Assessment of atherosclerotic plaque burden: comparison of AI-QCT versus SIS, CAC, visual and CAD-RADS stenosis categories.

This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as % stenosis, plaque volume, and plaque composition. AI-QCT plaque volume was then staged by recently validated prognostic thresholds, and compared with CAD-RADS 2.0 clinical methods of plaque evaluation (segment involvement score (SIS), coronary artery calcium score (CACS), visual assessment, and CAD-RADS percent (%) stenosis) by expert consensus blinded to the AI-QCT core lab reads. Average age of subjects were 59 ± 11 years; 44% women, with 50% of patients at CAD-RADS 1-2 and 21% at CAD-RADS 3 and above by expert consensus. AI-QCT quantitative plaque burden staging had excellent agreement of 93% (k = 0.87 95% CI: 0.79-0.96) with SIS. There was moderate agreement between AI-QCT quantitative plaque volume and categories of visual assessment (64.4%; k = 0.488 [0.38-0.60]), and CACS (66.3%; k = 0.488 [0.36-0.61]). Agreement between AI-QCT plaque volume stage and CAD-RADS % stenosis category was also moderate. There was discordance at small plaque volumes. With ongoing validation, these results demonstrate a potential for AI-QCT as a rapid, reproducible approach to quantify total plaque burden.

Prognostic value of a novel artificial intelligence-based coronary CTA-derived ischemia algorithm among patients with normal or abnormal myocardial perfusion.

BACKGROUND: Among patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CTA), downstream positron emission tomography (PET) perfusion imaging can be performed to assess the presence of myocardial ischemia. A novel artificial-intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCTischemia) aims to predict ischemia directly from coronary CTA images. We aimed to study the prognostic value of AI-QCTischemia among patients with obstructive CAD on coronary CTA and normal or abnormal downstream PET perfusion. METHODS: AI-QCTischemia was calculated by blinded analysts among patients from the retrospective coronary CTA cohort at Turku University Hospital, Finland, with obstructive CAD on initial visual reading (diameter stenosis ≥50%) being referred for downstream 15O-H2O-PET adenosine stress perfusion imaging. All coronary arteries with their side branches were assessed by AI-QCTischemia. Absolute stress myocardial blood flow ≤2.3 â€‹ml/g/min in ≥2 adjacent segments was considered abnormal. The primary endpoint was death, myocardial infarction, or unstable angina pectoris. The median follow-up was 6.2 [IQR 4.4-8.3] years. RESULTS: 662 of 768 (86%) patients had conclusive AI-QCTischemia result. In patients with normal 15O-H2O-PET perfusion, an abnormal AI-QCTischemia result (n â€‹= â€‹147/331) vs. normal AI-QCTischemia result (n â€‹= â€‹184/331) was associated with a significantly higher crude and adjusted rates of the primary endpoint (adjusted HR 2.47, 95% CI 1.17-5.21, p â€‹= â€‹0.018). This did not pertain to patients with abnormal 15O-H2O-PET perfusion (abnormal AI-QCTischemia result (n â€‹= â€‹269/331) vs. normal AI-QCTischemia result (n â€‹= â€‹62/331); adjusted HR 1.09, 95% CI 0.58-2.02, p â€‹= â€‹0.794) (p-interaction â€‹= â€‹0.039). CONCLUSION: Among patients with obstructive CAD on coronary CTA referred for downstream 15O-H2O-PET perfusion imaging, AI-QCTischemia showed incremental prognostic value among patients with preserved perfusion by 15O-H2O-PET imaging, but not among those with reduced perfusion.

Atherosclerosis evaluation and cardiovascular risk estimation using coronary computed tomography angiography.

Clinical risk scores based on traditional risk factors of atherosclerosis correlate imprecisely to an individual's complex pathophysiological predisposition to atherosclerosis and provide limited accuracy for predicting major adverse cardiovascular events (MACE). Over the past two decades, computed tomography scanners and techniques for coronary computed tomography angiography (CCTA) analysis have substantially improved, enabling more precise atherosclerotic plaque quantification and characterization. The accuracy of CCTA for quantifying stenosis and atherosclerosis has been validated in numerous multicentre studies and has shown consistent incremental prognostic value for MACE over the clinical risk spectrum in different populations. Serial CCTA studies have advanced our understanding of vascular biology and atherosclerotic disease progression. The direct disease visualization of CCTA has the potential to be used synergistically with indirect markers of risk to significantly improve prevention of MACE, pending large-scale randomized evaluation.

