Artificial Intelligence-Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome.

BACKGROUND: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization. OBJECTIVES: This study sought to investigate the additive value of artificial intelligence-enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA). METHODS: Among ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort. RESULTS: Among 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA. CONCLUSIONS: AI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328).

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.

Diabetes, Atherosclerosis, and Stenosis by AI.

OBJECTIVE: 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. RESEARCH DESIGN AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy.

BACKGROUND: Clinical reads of coronary computed tomography angiography (CTA), especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. Artificial intelligence (AI)-based solutions applied to coronary CTA may overcome these limitations. OBJECTIVES: This study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography (AI-QCT) angiography analyses to core lab-interpreted coronary CTA, core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. CONCLUSIONS: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).

Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard.

BACKGROUND. Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. OBJECTIVE. The purpose of our study was to compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography and to assess the downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. METHODS. This study entailed a retrospective post hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The study included 301 patients (88 women and 213 men; mean age, 64.4 ± 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. RESULTS. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥ 50%) stenosis in 54% of patients, including severe (≥ 70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified nonobstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p < .001) than MPI for predicting ≥ 50% stenosis by QCA (0.88 vs 0.66), ≥ 70% stenosis by QCA (0.92 vs 0.81), and FFR < 0.80 (0.90 vs 0.71). An AI-QCT result of ≥ 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥ 50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AIQCT in all patients and those showing ≥ 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥ 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. CONCLUSION. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. CLINICAL IMPACT. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs. TRIAL REGISTRATION. Clinicaltrials.gov NCT02173275.

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

OBJECTIVES: To 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. BACKGROUND: CCTA 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. METHODS: CCTA 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). RESULTS: Within 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. CONCLUSION: The 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 ABSTRACT: An 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.

Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence.

OBJECTIVE: The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT). METHODS: This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (<50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age <65 and ≥65 years. RESULTS: The cohort was 64.4±10.2 years and 29% women. Overall, patients >65 had more PV and CP than patients <65. On a lesion level, patients >65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p<0.04) and non-obstructive lesions (22.1 mm3 vs 49.4 mm3; p<0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p<0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients. CONCLUSION: AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment.

Stress Myocardial Perfusion Imaging vs Coronary Computed Tomographic Angiography for Diagnosis of Invasive Vessel-Specific Coronary Physiology: Predictive Modeling Results From the Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) Trial.

Importance: Stress imaging has been the standard for diagnosing functionally significant coronary artery disease. It is unknown whether novel, atherosclerotic plaque measures improve accuracy beyond coronary stenosis for diagnosing invasive fractional flow reserve (FFR) measurement. Objective: To compare the diagnostic accuracy of comprehensive anatomic (obstructive and nonobstructive atherosclerotic plaque) vs functional imaging measures for estimating vessel-specific FFR. Design, Setting, and Participants: Controlled clinical trial of diagnostic accuracy with a multicenter derivation-validation cohort of patients referred for nonemergent invasive coronary angiography. A total of 612 patients (64 [10] years; 30% women) with signs and symptoms suggestive of myocardial ischemia from 23 sites were included. Patients were recruited from 2014 to 2017. Data analysis began in August 2018. Interventions: Patients underwent invasive coronary angiography with measurement of invasive FFR, coronary computed tomographic angiography (CCTA) quantification of atherosclerotic plaque and FFR by CT (FFR-CT), and semiquantitative scoring of rest/stress myocardial perfusion imaging (by magnetic resonance, positron emission tomography, or single photon emission CT). Multivariable generalized linear mixed models were derived and validated calculating the area under the receiver operating characteristics curve. Main Outcomes and Measures: The primary end point was invasive FFR of 0.80 or less. Results: Of the 612 patients, the mean (SD) age was 64 (10) years, and 426 (69.9%) were men. An invasive FFR of 0.80 or less was measured in 26.5% of 1727 vessels. In the derivation cohort, CCTA vessel-specific factors associated with FFR 0.80 or less were stenosis severity, percentage of noncalcified atheroma volume, lumen volume, the number of lesions with high-risk plaque (≥2 of low attenuation plaque, positive remodeling, napkin ring sign, or spotty calcification), and the number of lesions with stenosis greater than 30%. Fractional flow reserve-CT was not additive to this model including stenosis and atherosclerotic plaque. Significant myocardial perfusion imaging predictors were the summed rest and difference scores. In the validation cohort, the areas under the receiver operating characteristic curve were 0.81 for CCTA vs 0.67 for myocardial perfusion imaging (P < .001). Conclusions and Relevance: A comprehensive anatomic interpretation with CCTA, including quantification of obstructive and nonobstructive atherosclerotic plaque, was superior to functional imaging in the diagnosis of invasive FFR. Comprehensive CCTA measures improve prediction of vessel-specific coronary physiology more so than stress-induced alterations in myocardial perfusion. Trial Registration: ClinicalTrials.gov Identifier: NCT02173275.

