences exist in coronary artery plaque burden and distribution. We aimed to explore sex differences in quantitative plaque burden by coronary CT angiography (CCTA) in relation to ischemia by invasive fractional flow reserve (FFR). MethodsThis post-hoc analysis of the PACIFIC trial included 581 vessels in 203 patients (mean age 58.1 ​± ​8.7 years, 63.5% male) who underwent CCTA and per-vessel invasive FFR. Quantitative assessment of total, calcified, non-calcified, and low-density non-calcified plaque burden were performed using semiautomated software. Significant ischemia was defined as invasive FFR ≤0.8. ResultsThe per-vessel frequency of ischemia was higher in men than women (33.5% vs. 7.5%, p ​< ​0.001). Women had a smaller burden of all plaque subtypes (all p ​< ​0.01). There was no sex difference on total, calcified, or non-calcified plaque burdens in vessels with ischemia; only low-density non-calcified plaque burden was significantly lower in women (beta: -0.183, p ​= ​0.035). The burdens of all plaque subtypes were independently associated with ischemia in both men and women (For total plaque burden (5% increase): Men, OR: 1.15, 95%CI: 1.06–1.24, p ​= ​0.001; Women, OR: 1.96, 95%CI: 1.11–3.46, p ​= ​0.02). No significant interaction existed between sex and total plaque burden for predicting ischemia (interaction p ​= ​0.108). The addition of quantitative plaque burdens to stenosis severity and adverse plaque characteristics improved the discrimination of ischemia in both men and women. ConclusionsIn symptomatic patients with suspected CAD, women have a lower CCTA-derived burden of all plaque subtypes compared to men. Quantitative plaque burden provides independent and incremental predictive value for ischemia, irrespective of sex.

Serial analysis of coronary artery disease progression by artificial intelligence assisted coronary computed tomography angiography: early clinical experience

BackgroundStudies have shown that quantitative evaluation of coronary artery plaque on Coronary Computed Tomography Angiography (CCTA) can identify patients at risk of cardiac events. Recent demonstration of artificial intelligence (AI) assisted CCTA shows that it allows for evaluation of CAD and plaque characteristics. Based on publications to date, we are the first group to perform AI augmented CCTA serial analysis of changes in coronary plaque characteristics over 13 years. We evaluated whether AI assisted CCTA can accurately assess changes in coronary plaque progression, which has potential clinical prognostic value in CAD management.Case presentation51-year-old male with hypertension, hyperlipidemia and family history of myocardial infarction, underwent CCTA exams for anginal symptom evaluation and CAD assessment. 5 CCTAs were performed between 2008 and 2021. Quantitative atherosclerosis plaque characterization (APC) using an AI platform (Cleerly), was performed to assess CAD burden. Total plaque volume (TPV) change-over-time demonstrated decreasing low-density non-calcified plaque (LD-NCP) with increasing overall NCP and calcified-plaque (CP). Examination of individual segments revealed a proximal-LAD lesion with decreasing NCP over-time and increasing CP. In contrast, although the D2/D1/ramus lesions showed increasing stenosis, CP, and total plaque, there were no significant differences in NCP over-time, with stable NCP and increased CP. Remarkably, we also consistently visualized small plaques, which typically readers may interpret as false positives due to artifacts. But in this case, they reappeared each study in the same locations, generally progressing in size and demonstrating expected plaque transformation over-time.ConclusionsWe performed the first AI augmented CCTA based serial analysis of changes in coronary plaque characteristics over 13 years. We were able to consistently assess progression of plaque volumes, stenosis, and APCs with this novel methodology. We found a significant increase in TPV composed of decreasing LD-NCP, and increasing NCP and CP, with variations in the evolution of APCs between vessels. Although the significance of evolving APCs needs to be investigated, this case demonstrates AI-based CCTA analysis can serve as valuable clinical tool to accurately define unique CAD characteristics over time. Prospective trails are needed to assess whether quantification of APCs provides prognostic capabilities to improve clinical care.

Abstract 13674: Optimal Hounsfield Threshold for Lipid-Rich Plaque by Artificial Intelligence-Enabled Quantitative CT Using Near-Infrared Spectroscopy

