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Background and Aims: Different methodologies to report whole-heart atherosclerotic plaque on coronary computed tomography angiography (CCTA) have been utilized. We examined which of the three commonly used plaque burden definitions was least affected by differences in body surface area (BSA) and sex., Methods: The PARADIGM study includes symptomatic patients with suspected coronary atherosclerosis who underwent serial CCTA >2 years apart. Coronary lumen, vessel, and plaque were quantified from the coronary tree on a 0.5 mm cross-sectional basis by a core-lab, and summed to per-patient. Three quantitative methods of plaque burden were employed: (1) total plaque volume (PV) in mm 3 , (2) percent atheroma volume (PAV) in % [which equaled: PV/vessel volume * 100%], and (3) normalized total atheroma volume (TAV norm ) in mm 3 [which equaled: PV/vessel length * mean population vessel length]. Only data from the baseline CCTA were used. PV, PAV, and TAV norm were compared between patients in the top quartile of BSA vs the remaining, and between sexes. Associations between vessel volume, BSA, and the three plaque burden methodologies were assessed., Results: The study population comprised 1479 patients (age 60.7 ± 9.3 years, 58.4% male) who underwent CCTA. A total of 17,649 coronary artery segments were evaluated with a median of 12 (IQR 11-13) segments per-patient (from a 16-segment coronary tree). Patients with a large BSA (top quartile), compared with the remaining patients, had a larger PV and TAV norm , but similar PAV. The relation between larger BSA and larger absolute plaque volume (PV and TAV norm ) was mediated by the coronary vessel volume. Independent from the atherosclerotic cardiovascular disease risk (ASCVD) score, vessel volume correlated with PV (P < 0.001), and TAV norm (P = 0.003), but not with PAV (P = 0.201). The three plaque burden methods were equally affected by sex., Conclusions: PAV was less affected by patient's body surface area then PV and TAV norm and may be the preferred method to report coronary atherosclerotic burden., Competing Interests: Declaration of competing interest Dr. James K. Min receives funding from the Dalio Foundation, National Institutes of Health, and GE Healthcare. Dr. Min serves on the scientific advisory board of Arineta and GE Healthcare, and has an equity interest in Cleerly. Dr. Habib Samady serves on the scientific advisory board of Philips, has equity interest in Covanos Inc., and has a research grant from Medtronic. Dr. Kavitha Chinnaiyan is a non-compensated medical advisory board member of Heartflow Inc. The remaining authors have no relevant disclosures., (Copyright © 2020. Published by Elsevier Inc.)

Background: The association between triglyceride glucose (TyG) index and coronary atherosclerotic change remains unclear. We aimed to evaluate the association between TyG index and coronary plaque progression (PP) using serial coronary computed tomography angiography (CCTA)., Methods: A total of 1143 subjects (aged 60.7 ± 9.3 years, 54.6% male) who underwent serial CCTA with available data on TyG index and diabetic status were analyzed from The Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography IMaging (PARADIGM) registry. PP was defined as plaque volume (PV) (mm 3 ) at follow-up minus PV at index > 0. Annual change of PV (mm 3 /year) was defined as PV change divided by inter-scan period. Rapid PP was defined as the progression of percent atheroma volume (PV divided by vessel volume multiplied by 100) ≥ 1.0%/year., Results: The median inter-scan period was 3.2 (range 2.6-4.4) years. All participants were stratified into three groups based on TyG index tertiles. The overall incidence of PP was 77.3%. Baseline total PV (group I [lowest]: 30.8 (0.0-117.7), group II: 47.2 (6.2-160.4), and group III [highest]: 57.5 (8.4-154.3); P < 0.001) and the annual change of total PV (group I: 5.7 (0.0-20.2), group II: 7.6 (0.5-23.5), and group III: 9.4 (1.4-27.7); P = 0.010) were different among all groups. The risk of PP (odds ratio [OR] 1.648; 95% confidence interval [CI] 1.167-2.327; P = 0.005) and rapid PP (OR 1.777; 95% CI 1.288-2.451; P < 0.001) was increased in group III compared to that in group I. TyG index had a positive and significant association with an increased risk of PP and rapid PP after adjusting for confounding factors., Conclusion: TyG index is an independent predictive marker for the progression of coronary atherosclerosis. Clinical registration ClinicalTrials.gov NCT02803411.

