Background: We examined age differences in whole-heart volumes of non-calcified and calcified atherosclerosis by coronary computed tomography angiography (CCTA) of patients with future ACS. Methods... Show moreBackground: We examined age differences in whole-heart volumes of non-calcified and calcified atherosclerosis by coronary computed tomography angiography (CCTA) of patients with future ACS. Methods: A total of 234 patients with core-lab adjudicated ACS after baseline CCTA were enrolled. Atherosclerotic plaque was quantified and characterized from the main epicardial vessels and side branches on a 0.5 mm cross-sectional basis. Calcified plaque and non-calcified plaque were defined by above or below 350 Hounsfield units. Patients were categorized according to their age by deciles. Also, coronary artery calcium scores (CACS) were evaluated when available. Results: Patients were on average 62.2 +/- 11.5 years old. On the pre-ACS CCTA, patients showed diffuse, multi-site, predominantly non-obstructive atherosclerosis across all age categories, with plaque being detected in 93.5% of all ACS cases. The proportion calcified plaque from the total plaque burden increased significantly with older presentation (10% calcification in those <50 years, and 50% calcification in those >80 years old). Patients with ACS <50 years had remarkably lower atherosclerotic burden compared with older patients, but a high proportion of high risk markers such as low-attenuation plaque. CACS was >0 in 85% of the patients older than 50 years, and in 57% of patients younger than 50 years. Conclusion: The proportion of calcified plaque varied depending on patient age at the time of ACS. Only a small proportion of plaque was calcified when ACS occurred at <50 years old, while this increased gradually with older age. Purely non-calcified atherosclerotic plaque was not uncommon in patients <50 years. Show less
Aims: The temporal instability of coronary atherosclerotic plaque preceding an incident acute coronary syndrome (ACS) is not well defined. We sought to examine differences in the volume and... Show moreAims: The temporal instability of coronary atherosclerotic plaque preceding an incident acute coronary syndrome (ACS) is not well defined. We sought to examine differences in the volume and composition of coronary atherosclerosis between patients experiencing an early (<= 90 days) versus late ACS (>90 days) after baseline coronary computed tomography angiography (CCTA). Methods and results: From a multicenter study, we enrolled patients who underwent a clinically indicated baseline CCTA and experienced ACS during follow-up. Separate core laboratories performed blinded adjudication of ACS events and quantification of CCTA including compositional plaque volumes by Hounsfield units (HU): calcified plaque >350 HU, fibrous plaque 131-350 HU, fibrofatty plaque 31-130 HU and necrotic core <30 HU. In 234 patients (mean age 62 +/- 12 years, 36% women), early and late ACS occurred in 129 and 105 patients after a mean of 395 +/- 622 days, respectively. Patients with early ACS had a greater maximal diameter stenosis and maximal cross-sectional plaque burden as compared to patients with late ACS (P < 0.05). Larger total, fibrous, fibrofatty, and necrotic core volumes were observed in the early ACS group (P < 0.05). Findings for total, fibrous, fibrofatty, and necrotic core volumes were reproduced in an external validation cohort (P < 0.05). Conclusions: Volumetric differences in composition of coronary atherosclerosis exist between ACS patients according to their timing antecedent to the acute event. These data support that a large burden of non-calcified plaque on CCTA is strongly associated with near-term plaque instability and ACS risk. Show less
IMPORTANCE Distinct plaque locations and vessel geometric features predispose to altered coronary flow hemodynamics. The association between these lesion-level characteristics assessed by coronary... Show moreIMPORTANCE Distinct plaque locations and vessel geometric features predispose to altered coronary flow hemodynamics. The association between these lesion-level characteristics assessed by coronary computed tomographic angiography (CCTA) and risk of future acute coronary syndrome (ACS) is unknown.OBJECTIVE To examine whether CCTA-derived adverse geometric characteristics (AGCs) of coronary lesions describing location and vessel geometry add to plaque morphology and burden for identifying culprit lesion precursors associated with future ACS.DESIGN, SETTING, AND PARTICIPANTS This substudy of ICONIC (Incident Coronary Syndromes Identified by Computed Tomography), a multicenter nested case-control cohort study, included patients with ACS and a culprit lesion precursor identified on baseline CCTA (n = 116) and propensity score-matched non-ACS controls (n = 116). Data were collected