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Differential progression of coronary atherosclerosis according to plaque composition: a cluster analysis of PARADIGM registry data.

ABSTRACT: Patient-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 mm³), with necrotic core and fibro-fatty PV regression (- 5.7 mm³ and - 5.6 mm³, respectively). Cluster 2 (n = 219), which contained largely fibro-fatty (39.2%) and fibrous plaque (46.8%), showed fibro-fatty PV regression (- 2.4 mm³). Cluster 3 (n = 376), which comprised mostly fibrous (62.7%) and calcified plaque (23.6%), showed increasingly prominent calcified PV progression (+ 21.4 mm³). Cluster 4 (n = 300), which comprised mostly calcified plaque (58.7%), demonstrated the greatest total PV increase (+ 50.7mm³), predominantly increasing in calcified PV (+ 35.9 mm³). 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.

SUBMITTER: Yoon YE

PROVIDER: S-EPMC8385056 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The baseline coronary plaque burden is the most important factor for rapid plaque progression (RPP) in the coronary artery. However, data on the independent predictors of RPP in the absence of a baseline coronary plaque burden are limited. Thus, this study aimed to investigate the predictors for RPP in patients without coronary plaques on baseline coronary computed tomography angiography (CCTA) images. A total of 402 patients (mean age: 57.6 ± 10.0 years, 49.3% men) without coronary plaques at baseline who underwent serial coronary CCTA were identified from the Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry and included in this retrospective study. RPP was defined as an annual change of ≥ 1.0%/year in the percentage atheroma volume (PAV). During a median inter-scan period of 3.6 years (interquartile range: 2.7-5.0 years), newly developed coronary plaques and RPP were observed in 35.6% and 4.2% of the patients, respectively. The baseline traditional risk factors, i.e., advanced age (≥ 60 years), male sex, hypertension, diabetes mellitus, hyperlipidemia, obesity, and current smoking status, were not significantly associated with the risk of RPP. Multivariate linear regression analysis showed that the serum hemoglobin A1c level (per 1% increase) measured at follow-up CCTA was independently associated with the annual change in the PAV (β: 0.098, 95% confidence interval [CI]: 0.048-0.149; P < 0.001). The multiple logistic regression models showed that the serum hemoglobin A1c level had an independent and positive association with the risk of RPP. The optimal predictive cut-off value of the hemoglobin A1c level for RPP was 7.05% (sensitivity: 80.0%, specificity: 86.7%; area under curve: 0.816 [95% CI: 0.574-0.999]; P = 0.017). In this retrospective case-control study, the glycemic control status was strongly associated with the risk of RPP in patients without a baseline coronary plaque burden. This suggests that regular monitoring of the glycemic control status might be helpful for preventing the rapid progression of coronary atherosclerosis irrespective of the baseline risk factors. Further randomized investigations are necessary to confirm the results of our study. ClinicalTrials.gov NCT02803411.

Project description: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 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.

