Quantitative Coronary Computed Tomography Angiography Research Articles

Quantitative coronary computed tomography assessment for differentiating between total occlusions and severe stenoses

BackgroundsThe impact of quantitative assessment to differentiate total occlusions (TOs) from severe stenoses on coronary computed tomography angiography (CCTA) remains unknown. ObjectiveThis study investigated whether quantitative characteristics assessed on CCTA could help differentiate a TO from a severe stenosis on invasive coronary angiography (ICA). MethodsThis study is a sub-analysis of the FASTTRACK CABG (NCT04142021) in which both CCTA and ICA were routinely performed. Quantitative analysis was performed with semi-automated CCTA plaque-analysis software. Blinded analysts compared TOs on CCTA, defined as a complete lack of contrast opacification within the coronary occlusion, with corresponding ICA. ResultsEighty-four TOs were seen on CCTA in 59 of the 114 patients enrolled in the trial. The concordance in diagnosing a TO between ICA and CCTA was 56.0% (n ​= ​47). Compared to severe stenoses, TOs had a significantly longer lesion length (25.1 ​± ​23.0 ​mm vs 9.4 ​± ​11.2 ​mm, P ​< ​0.001). The best cut-off value to differentiate a TO from severe stenosis was a lesion length of 5.5 ​mm (area under the curve 0.77, 95% CI: 0.66–0.87), with a 91.1% sensitivity and 61.1% specificity. Dense calcium percentage atheroma volume (PAV) was significantly higher in TOs compared to severe stenoses (18.7 ​± ​19.6% vs. 6.6 ​± ​13.0%, P ​< ​0.001), whilst the opposite was seen for fibro-fatty PAV (31.3 ​± ​14.2% vs. 19.5 ​± ​10.5%, P ​< ​0.001). On a multivariable logistic regression analysis, lesion length (>5.5 ​mm) was the only parameter associated with differentiating a TO from a severe stenosis. ConclusionIn quantitative CCTA analysis, a lesion length >5.5 ​mm was the only independent predictor differentiating a TO from a severe stenosis. NCT registration numberNCT04142021.

Plasma Lipidomics and Coronary Plaque Changes: A Substudy of the SMARTool Clinical Trial.

To date, no studies have investigated the association between lipid species and coronary plaque changes over time, quantitatively assessed by serial imaging. We aimed to prospectively determine the association between lipid species quantified by plasma lipidomic analysis, with coronary plaque changes according to composition assessed by quantitative serial analysis of coronary computed tomography angiography (CTA). Patients with suspected coronary artery disease (CAD) undergoing baseline coronary CTA were prospectively enrolled by 7 EU Centers in the SMARTool study and submitted to clinical, molecular and coronary CTA re-evaluation at follow-up (interscan period 6.39 ± 1.17 years). From the 202 patients that were analysed in the SMARTool main clinical study, lipidomic analysis was performed in 154 patients before the baseline coronary CTA, and this group was included in the present study. Quantitative CTA analysis was performed by a separate core laboratory blinded from clinical data. In univariable analysis, no lipid species were significantly associated with annual total and calcified plaque changes. After adjusting for clinical variables at baseline and statin use, 3 lipid species were significantly associated with non-calcified plaque progression. In detail, cholesteryl ester (CE)(20:3), sphingomyelin (SM)(40:3) and SM(41:1) were found positively related to non-calcified plaque progression (Bonferroni adjusted P-value = 0.005, 0.016 and 0.004, respectively). The current study showed an independent relationship between specific lipid species determined by plasma lipidomic analysis, and non-calcified coronary plaque progression assessed by serial, quantitative coronary CTA analysis.

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

BackgroundNoninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs. ObjectivesThis 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). MethodsA 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. ResultsIn 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. ConclusionsThis 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.

