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Competing Interests: Declaration of competing interest Maros Ferencik reports consulting fees from Siemens Healthineers, Elucid, Heartflow, BioMarin, and Cleerly, advisory board Cleerly, and stock options Elucid. ADC reports consulting from Amgen, Siemens Healthineers and Cleerly, equity in Cleerly and grant support from GW Heart and Vascular Institute. MCW is supported by the British Heart Foundation (FS/ICRF/20/26002). MCW has given talks for Canon Medical Systems, Siemens Healthineers, GE Healthcare, and Novartis and performed consultancy for FEOPS and Canon Medical Systems. The remaining authors declare no relevant competing interests.

Competing Interests: Declaration of competing interest Conflict of interest information will be provided by Stacy De La O, CAE, Consultant, SCCT Guidelines sdelao@scct.org.

Importance: Lipoprotein(a) (Lp[a]) is a causal risk factor for cardiovascular disease; however, long-term effects on coronary atherosclerotic plaque phenotype, high-risk plaque formation, and pericoronary adipose tissue inflammation remain unknown., Objective: To investigate the association of Lp(a) levels with long-term coronary artery plaque progression, high-risk plaque, and pericoronary adipose tissue inflammation., Design, Setting, and Participants: This single-center prospective cohort study included 299 patients with suspected coronary artery disease (CAD) who underwent per-protocol repeated coronary computed tomography angiography (CCTA) imaging with an interscan interval of 10 years. Thirty-two patients were excluded because of coronary artery bypass grafting, resulting in a study population of 267 patients. Data for this study were collected from October 2008 to October 2022 and analyzed from March 2023 to March 2024., Exposures: The median scan interval was 10.2 years. Lp(a) was measured at follow-up using an isoform-insensitive assay. CCTA scans were analyzed with a previously validated artificial intelligence-based algorithm (atherosclerosis imaging-quantitative computed tomography)., Main Outcome and Measures: The association between Lp(a) and change in percent plaque volumes was investigated in linear mixed-effects models adjusted for clinical risk factors. Secondary outcomes were presence of low-density plaque and presence of increased pericoronary adipose tissue attenuation at baseline and follow-up CCTA imaging., Results: The 267 included patients had a mean age of 57.1 (SD, 7.3) years and 153 were male (57%). Patients with Lp(a) levels of 125 nmol/L or higher had twice as high percent atheroma volume (6.9% vs 3.0%; P = .01) compared with patients with Lp(a) levels less than 125 nmol/L. Adjusted for other risk factors, every doubling of Lp(a) resulted in an additional 0.32% (95% CI, 0.04-0.60) increment in percent atheroma volume during the 10 years of follow-up. Every doubling of Lp(a) resulted in an odds ratio of 1.23 (95% CI, 1.00-1.51) and 1.21 (95% CI, 1.01-1.45) for the presence of low-density plaque at baseline and follow-up, respectively. Patients with higher Lp(a) levels had increased pericoronary adipose tissue attenuation around both the right coronary artery and left anterior descending at baseline and follow-up., Conclusions and Relevance: In this long-term prospective serial CCTA imaging study, higher Lp(a) levels were associated with increased progression of coronary plaque burden and increased presence of low-density noncalcified plaque and pericoronary adipose tissue inflammation. These data suggest an impact of elevated Lp(a) levels on coronary atherogenesis of high-risk, inflammatory, rupture-prone plaques over the long term.

