Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis : From the PARADIGM Registry

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 651.0%. 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

Topological data analysis of coronary plaques demonstrates the natural history of coronary atherosclerosis

OBJECTIVES This study sought to identify distinct patient groups and their association with outcome based on the patient similarity network using quantitative coronary plaque characteristics from coronary computed tomography angiography (CTA).BACKGROUND Coronary CTA can noninvasively assess coronary plaques quantitatively.METHODS Patients who underwent 2 coronary CTAs at a minimum of 24 months' interval were analyzed (n = 1,264). A similarity Mapper network of patients was built by topological data analysis (TDA) based on the whole-heart quantitative coronary plaque analysis on coronary CTA to identify distinct patient groups and their association with outcome.RESULTS Three distinct patient groups were identified by TDA, and the patient similarity network by TDA showed a dosed loop, demonstrating a continuous trend of coronary plaque progression. Group A had the least coronary plaque amount (median 12.4 mm(3) [interquartile range (IQR): 0.0 to 39.6 mm(3)]) in the entire coronary tree. Group B had a moderate coronary plaque amount (31.7 mm(3) [IQR: 0.0 to 127.4 mm(3)]) with relative enrichment of fibrofatty and necrotic core (32.6% [IQR: 16.7% to 46.2%] and 2.7% [IQR: 0.1% to 6.9%] of the total plaque, respectively) components. Group C had the largest coronary plaque amount (187.0 mm(3) [IQR: 96.7 to 306.4 mm(3)]) and was enriched for dense calcium component (46.8% [IQR: 32.0% to 63.7%] of the total plaque). At follow-up, total plaque volume, fibrous, and dense calcium volumes increased in all groups, but the proportion of fibrofatty component decreased in groups B and C, whereas the necrotic core portion decreased in only group B (all p< 0.05). Group B showed a higher acute coronary syndrome incidence than other groups (0.3% vs. 2.6% vs. 0.6%; p= 0.009) but both group B and C had a higher revascularization incidence than group A (3.1% vs. 15.5% vs. 17.8%; p < 0.001). Incorporating group information from TDA demonstrated increase of model fitness for predicting acute coronary syndrome or revascularization compared with that incorporating clinical risk factors, percentage diameter stenosis, and high-risk plaque features.CONCLUSIONS The TDA of quantitative whole-heart coronary plaque characteristics on coronary CTA identified distinct patient groups with different plaque dynamics and clinical outcomes. (Progression of AtheRosclerotic PlAque Determined by Computed TomoGraphic Angiography Imaging [PARADIGM]; NCT02803411) (C) 2021 by the American College of Cardiology Foundation.Cardiolog

A quantitative systems pharmacology consortium approach to managing immunogenicity of therapeutic proteins

Immunogenicity is a major challenge in drug development and patient care. Currently, most efforts are dedicated to the elimination of the unwanted immune responses through T‐cell epitope prediction and protein engineering. However, because it is unlikely that this approach will lead to complete eradication of immunogenicity, we propose that quantitative systems pharmacology models should be developed to predict and manage immunogenicity. The potential impact of such a mechanistic model‐based approach is precedented by applications of physiologically‐based pharmacokinetics

Coronary Atherosclerotic Precursors of Acute Coronary Syndromes

Cardiolog

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 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.Cardiolog

Differences in Progression to Obstructive Lesions per High-Risk Plaque Features and Plaque Volumes With CCTA

OBJECTIVES This study explored whether the pattern of nonobstructive lesion progression into obstructive lesions would differ according to the presence of high-risk plaque (HRP).BACKGROUND It is still debatable whether HRP simply represents a certain phase during the natural history of coronary atherosclerotic plaques or if disease progression would differ according to the presence of HRP.METHODS Patients with nonobstructive coronary artery disease, defined as percent diameter stenosis (%DS) = 2 years. HRP was defined as lesions with >= 2 features of positive remodeling, spotty calcification, or low-attenuation plaque. Quantitative total and compositional percent atheroma volume (PAV) at baseline and annualized PAV change were compared between non-HRP and HRP lesions.RESULTS A total of 3,049 nonobstructive lesions were identified from 1,297 patients (mean age 60.3 +/- 9.3 years; 56.8% men). There were 2,624 non-HRP and 425 HRP lesions. HRP lesions had a greater total PAV and all noncalcified components of PAV and %DS at baseline compared with non-HRP lesions. However, the annualized total PAV changes were greater in non-HRP lesions than in HRP lesions. On multivariate analysis adjusted for clinical risk factors, drug use, change in lipid level, total PAV, %DS, and HRP, only the baseline total PAV and %DS independently predicted the development of obstructive lesions (hazard ratio [HR]: 1.04; 95% confidence interval [CI]: 1.02 to 1.07, and HR: 1.07; 95% CI: 1.04 to 1.10, respectively, all p 0.05).CONCLUSIONS The pattern of individual coronary atherosclerotic plaque progression differed according to the presence of HRP. Baseline PAV, not the presence of HRP features, was the most important predictor of lesions developing into obstructive lesions. (Progression of Atherosclerotic Plaque Determined By Computed Tomographic Angiography Imaging [PARADIGM]; NCT02803411) (c) 2020 by the American College of Cardiology Foundation.Cardiolog

Impact of Non-obstructive left main disease on the progression of coronary artery disease: A PARADIGM substudy

Cardiolog

Differences in Progression to Obstructive Lesions per High-Risk Plaque Features and Plaque Volumes With CCTA

This study explored whether the pattern of nonobstructive lesion progression into obstructive lesions would differ according to the presence of high-risk plaque (HRP)