Quantitative Coronary Computed Tomography Angiography And Dynamic Czt Spect Derived Myocardial Blood Flow In Patients With Non-obstructive Coronary Artery Disease

Quantitative Coronary Computed Tomography Angiography And Dynamic Czt Spect Derived Myocardial Blood Flow In Patients With Non-obstructive Coronary Artery Disease

CT pericoronary adipose tissue density predicts coronary allograft vasculopathy and adverse clinical outcomes after cardiac transplantation

Abstract Background Coronary allograft vasculopathy (CAV) is a common cause of heart transplant failure. Coronary Computed Tomography Angiography (CCTA) is used to assess for luminal stenoses in patients with suspected CAV, but additional non-invasive markers are needed to detect early disease and guide the use of treatments that can prevent or slow CAV progression. Purpose We tested the hypotheses that: 1) coronary artery inflammation assessed by pericoronary adipose tissue (PCAT) density and/or total vessel volume-to-myocardial mass ratio (V/M) measured by CCTA could improve the ability to detect CAV in heart transplant patients; and 2) these semi-automated quantitative CCTA metrics would be associated with adverse clinical outcomes. Methods In this retrospective observational cohort study, PCAT density, V/M, and lesion composition were measured in consecutive CCTAs from patients who underwent heart transplantation at a single site between 2010 to 2021. These parameters were incorporated into predictive models for CAV as defined by International Society for Heart &amp; Lung Transplant criteria, and evaluated against long-term clinical outcomes. Results A total of 126 CCTAs were analysed from 94 patients after heart transplant (mean age 49 [SD 14.5] years, 40% female) without CAV (n=86) and with CAV (n=40). PCAT density was higher in transplant patients with CAV (-73.0 HU [SD 9.26]) than without CAV (-77.9 HU [SD 8.23]), and age and sex-matched controls (n=12; -86.19 HU, [SD 5.38]), p&amp;lt;0.001 for both. Unlike patients with atherosclerotic coronary artery disease (n=32), CAV lesions were predominately non-calcified, comprised of mostly fibrous or fibrofatty tissue. V/M was lower in patients with CAV than without (32.4 mm3/g vs. 44.4 mm3/g, p&amp;lt;0.001). PCAT density and V/M improved the ability to predict CAV from AUC 0.78 to 0.87 on receiver operating characteristic analysis (Figure A) when added to donor age and donor/recipient hypertension status (p&amp;lt;0.0001). PCAT density above -66 HU was associated with a greater incidence of all-cause mortality (OR 16.6 95%CI 2.88-109.98, p&amp;lt;0.01; Figure B, box-plot) and the composite endpoint of death, CAV progression, acute rejection, and coronary revascularisation (OR 6.3 95%CI 1.6-32.9, p=0.01) over 5.25 (SD 2.1) years. Conclusions Heart transplant patients with CAV have higher PCAT density and lower V/M than those without. Patients with very high PCAT density on CT had markedly increased incidence of all-cause mortality and major adverse clinical events. These semi-automated metrics could be a useful addition to standard clinical CCTA reporting for diagnosis and monitoring of CAV.Diagnostic Benefit of CTCA MetricsPCAT is predictor of Future Mortality

Association of oxidized ApoB and oxidized ApoA-I with high-risk coronary plaque features in cardiovascular disease.

Oxidized apolipoprotein B (oxLDL) and oxidized ApoA-I (oxHDL) are proatherogenic. Their prognostic value for assessing high-risk plaques by coronary computed tomography angiography (CCTA) is missing. In a prospective, observational study, 306 participants with cardiovascular disease (CVD) had extensive lipoprotein profiling. Proteomics analysis was performed on isolated oxHDL, and atherosclerotic plaque assessment was accomplished by quantitative CCTA. Patients were predominantly White, overweight men (58.5%) on statin therapy (43.5%). Increase in LDL-C, ApoB, small dense LDL-C (P < 0.001 for all), triglycerides (P = 0.03), and lower HDL function were observed in the high oxLDL group. High oxLDL associated with necrotic burden (NB; β = 0.20; P < 0.0001) and fibrofatty burden (FFB; β = 0.15; P = 0.001) after multivariate adjustment. Low oxHDL had a significant reverse association with these plaque characteristics. Plasma oxHDL levels better predicted NB and FFB after adjustment (OR, 2.22; 95% CI, 1.27-3.88, and OR, 2.80; 95% CI, 1.71-4.58) compared with oxLDL and HDL-C. Interestingly, oxHDL associated with fibrous burden (FB) change over 3.3 years (β = 0.535; P = 0.033) when compared with oxLDL. Combined Met136 mono-oxidation and Trp132 dioxidation of HDL showed evident association with coronary artery calcium score (r = 0.786; P < 0.001) and FB (r = 0.539; P = 0.012) in high oxHDL, whereas Met136 mono-oxidation significantly associated with vulnerable plaque in low oxHDL. Our findings suggest that the investigated oxidized lipids are associated with high-risk coronary plaque features and progression over time in patients with CVD. gov NCT01621594. National Heart, Lung, and Blood Institute at the NIH Intramural Research Program.