Introduction: By near-infrared spectroscopy (NIRS), plaques with 4-mm maximum lipid-core burden index (maxLCBI4 mm ) ≥ 400 are lipid-rich plaque. By CCTA, the optimal Hounsfield threshold (HU) for lipid-rich plaques defined by presence of low-density non-calcified plaque (LD-NCP) is still unclear. Hypothesis: Artificial intelligence-enabled quantitative CT (AI-QCT) will enable identification of HU for lipid-rich plaques when compared to NIRS reference standard. Methods: 128 atherosclerotic plaques from 47 patients were enrolled prospectively. MaxLCBI 4mm (cutoff was defined as 400) and LD-NCP derived from AI-QCT (Cleerly LABS, Denver CO) were compared. LD-NCP using 3 different thresholds (&lt;30, &lt;50, and &lt;70 HU) were analyzed by sensitivity, specificity, positive and negative predictive value, accuracy, and area Under the Curve (AUC). Results: Amongst 128 atherosclerotic plaques evaluated, 26 (20.3%) met NIRS criteria for lipid rich plaque (maxLCBI 4mm ≥400). The volume of LD-NCP was measure using three different thresholds, &lt;30, &lt;50, and &lt;70 HU as well as criteria of PR &gt; 1.1 and LD-NCP volume &gt;1.8 mm 3 (LD-NCP volume threshold identified from AUC). This yielded a sensitivity, specificity, positive and negative predictive value, accuracy, and AUC of 92%, 88%, 67%, 98%, 89%, and 0.944 at &lt;30HU, 96%, 79%, 53%,99% 82%, 0.921 at &lt;50HU, and 96% 72%, 46%, 99%, 77%, and 0.907 respectively at &lt;70HU (30 vs. 50 HU: p=0.008; 30 vs. 70HU: p=0.002 (Figure 1). Conclusions: A &lt;30 HU threshold for definition of LD-NCP on AI-QCT correlates optimally with NIRS proven lipid-rich plaques.

Abstract 13665: Determination of Lipid-Rich Plaque by Artificial Intelligence-Enabled Quantitative Computed Tomography Using Near-Infrared Spectroscopy

Introduction: By near-infrared spectroscopy (NIRS), plaques with 4-mm maximum lipid-core burden index (maxLCBI4 mm ) ≥400 are lipid-rich plaques. By CCTA, high-risk plaques are defined by presence of low-density non-calcified plaque (LD-NCP) and positive remodeling (PR) &gt;1.1. Hypothesis: Artificial intelligence-enabled quantitative CT (AI-QCT) will enable identification of lipid-rich plaques when compared to NIRS reference standard. Methods: 128 atherosclerotic plaques from 47 patients were prospectively enrolled. MaxLCBI 4mm (cutoff was defined as 400) and LD-NCP derived from AI-QCT (Cleerly LABS, Denver CO) were compared. We also analyzed vessel area, plaque burden, minimal lumen area and plaque length between intravascular ultrasound (IVUS) and AI-QCT. Results: Amongst 128 plaques evaluated, maxLCBI 4mm ≥400 was 26 plaques (20.3%). Use of current LD-NCP (&lt;30 Hounsfield threshold (HU) and LD-NCP volume &gt;0 mm 3 ) and PR ≥1.1, yielded a sensitivity, specificity, positive and negative predictive value and accuracy of 100%, 28%, 26%, 99%, and 43%. LD-NCP volume threshold was identified from AUC as 1.8 mm 3 . With LD-NCP volume ≥1.8 mm 3 , the performance of AI-QCT was improved to 92%, 87%, 65%, 98% and 88%, respectively (Figure 1). We noted very good to excellent correlation between AI-QCT and IVUS for vessel area (R2 =0.776), plaque burden (0.699), minimal lumen area (0.815) and plaque length (0.844). Conclusions: The current CCTA definition of NIRS-verified lipid-rich plaques has limited specificity and accuracy; use of a LD-NCP volume threshold, enabled only by AI-QCT, substantially improves performance for detection and characterization.

Abstract 9401: Impact of Visceral Abdominal Tissue on Coronary Artery Disease as Defined by Quantitative Computed Tomographic Angiography

Introduction: Obesity has long been associated with all-cause mortality and cardiovascular disease (CVD). Visceral abdominal tissue (VAT) has been proposed as an important CVD risk stratification metric and has been shown to correlate with the extent and severity of coronary artery plaque. This study aims to clarify the relationship between the presence and severity of VAT with the presence and severity of coronary artery plaque as defined by total plaque volume (TPV), calcified plaque (CP) volume, non-calcified plaque (NCP) volume, and high risk low-density non-calcified plaque (LD-NCP) using quantitative coronary computed tomography atherosclerosis imaging (AI-QCT). Methods: We reviewed 145 consecutive patients who had undergone CCTA and CT visceral fat analysis for health screening purposes. Atherosclerosis imaging - quantitative computed tomography (AI-QCT) was performed for TPV, percent atheroma volume (PAV), and the presence of stenosis. We segmented atherosclerosis by composition for CP, NCP and LD-NCP. Results: The mean age of the patients was 56.1 ± 8.5 years, 84.0% male. Overall, 3.5% had a history of diabetes, 19% hypertension, 38% dyslipidemia, 8% current smokers, and 34% had a family history of CAD. There was a stepwise progression of the median coronary plaque volume for each quartile of visceral fat including total plaque volume (Q1: 19.8, Q2: 48.1, Q3: 86.4, and Q4: 136.6 mm 3 (P=0.0098)), NCP (Q1: 15.7, Q2: 35.4, Q3: 86.4, and Q4: 136.6 mm 3 (P=0.0032)) and LD-NCP (Q1: 0.6, Q2: 0.81, Q3: 2.0, and Q4: 5.0 mm 3 (P&lt;0.0001)). Conclusion: Our findings represent the first demonstration of a stepwise progression with regards to visceral abdominal tissue and total plaque volume, non-calcified plaque volume, and, particularly of note, low density non-calcified plaque volume. A stepwise progression as demonstrated here may indicate the future usefulness of VAT in CVD risk stratification and its role in longitudinal preventive care.