Background: The longitudinal relation between coronary artery disease (CAD) polygenic risk score (PRS) and long-term plaque progression and high-risk plaque (HRP) features is unknown., Objectives: The goal of this study was to investigate the impact of CAD PRS on long-term coronary plaque progression and HRP., Methods: Patients underwent CAD PRS measurement and prospective serial coronary computed tomography angiography (CTA) imaging. Coronary CTA scans were analyzed with a previously validated artificial intelligence-based algorithm (atherosclerosis imaging-quantitative computed tomography imaging). The relationship between CAD PRS and change in percent atheroma volume (PAV), percent noncalcified plaque progression, and HRP prevalence was investigated in linear mixed-effect models adjusted for baseline plaque volume and conventional risk factors., Results: A total of 288 subjects (mean age 58 ± 7 years; 60% male) were included in this study with a median scan interval of 10.2 years. At baseline, patients with a high CAD PRS had a more than 5-fold higher PAV than those with a low CAD PRS (10.4% vs 1.9%; P < 0.001). Per 10 years of follow-up, a 1 SD increase in CAD PRS was associated with a 0.69% increase in PAV progression in the multivariable adjusted model. CAD PRS provided additional discriminatory benefit for above-median noncalcified plaque progression during follow-up when added to a model with conventional risk factors (AUC: 0.73 vs 0.69; P = 0.039). Patients with high CAD PRS had an OR of 2.85 (95% CI: 1.14-7.14; P = 0.026) and 6.16 (95% CI: 2.55-14.91; P < 0.001) for having HRP at baseline and follow-up compared with those with low CAD PRS., Conclusions: Polygenic risk is strongly associated with future long-term plaque progression and HRP in patients suspected of having CAD., Competing Interests: Funding Support and Author Disclosures Dr Nurmohamed is supported by grants from the Dutch Heart Foundation (Dekker grant number 03-007-2023-0068) and European Atherosclerosis Society (2023). Dr Fahed is supported by grants K08HL161448 and R01HL164629 from the National Heart, Lung, and Blood Institute. Dr Nurmohamed has received research funding/speaker fees from Cleerly, Daiichi Sankyo, and Novartis; and is co-founder of Lipid Tools. Dr Choi has received grant support from Cleerly Inc and GW Heart and Vascular Institute; has equity in Cleerly, Inc; and reports consulting with Siemens Healthineers. Dr Natarajan has received investigator-initiated grants from Allelica, Amgen, Apple, AstraZeneca, Boston Scientific, and Novartis; has received personal fees from Allelica, Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, GV, HeartFlow, Novartis, and Roche/Genentech; is a co-founder of TenSixteen Bio; is a scientific advisory board member of Esperion Therapeutics, Preciseli, and TenSixteen Bio; and reports spousal employment at Vertex Pharmaceuticals. Drs Earls and Min are employees of and hold equity in Cleerly Inc. Dr Stroes has received lecturing/advisory board fees from Amgen, Novartis, Esperion, Sanofi-Regeneron, and Akcea. Dr Knaapen has received research grants from HeartFlow, Inc and Cleerly Inc. Dr Reeskamp is cofounder of Lipid Tools; and has received speaker fees from Novartis, Daiichi Sankyo, and Ultragenyx. Dr Fahed is co-founder of Goodpath. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs., Objectives: This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCT ISCHEMIA ) and to evaluate its prognostic utility for major adverse cardiovascular events (MACE)., Methods: A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFR CT ), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCT ISCHEMIA model was developed in the derivation cohort of the CREDENCE study, and its diagnostic performance for coronary ischemia (FFR ≤0.80) was evaluated in the CREDENCE validation cohort and PACIFIC-1. Its prognostic value was investigated in PACIFIC-1., Results: In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level was 0.80 (95% CI: 0.75-0.85) for AI-QCT ISCHEMIA , 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFR CT , and 0.65 (95% CI: 0.59-0.71; P < 0.001) for MPI. In PACIFIC-1 (n = 208, age 58.1 ± 8.7 years, 132 [63%] male), the AUCs were 0.85 (95% CI: 0.79-0.91) for AI-QCT ISCHEMIA , 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFR CT , 0.89 (95% CI: 0.84-0.93; P = 0.262) for PET, and 0.72 (95% CI: 0.67-0.78; P < 0.001) for SPECT. Adjusted for clinical risk factors and coronary CTA-determined obstructive stenosis, a positive AI-QCT ISCHEMIA test was associated with aHR: 7.6 (95% CI: 1.2-47.0; P = 0.030) for MACE., Conclusions: This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis., Competing Interests: Funding Support and Author Disclosures This project has been supported by the Foundation “De Drie Lichten” in the Netherlands. Dr Nurmohamed has received grants from the Dutch Heart Foundation (Dekker 03-007-2023-0068) and grants from the European Atherosclerosis Society (2023); and is co-founder of Lipid Tools. Mr Wang, Dr Chan, Ms Crabtree, Ms Aquino, Dr Min, and Dr Earls are employees of Cleerly Inc. Dr Choi has received grant support from GW Heart and Vascular Institute; equity in Cleerly, Inc; and has received consulting fees from Siemens Healthineers. Dr Knaapen has received research grants from HeartFlow, Inc. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The use of cardiac CT and MRI is rapidly expanding based on strong evidence from large international trials. The number of physicians competent to interpret cardiac CT and MRI may be unable to keep pace with the increasing demand. Societies and organizations have prescribed training requirements for interpreting cardiac CT and MRI, with recent updates focusing on the increased breadth of competency that is now required due to ongoing imaging advances. In this AJR Expert Panel Narrative Review, we discuss several aspects of cardiac CT and MRI training, focusing on topics that are uncertain or not addressed in existing society statements and guidelines, including determination of competency in different practice types in real-world settings and the impact of artificial intelligence on training and education. The article is intended to guide updates in professional society training requirements and also inform institutional verification processes.