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) <50%, were enrolled from a prospective, multinational registry of consecutive patients who underwent serial coronary computed tomography angiography at an interscan interval of ≥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), whereas the presence of HRP did not (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)., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Objectives: To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation., Background: Segmentation 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., Methods: Images 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., Results: The 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., Conclusions: An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level., Competing Interests: James K. Min received funding from the Dalio Foundation. Dr. Pontone receives institutional research grant support and is a speaker for Heartflow, Medtronic, GE Healthcare, Bracco Diagnostics, and Bayer Life Sciences. Dr. Jonathon Leipsic is a consultant and has reported stock options with Circle CVI and HeartFlow. Gabriel Maliakal previously worked at Weill Cornell Medicine but is now an employee at Cleerly Health. The work done in this study was during Gabriel Maliakal’s full-time employment at Weill Cornell Medicine. Dr. Min previously worked at Weill Cornell Medicine but is now an employee and has an equity interest in Cleerly Health. The work done in this study was during Dr.Min’s full-time employment at Weill Cornell Medicine. Dr. Shaw has an equity interest in Cleerly Health. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products to declare.

Aims: Symptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA)., Methods and Results: The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features., Conclusion: A ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)

Objectives: The 1-year data from the international ADVANCE (Assessing Diagnostic Value of Non-invasive FFR CT in Coronary Care) Registry of patients undergoing coronary computed tomography angiography (CTA) was used to evaluate the relationship of fractional flow reserve derived from coronary CTA (FFR CT ) with downstream care and clinical outcomes., Background: Guidelines for management of chest pain using noninvasive imaging pathways are based on short- to intermediate-term outcomes., Methods: Patients (N = 5,083) evaluated for clinically suspected coronary artery disease and in whom atherosclerosis was identified by coronary CTA were prospectively enrolled at 38 international sites from July 15, 2015, to October 20, 2017. Demographics, symptom status, coronary CTA and FFR CT findings and resultant site-based treatment plans, and clinical outcomes through 1 year were recorded and adjudicated by a blinded core laboratory. Major adverse cardiac events (MACE), death, myocardial infarction (MI), and acute coronary syndrome leading to urgent revascularization were captured., Results: At 1 year, 449 patients did not have follow-up data. Revascularization occurred in 1,208 (38.40%) patients with an FFR CT  ≤0.80 and in 89 (5.60%) with an FFR CT >0.80 (relative risk [RR]: 6.87; 95% confidence interval [CI]: 5.59 to 8.45; p < 0.001). MACE occurred in 55 patients, 43 events occurred in patients with an FFR CT  ≤0.80 and 12 occurred in those with an FFR CT >0.80 (RR: 1.81; 95% CI: 0.96 to 3.43; p = 0.06). Time to first event (all-cause death or MI) occurred in 38 (1.20%) patients with an FFR CT  ≤0.80 compared with 10 (0.60%) patients with an FFR CT >0.80 (RR: 1.92; 95% CI: 0.96 to 3.85; p = 0.06). Time to first event (cardiovascular death or MI) occurred cardiovascular death or MI occurred more in patients with an FFR CT  ≤0.80 compared with patients with an FFR CT >0.80 (25 [0.80%] vs. 3 [0.20%]; RR: 4.22; 95% CI: 1.28 to 13.95; p = 0.01)., Conclusions: The 1-year outcomes from the ADVANCE FFR CT Registry show low rates of events in all patients, with less revascularization and a trend toward lower MACE and significantly lower cardiovascular death or MI in patients with a negative FFR CT compared with patients with abnormal FFR CT values. (Assessing Diagnostic Value of Non-invasive FFRCT in Coronary Wave [ADVANCE]; NCT02499679)., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