Distal-vessel fractional flow reserve by computed tomography to monitor epicardial coronary artery disease.

Coronary computed tomography angiography (CTA) and fractional flow reserve by computed tomography (FFR-CT) are increasingly utilized to characterize coronary artery disease (CAD). We evaluated the feasibility of distal-vessel FFR-CT as an integrated measure of epicardial CAD that can be followed serially, assessed the CTA parameters that correlate with distal-vessel FFR-CT, and determined the combination of clinical and CTA parameters that best predict distal-vessel FFR-CT and distal-vessel FFR-CT changes. Patients (n = 71) who underwent serial CTA scans at ≥2 years interval (median = 5.2 years) over a 14-year period were included in this retrospective study. Coronary arteries were analysed blindly using artificial intelligence-enabled quantitative coronary CTA. Two investigators jointly determined the anatomic location and corresponding distal-vessel FFR-CT values at CT1 and CT2. A total of 45.3% had no significant change, 27.8% an improvement, and 26.9% a worsening in distal-vessel FFR-CT at CT2. Stepwise multiple logistic regression analysis identified a four-parameter model consisting of stenosis diameter ratio, lumen volume, low density plaque volume, and age, that best predicted distal-vessel FFR-CT ≤ 0.80 with an area under the curve (AUC) = 0.820 at CT1 and AUC = 0.799 at CT2. Improvement of distal-vessel FFR-CT was captured by a decrease in high-risk plaque and increases in lumen volume and remodelling index (AUC = 0.865), whereas increases in stenosis diameter ratio, medium density calcified plaque volume, and total cholesterol presaged worsening of distal-vessel FFR-CT (AUC = 0.707). Distal-vessel FFR-CT permits the integrative assessment of epicardial atherosclerotic plaque burden in a vessel-specific manner and can be followed serially to determine changes in global CAD.

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

BackgroundThe recent development of artificial intelligence–guided quantitative coronary computed tomography angiography analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics. ObjectivesThis 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. MethodsThis 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. ResultsThe 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). ConclusionsThrough 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.

Estimated sdLDL-C for predicting high-risk coronary plaque features in psoriasis: a prospective observational study

BackgroundPsoriasis (PSO) is a skin disorder with systemic inflammation and high coronary artery disease risk. A distinct lipid phenotype occurs in psoriasis, which is characterized by high plasma triglycerides (TGs) with typically normal or even low LDL-C. The extent to which cholesterol on LDL subfractions, such as small dense LDL-C (sdLDL-C), are associated with vulnerable coronary plaque characteristics in PSO remains elusive.MethodsA recently developed equation for estimating sdLDL-C from the standard lipid panel was utilized in a PSO cohort (n = 200) with 4-year follow-up of 75 subjects. Coronary plaque burden was assessed by quantitative coronary computed tomography angiography (CCTA). Multivariate regression analyses were used for establishing associations and prognostic value of estimated sdLDL-C.ResultsEstimated sdLDL-C was positively associated with non-calcified burden (NCB) and fibro-fatty burden (FFB), which remained significant after multivariate adjustment for NCB (β = 0.37; P = 0.050) and LDL-C adjustment for FFB (β = 0.29; P < 0.0001). Of note, total LDL-C calculated by the Friedewald equation was not able to capture these associations in the study cohort. Moreover, in the regression modelling estimated sdLDL-C was significantly predicting necrotic burden progression over 4 years follow-up (P = 0.015), whereas LDL-C did not. Finally, small LDL particles (S-LDLP) and small HDL particles (S-HDLP), along with large and medium TG-rich lipoproteins (TRLPs) had the most significant positive correlation with estimated sdLDL-C.ConclusionsEstimated sdLDL-C has a stronger association than LDL-C with high-risk features of coronary atherosclerotic plaques in psoriasis patients.Clinical trial registrationURL: https://www.clinicaltrials.gov. Unique identifiers: NCT01778569

Relationship between coronary volume, myocardial mass, and post-PCI fractional flow reserve.