Aims: Recovery of cardiovascular diagnostic testing in Italy after the coronavirus disease-2019 (COVID-19) pandemic has not been quantified. The study aims to describe cardiac diagnostic procedure volumes, centres practice and protocols, and staff members' well-being 1 year after COVID-19 outbreak in Italy., Methods and Results: A global survey was conducted by the International Atomic Energy Agency to evaluate changes in cardiac diagnostic procedure volumes in April 2021. Evaluated procedures were transoesophageal echocardiogram, coronary computed tomography angiography, coronary artery calcium scanning, nuclear medicine infection studies, invasive coronary angiography, rest and stress transthoracic echocardiogram, cardiac magnetic resonance, single-photon emission computed tomography and positron emission tomography, and stress electrocardiogram. Data were compared with April 2020 and March 2019. Forty-two Italian centres took part in the survey. In April 2020, there was a 72% decrease of median volumes of cardiac diagnostic procedures compared with March 2019. In April 2021, volumes of cardiac diagnostic procedures remained decreased by 3% when compared with March 2019. Stress electrocardiogram, coronary computed tomography angiography, and stress cardiac magnetic resonance volumes increased in April 2021 compared with baseline (29%, 6%, and 16%, respectively). The majority of centres had adopted physical distancing measures (93%), COVID-19 screening through questionnaires (76%), or temperature checks (93%). Twenty-five per cent of physicians at Italian responding sites reported excessive levels of psychological stress., Conclusion: In April 2021, volumes of cardiac diagnostic procedures at Italian responding sites were still recovering. Centres had implemented several adaptations to ensure the provision of care to their patients. Even 1 year after the pandemic, a substantial minority of Italian healthcare providers were still experiencing excessive psychological stress., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as % stenosis, plaque volume, and plaque composition. AI-QCT plaque volume was then staged by recently validated prognostic thresholds, and compared with CAD-RADS 2.0 clinical methods of plaque evaluation (segment involvement score (SIS), coronary artery calcium score (CACS), visual assessment, and CAD-RADS percent (%) stenosis) by expert consensus blinded to the AI-QCT core lab reads. Average age of subjects were 59 ± 11 years; 44% women, with 50% of patients at CAD-RADS 1-2 and 21% at CAD-RADS 3 and above by expert consensus. AI-QCT quantitative plaque burden staging had excellent agreement of 93% (k = 0.87 95% CI: 0.79-0.96) with SIS. There was moderate agreement between AI-QCT quantitative plaque volume and categories of visual assessment (64.4%; k = 0.488 [0.38-0.60]), and CACS (66.3%; k = 0.488 [0.36-0.61]). Agreement between AI-QCT plaque volume stage and CAD-RADS % stenosis category was also moderate. There was discordance at small plaque volumes. With ongoing validation, these results demonstrate a potential for AI-QCT as a rapid, reproducible approach to quantify total plaque burden., (© 2024. The Author(s).)

