Age- and Sex-Specific Nomographic CT Quantitative Plaque Data From a Large International Cohort.

With growing adoption of coronary computed tomographic angiography (CTA), there is increasing evidence for and interest in the prognostic importance of atherosclerotic plaque volume. Manual tools for plaque segmentation are cumbersome, and their routine implementation in clinical practice is limited. The aim of this study was to develop nomographic quantitative plaque values from a large consecutive multicenter cohort using coronary CTA. Quantitative assessment of total atherosclerotic plaque and plaque subtype volumes was performed in patients undergoing clinically indicated coronary CTA, using an Artificial Intelligence-Enabled Quantitative Coronary Plaque Analysis tool. A total of 11,808 patients were included in the analysis; their mean age was 62.7 ± 12.2 years, and 5,423 (45.9%) were women. The median total plaque volume was 223 mm <sup>3</sup> (IQR: 29-614 mm <sup>3</sup> ) and was significantly higher in male participants (360 mm <sup>3</sup> ; IQR: 78-805 mm <sup>3</sup> ) compared with female participants (108 mm <sup>3</sup> ; IQR: 10-388 mm <sup>3</sup> ) (P < 0.0001). Total plaque increased with age in both male and female patients. Younger patients exhibited a higher prevalence of noncalcified plaque. The distribution of total plaque volume and its components was reported in every decile by age group and sex. The authors developed pragmatic age- and sex-stratified percentile nomograms for atherosclerotic plaque measures using findings from coronary CTA. The impact of age and sex on total plaque and its components should be considered in the risk-benefit analysis when treating patients. Artificial Intelligence-Enabled Quantitative Coronary Plaque Analysis work flows could provide context to better interpret coronary computed tomographic angiographic measures and could be integrated into clinical decision making

Data on the impact of scan quality on the diagnostic performance of CCTA, SPECT, and PET for diagnosing myocardial ischemia defined by fractional flow reserve on a per vessel level

Scan quality directly impacts the diagnostic performance of non-invasive imaging modalities as reported in a substudy of the PACIFC-trial: "Impact of Scan Quality on the Diagnostic Performance of CCTA, SPECT, and PET for Diagnosing Myocardial Ischemia Defined by Fractional Flow Reserve" [1]. This Data-in-Brief paper supplements the hereinabove mentioned article by presenting the diagnostic performance of CCTA, SPECT, and PET on a per vessel level for the detection of hemodynamic significant coronary artery disease (CAD) when stratified according to scan quality and vascular territory

Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease

PurposeTraditionally, interpretation of myocardial perfusion imaging (MPI) is based on visual assessment. Computer-based automated analysis might be a simple alternative obviating the need for extensive reading experience. Therefore, the aim of the present study was to compare the diagnostic performance of automated analysis with that of expert visual reading for the detection of obstructive coronary artery disease (CAD).Methods206 Patients (64% men, age 58.2 ± 8.7 years) with suspected CAD were included prospectively. All patients underwent 99mTc-tetrofosmin single-photon emission computed tomography (SPECT) and invasive coronary angiography with fractional flow reserve (FFR) measurements. Non-corrected (NC) and attenuation-corrected (AC) SPECT images were analyzed both visually as well as automatically by commercially available SPECT software. Automated analysis comprised a segmental summed stress score (SSS), summed difference score (SDS), stress total perfusion deficit (S-TPD), and ischemic total perfusion deficit (I-TPD), representing the extent and severity of hypoperfused myocardium. Subsequently, software was optimized with an institutional normal database and thresholds. Diagnostic performances of automated and visual analysis were compared taking FFR as a reference.ResultsSensitivity did not differ significantly between visual reading and most automated scoring parameters, except for SDS, which was significantly higher than visual assessment (p ConclusionAutomated analysis of myocardial perfusion SPECT can be as accurate as visual interpretation by an expert reader in detecting significant CAD defined by FFR.</div

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&lt;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

Comparison between the performance of quantitative flow ratio and perfusion imaging for diagnosing myocardial ischemia