Fractional flow reserve (FFR) measured after percutaneous coronary intervention(PCI) carries prognostic information. Yet, myocardial mass subtended by a stenosis influences FFR. We hypothesized that a smaller coronary lumen volume and a large myocardial mass might be associated with lower post-PCI FFR. We sought to assess the relationship between vessel volume, myocardial mass, and post-PCIFFR. This was a subanalysis with an international prospective study of patients with significant lesions (FFR ≤ 0.80) undergoing PCI. Territory-specific myocardial mass was calculated from coronary computed tomography angiography (CCTA) using the Voronoi's algorithm. Vessel volume was extracted from quantitative CCTA analysis. Resting full-cycle ratio (RFR) and FFR were measured before and after PCI. We assessed the association between coronary lumen volume (V) and its related myocardial mass (M), and the percentof total myocardial mass (%M) with post-PCI FFR. We studied 120 patients (123 vessels: 94 left anterior descending arteries, 13 left Circumflex arteries, 16 right coronary arteries). Mean vessel-specific mass was 61 ± 23.1 g (%M 39.6 ± 11.7%). The mean post-PCI FFR was 0.88 ± 0.06 FFR units. Post-PCI FFR values were lower in vessels subtending higher mass (0.87 ± 0.05 vs. 0.89 ± 0.07, p = 0.047), and with lower V/M ratio (0.87 ± 0.06 vs. 0.89 ± 0.07, p = 0.02). V/M ratio correlated significantly with post-PCI RFR and FFR (RFR r = 0.37, 95% CI: 0.21-0.52,p < 0.001 and FFR r = 0.41, 95% CI: 0.26-0.55, p < 0.001). Post-PCI RFR and FFR are associated with the subtended myocardial mass and the coronary volume to mass ratio. Vessels with higher mass and lower V/M ratio have lower post-PCI RFR and FFR.

Socioeconomic Deprivation And Quantitative Coronary Computed Tomography Angiography In The SCOT-HEART Trial

Socioeconomic Deprivation And Quantitative Coronary Computed Tomography Angiography In The SCOT-HEART Trial

411 AI QCT: Novel Plaque Thresholds To Predict Cardiac Outcomes: On Behalf Of The Conserve Investigators

Introduction: Applying atherosclerosis imaging quantitative coronary computed tomography angiography (AI-QCT) guided by artificial intelligence, we hypothesize that use of AI-QCT may enable identification of plaque thresholds that prognosticate major adverse cardiovascular events (MACE).

Complex association of apolipoprotein E-containing HDL with coronary artery disease burden in cardiovascular disease.

BackgroundAlthough traditional lipid parameters and coronary imaging techniques are valuable for cardiovascular disease (CVD) risk prediction, better diagnostic tests are still needed.MethodsIn a prospective, observational study, 795 individuals had extensive cardiometabolic profiling, including emerging biomarkers, such as apolipoprotein E–containing HDL-cholesterol (ApoE-HDL-C). Coronary artery calcium (CAC) score was assessed in the entire cohort, and quantitative coronary computed tomography angiography (CCTA) characterization of total burden, noncalcified burden (NCB), and fibrous plaque burden (FB) was performed in a subcohort (n = 300) of patients stratified by concentration of ApoE-HDL-C. Total and HDL-containing apolipoprotein C-III (ApoC-III) were also measured.ResultsMost patients had a clinical diagnosis of coronary artery disease (CAD) (n = 80.4% of 795), with mean age of 59 years, a majority being male (57%), and about half on statin treatment. The low ApoE-HDL-C group had more severe stenosis (11% vs. 2%, overall P < 0.001), with higher CAC as compared with high ApoE-HDL-C. On quantitative CCTA, the high ApoE-HDL-C group had lower NCB (β = –0.24, P = 0.0001), which tended to be significant in a fully adjusted model (β = –0.32, P = 0.001) and altered by ApoC-III in HDL levels. Low ApoE-HDL-C was significantly associated with LDL particle number (β = 0.31; P = 0.0001). Finally, when stratified by FB, ApoC-III in HDL showed a more robust predictive value of CAD over ApoE-HDL-C (AUC: 0.705, P = 0.0001) in a fully adjusted model.ConclusionApoE-containing HDL-C showed a significant association with early coronary plaque characteristics and is affected by the presence of ApoC-III, indicating that low ApoE-HDL-C and high ApoC-III may be important markers of CVD severity.Trial RegistrationClinicalTrials.gov: NCT01621594.FundingThis work was supported by the NHLBI at the NIH Intramural Research Program.