The 2018 American College of Cardiology (ACC)/American Heart Association (AHA) cholesterol management guideline recommends risk enhancers in the borderline-risk and statin recommended/intermediate-risk groups. We determined the risk reclassification by the presence and severity of coronary computed tomography angiography (CCTA)-visualized coronary artery disease (CAD) according to statin eligibility groups. Of 35,281 individuals who underwent CCTA, 1,303 asymptomatic patients (age 59, 65% male) were identified. Patients were categorized as low risk, borderline risk, statin recommended/intermediate risk or statin recommended/high risk according to the guideline. CCTA-visualized CAD was categorized as no CAD, nonobstructive, or obstructive. Major adverse cardiovascular events (MACE) were defined as a composite outcome of all-cause mortality, nonfatal myocardial infarction, and late coronary revascularization (>90 days). We tested a reclassification wherein no CAD reclassifies downward, and the presence of any CAD reclassifies upward. During a median follow-up of 2.9 years, 93 MACE events (7.1%) were observed. Among the borderline-risk and statin-recommended/intermediate-risk groups eligible for risk enhancers, the presence or absence of any CCTA-visualized CAD led to a net increase of 2.3% of cases and 22.4% of controls correctly classified (net reclassification index [NRI] 0.27, 95% CI 0.13 to 0.41, p = 0.0002). The NRI was not significant among low- or statin-recommended/high-risk patients (all p >0.05). The presence or absence of CCTA-visualized CAD, including both obstructive and nonobstructive CAD, significantly improves reclassification in patients eligible for risk enhancers in 2018 ACC/AHA guidelines. Patients in low- and high-risk groups derive no significant improvement in risk reclassification from CCTA., (Copyright © 2019. Published by Elsevier Inc.)

Introduction: Non-statin therapy (NST) is used as second-line treatment when statin monotherapy is inadequate or poorly tolerated., Objective: To determine the association of NST with plaque composition, alone or in combination with statins, in patients undergoing coronary computed tomography angiography (coronary CTA)., Methods: From the multicenter CONFIRM registry, we analyzed individuals who underwent coronary CTA with known lipid-lowering therapy status and without prior coronary artery disease at baseline. We created a propensity score for being on NST, followed by stepwise multivariate linear regression, adjusting for the propensity score as well as risk factors, to determine the association between NST and the number of coronary artery segments with each plaque type (non-calcified (NCP), partially calcified (PCP) or calcified (CP)) and segment stenosis score (SSS)., Results: Of the 27,125 subjects in CONFIRM, 4,945 met the inclusion criteria; 371 (7.5%) took NST. At baseline, patients on NST had more prevalent risk factors and were more likely to be on concomitant cardiac medications. After multivariate and propensity score adjustment, NST was not associated with plaque composition: NCP (0.07 increase, 95% CI: -0.05, 0.20; p = 0.26), PCP (0.10 increase, 95% CI: -0.10, 0.31; p = 0.33), CP (0.18 increase, 95% CI: -0.10, 0.46; p = 0.21) or SSS (0.45 increase, 95% CI: -0.02,0.93; p = 0.06). The absence of an effect of NST on plaque type was not modified by statin use (p for interaction > 0.05 for all)., Conclusion: In this cross-sectional study, non-statin therapy was not associated with differences in plaque composition as assessed by coronary CTA., (Copyright © 2019. Published by Elsevier Inc.)

Background: Data on the impact of glycemic status on coronary plaque progression have been limited. This study evaluated the association between glycemic status and coronary plaque volume change (PVC) using coronary computed tomography angiography (CCTA)., Methods: A total of 1296 subjects (61 ± 9, 56.9% male) who underwent serial CCTA with available glycemic status were enrolled and analyzed from the Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography IMaging (PARADIGM) registry. The median inter-scan period was 3.2 (2.6-4.4) years. Quantitative assessment of coronary plaques was performed at both scans. All participants were categorized into the following groups according to glycemic status: normal, pre-diabetes (pre-DM), and diabetes mellitus (DM)., Results: During the follow-up, significant differences in PVC (normal: 51.3 ± 83.3 mm 3 vs. pre-DM: 51.0 ± 84.3 mm 3 vs. DM: 72.6 ± 95.0 mm 3 ; p < 0.001) and annualized PVC (normal: 14.9 ± 24.9 mm 3 vs. pre-DM: 15.7 ± 23.8 mm 3 vs. DM: 21.0 ± 27.7 mm 3 ; p = 0.001) were observed among the 3 groups. Compared with normal individuals, individuals with pre-DM showed no significant differences in the adjusted odds ratio (OR) for plaque progression (PP) (1.338, 95% confidence interval [CI] 0.967-1.853; p = 0.079). However, the adjusted OR for PP was higher in DM individuals than in normal individuals (1.635, 95% CI 1.126-2.375; p = 0.010)., Conclusion: DM had an incremental impact on coronary PP, but pre-DM appeared to have no significant association with an increased risk of coronary PP after adjusting for confounding factors., Clinical Trial Registration: ClinicalTrials.govNCT02803411., (Copyright © 2018. Published by Elsevier Inc.)