OBJECTIVES This study compared the performance of the quantitative flow ratio (QFR) with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) myocardial perfusion imaging (MPI) for the diagnosis of fractional flow reserve (FFR)-defined coronary artery disease (CAD).BACKGROUND QFR estimates FFR solely based on cine contrast images acquired during invasive coronary angiography (ICA). Head-to-head studies comparing QFR with noninvasive MPI are lacking.METHODS A total of 208 (624 vessels) patients underwent technetium -99m tetrofosmin SPECT and [15O]H2O PET imaging before ICA in conjunction with FFR measurements. ICA was obtained without using a dedicated QFR acquisition protocol, and QFR computation was attempted in all vessels interrogated by FFR (552 vessels).RESULTS QFR computation succeeded in 286 (52%) vessels. QFR correlated well with invasive FFR overall (R = 0.79; p < 0.001) and in the subset of vessels with an intermediate (30% to 90%) diameter stenosis (R = 0.76; p < 0.001). Overall, per-vessel analysis demonstrated QFR to exhibit a superior sensitivity (70%) in comparison with SPECT (29%; p < 0.001), whereas it was similar to PET (75%; p = 1.000). Specificity of QFR (93%) was higher than PET (79%; p < 0.001) and not different from SPECT (96%; p = 1.000). As such, the accuracy of QFR (88%) was superior to both SPECT (82%; p = 0.010) and PET (78%; p = 0.004). Lastly, the area under the receiver operating characteristics curve of QFR, in the overall sample (0.94) and among vessels with an intermediate lesion (0.90) was higher than SPECT (0.63 and 0.61; p < 0.001 for both) and PET (0.82; p < 0.001 and 0.77; p = 0.002), respectively.CONCLUSIONS In this head-to-head comparative study, QFR exhibited a higher diagnostic value for detecting FFRdefined significant CAD compared with perfusion imaging by SPECT or PET. (J Am Coll Cardiol Img 2020;13:1976-85) (c) 2020 by the American College of Cardiology Foundation.Cardiovascular Aspects of Radiolog

Valve Academic Research Consortium 3: updated endpoint definitions for aortic valve clinical research

AIMS The Valve Academic Research Consortium (VARC), founded in 2010, was intended to (i) identify appropriate clinical endpoints and (ii) standardize definitions of these endpoints for transcatheter and surgical aortic valve clinical trials. Rapid evolution of the field, including the emergence of new complications, expanding clinical indications, and novel therapy strategies have mandated further refinement and expansion of these definitions to ensure clinical relevance. This document provides an update of the most appropriate clinical endpoint definitions to be used in the conduct of transcatheter and surgical aortic valve clinical research.METHODS AND RESULTS Several years after the publication of the VARC-2 manuscript, an in-person meeting was held involving over 50 independent clinical experts representing several professional societies, academic research organizations, the US Food and Drug Administration (FDA), and industry representatives to (i) evaluate utilization of VARC endpoint definitions in clinical research, (ii) discuss the scope of this focused update, and (iii) review and revise specific clinical endpoint definitions. A writing committee of independent experts was convened and subsequently met to further address outstanding issues. There were ongoing discussions with FDA and many experts to develop a new classification schema for bioprosthetic valve dysfunction and failure. Overall, this multi-disciplinary process has resulted in important recommendations for data reporting, clinical research methods, and updated endpoint definitions. New definitions or modifications of existing & nbsp;definitions are being proposed for repeat hospitalizations, access site-related complications, bleeding events, conduction disturbances, cardiac structural complications, and bioprosthetic valve dysfunction and failure (including valve leaflet thickening and thrombosis). A more granular 5-class grading scheme for paravalvular regurgitation (PVR) is being proposed to help refine the assessment of PVR. Finally, more specific recommendations on quality-of-life assessments have been included, which have been targeted to specific clinical study designs.CONCLUSIONS Acknowledging the dynamic and evolving nature of less-invasive aortic valve therapies, further refinements of clinical research processes are required. The adoption of these updated and newly proposed VARC-3 endpoints and definitions will ensure homogenous event reporting, accurate adjudication, and appropriate comparisons of clinical research studies involving devices and new therapeutic strategies.Cardiolog