Abstract 12348: High-Risk Low-Density Non-Calcified Plaque Morphology

Hypothesis The morphology of low-density non-calcified coronary plaque is associated with acute coronary syndrome (ACS) and culprit lesion precursors Methods: This was a post-hoc analysis of the multicenter ICONIC study. A subset of 94 patients suspected of coronary artery disease (CAD) underwent coronary CT angiography imaging (CCTA) with subsequent follow-up for the occurrence of the first ACS event were selected. At the time of ACS, the culprit lesion was adjudicated by invasive coronary angiography cardiologists blinded to the CCTA. Quantitative CT was performed by a validated software as a service (Cleerly Labs, Cleerly, Inc., Denver, CO). A level-III reader used multiplanar reformation (MPR) images from this analysis to qualitatively assess individual collections of low-density non-calcified (LDNC) plaques (-189 to 30 HU). The degree of embedded LDNC plaque (DELP) was the amount a LDNC plaque was surrounded by non-calcified plaque. DELP was categorized as 90, 180, 270, and 360. LDNC plaque shape was categorized as crescent, round, lobular, or bean-shaped. DELP and shape were qualitatively assessed using the cross-sectional MPR image with the greatest LDNC plaque area. LDNC plaques with &gt;270 DELP and round or bean-shaped were considered high-risk plaque (HRP) morphology. Results: ACS occurred in 64 patients. 247 LDNC plaques were analyzed. Patients without and with ACS had, on average 0.40±0.86 and 1.02±1.21 (p-value = 0.014) LDNC plaques with HRP morphology. The proportional hazard ratio associating the presence of one or more HRP morphology plaques with ACS was 2.03 (1.19, 3.48; p-value = 0.009), after controlling for diameter stenosis, age, sex, and family history of CAD. The odds ratio of the association of HRP morphology with culprit lesion precursors was 10.93 (3.77, 31.71; p-value &lt;0.001), after controlling for diameter stenosis, age, sex, and family history of CAD. Conclusion HRP morphology may improve the stratification of patients at risk for ACS.

Artificial intelligence-enabled comprehensive coronary phenotyping in patients with suspected CAD

Abstract Introduction The capabilities of artificial intelligence (AI) are rapidly progressing and the research community is getting increasingly interested in its possibilities. AI algorithms are able to work continuously and at high speed, reducing human workload and saving time that physicians can spend on more complex data or rarer cases. However, many clinical AI applications are currently only used in a research setting and lack proper testing and validation. Objectives This study aimed to determine the accuracy and performance of a novel AI-based software tool for CCTA analysis compared to conventional expert evaluation. Methods We evaluated 100 CCTA exams from a cohort of symptomatic patients with mild-to-moderately abnormal non-invasive ischemia test. Stenosis severity assessed by AI-based analysis (automatic evaluation, AEv) was compared with a level III expert CCTA interpretation (manual evaluation, MEv). AI-based analysis reported exact % stenosis and obstructive CAD was considered if maximal stenosis was ≥50%. Plaque phenotype was also estimated using AI algorithms. Results The study cohort was as follows: 52% male, mean age 68±10 years. The prevalence of hypertension, dyslipidemia and diabetes was 77%, 81% and 23%, respectively, and 10-year cardiovascular risk was 19±10% as predicted by Framingham risk score. Typical angina was present in 33%, of which 67% had a Canadian Cardiovascular Society angina grade ≥2. Overall prevalence of obstructive CAD determined by MEv and AEv was 25% and 21%, respectively, with a significant association between both assessments (p&amp;lt;0.001). When compared to MEv as reference, AEv method performed with a sensitivity, specificity, positive and negative predictive values of 0.56, 0.91, 0.58 and 0.86, respectively. Area under the curve was 0.871 (p&amp;lt;0.001) demonstrating high accuracy. AEv atherosclerosis quantification revealed significant differences between patients with and without obstructive CAD according to MEv: median total plaque volume (569 vs 115 mm3, p&amp;lt;0.001), calcified plaque volume (297 vs 19 mm3, p&amp;lt;0.001), non-calcified plaque volume (235 vs 71 mm, p&amp;lt;0.001), low-density non-calcified plaque volume (2.8 vs 1.0 mm3, p=0.023) and percent atheroma volume (16.1 vs 3.8 mm3, p&amp;lt;0.001). Conclusion In patients with suspected CAD and mild-to-moderately abnormal ischemia tests, a diagnostic strategy using AEv as a gatekeeper is effective, providing a quantitative stenosis evaluation with similar diagnostic performance for obstructive CAD when compared to MEv. AI-enabled approach additionally allows a fully automated quantification of coronary plaque volumes and composition, which would further enhance risk stratification. Funding Acknowledgement Type of funding sources: None.