from July 20, 2012, to April 30, 2017, and analyzed from October 1, 2020, to October 31, 2021.EXPOSURES Coronary lesions were evaluated for the following 3 AGCs: (1) distance from the coronary ostium to lesion; (2) location at vessel bifurcations; and (3) vessel tortuosity, defined as the presence of 1 bend of greater than 90 degrees or 3 curves of 45 degrees to 90 degrees using a 3-point angle within the lesion.MAIN OUTCOMES AND MEASURES Association between lesion-level AGCs and risk of future ACS-causing culprit lesions.RESULTS Of 548 lesions, 116 culprit lesion precursors were identified in 116 patients (80 [69.0%] men; mean [SD], age 62.7 [11.5] years). Compared with nonculprit lesions, culprit lesion precursors had a shorter distance from the ostium (median, 35.1 [IQR, 23.6-48.4] mm vs 44.5 [IQR, 28.2-70.8] mm), more frequently localized to bifurcations (85 [73.3%] vs 168 [38.9%]), and had more tortuous vessel segments (5 [4.3%] vs 6 [1.4%]; all P<.05). In multivariable Cox regression analysis, an increasing number of AGCs was associated with a greater risk of future culprit lesions (hazard ratio [HR] for 1 AGC, 2.90 [95% CI, 1.38-6.08]; P=.005; HR for >= 2 AGCs, 6.84 [95% CI, 3.33-14.04]; P<.001). Adverse geometric characteristics provided incremental discriminatory value for culprit lesion precursors when added to a model containing stenosis severity, adverse morphological plaque characteristics, and quantitative plaque characteristics (area under the curve, 0.766 [95% CI, 0.718-0.814] vs 0.733 [95% CI, 0.685-0.782]). In per-patient comparison, patients with ACS had a higher frequency of lesions with adverse plaque characteristics, AGCs, or both compared with control patients (>= 2 adverse plaque characteristics, 70 [60.3%] vs 50 [43.1%]; >= 2 AGCs, 92 [79.3%] vs 60 [51.7%]; >= 2 of both, 37 [31.9%] vs 20 [17.2%]; all P<.05).CONCLUSIONS AND RELEVANCE These findings support the concept that CCTA-derived AGCs capturing lesion location and vessel geometry are associated with risk of future ACS-causing culprit lesions. Adverse geometric characteristics may provide additive prognostic information beyond plaque assessment in CCTA. Show less
Patient-specific phenotyping of coronary atherosclerosis would facilitate personalized risk assessment and preventive treatment. We explored whether unsupervised cluster analysis can categorize... Show morePatient-specific phenotyping of coronary atherosclerosis would facilitate personalized risk assessment and preventive treatment. We explored whether unsupervised cluster analysis can categorize patients with coronary atherosclerosis according to their plaque composition, and determined how these differing plaque composition profiles impact plaque progression. Patients with coronary atherosclerotic plaque (n = 947; median age, 62 years; 59% male) were enrolled from a prospective multi-national registry of consecutive patients who underwent serial coronary computed tomography angiography (median inter-scan duration, 3.3 years). K-means clustering applied to the percent volume of each plaque component and identified 4 clusters of patients with distinct plaque composition. Cluster 1 (n = 52), which comprised mainly fibro-fatty plaque with a significant necrotic core (median, 55.7% and 16.0% of the total plaque volume, respectively), showed the least total plaque volume (PV) progression (+ 23.3 mm(3)), with necrotic core and fibro-fatty PV regression (- 5.7 mm(3) and - 5.6 mm(3), respectively). Cluster 2 (n = 219), which contained largely fibro-fatty (39.2%) and fibrous plaque (46.8%), showed fibro-fatty PV regression (- 2.4 mm(3)). Cluster 3 (n = 376), which comprised mostly fibrous (62.7%) and calcified plaque (23.6%), showed increasingly prominent calcified PV progression (+ 21.4 mm(3)). Cluster 4 (n = 300), which comprised mostly calcified plaque (58.7%), demonstrated the greatest total PV increase (+ 50.7mm(3)), predominantly increasing in calcified PV (+ 35.9 mm(3)). Multivariable analysis showed higher risk for plaque progression in Clusters 3 and 4, and higher risk for adverse cardiac events in Clusters 2, 3, and 4 compared to that in Cluster 1. Unsupervised clustering algorithms may uniquely characterize patient phenotypes with varied atherosclerotic plaque profiles, yielding distinct patterns of progressive disease and outcome. Show less
Shaw, L.J.; Blankstein, R.; Bax, J.J.; Ferencik, M.; Bittencourt, M.S.; Min, J.K.; ... ; Narula, J. 2021