Valve Academic Research Consortium 3: updated endpoint definitions for aortic valve clinical research

Aims The Valve Academic Research Consortium (VARC), founded in 2010, was intended to (i) identify appropriate clinical endpoints and (ii) standardize definitions of these endpoints for transcatheter and surgical aortic valve clinical trials. Rapid evolution of the field, including the emergence of new complications, expanding clinical indications, and novel therapy strategies have mandated further refinement and expansion of these definitions to ensure clinical relevance. This document provides an update of the most appropriate clinical endpoint definitions to be used in the conduct of transcatheter and surgical aortic valve clinical research.Methods and results Several years after the publication of the VARC-2 manuscript, an in-person meeting was held involving over 50 independent clinical experts representing several professional societies, academic research organizations, the US Food and Drug Administration (FDA), and industry representatives to (i) evaluate utilization of VARC endpoint definitions in clinical research, (ii) discuss the scope of this focused update, and (iii) review and revise specific clinical endpoint definitions. A writing committee of independent experts was convened and subsequently met to further address outstanding issues. There were ongoing discussions with FDA and many experts to develop a new classification schema for bioprosthetic valve dysfunction and failure. Overall, this multi-disciplinary process has resulted in important recommendations for data reporting, clinical research methods, and updated endpoint definitions. New definitions or modifications of existing definitions are being proposed for repeat hospitalizations, access site-related complications, bleeding events, conduction disturbances, cardiac structural complications, and bioprosthetic valve dysfunction and failure (including valve leaflet thickening and thrombosis). A more granular 5-class grading scheme for paravalvular regurgitation (PVR) is being proposed to help refine the assessment of PVR. Finally, more specific recommendations on quality-of-life assessments have been included, which have been targeted to specific clinical study designs.Conclusions Acknowledging the dynamic and evolving nature of less-invasive aortic valve therapies, further refinements of clinical research processes are required. The adoption of these updated and newly proposed VARC-3 endpoints and definitions will ensure homogenous event reporting, accurate adjudication, and appropriate comparisons of clinical research studies involving devices and new therapeutic strategies.Cardiolog

Effects of chronic kidney disease and declining renal function on coronary atherosclerotic plaque progression: a PARADIGM substudy

Aims To investigate the change in atherosclerotic plaque volume in patients with chronic kidney disease (CKD) and declining renal function, using coronary computed tomography angiography (CCTA).Methods and results In total, 891 participants with analysable serial CCTA and available glomerular filtration rate (GFR, derived using Cockcroft-Gault formulae) at baseline (CCTA 1) and follow-up (CCTA 2) were included. CKD was defined as GFR = 10% drop in GFR from the baseline. Quantitative assessment of plaque volume and composition were performed on both scans. There were 203 participants with CKD and 688 without CKD. CKD was associated with higher baseline total plaque volume, but similar plaque progression, measured by crude (57.53.4 vs. 65.9 +/- 7.7 mm(3)/year, P = 0.28) or annualized (17.3 +/- 1.0 vs. 19.9 +/- 2.0 mm(3)/year, P = 0.25) change in total plaque volume. There were 709 participants with stable GFR and 182 with declining GFR. Declining renal function was independently associated with plaque progression, with higher crude (54.1 +/- 3.2 vs. 80.2 +/- 9.0 mm(3)/year, P < 0.01) or annualized (16.4 +/- 0.9 vs. 23.9 +/- 2.6 mm(3)/year, P < 0.01) increase in total plaque volume. In CKD, plaque progression was driven by calcified plaques whereas in patients with declining renal function, it was driven by non-calcified plaques.Conclusion Decline in renal function was associated with more rapid plaque progression, whereas the presence of CKD was not.Cardiolog

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

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 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.Cardiolog

Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: results from the PARADIGM registry

Background and HypothesisThe recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner.MethodsFrom the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model.ResultsThe 90th percentiles of the DS of the three vessels and their maximum DS change were 41%–50% and 5.6%–7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis.ConclusionsThis study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression.Cardiolog