Development and Validation of a Quantitative Coronary CT Angiography Model for Diagnosis of Vessel-Specific Coronary Ischemia.

BACKGROUND: Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs. OBJECTIVES: This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCTISCHEMIA) and to evaluate its prognostic utility for major adverse cardiovascular events (MACE). METHODS: A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFRCT), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCTISCHEMIA model was developed in the derivation cohort of the CREDENCE study, and its diagnostic performance for coronary ischemia (FFR ≤0.80) was evaluated in the CREDENCE validation cohort and PACIFIC-1. Its prognostic value was investigated in PACIFIC-1. RESULTS: In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level was 0.80 (95% CI: 0.75-0.85) for AI-QCTISCHEMIA, 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFRCT, and 0.65 (95% CI: 0.59-0.71; P < 0.001) for MPI. In PACIFIC-1 (n = 208, age 58.1 ± 8.7 years, 132 [63%] male), the AUCs were 0.85 (95% CI: 0.79-0.91) for AI-QCTISCHEMIA, 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFRCT, 0.89 (95% CI: 0.84-0.93; P = 0.262) for PET, and 0.72 (95% CI: 0.67-0.78; P < 0.001) for SPECT. Adjusted for clinical risk factors and coronary CTA-determined obstructive stenosis, a positive AI-QCTISCHEMIA test was associated with an HR of 7.6 (95% CI: 1.2-47.0; P = 0.030) for MACE. CONCLUSIONS: This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis.

Impact of atherosclerosis imaging-quantitative computed tomography on diagnostic certainty, downstream testing, coronary revascularization, and medical therapy: the CERTAIN study.

AIMS: The incremental impact of atherosclerosis imaging-quantitative computed tomography (AI-QCT) on diagnostic certainty and downstream patient management is not yet known. The aim of this study was to compare the clinical utility of the routine implementation of AI-QCT versus conventional visual coronary CT angiography (CCTA) interpretation. METHODS AND RESULTS: In this multi-centre cross-over study in 5 expert CCTA sites, 750 consecutive adult patients referred for CCTA were prospectively recruited. Blinded to the AI-QCT analysis, site physicians established patient diagnoses and plans for downstream non-invasive testing, coronary intervention, and medication management based on the conventional site assessment. Next, physicians were asked to repeat their assessments based upon AI-QCT results. The included patients had an age of 63.8 ± 12.2 years; 433 (57.7%) were male. Compared with the conventional site CCTA evaluation, AI-QCT analysis improved physician's confidence two- to five-fold at every step of the care pathway and was associated with change in diagnosis or management in the majority of patients (428; 57.1%; P < 0.001), including for measures such as Coronary Artery Disease-Reporting and Data System (CAD-RADS) (295; 39.3%; P < 0.001) and plaque burden (197; 26.3%; P < 0.001). After AI-QCT including ischaemia assessment, the need for downstream non-invasive and invasive testing was reduced by 37.1% (P < 0.001), compared with the conventional site CCTA evaluation. Incremental to the site CCTA evaluation alone, AI-QCT resulted in statin initiation/increase an aspirin initiation in an additional 28.1% (P < 0.001) and 23.0% (P < 0.001) of patients, respectively. CONCLUSION: The use of AI-QCT improves diagnostic certainty and may result in reduced downstream need for non-invasive testing and increased rates of preventive medical therapy.

Beyond early LDL cholesterol lowering to prevent coronary atherosclerosis in familial hypercholesterolaemia.