Atherosclerotic plaque characteristics on quantitative coronary computed tomography angiography associated with ischemia on positron emission tomography in diabetic patients

Patients with diabetes mellitus (DM) may show diffuse coronary artery atherosclerosis on coronary computed tomography angiography (CTA). The present study aimed at quantification of atherosclerotic plaque with CTA and its association with myocardial ischemia on positron emission tomography (PET) in DM patients. Of 922 symptomatic outpatients without previously known coronary artery disease who underwent CTA, 115 with DM (mean age 65 ± 8 years, 58% male) who had coronary atherosclerosis and underwent both quantified CTA (QCTA) and PET were included in the study. QCTA analysis was performed on a per-vessel basis and the most stenotic lesion of each vessel was considered. Myocardial ischemia on PET was based on absolute myocardial blood flow at stress ≤ 2.4 ml/g/min. Of the 345 vessels included in the analysis, 135 (39%) had flow-limiting stenosis and were characterized by having longer lesions, higher plaque volume, more extensive plaque burden and higher percentage of dense calcium (37 ± 22% vs 28 ± 22%, p = 0.001). On univariable analysis, QCTA parameters indicating the degree of stenosis, the plaque extent and composition were associated with presence of ischemia. The addition of the QCTA degree of stenosis parameters (x2 36.45 vs 88.18, p < 0.001) and the QCTA plaque extent parameters (x2 88.18 vs 97.44, p = 0.01) to a baseline model increased the association with ischemia. In DM patients, QCTA variables of vessel stenosis, plaque extent and composition are associated with ischemia on PET and characterize the hemodynamic significant atherosclerotic lesion.

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

ObjectivesTo determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. BackgroundCCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. MethodsCCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). ResultsWithin the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had <50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (<350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370–400 mg/ml 90.0%, >400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. ConclusionThe diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstractAn AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.

Coronary Artery Dimensions in Endurance Athletes by Computed Tomography Angiography: A Quantitative Analysis.

(1) Background: The athlete’s heart may develop permanent vessel enlargement. The purpose of our study was to define normal values for coronary artery dimensions of endurance athletes by coronary computed tomography angiography (CTA). (2) Methods: Ninety-eight individuals (56.2 ± 11 years) were included into this retrospective matched case-controlled-study. Endurance athletes had regular training volumes of ≥1 h per unit, ≥3–7 times per week (either cycling, running or mountain-endurance). Athletes were matched for age and gender with sedentary controls using propensity score. Quantitative CTA analysis included coronary vessel dimensions (two diameters and area) of the LM, LAD, CX and RCA for all AHA-16-segments. (3) Results: Proximal LAD area and diameter (p = 0.019); proximal/mid CX (diameter and area; p = 0.026 and p = 0.018/p = 0.008 and p = 0.009); mid RCA diameter and area; and proximal RCA diameter were significantly larger in endurance athletes (p < 0.05). The left main area (p = 0.708) and diameter (p = 0.809) as well as the mid LAD and distal segments were not different. We present the histograms and data for normal values ±1 and ± 2 SD. (4) Conclusions: Endurance athletes have larger proximal LAD, proximal/mid CX and RCA vessel dimensions, while LM and distal segments are similar. Hence, dilated coronary arteries in endurance athletes (“Athlete’s arteries”) have to be distinguished from diffuse ectatic segments developing during Kawasaki disease or multisystemic inflammation syndrome after COVID-19.

Machine learning from quantitative coronary computed tomography angiography predicts ischemia and impaired myocardial blood flow