Background: The aim of the study is examine the impact of non-obstructive (<50%stenosis) left main (LM) disease on the natural history of coronary artery disease using serial coronary computed tomography angiography (CTA)., Methods: CTAs from the PARADIGM (Progression of atherosclerotic plaque determined by computed tomographic angiography imaging) study, a prospective multinational registry of patients who underwent serial CTA at a ≥2 year interval were analyzed. Those without evidence of CAD on their baseline scan were excluded, as were those with obstructive left main disease. Coronary artery vessels and their branches underwent quantification of: plaque volume and composition; diameter stenosis; presence of high-risk plaque., Results: Of 944 (62 ± 9 years, 60% male) who had evidence of CAD at baseline, 444 (47%) had LM disease. Those with LM disease had a higher baseline plaque volume (194.8 ± 221mm3 versus 72.9 ± 84.3mm3, p < 0.001) and a higher prevalence of high-risk plaque (17.5% versus 13%, p < 0.001) than those without LM disease. On multivariable general linear model, patients with LM disease had greater annual rates of progression of total (26.5 ± 31.4mm3/yr versus 14.9 ± 20.1mm3/yr, p < 0.001) and calcified plaque volume (17 ± 24mm3/yr versus 7 ± 11mm3/yr, p < 0.001), with no difference in fibrous, fibrofatty or necrotic core plaque components., Conclusion: The presence of non-obstructive LM disease is associated with greater rates of plaque progression and a higher prevalence of high-risk plaque throughout the entire coronary artery tree compared to CAD without LM involvement. Our data suggests that non-obstructive LM disease may be a marker for an aggressive phenotype of CAD that may benefit from more intensive treatment strategies., (Copyright © 2018. Published by Elsevier Inc.)

Introduction: Machine learning (ML) is a field in computer science that demonstrated to effectively integrate clinical and imaging data for the creation of prognostic scores. The current study investigated whether a ML score, incorporating only the 16 segment coronary tree information derived from coronary computed tomography angiography (CCTA), provides enhanced risk stratification compared with current CCTA based risk scores., Methods: From the multi-center CONFIRM registry, patients were included with complete CCTA risk score information and ≥3 year follow-up for myocardial infarction and death (primary endpoint). Patients with prior coronary artery disease were excluded. Conventional CCTA risk scores (conventional CCTA approach, segment involvement score, duke prognostic index, segment stenosis score, and the Leaman risk score) and a score created using ML were compared for the area under the receiver operating characteristic curve (AUC). Only 16 segment based coronary stenosis (0%, 1-24%, 25-49%, 50-69%, 70-99% and 100%) and composition (calcified, mixed and non-calcified plaque) were provided to the ML model. A boosted ensemble algorithm (extreme gradient boosting; XGBoost) was used and the entire data was randomly split into a training set (80%) and testing set (20%). First, tuned hyperparameters were used to generate a trained model from the training data set (80% of data). Second, the performance of this trained model was independently tested on the unseen test set (20% of data)., Results: In total, 8844 patients (mean age 58.0 ± 11.5 years, 57.7% male) were included. During a mean follow-up time of 4.6 ± 1.5 years, 609 events occurred (6.9%). No CAD was observed in 48.7% (3.5% event), non-obstructive CAD in 31.8% (6.8% event), and obstructive CAD in 19.5% (15.6% event). Discrimination of events as expressed by AUC was significantly better for the ML based approach (0.771) vs the other scores (ranging from 0.685 to 0.701), P < 0.001. Net reclassification improvement analysis showed that the improved risk stratification was the result of down-classification of risk among patients that did not experience events (non-events)., Conclusion: A risk score created by a ML based algorithm, that utilizes standard 16 coronary segment stenosis and composition information derived from detailed CCTA reading, has greater prognostic accuracy than current CCTA integrated risk scores. These findings indicate that a ML based algorithm can improve the integration of CCTA derived plaque information to improve risk stratification., (Published by Elsevier Inc.)