AIMS: Familial hypercholesterolaemia (FH) patients are subjected to a high lifetime exposure to low density lipoprotein cholesterol (LDL-C), despite use of lipid-lowering therapy (LLT). This study aimed to quantify the extent of subclinical atherosclerosis and to evaluate the association between lifetime cumulative LDL-C exposure and coronary atherosclerosis in young FH patients. METHODS AND RESULTS: Familial hypercholesterolaemia patients, divided into a subgroup of early treated (LLT initiated <25 years) and late treated (LLT initiated ≥25 years) patients, and an age- and sex-matched unaffected control group, underwent coronary CT angiography (CCTA) with artificial intelligence-guided analysis. Ninety genetically diagnosed FH patients and 45 unaffected volunteers (mean age 41 ± 3 years, 51 (38%) female) were included. Familial hypercholesterolaemia patients had higher cumulative LDL-C exposure (181 ± 54 vs. 105 ± 33 mmol/L ∗ years) and higher prevalence of coronary plaque compared with controls (46 [51%] vs. 10 [22%], OR 3.66 [95%CI 1.62-8.27]). Every 75 mmol/L ∗ years cumulative exposure to LDL-C was associated with a doubling in per cent atheroma volume (total plaque volume divided by total vessel volume). Early treated patients had a modestly lower cumulative LDL-C exposure compared with late treated FH patients (167 ± 41 vs. 194 ± 61 mmol/L ∗ years; P = 0.045), without significant difference in coronary atherosclerosis. Familial hypercholesterolaemia patients with above-median cumulative LDL-C exposure had significantly higher plaque prevalence (OR 3.62 [95%CI 1.62-8.27]; P = 0.001), compared with patients with below-median exposure. CONCLUSION: Lifetime exposure to LDL-C determines coronary plaque burden in FH, underlining the need of early as well as potent treatment initiation. Periodic CCTA may offer a unique opportunity to monitor coronary atherosclerosis and personalize treatment in FH.

This study reveals that young patients with familial hypercholesterolaemia (FH), as compared with individuals without FH, have a higher build-up of coronary artery plaque, linked directly to their increased lifetime exposure to LDL cholesterol. Genetically confirmed FH patients have a higher coronary plaque burden than those without FH, with every 75 mmol/L ∗ years increase in lifetime cumulative LDL cholesterol exposure resulting in a two-fold increase in total plaque volume. Early and potent LDL cholesterol lowering treatments are crucial for FH patients to prevent future cardiovascular diseases.

AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD.

BACKGROUND: The recent development of artificial intelligence-guided quantitative coronary computed tomography angiography analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics. OBJECTIVES: This study set out to investigate the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and to compare the spectrum of plaque to manually assessed coronary computed tomography angiography (CCTA), coronary artery calcium scoring (CACS), and clinical risk characteristics. METHODS: This was a long-term follow-up study of 536 patients referred for suspected coronary artery disease. CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0% percentage atheroma volume [PAV]; stage 1: >0%-5% PAV; stage 2: >5%-15% PAV; stage 3: >15% PAV). The primary major adverse cardiac event (MACE) outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and all-cause mortality. RESULTS: The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 years (IQR: 8.6-11.5 years), 114 patients (21%) experienced the primary outcome. Compared to stages 0 and 1, patients with stage 3 PAV and percentage of noncalcified plaque volume of >7.5% had a more than 3-fold (adjusted HR: 3.57; 95% CI 2.12-6.00; P < 0.001) and 4-fold (adjusted HR: 4.37; 95% CI: 2.51-7.62; P < 0.001) increased risk of MACE, respectively. Addition of AI-QCT improved a model with clinical risk factors and CACS at different time points during follow-up (10-year AUC: 0.82 [95% CI: 0.78-0.87] vs 0.73 [95% CI: 0.68-0.79]; P < 0.001; net reclassification improvement: 0.21 [95% CI: 0.09-0.38]). Furthermore, AI-QCT achieved an improved area under the curve compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.023) and manual QCT (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.040), although net reclassification improvement was modest (0.09 [95% CI: -0.02 to 0.29] and 0.04 [95% CI: -0.05 to 0.27], respectively). CONCLUSIONS: Through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment.

Rationale and design of the CONFIRM2 (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) study.