Abstract Background Atherosclerotic plaque characteristics influence the hemodynamic consequences of coronary lesions. This study sought to assess the performance of a machine learning (ML) score integrating coronary computed tomography angiography (CCTA)-based quantitative plaque features for the prediction of ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by [15O]H2O positron emission tomography (PET). Methods This post-hoc analysis of the PACIFIC (Prospective Comparison of Cardiac PET/CT, SPECT/CT Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography) trial included 208 patients with suspected coronary artery disease who underwent CCTA, [15O]H2O PET, and 3-vessel invasive FFR. Plaque quantification from CCTA was performed using semiautomated software. A boosted ensemble ML algorithm (XGBoost) trained on data from the NXT (Analysis of Coronary Blood Flow using CT Angiography: Next Steps) trial was used to develop a ML score for the prediction of per-vessel ischemia (invasive FFR ≤0.80). The performance of the ML score was evaluated in 551 vessels from the PACIFIC trial for external validation. Thereafter, we assessed the discriminative ability of the ML score for per-vessel impaired hyperemic MBF (≤2.30 mL/min/g). Results In total, 138 (25.0%) vessels had ischemia and 195 (35.4%) vessels had impaired hyperemic MBF. CCTA-derived quantitative percent diameter stenosis and low-density noncalcified plaque (LDNCP) volume were higher in ischemic vessels compared with non-ischemic vessels (60.8% vs. 19.9%; and 42.3 mm3 vs. 9.1 mm3; both p&amp;lt;0.001). The ML score demonstrated a significantly higher area under the receiver-operating characteristic curve (AUC) for predicting ischemia (0.92, 95% confidence interval [CI] 0.89–0.94) compared with visual stenosis grade (0.84, 95% CI 0.80–0.87; p&amp;lt;0.001). Overall, quantitative percent diameter stenosis and LDNCP volume had greatest feature importance for ML, followed by percent area stenosis, minimum luminal diameter, and contrast density drop (Figure 1). An individualized explanation of ML ischemia prediction is shown in Figure 2. When applied for impaired MBF discrimination, the ML score exhibited an AUC of 0.82 (95% CI 0.78–0.85) and was superior to visual stenosis grade (AUC 0.76, 95% CI 0.72–0.80; p=0.03). Conclusions An externally validated ML score integrating CCTA-based quantitative plaque features accurately predicts FFR-defined ischemia and abnormal MBF by PET, outperforming standard visual CCTA interpretation. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart, Lung, and Blood Institute, United States Performance of the ML scoreIndividual explanation of ML prediction

Prognostic Value of RCA Pericoronary Adipose Tissue CT-Attenuation Beyond High-Risk Plaques, Plaque Volume, and Ischemia

ObjectivesThis study was designed to assess the prognostic value of pericoronary adipose tissue computed tomography attenuation (PCATa) beyond quantitative coronary computed tomography angiography (CCTA)–derived plaque volume and positron emission tomography (PET) determined ischemia. BackgroundInflammation plays a crucial role in atherosclerosis. PCATa has been shown to assess coronary-specific inflammation and is of prognostic value in patients with suspected coronary artery disease (CAD). MethodsA total of 539 patients who underwent CCTA and [15O]H2O PET perfusion imaging because of suspected CAD were included. Imaging assessment included coronary artery calcium score (CACS), presence of obstructive CAD (≥50% stenosis) and high-risk plaques (HRPs), total plaque volume (TPV), calcified/noncalcified plaque volume (CPV/NCPV), PCATa, and myocardial ischemia. The endpoint was a composite of death and nonfatal myocardial infarction. Prognostic thresholds were determined for quantitative CCTA variables. ResultsDuring a median follow-up of 5.0 (interquartile range: 4.7 to 5.0) years, 33 events occurred. CACS >59 Agatston units, obstructive CAD, HRPs, TPV >220 mm3, CPV >110 mm3, NCPV >85 mm3, and myocardial ischemia were associated with shorter time to the endpoint with unadjusted hazard ratios (HRs) of 4.17 (95% confidence interval [CI]: 1.80 to 9.64), 4.88 (95% CI: 1.88 to 12.65), 3.41 (95% CI: 1.72 to 6.75), 7.91 (95% CI: 3.05 to 20.49), 5.82 (95% CI: 2.40 to 14.10), 8.07 (95% CI: 3.33 to 19.55), and 4.25 (95% CI: 1.84 to 9.78), respectively (p < 0.05 for all). Right coronary artery (RCA) PCATa above scanner specific thresholds was associated with worse prognosis (unadjusted HR: 2.84; 95% CI: 1.44 to 5.63; p = 0.003), whereas left anterior descending artery and circumflex artery PCATa were not related to outcome. RCA PCATa above scanner specific thresholds retained is prognostic value adjusted for imaging variables and clinical characteristics associated with the endpoint (adjusted HR: 2.45; 95% CI: 1.23 to 4.93; p = 0.011). ConclusionsParameters associated with atherosclerotic burden and ischemia were more strongly associated with outcome than RCA PCATa. Nonetheless, RCA PCATa was of prognostic value beyond clinical characteristics, CACS, obstructive CAD, HRPs, TPV, CPV, NCPV, and ischemia.