Rationale and Design of the CREDENCE Trial: computed TomogRaphic evaluation of atherosclerotic DEtermiNants of myocardial IsChEmia.

BACKGROUND: Coronary computed tomography angiography (CCTA) allows for non-invasive assessment of obstructive coronary artery disease (CAD) beyond measures of stenosis severity alone. This assessment includes atherosclerotic plaque characteristics (APCs) and calculation of fractional flow reserve (FFR) from CCTA (FFRCT). Similarly, stress imaging by myocardial perfusion scintigraphy (MPS) provides vital information. To date, the diagnostic performance of integrated CCTA assessment versus integrated MPS assessment for diagnosis of vessel-specific ischemia remains underexplored. METHODS: CREDENCE will enroll adult individuals with symptoms suspicious of CAD referred for non-emergent invasive coronary angiography (ICA), but without known CAD. All participants will undergo CCTA, MPS, ICA and FFR. FFR will be performed for lesions identified at the time of ICA to be ≥40 and <90 % stenosis, or those clinically indicated for evaluation. Study analyses will focus on diagnostic performance of CCTA versus MPS against invasive FFR reference standard. An integrated stenosis-APC-FFRCT metric by CCTA for vessel-specific ischemia will be developed from derivation cohort and tested against a validation cohort. Similarly, integrated metric by MPS for vessel-specific ischemia will be developed, validated and compared. An FFR value of ≤0.80 will be considered as ischemia causing. The primary endpoint will be the diagnostic accuracy of vessel territory-specific ischemia of integrated stenosis-APC-FFRCT measure by CCTA, compared with perfusion or perfusion-myocardial blood flow stress imaging testing, against invasive FFR. DISCUSSION: CREDENCE will determine the performance of integrated CCTA metric compared to integrated MPS measure for diagnosis of vessel-specific ischemia. If proven successful, this study may reduce the number of missed diagnoses and help to optimally predict ischemia-causing lesions. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02173275 . Registered on June 23, 2014.

SYNTAX Score Derived From Coronary CT Angiography for Prediction of Complex Percutaneous Coronary Interventions.

RATIONALE AND OBJECTIVES: SYNTAX score is a useful metric determined at the time of invasive coronary angiography (ICA) to assess the complexity of coronary artery disease, and improves prediction of complications at the time of percutaneous complex intervention (PCI). We aimed to determine whether SYNTAX score can be reliably determined from coronary computed tomography angiography (CCTA) and whether a CCTA-derived SYNTAX score can predict complex PCI. MATERIALS AND METHODS: SYNTAX scores were calculated on per-patient, per-vessel, and per-segment basis in 154 consecutive patients who underwent CCTA and ICA. PCI complexity in 113 patients who underwent intervention was defined by total fluoroscopy time and contrast volume. RESULTS: Compared to ICA, CCTA detected 285 of 302 (94%) obstructive lesions in 230 vessels, for which PCI was performed for 154 lesions in 131 vessels. Overall, on a per-patient basis, ICA-derived SYNTAX score was lower in comparison to CCTA-derived score (10.2 ± 8.0 vs 10.9 ± 8.3, P = 0.001). As compared to lesions in the lowest CCTA-derived segmental SYNTAX tertile, lesions in the highest tertile required longer fluoroscopy time (17.5 ± 12 min vs 11.5 ± 7.9 min, P = 0.01) and greater contrast volume (215.4 ± 125.5 mL vs 144.3 ± 49 mL, P = 0.02). CONCLUSION: SYNTAX scores derived from CCTA are concordant with those derived from ICA and correspond with complex PCI.

