Coronary Atherosclerotic Precursors of Acute Coronary Syndromes.

BACKGROUND:The association of atherosclerotic features with first acute coronary syndromes (ACS) has not accounted for plaque burden. OBJECTIVES:The purpose of this study was to identify atherosclerotic features associated with precursors of ACS. METHODS:We performed a nested case-control study within a cohort of 25,251 patients undergoing coronary computed tomographic angiography (CTA) with follow-up over 3.4 ± 2.1 years. Patients with ACS and nonevent patients with no prior coronary artery disease (CAD) were propensity matched 1:1 for risk factors and coronary CTA-evaluated obstructive (≥50%) CAD. Separate core laboratories performed blinded adjudication of ACS and culprit lesions and quantification of baseline coronary CTA for percent diameter stenosis (%DS), percent cross-sectional plaque burden (PB), plaque volumes (PVs) by composition (calcified, fibrous, fibrofatty, and necrotic core), and presence of high-risk plaques (HRPs). RESULTS:We identified 234 ACS and control pairs (age 62 years, 63% male). More than 65% of patients with ACS had nonobstructive CAD at baseline, and 52% had HRP. The %DS, cross-sectional PB, fibrofatty and necrotic core volume, and HRP increased the adjusted hazard ratio (HR) of ACS (1.010 per %DS, 95% confidence interval [CI]: 1.005 to 1.015; 1.008 per percent cross-sectional PB, 95% CI: 1.003 to 1.013; 1.002 per mm3 fibrofatty plaque, 95% CI: 1.000 to 1.003; 1.593 per mm3 necrotic core, 95% CI: 1.219 to 2.082; all p < 0.05). Of the 129 culprit lesion precursors identified by coronary CTA, three-fourths exhibited <50% stenosis and 31.0% exhibited HRP. CONCLUSIONS:Although ACS increases with %DS, most precursors of ACS cases and culprit lesions are nonobstructive. Plaque evaluation, including HRP, PB, and plaque composition, identifies high-risk patients above and beyond stenosis severity and aggregate plaque burden

Coronary Atherosclerotic Precursors of Acute Coronary Syndromes

Background: The association of atherosclerotic features with first acute coronary syndromes (ACS) has not accounted for plaque burden. Objectives: The purpose of this study was to identify atherosclerotic features associated with precursors of ACS. Methods: We performed a nested case-control study within a cohort of 25,251 patients undergoing coronary computed tomographic angiography (CTA) with follow-up over 3.4 ± 2.1 years. Patients with ACS and nonevent patients with no prior coronary artery disease (CAD) were propensity matched 1:1 for risk factors and coronary CTA–evaluated obstructive (≥50%) CAD. Separate core laboratories performed blinded adjudication of ACS and culprit lesions and quantification of baseline coronary CTA for percent diameter stenosis (%DS), percent cross-sectional plaque burden (PB), plaque volumes (PVs) by composition (calcified, fibrous, fibrofatty, and necrotic core), and presence of high-risk plaques (HRPs). Results: We identified 234 ACS and control pairs (age 62 years, 63% male). More than 65% of patients with ACS had nonobstructive CAD at baseline, and 52% had HRP. The %DS, cross-sectional PB, fibrofatty and necrotic core volume, and HRP increased the adjusted hazard ratio (HR) of ACS (1.010 per %DS, 95% confidence interval [CI]: 1.005 to 1.015; 1.008 per percent cross-sectional PB, 95% CI: 1.003 to 1.013; 1.002 per mm3 fibrofatty plaque, 95% CI: 1.000 to 1.003; 1.593 per mm3 necrotic core, 95% CI: 1.219 to 2.082; all p < 0.05). Of the 129 culprit lesion precursors identified by coronary CTA, three-fourths exhibited <50% stenosis and 31.0% exhibited HRP. Conclusions: Although ACS increases with %DS, most precursors of ACS cases and culprit lesions are nonobstructive. Plaque evaluation, including HRP, PB, and plaque composition, identifies high-risk patients above and beyond stenosis severity and aggregate plaque burden

A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA.

ObjectivesThis study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography-based plaque characteristics.BackgroundCoronary 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.MethodsUtilizing the ICONIC (Incident COroNary Syndromes Identified by Computed Tomography) study, a nested case-control 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 this model 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.ResultsCL 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 &lt; 0.01), model 2 (0.532; 95% CI: 0.501 to 0.563; p &lt; 0.01), and model 3 (0.672; 95% CI: 0.662 to 0.682; p &lt; 0.01). When applied to the non-ACS cohort, the ML model had a specificity of 89.3% for excluding CLs.ConclusionsIn a high-risk cohort, a boosted ensemble algorithm can be used to predict CL from non-CL precursors on coronary CTA

A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA.

ObjectivesThis study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography-based plaque characteristics.BackgroundCoronary 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.MethodsUtilizing the ICONIC (Incident COroNary Syndromes Identified by Computed Tomography) study, a nested case-control 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 this model 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.ResultsCL 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 &lt; 0.01), model 2 (0.532; 95% CI: 0.501 to 0.563; p &lt; 0.01), and model 3 (0.672; 95% CI: 0.662 to 0.682; p &lt; 0.01). When applied to the non-ACS cohort, the ML model had a specificity of 89.3% for excluding CLs.ConclusionsIn a high-risk cohort, a boosted ensemble algorithm can be used to predict CL from non-CL precursors on coronary CTA

Age- and sex-related features of atherosclerosis from coronary computed tomography angiography in patients prior to acute coronary syndrome: results from the ICONIC study

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 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 mm3, 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 mm3, 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

A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA

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 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 case-control 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 this model 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

Association of High-Density Calcified 1K Plaque with Risk of Acute Coronary Syndrome

Importance: Plaque 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. Objective: To ascertain whether the increasing density in calcified coronary plaque is associated with risk for ACS. Design, Setting, and Participants: This 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. Exposures: Whole-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 Measures: Association between calcium density and future ACS risk. Results: A 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] mm3 vs 99.0 [156.1] mm3; P =.32), but patients who experienced ACS exhibited less 1K plaque (>1000 HU) compared with controls (3.9 [8.3] mm3 vs 9.4 [23.2] mm3; 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] mm3 vs 7.6 [20.3] mm3; P =.01). The per-patient and per-lesion results were similar between the 2 groups when restricted to myocardial infarction cases. Conclusions and Relevance: Results 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