Project description:ImportanceThe density of atherosclerotic plaque forms the basis for categorizing calcified and noncalcified morphology of plaques.ObjectiveTo assess whether alterations in plaque across a range of density measurements provide a more detailed understanding of atherosclerotic disease progression.Design, setting, and participantsThis cohort study enrolled 857 patients who underwent serial coronary computed tomography angiography 2 or more years apart and had quantitative measurements of coronary plaques throughout the entire coronary artery tree. The study was conducted from 2013 to 2016 at 13 sites in 7 countries.Main outcomes and measuresThe main outcome was progression of plaque composition of individual coronary plaques. Six plaque composition types were defined on a voxel-level basis according to the plaque attenuation (expressed in Hounsfield units [HU]): low attenuation (-30 to 75 HU), fibro-fatty (76-130 HU), fibrous (131-350 HU), low-density calcium (351-700 HU), high-density calcium (701-1000 HU), and 1K (>1000 HU). The progression rates of these 6 compositional plaque types were evaluated according to the interaction between statin use and baseline plaque volume, adjusted for risk factors and time interval between scans. Plaque progression was also examined based on baseline calcium density. Analysis was performed among lesions matched at baseline and follow-up. Data analyses were conducted from August 2019 through March 2020.ResultsIn total, 2458 coronary lesions in 857 patients (mean [SD] age, 62.1 [8.7] years; 540 [63.0%] men; 548 [63.9%] received statin therapy) were included. Untreated coronary lesions increased in volume over time for all 6 compositional types. Statin therapy was associated with volume decreases in low-attenuation plaque (β, -0.02; 95% CI, -0.03 to -0.01; P = .001) and fibro-fatty plaque (β, -0.03; 95% CI, -0.04 to -0.02; P < .001) and greater progression of high-density calcium plaque (β, 0.02; 95% CI, 0.01-0.03; P < .001) and 1K plaque (β, 0.02; 95% CI, 0.01-0.03; P < .001). When analyses were restricted to lesions without low-attenuation plaque or fibro-fatty plaque at baseline, statin therapy was not associated with a change in overall calcified plaque volume (β, -0.03; 95% CI, -0.08 to 0.02; P = .24) but was associated with a transformation toward more dense calcium. Interaction analysis between baseline plaque volume and calcium density showed that more dense coronary calcium was associated with less plaque progression.Conclusions and relevanceThe results suggest an association of statin use with greater rates of transformation of coronary atherosclerosis toward high-density calcium. A pattern of slower overall plaque progression was observed with increasing density. All findings support the concept of reduced atherosclerotic risk with increased densification of calcium.

Project description:ObjectivesThe aim of this study was to identify the determinants of plaque structural stress (PSS) and the relationship between PSS and plaques with rupture.BackgroundPlaque rupture is the most common cause of myocardial infarction, occurring particularly in higher risk lesions such as fibroatheromas. However, prospective intravascular ultrasound-virtual histology studies indicate that <10% higher risk plaques cause clinical events over 3 years, indicating that other factors also determine plaque rupture. Plaque rupture occurs when PSS exceeds its mechanical strength; however, the determinants of PSS and its association with plaques with proven rupture are not known.MethodsWe analyzed plaque structure and composition in 4,053 virtual histology intravascular ultrasound frames from 32 fibroatheromas with rupture from the intravascular ultrasound-virtual histology in Vulnerable Atherosclerosis study and 32 fibroatheromas without rupture on optical coherence tomography from a stable angina cohort. Mechanical loading in the periluminal region was estimated by calculating maximum principal PSS by finite element analysis.ResultsPSS increased with increasing lumen area (r = 0.46; p = 0.001), lumen eccentricity (r = 0.32; p = 0.001), and necrotic core ≥10% (r = 0.12; p = 0.001), but reduced when dense calcium was ≥10% (r = -0.12; p = 0.001). Ruptured fibroatheromas showed higher PSS (133 kPa [quartiles 1 to 3: 90 to 191 kPa] vs. 104 kPa [quartiles 1 to 3: 75 to 142 kPa]; p = 0.002) and variation in PSS (55 kPa [quartiles 1 to 3: 37 to 75 kPa] vs. 43 kPa [quartiles 1 to 3: 34 to 59 kPa]; p = 0.002) than nonruptured fibroatheromas, with rupture primarily occurring either proximal or immediately adjacent to the minimal luminal area (87.5% vs. 12.5%; p = 0.001). PSS was higher in segments proximal to the rupture site (143 kPa [quartiles 1 to 3: 101 to 200 kPa] vs. 120 kPa [quartiles 1 to 3: 78 to 180 kPa]; p = 0.001) versus distal segments, associated with increased necrotic core (19.1% [quartiles 1 to 3: 11% to 29%] vs. 14.3% [quartiles 1 to 3: 8% to 23%]; p = 0.001) but reduced fibrous/fibrofatty tissue (63.6% [quartiles 1 to 3: 46% to 78%] vs. 72.7% [quartiles 1 to 3: 54% to 86%]; p = 0.001). PSS >135 kPa was a good predictor of rupture in higher risk regions.ConclusionsPSS is determined by plaque composition, plaque architecture, and lumen geometry. PSS and PSS variability are increased in plaques with rupture, particularly at proximal segments. Incorporating PSS into plaque assessment may improve identification of rupture-prone plaques.

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Differential progression of coronary atherosclerosis according to plaque composition: a cluster analysis of PARADIGM registry data.

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