BACKGROUND: In the last 15 years, large registries and several randomized clinical trials have demonstrated the diagnostic and prognostic value of coronary computed tomography angiography (CCTA). Advances in CT scanner technology and developments of analytic tools now enable accurate quantification of coronary artery disease (CAD), including total coronary plaque volume and low attenuation plaque volume. The primary aim of CONFIRM2, (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is to perform comprehensive quantification of CCTA findings, including coronary, non-coronary cardiac, non-cardiac vascular, non-cardiac findings, and relate them to clinical variables and cardiovascular clinical outcomes. DESIGN: CONFIRM2 is a multicenter, international observational cohort study designed to evaluate multidimensional associations between quantitative phenotype of cardiovascular disease and future adverse clinical outcomes in subjects undergoing clinically indicated CCTA. The targeted population is heterogenous and includes patients undergoing CCTA for atherosclerotic evaluation, valvular heart disease, congenital heart disease or pre-procedural evaluation. Automated software will be utilized for quantification of coronary plaque, stenosis, vascular morphology and cardiac structures for rapid and reproducible tissue characterization. Up to 30,000 patients will be included from up to 50 international multi-continental clinical CCTA sites and followed for 3-4 years. SUMMARY: CONFIRM2 is one of the largest CCTA studies to establish the clinical value of a multiparametric approach to quantify the phenotype of cardiovascular disease by CCTA using automated imaging solutions.

Prognostic Value of a Novel Artificial Intelligence-Based Coronary Computed Tomography Angiography-Derived Ischemia Algorithm for Patients with Suspected Coronary Artery Disease.

AIMS: Coronary computed tomography angiography (CTA) imaging is used to diagnose patients with suspected coronary artery disease (CAD). A novel artificial-intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCTischemia) aims to identify myocardial ischemia directly from CTA images and may be helpful to improve risk stratification. The aims were 1) the prognostic value of AI-QCTischemia among symptomatic patients with suspected CAD entering diagnostic imaging with coronary CTA, and 2) the prognostic value of AI-QCTischemia separately among patients with no/non-obstructive CAD (≤50% visual diameter stenosis) and obstructive CAD (>50% visual diameter stenosis). METHODS AND RESULTS: For this cohort study, AI-QCTischemia was calculated by blinded analysts among patients with suspected CAD undergoing coronary CTA. The primary endpoint was the composite of death, myocardial infarction (MI), or unstable angina pectoris (uAP) (median follow-up 6.9 years). 1880/2271 (83%) patients were analyzable by AI-QCTischemia. Patients with an abnormal AI-QCTischemia result (n = 509/1880) vs. patients with a normal AI-QCTischemia result (n = 1371/1880) had significantly higher crude and adjusted rates of the primary endpoint (HRadj 1.96,95% CI 1.46-2.63, p < 0.001; covariates: age/sex/hypertension/diabetes/smoking/typical angina). An abnormal AI-QCTischemia result was associated with significantly higher crude and adjusted rates of the primary endpoint among patients with no/non-obstructive CAD (n = 1373/1847) (HRadj 1.81,95% CI 1.09-3.00, p = 0.022), but not among those with obstructive CAD (n = 474/1847) (HRadj 1.26,95% CI 0.75-2.12, p = 0.386) (p-interaction = 0.032). CONCLUSION: Among patients with suspected CAD, an abnormal AI-QCTischemia result was associated with a 2-fold increased adjusted rate of long-term death, MI, or uAP. AI-QCTischemia may be useful to improve risk stratification, especially among patients with no/non-obstructive CAD on coronary CTA.

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

BACKGROUND AND AIMS: Artificial 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). METHODS: The 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. RESULTS: This 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. CONCLUSIONS: AI-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.

How early can atherosclerosis be detected by coronary CT angiography? Insights from quantitative CT analysis of serial scans in the PARADIGM trial.