Coronary computed tomographic angiography (CCTA) provides a wealth of clinically meaningful information beyond anatomic stenosis alone, including the presence or absence of nonobstructive... Show moreCoronary computed tomographic angiography (CCTA) provides a wealth of clinically meaningful information beyond anatomic stenosis alone, including the presence or absence of nonobstructive atherosclerosis and high-risk plaque features as precursors for incident coronary events. There is, however, no uniform agreement on how to identify and quantify these features or their use in evidence-based clinical decision-making. This statement from the Society of Cardiovascular Computed Tomography and North American Society of Cardiovascular Imaging addresses this gap and provides a comprehensive review of the available evidence on imaging of coronary atherosclerosis. In this statement, we provide standardized definitions for high-risk plaque (HRP) features and distill the evidence on the effectiveness of risk stratification into usable practice points. This statement outlines how this information should be communicated to referring physicians and patients by identifying critical elements to include in a structured CCTA report - the presence and severity of atherosclerotic plaque (descriptive statements, CAD-RADS (TM) categories), the segment involvement score, HRP features (e.g., low attenuation plaque, positive remodeling), and the coronary artery calcium score (when performed). Rigorous documentation of atherosclerosis on CCTA provides a vital opportunity to make recommendations for preventive care and to initiate and guide an effective care strategy for at-risk patients. (C) 2020 Published by Elsevier Inc. on behalf of Society of Cardiovascular Computed Tomography. Show less
Aims Although there is increasing evidence supporting coronary atherosclerosis evaluation by coronary computed tomography angiography (CCTA), no data are available on age and sex differences for... Show moreAims Although there is increasing evidence supporting coronary atherosclerosis evaluation by coronary computed tomography angiography (CCTA), no data are available on age and sex differences for quantitative plaque features. The aim of this study was to investigate sex and age differences in both qualitative and quantitative atherosclerotic features from CCTA prior to acute coronary syndrome (ACS).Methods and results Within the ICONIC study, in which 234 patients with subsequent ACS were propensity matched 1:1 with 234 non-event controls, our current subanalysis included only the ACS cases. Both qualitative and quantitative advance plaque analysis by CCTA were performed by a core laboratory. In 129 cases, culprit lesions identified by invasive coronary angiography at the time of ACS were co-registered to baseline CCTA precursor lesions. The study population was then divided into subgroups according to sex and age (<65 vs. = 65 years old) for analysis. Older patients had higher total plaque volume than younger patients. Within specific subtypes of plaque volume, however, only calcified plaque volume was higher in older patients (135.9 +/- 163.7 vs. 63.8 +/- 94.2 mm(3), P < 0.0001, respectively). Although no sex-related differences were recorded for calcified plaque volume, females had lower fibrous and fibrofatty plaque volume than males (Fibrofatty volume 29.6 +/- 44.1 vs. 75.3 +/- 98.6 mm(3), P = 0.0001, respectively). No sex-related differences in the prevalence of qualitative high-risk plaque features were found, even after separate analyses considering age were performed.Conclusion Our data underline the importance of age- and sex-related differences in coronary atherosclerosis presentation, which should be considered during CCTA-based atherosclerosis quantification. Show less
OBJECTIVES This study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography-based plaque... Show moreOBJECTIVES This study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography-based plaque characteristics.BACKGROUND Coronary computed tomography angiography (CTA) has been validated for patient-level prediction of ACS. However, the applicability of coronary CTA to CL assessment is not known.METHODS Utilizing the ICONIC (Incident COroNary Syndromes Identified by Computed Tomography) study, a nested casecontrol study of 468 patients with baseline coronary CTA, the study included ACS patients with invasive coronary angiography-adjudicated CLs that could be aligned to CL precursors on baseline coronary CTA. Separate blinded core laboratories adjudicated CLs and performed atherosclerotic plaque evaluation. Thereafter, the study used a boosted ensemble algorithm (XGBoost) to develop a predictive model of CLs. Data were randomly split into a training set (80%) and a test set (20%). The area under the receiver-operating characteristic curve of thismodel was compared with that of diameter stenosis (model 1), high-risk plaque features (model 2), and lesion-level features of CL precursors from the ICONIC study (model 3). Thereafter, the machine learning (ML) model was applied to 234 non-ACS patients with 864 lesions to determine model performance for CL exclusion.RESULTS CL precursors were identified by both coronary angiography and baseline coronary CTA