Pericoronary adipose tissue attenuation leads to improved prognostication beyond atherosclerotic burden and high-risk plaques in patients with suspected coronary artery disease

Abstract Funding Acknowledgements Type of funding sources: None. Background Inflammation is a key component in the atherosclerotic process, initiating and sustaining plaques and serving as a trigger for plaque rupture leading to myocardial infarction. Coronary computed tomography angiography (CCTA) derived pericoronary adipose tissue attenuation (PCATa) has been proposed as surrogate marker for coronary inflammation and might improve risk assessment on top of CCTA derived cardiovascular risk-factors: atherosclerotic burden and plaque vulnerability. Purpose To assess the prognostic value of PCATa beyond atherosclerotic burden and high-risk plaques (HRPs). Methods A total of 543 patients who underwent CCTA because of suspected CAD were included. CCTA assessment comprised coronary artery calcium score (CACS), presence of obstructive CAD (≥50% stenosis) and HRPs, total plaque volume (TPV), non-calcified plaque volume (NCPV), and PCATa. The endpoint was a composite of death and non-fatal myocardial infarction (MI). Prognostic thresholds were determined for quantitative CCTA variables. Results During a median follow-up of 6.6 [interquartile range: 4.7-7.8] years, the endpoint was observed in 42 (20 MI/22 death) patients. CACS &amp;gt;83, obstructive CAD, HRPs, TPV &amp;gt;269mm3, and NCPV &amp;gt;83mm3 were associated with shorter time to the endpoint with unadujsted hazard ratio’s (HR) of 5.37 (95% confidence interval (CI): 2.56-11.29), 5.70 (95% CI: 2.40-13.55), 3.31 (95% CI: 1.80-6.07), 7.76 (95% CI: 3.59-16.81), and 6.77 (95% CI: 3.24-14.16), respectively (p &amp;lt; 0.001 for all). PCATa of the RCA &amp;gt;-74.4 Hounsfield units was associated with worse prognosis (unadjusted HR: 1.99, 95% CI: 1.04-3.79, p = 0.037), whereas PCATa of the LAD and Cx were not associated with prognosis. PCATa of the RCA remained a significant predictor of death and non-fatal MI corrected for CCTA variables and clincal chacteristics associated with the endpoint (adjusted HR: 2.11, 95% CI: 1.11-4.04, p = 0.024). Conclusion Coronary inflammation determined by PCATa of the RCA provides incremental prognostic value beyond clinical characteristics and comprehensive CCTA assessment. Abstract Figure. Take-home figure

Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score.

The machine learning ischemia risk score (ML-IRS) is a machine learning-based algorithm designed to identify hemodynamically significant coronary disease using quantitative coronary computed tomography angiography (CCTA). The purpose of this study was to examine whether the ML-IRS can predict revascularization in patients referred for invasive coronary angiography (ICA) after CCTA. This study was a post hoc analysis of a prospective dual-center registry of sequential patients undergoing CCTA followed by ICA within 3 months, referred from inpatient, outpatient, and emergency department settings (n = 352, age 63 ± 10 years, 68% male). The primary outcome was revascularization by either percutaneous coronary revascularization or coronary artery bypass grafting. Blinded readers performed semi-automated quantitative coronary plaque analysis. The ML-IRS was automatically computed. Relationships between clinical risk factors, coronary plaque features, and ML-IRS with revascularization were examined. The study cohort consisted of 352 subjects with 1056 analyzable vessels. The ML-IRS ranged between 0 and 81% with a median of 18.7% (6.4-34.8). Revascularization was performed in 26% of vessels. Vessels receiving revascularization had higher ML-IRS (33.6% (21.1-55.0) versus 13.0% (4.5-29.1), p < 0.0001), as well as higher contrast density difference, and total, non-calcified, calcified, and low-density plaque burden. ML-IRS, when added to a traditional risk model based on clinical data and stenosis to predict revascularization, resulted in increased area under the curve from 0.69 (95% CI: 0.65-0.72) to 0.78 (95% CI: 0.75-0.81) (p < 0.0001), with an overall continuous net reclassification improvement of 0.636 (95% CI: 0.503-0.769; p < 0.0001). ML-IRS from quantitative coronary CT angiography improved the prediction of future revascularization and can potentially identify patients likely to receive revascularization if referred to cardiac catheterization. • Machine learning ischemia risk from quantitative coronary CT angiography was significantly higher in patients who received revascularization versus those who did not receive revascularization. • The machine learning ischemia risk score was significantly higher in patients with invasive fractional flow ≤ 0.8 versus those with > 0.8. • The machine learning ischemia risk score improved the prediction of future revascularization significantly when added to a standard prediction model including stenosis.