BACKGROUND: Non-obstructing small coronary plaques may not be well recognized by expert readers during coronary computed tomography angiography (CCTA) evaluation. Recent developments in atherosclerosis imaging quantitative computed tomography (AI-QCT) enabled by machine learning allow for whole-heart coronary phenotyping of atherosclerosis, but its diagnostic role for detection of small plaques on CCTA is unknown. METHODS: We performed AI-QCT in patients who underwent serial CCTA in the multinational PARADIGM study. AI-QCT results were verified by a level III experienced reader, who was blinded to baseline and follow-up status of CCTA. This retrospective analysis aimed to characterize small plaques on baseline CCTA and evaluate their serial changes on follow-up imaging. Small plaques were defined as a total plaque volume <50 â€‹mm3. RESULTS: A total of 99 patients with 502 small plaques were included. The median total plaque volume was 6.8 â€‹mm3 (IQR 3.5-13.9 â€‹mm3), most of which was non-calcified (median 6.2 â€‹mm3; 2.9-12.3 â€‹mm3). The median age at the time of baseline CCTA was 61 years old and 63% were male. The mean interscan period was 3.8 â€‹± â€‹1.6 years. On follow-up CCTA, 437 (87%) plaques were present at the same location as small plaques on baseline CCTA; 72% were larger and 15% decreased in volume. The median total plaque volume and non-calcified plaque volume increased to 18.9 â€‹mm3 (IQR 8.3-45.2 â€‹mm3) and 13.8 â€‹mm3 (IQR 5.7-33.4 â€‹mm3), respectively, among plaques that persisted on follow-up CCTA. Small plaques no longer visualized on follow-up CCTA were significantly more likely to be of lower volume, shorter in length, non-calcified, and more distal in the coronary artery, as compared with plaques that persisted at follow-up. CONCLUSION: In this retrospective analysis from the PARADIGM study, small plaques (<50 â€‹mm3) identified by AI-QCT persisted at the same location and were often larger on follow-up CCTA.

Rationale and design of the INVICTUS Registry: (Multicenter Registry of Invasive and Non-Invasive imaging modalities to compare Coronary Computed Tomography Angiography, Intravascular Ultrasound and Optical Coherence Tomography for the determination of Severity, Volume and Type of coronary atherosclerosiS).

BACKGROUND: Coronary CT angiography (CCTA) is a first-line noninvasive imaging modality for evaluating coronary artery disease (CAD). Recent advances in CCTA technology enabled semi-automated detection of coronary arteries and atherosclerosis. However, there have been to date no large-scale validation studies of automated assessment of coronary atherosclerosis phenotype and coronary artery dimensions by artificial intelligence (AI) compared to current standard invasive imaging. METHODS: INVICTUS registry is a multicenter, retrospective, and prospective study designed to evaluate the dimensions of coronary arteries, as well as the characteristic, volume, and phenotype of coronary atherosclerosis by CCTA, compared with the invasive imaging modalities including intravascular ultrasound (IVUS), near-infrared spectroscopy (NIRS)-IVUS and optical coherence tomography (OCT). All patients clinically underwent both CCTA and invasive imaging modalities within three months. RESULTS: Patients data are sent to the core-laboratories to analyze for stenosis severity, plaque characteristics and volume. The variables for CCTA are measured using an AI-based automated software and assessed independently with the variables measured at the imaging core laboratories for IVUS, NIRS-IVUS, and OCT in a blind fashion. CONCLUSION: The INVICTUS registry will provide new insights into the diagnostic value of CCTA for determining coronary atherosclerosis phenotype and coronary artery dimensions compared to IVUS, NIRS-IVUS, and OCT. Our findings will potentially shed new light on precision medicine informed by an AI-based coronary CTA assessment of coronary atherosclerosis burden, composition, and severity. (ClinicalTrials.gov: NCT04066062).

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

OBJECTIVE: 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. METHODS: 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. RESULTS: 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]). CONCLUSIONS: 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 ± 10.2 years (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.

Endothelial shear stress computed from coronary computed tomography angiography: A direct comparison to intravascular ultrasound.