in 124 of 234 (53.0%) patients, with a total of 582 lesions (containing 124 CLs) included in the analysis. The ML model demonstrated significantly higher area under the receiver-operating characteristic curve for discriminating CL precursors (0.774; 95% confidence interval [CI]: 0.758 to 0.790) compared with model 1 (0.599; 95% CI: 0.599 to 0.599; p < 0.01), model 2 (0.532; 95% CI: 0.501 to 0.563; p < 0.01), and model 3 (0.672; 95% CI: 0.662 to 0.682; p < 0.01). When applied to the non-ACS cohort, the ML model had a specificity of 89.3% for excluding CLs.CONCLUSIONS In a high-risk cohort, a boosted ensemble algorithm can be used to predict CL from non-CL precursors on coronary CTA. (c) 2020 by the American College of Cardiology Foundation. Show less
Aims High-risk plaque (HRP) and non-obstructive coronary artery disease independently predict adverse events, but their importance to future culprit lesions has not been resolved. We sought to... Show moreAims High-risk plaque (HRP) and non-obstructive coronary artery disease independently predict adverse events, but their importance to future culprit lesions has not been resolved. We sought to determine in patients prior to confirmed acute coronary syndrome (ACS) the association between lesion percent diameter stenosis (%DS), and the absolute number and prevalence of HRP. The secondary objective was to examine the relative importance of non-obstructive HRP in future culprit lesions.Methods and results Within the ICONIC study, a nested case-control study of patients undergoing coronary computed tomographic angiography (coronary CT), we included ACS cases with culprit lesions confirmed by invasive coronary angiography and coregistered to baseline coronary CT. Quantitative CT was used to evaluate obstructive (>= 50%) and non-obstructive (<50%) diameter stenosis, with HRP defined as >= 2 features of spotty calcification, positive remodelling, or low-attenuation plaque at baseline. A total of 234 patients with downstream ACS over 54 (interquartile range 5-525.5) days exhibited 198/898 plaques with HRP on coronary CT. While HRP was less prevalent in non-obstructive (19.7%, 161/819) than obstructive lesions (46.8%, 37/79, P < 0.001), non-obstructive plaque comprised 81.3% (161/198) of HRP lesions overall. Among the 128 patients with identifiable culprit lesion precursors, the adjusted hazard ratio (HR) was 1.85 [95% confidence interval (CI) 1.26-2.72] for HRP, with no interaction between %DS and HRP (P = 0.82). Compared to non-obstructive HRP lesions, obstructive lesions without HRP exhibited a non-significant HR of 1.41 (95% CI 0.61-3.25, P = 0.42).Conclusions While HRP is more prevalent among obstructive lesions, non-obstructive HRP lesions outnumber those that are obstructive and confer risk clinically approaching that of obstructive lesions without HRP. 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
OBJECTIVES This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification.BACKGROUND Segmentation of cardiac structures from coronary computed... Show moreOBJECTIVES This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification.BACKGROUND Segmentation of cardiac structures from coronary computed tomography angiography (CCTA) images is laborious. We designed an end-to-end deep-learning solution.METHODS Scans were obtained from multicenter registries of 166 patients who underwent clinically indicated CCTA. Left ventricular volume (LVV) and right ventricular volume (RVV), left atrial volume (LAV) and right atrial volume (RAV), and left ventricular myocardial mass (LVM) were manually annotated as ground truth. A U-Net-inspired, deep-learning model was trained, validated, and tested in a 70:20:10 split.RESULTS Mean age was 61.1 +/- 8.4 years, and 49% were women. A combined overall median Dice score of 0.9246 (interquartile range: 0.8870 to 0.9475) was achieved. The median Dice scores for LVV, RVV, LAV, RAV, and LVM were 0.938 (interquartile range: 0.887 to 0.958), 0.927 (interquartile range: 0.916 to 0.946), 0.934 (interquartile range: 0.899 to 0.950), 0.915 (interquartile range: 0.890 to 0.920), and 0.920 (interquartile range: 0.811 to 0.944), respectively. Model prediction correlated and agreed well with manual annotation for LVV (r = 0.98), RVV (r = 0.97), LAV (r = 0.78), RAV (r = 0.97), and LVM (r = 0.94) (p < 0.05 for all). Mean difference and limits of agreement for LVV, RVV, LAV, RAV, and LVM were 1.20 ml (95% CI: -7.12 to 9.51), -0.78 ml (95% CI: -10.08 to 8.52), -3.75 ml (95% CI: -21.53 to 14.03), 0.97 ml (95% CI: -6.14 to 8.09), and 6.41 g (95% CI: -8.71 to 21.52), respectively.CONCLUSIONS A deep-learning model rapidly segmented and quantified cardiac structures. This was done with high accuracy on a pixel level, with good agreement with manual annotation, facilitating its expansion into areas of research and clinical import. (C) 2020 by the American College of Cardiology Foundation. Show less
Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single... Show moreBackground Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP.Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume >= 1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128).Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP. Show less
This case-control cohort study analyzes patients with suspected coronary atherosclerosis and control patients to identify the factors associated with higher or lower risks for adverse... Show moreThis case-control cohort study analyzes patients with suspected coronary atherosclerosis and control patients to identify the factors associated with higher or lower risks for adverse cardiovascular events and acute coronary syndrome.Key PointsQuestionIs the density of coronary calcified plaque associated with future development of acute coronary syndrome? FindingsIn this case-control study of 189 patients who experienced vs 189 control individuals who did not experience an acute coronary syndrome after baseline coronary computed tomography angiography imaging, the volume of plaque with more than 1000 Hounsfield unit (termed 1K plaque) was associated with lower risk for acute coronary syndrome. The specific acute coronary syndrome precursor culprit lesion had less 1K plaque compared with the most stenotic lesion in control individuals. MeaningThis study's findings suggest that 1K plaque detected by coronary computed tomography angiography is associated with lower risk of future occurrence of acute coronary syndrome.ImportancePlaque morphologic measures on coronary computed tomography angiography (CCTA) have been associated with future acute coronary syndrome (ACS). However, the evolution of calcified coronary plaques by noninvasive imaging is not known. ObjectiveTo ascertain whether the increasing density in calcified coronary plaque is associated with risk for ACS. Design, Setting, and ParticipantsThis multicenter case-control cohort study included individuals enrolled in ICONIC (Incident Coronary Syndromes Identified by Computed Tomography), a nested case-control study of patients drawn from the CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter) registry, which included 13 study sites in 8 countries. Patients who experienced core laboratory-verified ACS after baseline CCTA (n=189) and control individuals who did not experience ACS after baseline CCTA (n=189) were included. Patients and controls were matched 1:1 by propensity scores for age; male sex; presence of hypertension, hyperlipidemia, and diabetes; family history of premature coronary artery disease (CAD); current smoking status; and CAD severity. Data were analyzed from November 2018 to March 2019. ExposuresWhole-heart atherosclerotic plaque volume was quantitated from all coronary vessels and their branches. For patients who underwent invasive angiography at the time of ACS, culprit lesions were coregistered to baseline CCTA lesions by a blinded independent reader. Low-density plaque was defined as having less than 130 Hounsfield units (HU); calcified plaque, as having more than 350 HU and subcategorized on a voxel-level basis into 3 strata: 351 to 700 HU, 701 to 1000 HU, and more than 1000 HU (termed 1K plaque). Main Outcomes and MeasuresAssociation between calcium density and future ACS risk. ResultsA total of 189 patients and 189 matched controls (mean [SD] age of 59.9[9.8] years; 247 [65.3%] were male) were included in the analysis and were monitored during a mean (SD) follow-up period of 3.9 (2.5) years. The overall mean (SD) calcified plaque volume (>350 HU) was similar between patients and controls (76.4 [101.6] mm(3) vs 99.0 [156.1] mm(3); P=.32), but patients who experienced ACS exhibited less 1K plaque (>1000 HU) compared with controls (3.9 [8.3] mm(3) vs 9.4 [23.2] mm(3); P=.02). Individuals within the highest quartile of 1K plaque exhibited less low-density plaque, as a percentage of total plaque, when compared with patients within the lower 3 quartiles (12.6% [10.4%] vs 24.9% [20.6%]; P<.001). For 93 culprit precursor lesions detected by CCTA, the volume of 1K plaque was lower compared with the maximally stenotic lesion in controls (2.6 [7.2] mm(3) vs 7.6 [20.3] mm(3); P=.01). The per-patient and per-lesion results were similar between the 2 groups when restricted to myocardial infarction cases. Conclusions and RelevanceResults of this study suggest that, on a per-patient and per-lesion basis, 1K plaque was associated with a lower risk for future ACS and that measurement of 1K plaque may improve risk stratification beyond plaque burden. Show less
The purpose of this review is to summarize the work published by the Journal of Cardiovascular Computed Tomography (JCCT) for the year 2019, highlighting original research and new guidelines.