Diagnostic Accuracy, Image Quality, and Patient Comfort for Coronary CT Angiography Performed Using Iso-Osmolar versus Low-Osmolar Iodinated Contrast: A Prospective International Multicenter Randomized Controlled Trial.

RATIONALE AND OBJECTIVES: The impact of iso-osmolar versus low-osmolar iodinated contrast on diagnostic accuracy for coronary computed tomography angiography (CCTA), against the reference standard of invasive coronary angiography (ICA), has not been determined. We sought to compare in an international multicenter randomized controlled trial the impact of iso-osmolar iodixanol versus low-osmolar iopamidol on diagnostic accuracy, image quality, patient symptoms, and heart rate variability. MATERIALS AND METHODS: Adult patients who were clinically referred for ICA were randomly assigned to receive either iodixanol (n = 133) or iopamidol (n = 133) with an investigational CCTA. CCTA stenosis and image quality were scored by consensus of independent blinded core laboratory readers. Degree of stenosis by ICA was evaluated using quantitative coronary angiography and used to calculate diagnostic accuracy. Heart rate variability and patient-reported symptom questionnaires were compared between the two groups. RESULTS: A total of 266 subjects underwent both CCTA and ICA (57 ± 11 years, 58% male). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for detecting coronary artery disease were 86.8%, 93.7%, 84.6%, 94.7%, and 91.7% for iodixanol and 94.7%, 88.4%, 76.6%, 97.7%, and 90.2% for iopamidol, respectively, on a per-patient level. These values were not significantly different between the two groups. There was no significant difference in image quality and heart rate increase or variability. The majority of patients reported symptoms (59.4%), with no differences in the overall or individual rate of any or moderate to severe symptoms between the two groups. Patients receiving iodixanol reported lower incidence of moderate to severe flushing (3.0% vs. 12.8%, P = .005). Lower rates of moderate to severe symptoms were particularly evident for patients with ≥55 years receiving iodixanol versus iopamidol (8.5% vs. 24.6%, P = .01). CONCLUSIONS: Diagnostic performance and image quality were similar for CCTA performed with iso-osmolar versus low-osmolar iodinated contrast. Indices of patient comfort were improved with iso-osmolar iodinated contrast.

CT Angiography for the Prediction of Hemodynamic Significance in Intermediate and Severe Lesions: Head-to-Head Comparison With Quantitative Coronary Angiography Using Fractional Flow Reserve as the Reference Standard.

OBJECTIVES: The goal of this study was to compare the diagnostic performance of coronary computed tomography angiography (CTA) versus quantitative coronary angiography (QCA) for the detection of lesion-specific ischemia using fractional flow reserve (FFR) as the gold standard. BACKGROUND: Coronary CTA has emerged as a noninvasive method for accurate detection and exclusion of high-grade coronary stenoses. FFR is the gold standard for determining lesion-specific ischemia and has been shown to improve clinical outcomes when guiding revascularization. METHODS: A total of 252 patients from 5 countries were prospectively enrolled (mean age 63 years; 71% male). Patients underwent coronary CTA and invasive coronary angiography (ICA) with FFR in 407 lesions. Coronary CTA, QCA, and FFR were interpreted by independent core laboratories. Stenosis severity according to coronary CTA and QCA were graded as 0% to 29%, 30% to 49%, 50% to 69%, and 70% to 100%; stenosis ≥50% was considered anatomically obstructive. Lesion-specific ischemia was defined according to FFR ≤0.8, whereas QCA and coronary CTA stenosis ≥50% were considered obstructive. Diagnostic accuracy and areas under the receiver-operating characteristics curve (AUC) for lesion-specific ischemia was assessed. RESULTS: According to FFR, ischemia was present in 151 (37%) of 407 lesions. Diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 69%, 79%, 63%, 55%, and 83% for coronary CTA; and 71%, 74%, 70%, 59%, and 82% for QCA. AUC for identification of ischemia-causing lesions was similar: 0.75 for coronary CTA and 0.77 for QCA (p = 0.6). No differences between CTA and QCA existed for discrimination of ischemia within the left anterior descending artery (AUC 0.71 vs. 0.73; p = 0.6), left circumflex artery (AUC 0.78 vs. 0.85; p = 0.4), and right coronary artery (AUC 0.80 vs. 0.83; p = 0.6). CONCLUSIONS: CTA and ICA exhibited similar diagnostic performance for the detection and exclusion of lesion-specific ischemia. Using a true reference standard to determine appropriate revascularization targets, 3-dimensional coronary CTA performed as well as 2-dimensional ICA.

Additive diagnostic value of atherosclerotic plaque characteristics to non-invasive FFR for identification of lesions causing ischaemia: results from a prospective international multicentre trial.

AIMS: We evaluated the association between atherosclerotic plaque characteristics (APCs) by CT -including positive remodelling (PR), low attenuation plaque (LAP) and spotty calcification (SC)- and lesion ischaemia by fractional flow reserve (FFR). METHODS AND RESULTS: Two hundred and fifty-two patients (17 centres, five countries) underwent CT, FFR derived from CT (FFRCT) with invasive FFR performed for 407 coronary lesions. FFR ≤0.8 was indicative of lesion-specific ischaemia. CT diameter ≥50% stenosis was considered obstructive. APCs by CT were defined as: (1) PR, lesion diameter/reference diameter >1.10; (2) LAP, any voxel <30 HU; and (3) SC, nodular calcified plaque <3 mm. Odds ratios (OR) and area under the ROC curve (AUC) of APCs for lesion-specific ischaemia were analysed. PR, LAP and SC were associated with ischaemia, with a three to fivefold higher prevalence than in non-ischaemic lesions. Among individual APC, PR (OR 4.7, p<0.001), but not SC or LAP, was strongly associated with lesion-specific ischaemia and provided incremental prediction for lesion-specific ischaemia over CT stenosis plus FFRCT (AUC 0.87 vs. 0.83, p=0.002). CONCLUSIONS: APCs' features -especially PR- by CT improve identification and reclassification of coronary lesions which cause ischaemia over CT stenosis and FFRCT.

Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography.

Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis [Formula: see text]. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis [Formula: see text]. Visual identification of lesions with stenosis [Formula: see text] by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.

Relationship of epicardial fat volume from noncontrast CT with impaired myocardial flow reserve by positron emission tomography.

BACKGROUND: Impaired myocardial flow reserve (MFR) is a marker of coronary vascular dysfunction with prognostic significance. OBJECTIVES: We aimed to investigate the relationship between epicardial fat volume (EFV) measured from noncontrast CT and impaired MFR derived from rest-stress Rb-82 positron emission tomography (PET). METHODS: We retrospectively studied 85 consecutive patients without known coronary artery disease who underwent rest-stress Rb-82 myocardial PET/CT and were subsequently referred for invasive coronary angiography. EFV was computed from noncontrast CT by validated software and indexed to body surface area (EFVi, cm3/m2). Global stress and rest MFR were automatically derived from PET. Patient age, sex, cardiovascular risk factors, coronary calcium score (CCS), and EFVi were combined by boosted ensemble machine learning algorithm into a novel composite risk score, using 10-fold cross-validation, to predict impaired global MFR (MFR ≤2.0) by PET. RESULTS: Patients with impaired MFR (44 of 85; 52%) were older (71 vs. 65 years; P = .03) and had higher frequency of CCS (≥400; P = .02) with significantly higher EFVi (63.1 ± 20.4 vs. 51.3 ± 14.1 cm3/m2; P = .003). On multivariate logistic regression (with age, sex, number of risk factors, CCS, and EFVi), EFVi was the only independent predictor of impaired MFR (odds ratio, 7.39; P = .02). The machine learning composite risk score significantly improved risk reclassification of impaired MFR compared to CCS or EFVi alone (integrated discrimination improvement = 0.19; P = .007 and IDI = 0.22; P = .002, respectively). CONCLUSIONS: Increased EFVi and composite risk score combining EFVi and CCS significantly improve identification of impaired global MFR by PET.

Automated Quantitative Plaque Burden from Coronary CT Angiography Noninvasively Predicts Hemodynamic Significance by using Fractional Flow Reserve in Intermediate Coronary Lesions.

PURPOSE: To evaluate the utility of multiple automated plaque measurements from coronary computed tomographic (CT) angiography in determining hemodynamic significance by using invasive fractional flow reserve (FFR) in patients with intermediate coronary stenosis. MATERIALS AND METHODS: The study was approved by the institutional review board. All patients provided written informed consent. Fifty-six intermediate lesions (with 30%-69% diameter stenosis) in 56 consecutive patients (mean age, 62 years; range, 46-88 years), who subsequently underwent invasive coronary angiography with assessment of FFR (values ≤0.80 were considered hemodynamically significant) were analyzed at coronary CT angiography. Coronary CT angiography images were quantitatively analyzed with automated software to obtain the following measurements: volume and burden (plaque volume × 100 per vessel volume) of total, calcified, and noncalcified plaque; low-attenuation (<30 HU) noncalcified plaque; diameter stenosis; remodeling index; contrast attenuation difference (maximum percent difference in attenuation per unit area with respect to the proximal reference cross section); and lesion length. Logistic regression adjusted for potential confounding factors, receiver operating characteristics, and integrated discrimination improvement were used for statistical analysis. RESULTS: FFR was 0.80 or less in 21 (38%) of the 56 lesions. Compared with nonischemic lesions, ischemic lesions had greater diameter stenosis (65% vs 52%, P = .02) and total (49% vs 37%, P = .0003), noncalcified (44% vs 33%, P = .0004), and low-attenuation noncalcified (9% vs 4%, P < .0001) plaque burden. Calcified plaque and remodeling index were not significantly different. In multivariable analysis, only total, noncalcified, and low-attenuation noncalcified plaque burden were significant predictors of ischemia (P < .015). For predicting ischemia, the area under the receiver operating characteristics curve was 0.83 for total plaque burden versus 0.68 for stenosis (P = .04). CONCLUSION: Compared with stenosis grading, automatic quantification of total, noncalcified, and low-attenuation noncalcified plaque burden substantially improves determination of lesion-specific hemodynamic significance by FFR in patients with intermediate coronary lesions.

Atherosclerotic plaque characteristics by CT angiography identify coronary lesions that cause ischemia: a direct comparison to fractional flow reserve.

OBJECTIVES: This study evaluated the association between atherosclerotic plaque characteristics (APCs) by coronary computed tomographic angiography (CTA), and lesion ischemia by fractional flow reserve (FFR). BACKGROUND: FFR is the gold standard for determining lesion ischemia. Although APCs by CTA-including aggregate plaque volume % (%APV), positive remodeling (PR), low attenuation plaque (LAP), and spotty calcification (SC)-are associated with future coronary syndromes, their relationship to lesion ischemia is unclear. METHODS: 252 patients (17 centers, 5 countries; mean age 63 years; 71% males) underwent coronary CTA, with FFR performed for 407 coronary lesions. Coronary CTA was interpreted for <50% and ≥50% stenosis, with the latter considered obstructive. APCs by coronary CTA were defined as: 1) PR, lesion diameter/reference diameter >1.10; 2) LAP, any voxel <30 Hounsfield units; and 3) SC, nodular calcified plaque <3 mm. Odds ratios (OR) and net reclassification improvement of APCs for lesion ischemia, defined by FFR ≤0.8, were analyzed. RESULTS: By FFR, ischemia was present in 151 lesions (37%). %APV was associated with a 50% increased risk of ischemia per 5% additional APV. PR, LAP, and SC were associated with ischemia, with a 3 to 5 times higher prevalence than in nonischemic lesions. In multivariable analyses, a stepwise increased risk of ischemia was observed for 1 (OR: 4.0, p < 0.001) and ≥2 (OR: 12.1, p < 0.001) APCs. These findings were APC dependent, with PR (OR: 5.3, p < 0.001) and LAP (OR: 2.1, p = 0.038) associated with ischemia, but not SC. When examined by stenosis severity, PR remained a predictor of ischemia for all lesions, whereas %APV and LAP were associated with ischemia for only ≥50%, but not for <50%, stenosis. CONCLUSIONS: %APV and APCs by coronary CTA improve identification of coronary lesions that cause ischemia. PR is associated with all ischemia-causing lesions, whereas %APV and LAP are only associated with ischemia-causing lesions ≥50%. (Determination of Fractional Flow Reserve by Anatomic Computed Tomographic Angiography; NCT01233518).

Atherosclerotic plaque characterization by CT angiography for identification of high-risk coronary artery lesions: a comparison to optical coherence tomography.

AIMS: Adverse plaque characteristics (APCs) by coronary computed tomography (CT) angiography (CTA) are associated with myocardial ischaemia and future acute coronary syndromes. The overall objective was to determine whether APCs on non-invasive CTA are associated with vulnerable plaque features by invasive optical coherence tomography (OCT). METHODS AND RESULTS: Sixty-eight coronary plaques in 45 patients were evaluated by CTA and OCT. APCs by CTA were: positive remodelling (PR), remodelling index ≥1.10; low attenuation plaque (LAP), any intraplaque voxel <30 Hounsfield units; spotty calcification (SC), intraplaque calcification ≤3 mm; and 'napkin-ring' sign, low intraplaque attenuation surrounded by a higher attenuation rim. OCT evaluated plaques for thin-cap fibroatheroma (TCFA, ≤65 µm, lipid arch >90°) and macrophage infiltration. Increasing plaque vulnerability was graded by OCT as having no TCFA, TCFA without macrophage infiltration, and TCFA with macrophage infiltration. OCT lesions included those with no TCFA (n = 44), TCFA without macrophage infiltration (n = 7), and TCFA with macrophage infiltration (n = 17). Increasing plaque vulnerability grade by OCT was associated with higher diameter stenosis (43.6 vs. 40.7 vs. 57.3%, P = 0.01), and greater prevalence of PR (11 vs. 43 vs. 71%, P < 0.001), LAP (11 vs. 29 vs. 59%, P = 0.001), and SC (2 vs. 29 vs. 18%, P = 0.02), but not for napkin-ring sign (P = 0.18). In multivariable analysis, PR [odds ratio (OR) 16.9, 95% confidence interval (CI) 3.9-73.3, P < 0.001] and LAP (OR 11.2, 95% CI 2.8-44.3, P = 0.001) predicted TCFA with macrophage infiltration, whereas SC and napkin-ring sign did not. CONCLUSION: Plaques demonstrating PR and LAP by CTA are associated with TCFA with macrophage infiltration by OCT.