INTRODUCTION: Intravascular ultrasound (IVUS) studies have shown that biomechanical variables, particularly endothelial shear stress (ESS), add synergistic prognostic insight when combined with anatomic high-risk plaque features. Non-invasive risk assessment of coronary plaques with coronary computed tomography angiography (CCTA) would be helpful to enable broad population risk-screening. AIM: To compare the accuracy of ESS computation of local ESS metrics by CCTA vs IVUS imaging. METHODS: We analyzed 59 patients from a registry of patients who underwent both IVUS and CCTA for suspected CAD. CCTA images were acquired using either a 64- or 256-slice scanner. Lumen, vessel, and plaque areas were segmented from both IVUS and CCTA (59 arteries, 686 3-mm segments). Images were co-registered and used to generate a 3-D arterial reconstruction, and local ESS distribution was assessed by computational fluid dynamics (CFD) and reported in consecutive 3-mm segments. RESULTS: Anatomical plaque characteristics (vessel, lumen, plaque area and minimal luminal area [MLA] per artery) were correlated when measured with IVUS and CCTA: 12.7 â€‹± â€‹4.3 vs 10.7 â€‹± â€‹4.5 â€‹mm2, r â€‹= â€‹0.63; 6.8 â€‹± â€‹2.7 vs 5.6 â€‹± â€‹2.7 â€‹mm2, r â€‹= â€‹0.43; 5.9 â€‹± â€‹2.9 vs 5.1 â€‹± â€‹3.2 â€‹mm2, r â€‹= â€‹0.52; 4.5 â€‹± â€‹1.3 vs 4.1 â€‹± â€‹1.5 â€‹mm2, r â€‹= â€‹0.67 respectively. ESS metrics of local minimal, maximal, and average ESS were also moderately correlated when measured with IVUS and CCTA (2.0 â€‹± â€‹1.4 vs 2.5 â€‹± â€‹2.6 â€‹Pa, r â€‹= â€‹0.28; 3.3 â€‹± â€‹1.6 vs 4.2 â€‹± â€‹3.6 â€‹Pa, r â€‹= â€‹0.42; 2.6 â€‹± â€‹1.5 vs 3.3 â€‹± â€‹3.0 â€‹Pa, r â€‹= â€‹0.35, respectively). CCTA-based computation accurately identified the spatial localization of local ESS heterogeneity compared to IVUS, with Bland-Altman analyses indicating that the absolute ESS differences between the two CCTA methods were pathobiologically minor. CONCLUSION: Local ESS evaluation by CCTA is possible and similar to IVUS; and is useful for identifying local flow patterns that are relevant to plaque development, progression, and destabilization.

Plaque Progression Differences Between Apixaban and Rivaroxaban in Patients With Atrial Fibrillation Measured With Cardiac Computed Tomography and Plaque Quantification.

BACKGROUND: Direct oral anticoagulants (DOACs) have been associated with less calcification and coronary plaque progression than warfarin. Whether different DOACs have different effects on coronary plaque burden and progression is not known. We compared the 12-month effects of apixaban and rivaroxaban on plaque characteristics and vascular morphology in patients with atrial fibrillation through quantitative cardiac computed tomographic angiography. STUDY QUESTION: In patients with nonvalvular atrial fibrillation using apixaban or rivaroxaban, are there differences in plaque quantification and progression measured with cardiac computed tomography? STUDY DESIGN: This is a post hoc analysis of 2 paired prospective, single-centered, randomized, open-label trials with blinded adjudication of results. In total, 74 patients were prospectively randomized in parallel trials: 29 to apixaban (2.5-5 mg BID) and 45 to rivaroxaban (20 mg QD). Serial cardiac computed tomographic angiography was performed at baseline and 52 weeks. MEASURES AND OUTCOMES: Comprehensive whole-heart analysis was performed for differences in the progression of percent atheroma volume (PAV), calcified plaque (CP) PAV, noncalcified plaque (NCP) PAV, positive arterial remodeling (PR) ≥1.10, and high-risk plaque (Cleerly Labs, New York, NY). RESULTS: Both groups had progression of all 3 plaque types (apixaban: CP 8.7 mm 3 , NCP 69.7 mm 3 , and LD-NCP 27.2 mm 3 ; rivaroxaban: CP 22.9 mm 3 , NCP 66.3 mm 3 , and LD-NCP 11.0 mm 3 ) and a total annual plaque PAV change (apixaban: PAV 1.5%, PAV-CP 0.12%, and PAV-NCP 0.92%; rivaroxaban: PAV 2.1%, PAV-CP 0.46%, and PAV-NCP 1.40%). There was significantly lower PAV-CP progression in the apixaban group compared with the rivaroxaban group (0.12% vs. 0.46% P = 0.02). High-risk plaque characteristics showed a significant change in PR of apixaban versus rivaroxaban ( P = 0.01). When the propensity score weighting model is applied, only PR changes are statistically significant ( P = 0.04). CONCLUSIONS: In both groups, there is progression of all types of plaque. There was a significant difference between apixaban and rivaroxaban on coronary calcification, with significantly lower calcific plaque progression in the apixaban group, and change in positive remodeling. With weighted modeling, only PR changes are statistically significant between the 2 DOACs.

Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial.

AIMS: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). METHODS: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. RESULTS: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively. CONCLUSIONS: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE.