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... Show moreIt 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. Show less
OBJECTIVEThis study evaluates the relationship between atherosclerotic plaque characteristics (APCs) and angiographic stenosis severity in patients with and without diabetes. Whether APCs differ... Show moreOBJECTIVEThis 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 METHODSWe 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.RESULTSAmong 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.CONCLUSIONSPatients 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. Show less
Key PointsQuestionAre atherosclerotic plaque measurements associated with physiologic measures of invasive fractional flow reserve? FindingsIn this analysis of the CREDENCE clinical trial that... Show moreKey PointsQuestionAre atherosclerotic plaque measurements associated with physiologic measures of invasive fractional flow reserve? FindingsIn this analysis of the CREDENCE clinical trial that included 612 patients, nonobstructive and obstructive measures of atherosclerotic plaque were significantly associated with invasive fractional flow reserve. A comprehensive set of atherosclerotic plaque features improved the accuracy of classifying vessel-specific reduced fractional flow reserve vs rest/stress myocardial perfusion imaging measurements. MeaningUsing coronary computed tomographic angiography for detection of atherosclerotic plaque features associated with coronary physiology may improve diagnostic certainty and guide clinical management of symptomatic patients.ImportanceStress 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. ObjectiveTo compare the diagnostic accuracy of comprehensive anatomic (obstructive and nonobstructive atherosclerotic plaque) vs functional imaging measures for estimating vessel-specific FFR. Design, Setting, and ParticipantsControlled 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. InterventionsPatients 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 MeasuresThe primary end point was invasive FFR of 0.80 or less. ResultsOf 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 RelevanceA 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 RegistrationClinicalTrials.gov Identifier: NCT02173275.This analysis of the CREDENCE trial compares the diagnostic accuracy of comprehensive anatomic (obstructive and nonobstructive atherosclerotic plaque) vs functional imaging measures for estimating vessel-specific fractional flow reserve. Show less
BackgroundSegmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography... Show moreBackgroundSegmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images.MethodsImages from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split.ResultsThe dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 +/- 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups.ConclusionsAn automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level. Show less
Kirpalani, H.; Ratcliffe, S.J.; Keszler, M.; Davis, P.G.; Foglia, E.E.; Pas, A. te; ... ; SAIL Site Investigators 2019