Aims: The incremental impact of atherosclerosis imaging-quantitative computed tomography (AI-QCT) on diagnostic certainty and downstream patient management is not yet known. The aim of this study was to compare the clinical utility of the routine implementation of AI-QCT versus conventional visual coronary CT angiography (CCTA) interpretation., Methods and Results: In this multi-centre cross-over study in 5 expert CCTA sites, 750 consecutive adult patients referred for CCTA were prospectively recruited. Blinded to the AI-QCT analysis, site physicians established patient diagnoses and plans for downstream non-invasive testing, coronary intervention, and medication management based on the conventional site assessment. Next, physicians were asked to repeat their assessments based upon AI-QCT results. The included patients had an age of 63.8 ± 12.2 years; 433 (57.7%) were male. Compared with the conventional site CCTA evaluation, AI-QCT analysis improved physician's confidence two- to five-fold at every step of the care pathway and was associated with change in diagnosis or management in the majority of patients (428; 57.1%; P < 0.001), including for measures such as Coronary Artery Disease-Reporting and Data System (CAD-RADS) (295; 39.3%; P < 0.001) and plaque burden (197; 26.3%; P < 0.001). After AI-QCT including ischaemia assessment, the need for downstream non-invasive and invasive testing was reduced by 37.1% (P < 0.001), compared with the conventional site CCTA evaluation. Incremental to the site CCTA evaluation alone, AI-QCT resulted in statin initiation/increase an aspirin initiation in an additional 28.1% (P < 0.001) and 23.0% (P < 0.001) of patients, respectively., Conclusion: The use of AI-QCT improves diagnostic certainty and may result in reduced downstream need for non-invasive testing and increased rates of preventive medical therapy., Competing Interests: Conflict of interest: N.S.N. is co-founder of Lipid Tools. M.A., U.H., J.K.M., and J.P.E. are employees of Cleerly Inc. A.D.C. reports grant support from GW Heart and Vascular Institute, equity in Cleerly, Inc., and consulting with Siemens Healthineers., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

The COVID-19 pandemic disrupted the delivery of cardiovascular care, including noninvasive testing protocols and test selection for the evaluation of coronary artery disease (CAD). Trends in test selection in traditional versus advanced noninvasive tests for CAD during the pandemic and in countries of varying income status have not been well studied. The International Atomic Energy Agency conducted a global survey to assess the pandemic-related changes in the practice of cardiovascular diagnostic testing. Site procedural volumes for noninvasive tests to evaluate CAD from March 2019 (prepandemic), April 2020 (onset), and April 2021 (initial recovery) were collected. We considered traditional testing modalities, such as exercise electrocardiography, stress echocardiography, and stress single-photon emission computed tomography, and advanced testing modalities, such as stress cardiac magnetic resonance, coronary computed tomography angiography, and stress positron emission tomography. Survey data were obtained from 669 centers in 107 countries, reporting the performance of 367,933 studies for CAD during the study period. Compared with 2019, traditional tests were performed 14% less frequently (recovery rate 82%) in 2021 versus advanced tests, which were performed 15% more frequently (128% recovery rate). Coronary computed tomography angiography, stress cardiac magnetic resonance, and stress positron emission tomography showed 14%, 25%, and 25% increases in volumes from 2019 to 2021, respectively. The increase in advanced testing was isolated to high- and upper middle-income countries, with 132% recovery in advanced tests by 2021 compared with 55% in lower income nations. The COVID-19 pandemic exacerbated economic disparities in CAD testing practice between wealthy and poorer countries. Greater recovery rates and even new growth were observed for advanced imaging modalities; however, this growth was restricted to wealthy countries. Efforts to reduce practice variations in CAD testing because of economic status are warranted., Competing Interests: Declaration of competing interest Dr. Villines was an employee of Elucid Bioimaging and has separately received authorship fees from Wolters Kluwer Healthcare-UpToDate. Dr. Williams has given talks for Canon Medical Systems, Siemens Healthineers and Novartis. Dr. Einstein reports receiving speaker's fee from Ionetix, consulting fees from W. L. Gore & Associates and authorship fees from Wolters Kluwer Healthcare—UpToDate and serving on a scientific advisory board for Canon Medical Systems USA; his institution has grants/grants pending from Attralus, Bruker, Canon Medical Systems USA, Eidos Therapeutics, GE Healthcare, Intellia Therapeutics, Ionis Pharmaceuticals, Neovasc, Pfizer, Roche Medical Systems, and W. L. Gore & Associates. The remaining authors have no competing interest to declare., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Aim: The aim of this study was to assess the recovery rates of diagnostic cardiac procedure volumes in the Oceania Region, midway through the coronavirus disease 2019 (COVID-19) pandemic., Methods: A survey was performed comparing procedure volumes between March 2019 (pre-pandemic), April 2020 (during first wave of COVID-19 pandemic), and April 2021 (1 year into the COVID-19 pandemic). A total of 31 health care facilities within Oceania that perform cardiac diagnostic procedures were surveyed, including a mixture of metropolitan and regional, hospital and outpatient, public and private sites, as well as teaching and non-teaching hospitals. A comparison was made with 549 centres in 96 countries in the rest of the world (RoW) outside of Oceania. The total number and median percentage change in procedure volume were measured between the three timepoints, compared by test type and by facility., Results: A total of 11,902 cardiac diagnostic procedures were performed in Oceania in April 2021 as compared with 11,835 pre-pandemic in March 2019 and 5,986 in April 2020; whereas, in the RoW, 499,079 procedures were performed in April 2021 compared with 497,615 pre-pandemic in March 2019 and 179,014 in April 2020. There was no significant difference in the median recovery rates for total procedure volumes between Oceania (-6%) and the RoW (-3%) (p=0.81). While there was no statistically significant difference in percentage recovery been functional ischaemia testing and anatomical coronary testing in Oceania as compared with the RoW, there was, however, a suggestion of poorer recovery in anatomical coronary testing in Oceania as compared with the RoW (CT coronary angiography -16% in Oceania vs -1% in RoW, and invasive coronary angiography -20% in Oceania vs -9% in RoW). There was no statistically significant difference in recovery rates in procedure volume between metropolitan vs regional (p=0.44), public vs private (p=0.92), hospital vs outpatient (p=0.79), or teaching vs non-teaching centres (p=0.73)., Conclusions: Total cardiology procedure volumes in Oceania normalised 1 year post-pandemic compared to pre-pandemic levels, with no significant difference compared with the RoW and between the different types of health care facilities., Competing Interests: Conflicts of Interest None disclosed., (Copyright © 2024 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). All rights reserved.)

Background: The recent development of artificial intelligence-guided quantitative coronary computed tomography angiography analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics., Objectives: This study set out to investigate the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and to compare the spectrum of plaque to manually assessed coronary computed tomography angiography (CCTA), coronary artery calcium scoring (CACS), and clinical risk characteristics., Methods: This was a long-term follow-up study of 536 patients referred for suspected coronary artery disease. CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0% percentage atheroma volume [PAV]; stage 1: >0%-5% PAV; stage 2: >5%-15% PAV; stage 3: >15% PAV). The primary major adverse cardiac event (MACE) outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and all-cause mortality., Results: The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 years (IQR: 8.6-11.5 years), 114 patients (21%) experienced the primary outcome. Compared to stages 0 and 1, patients with stage 3 PAV and percentage of noncalcified plaque volume of >7.5% had a more than 3-fold (adjusted HR: 3.57; 95% CI 2.12-6.00; P < 0.001) and 4-fold (adjusted HR: 4.37; 95% CI: 2.51-7.62; P < 0.001) increased risk of MACE, respectively. Addition of AI-QCT improved a model with clinical risk factors and CACS at different time points during follow-up (10-year AUC: 0.82 [95% CI: 0.78-0.87] vs 0.73 [95% CI: 0.68-0.79]; P < 0.001; net reclassification improvement: 0.21 [95% CI: 0.09-0.38]). Furthermore, AI-QCT achieved an improved area under the curve compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.023) and manual QCT (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.040), although net reclassification improvement was modest (0.09 [95% CI: -0.02 to 0.29] and 0.04 [95% CI: -0.05 to 0.27], respectively)., Conclusions: Through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment., Competing Interests: Funding Support and Author Disclosures Dr Nurmohamed is co-founder of Lipid Tools. Dr Stroes has received lecturing/advisory board fees from Amgen, Novartis, Esperion, Sanofi-Regeneron, and Akcea. Drs Min and Earls are employees of and hold equity in Cleerly Inc. Dr Choi has received grant support from GW Heart and Vascular Institute; holds equity in Cleerly, Inc; and has provided consulting services to Siemens Healthineers. Dr Knaapen has received research grants from HeartFlow, Inc. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: In the last 15 years, large registries and several randomized clinical trials have demonstrated the diagnostic and prognostic value of coronary computed tomography angiography (CCTA). Advances in CT scanner technology and developments of analytic tools now enable accurate quantification of coronary artery disease (CAD), including total coronary plaque volume and low attenuation plaque volume. The primary aim of CONFIRM2, (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is to perform comprehensive quantification of CCTA findings, including coronary, non-coronary cardiac, non-cardiac vascular, non-cardiac findings, and relate them to clinical variables and cardiovascular clinical outcomes., Design: CONFIRM2 is a multicenter, international observational cohort study designed to evaluate multidimensional associations between quantitative phenotype of cardiovascular disease and future adverse clinical outcomes in subjects undergoing clinically indicated CCTA. The targeted population is heterogenous and includes patients undergoing CCTA for atherosclerotic evaluation, valvular heart disease, congenital heart disease or pre-procedural evaluation. Automated software will be utilized for quantification of coronary plaque, stenosis, vascular morphology and cardiac structures for rapid and reproducible tissue characterization. Up to 30,000 patients will be included from up to 50 international multi-continental clinical CCTA sites and followed for 3-4 years., Summary: CONFIRM2 is one of the largest CCTA studies to establish the clinical value of a multiparametric approach to quantify the phenotype of cardiovascular disease by CCTA using automated imaging solutions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Alexander R. van Rosendael is a member, Cleerly Scientific Advisory Board. Tami Crabtree is an employee and equity holder, Cleerly, Inc. Gianluca Pontone has received honorarium as speaker/consultant and/or institutional research grant from GE Healthcare, Bracco, Medtronic, Novartis. Ronny R. Buechel reports receiving speaking honoraria from GE Healthcare, Pfizer, Gilead, and IBA. Christoph Gräni received funding from the Swiss National Science foundation, InnoSuisse, CAIM foundation, GAMBIT foundation, Novartis foundation for biomedical research, outside of the submitted work. Andrew D. Choi is a consultant for Siemens, holds equity in Cleerly, and receives grant support from the George Washing Heart and Vascular Institute.Carlos E. Rochitte reports receiving speaking honoraria for Pfizer, Edwards, GE, and Manole. David M. German reports no conflicts Himanshu Gupta reports no conflicts. Omar Khalique is a consultant for Edwards, Croivalve, Restore Medical, holds equity in Triflo, and has received honoraria for educational programs from Heartflow. Udo Hoffmann is an employee and equity holder, Cleerly, Inc., and has received honoraria from Stanford University, Clinical Cardiovascular Sciences, Rapid AI, MedTrace. James Earls is an employee and equity holder, Cleerly, Inc. James K. Min is an employee and equity holder, Cleerly, Inc. and a member of the Arineta Medical Advisory Board. Ibrahim Danad is a member, Cleerly Scientific Advisory Board. Hugo Marques is a consultant for Cleerly, Inc., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Non-obstructing small coronary plaques may not be well recognized by expert readers during coronary computed tomography angiography (CCTA) evaluation. Recent developments in atherosclerosis imaging quantitative computed tomography (AI-QCT) enabled by machine learning allow for whole-heart coronary phenotyping of atherosclerosis, but its diagnostic role for detection of small plaques on CCTA is unknown., Methods: We performed AI-QCT in patients who underwent serial CCTA in the multinational PARADIGM study. AI-QCT results were verified by a level III experienced reader, who was blinded to baseline and follow-up status of CCTA. This retrospective analysis aimed to characterize small plaques on baseline CCTA and evaluate their serial changes on follow-up imaging. Small plaques were defined as a total plaque volume <50 ​mm 3 ., Results: A total of 99 patients with 502 small plaques were included. The median total plaque volume was 6.8 ​mm 3 (IQR 3.5-13.9 ​mm 3 ), most of which was non-calcified (median 6.2 ​mm 3 ; 2.9-12.3 ​mm 3 ). The median age at the time of baseline CCTA was 61 years old and 63% were male. The mean interscan period was 3.8 ​± ​1.6 years. On follow-up CCTA, 437 (87%) plaques were present at the same location as small plaques on baseline CCTA; 72% were larger and 15% decreased in volume. The median total plaque volume and non-calcified plaque volume increased to 18.9 ​mm 3 (IQR 8.3-45.2 ​mm 3 ) and 13.8 ​mm 3 (IQR 5.7-33.4 ​mm 3 ), respectively, among plaques that persisted on follow-up CCTA. Small plaques no longer visualized on follow-up CCTA were significantly more likely to be of lower volume, shorter in length, non-calcified, and more distal in the coronary artery, as compared with plaques that persisted at follow-up., Conclusion: In this retrospective analysis from the PARADIGM study, small plaques (<50 ​mm 3 ) identified by AI-QCT persisted at the same location and were often larger on follow-up CCTA., (Copyright © 2023 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

Artificial intelligence (AI) applications are expanding in cardiac imaging. AI research has shown promise in workflow optimization, disease diagnosis, and integration of clinical and imaging data to predict patient outcomes. The diagnostic and prognostic paradigm of heart failure is heavily reliant on cardiac imaging. As AI becomes increasingly validated and integrated into clinical practice, AI influence on heart failure management will grow. This review discusses areas of current research and potential clinical applications in AI as applied to heart failure cardiac imaging., (Copyright © 2023 Elsevier Inc. All rights reserved.)

It is unknown whether gender influences the atherosclerotic plaque characteristics (APCs) of lesions of varying angiographic stenosis severity. This study evaluated the imaging data of 303 symptomatic patients from the derivation arm of the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial, all of whom underwent coronary computed tomographic angiography and clinically indicated nonemergent invasive coronary angiography upon study enrollment. Index tests were interpreted by 2 blinded core laboratories, one of which performed quantitative coronary computed tomographic angiography using an artificial intelligence application to characterize and quantify APCs, including percent atheroma volume (PAV), low-density noncalcified plaque (LD-NCP), noncalcified plaque (NCP), calcified plaque (CP), lesion length, positive arterial remodeling, and high-risk plaque (a combination of LD-NCP and positive remodeling ≥1.10); the other classified lesions as obstructive (≥50% diameter stenosis) or nonobstructive (<50% diameter stenosis) based on quantitative invasive coronary angiography. The relation between APCs and angiographic stenosis was further examined by gender. The mean age of the study cohort was 64.4 ± 10.2 years (29.0% female). In patients with obstructive disease, men had more LD-NCP PAV (0.5 ± 0.4 vs 0.3 ± 0.8, p = 0.03) and women had more CP PAV (11.7 ± 1.6 vs 8.0 ± 0.8, p = 0.04). Obstructive lesions had more NCP PAV compared with their nonobstructive lesions in both genders, however, obstructive lesions in women also demonstrated greater LD-NCP PAV (0.4 ± 0.5 vs 1.0 ± 1.8, p = 0.03), and CP PAV (17.4 ± 16.5 vs 25.9 ± 18.7, p = 0.03) than nonobstructive lesions. Comparing the composition of obstructive lesions by gender, women had more CP PAV (26.3 ± 3.4 vs 15.8 ± 1.5, p = 0.005) whereas men had more NCP PAV (33.0 ± 1.6 vs 26.7 ± 2.5, p = 0.04). Men had more LD-NCP PAV in nonobstructive lesions compared with women (1.2 ± 0.2 vs 0.6 ± 0.2, p = 0.02). In conclusion, there are gender-specific differences in plaque composition based on stenosis severity., Competing Interests: Declaration of Competing Interest Crabtree, Jennings, Earls, Hoffmann, and Min are employees of Cleerly Inc.; Earls, Marques, Choi, and Min have equity in Cleerly Inc. The remaining authors have no conflicts of interest to declare., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To characterize the recovery of diagnostic cardiovascular procedure volumes in U.S. and non-U.S. facilities in the year following the initial COVID-19 outbreak., Materials and Methods: The International Atomic Energy Agency (IAEA) coordinated a worldwide study called the IAEA Noninvasive Cardiology Protocols Study of COVID-19 2 (INCAPS COVID 2), collecting data from 669 facilities in 107 countries, including 93 facilities in 34 U.S. states, to determine the impact of the pandemic on diagnostic cardiovascular procedure volumes. Participants reported volumes for each diagnostic imaging modality used at their facility for March 2019 (baseline), April 2020, and April 2021. This secondary analysis of INCAPS COVID 2 evaluated differences in changes in procedure volume between U.S. and non-U.S. facilities and among U.S. regions. Factors associated with return to prepandemic volumes in the United States were also analyzed in a multivariable regression analysis., Results: Reduction in procedure volumes in April 2020 compared with baseline was similar for U.S. and non-U.S. facilities (-66% vs -71%, P = .27). U.S. facilities reported greater return to baseline in April 2021 than did all non-U.S. facilities (4% vs -6%, P = .008), but there was no evidence of a difference when comparing U.S. facilities with non-U.S. high-income country (NUHIC) facilities (4% vs 0%, P = .18). U.S. regional differences in return to baseline were observed between the Midwest (11%), Northeast (9%), South (1%), and West (-7%, P = .03), but no studied factors were significant predictors of 2021 change from prepandemic baseline., Conclusion: The reductions in cardiac testing during the early pandemic have recovered within a year to prepandemic baselines in the United States and NUHICs, while procedure volumes remain depressed in lower-income countries. Keywords: SPECT, Cardiac, Epidemiology, Angiography, CT Angiography, CT, Echocardiography, SPECT/CT, MR Imaging, Radionuclide Studies, COVID-19, Cardiovascular Imaging, Diagnostic Cardiovascular Procedure, Cardiovascular Disease, Cardiac Testing Supplemental material is available for this article. © RSNA, 2023., Competing Interests: Disclosures of conflicts of interest: C.B.H. No relevant relationships. S.D. Research grants to author’s institution from Pfizer, GE HealthCare, Attralus, Siemens, and Philips; consulting fees from AstraZeneca and Novo Nordisk; support from Novo Nordisk for travel to an advisory board meeting. L.J.S. No relevant relationships. T.C.V. No relevant relationships. A.D.C. Grant support from GW Heart and Vascular Institute; consultant for Siemens Healthineers; equity in Cleerly. N.B. No relevant relationships. R.J.C. No relevant relationships. G.K. No relevant relationships. J.V.V. No relevant relationships. M.C.W. Support from the British Heart Foundation (FS/ICRF/20/26002); payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or education events from Canon Medical Systems, Siemens Healthineers, and Novartis; member of the Radiology: Cardiothoracic Imaging editorial board. M.A.M. Research support from Siemens; consulting fees from Jubilant and Philips; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Jubilant. D.S.B. Receives software royalties from Cedars-Sinai Medical Center; consultant for General Electric and Bayer. A.B. No relevant relationships. R.W.B. No relevant relationships. P.E.B. No relevant relationships. M.J.B. Grant support from General Electric. R.P.B.P. Women as One grant ($25 000), paid to author’s institution; Knowledge to Practice honoraria ($500) for being a panelist during a virtual educational event; honoraria ($1000, $2500) from American College of Radiology for CME education products; member of American Society of Nuclear Cardiology board of directors, Intersocietal Accreditation Commission CT board of directors, and American College of Cardiology board of governors. M.Y.C. No relevant relationships. M.P.D. Salary support from Genentech for COVID-19 research. R.D. No relevant relationships. M.F. Grants or contracts from the National Institutes of Health (NIH) and the American Heart Association (AHA); consulting fees from Biography, Elucid, HeartFlow, and Siemens Healthineers; vice-president of the Society of Cardiovascular Computed Tomography; stock or stock options in Elucid. J.B.G. No relevant relationships. F.G.H. Grant support from GE HealthCare. R.C.H. No relevant relationships. L.K. Member of the Radiology: Cardiothoracic Imaging editorial board. V.L.M. Grants or contracts from the NIH, AHA, and Siemens; consulting fees from Siemens, INVIA Medical Imaging Solutions, and Ionetix; member of AHA Cardiovascular Radiology and Interventions Council Leadership Committee and the American Society of Nuclear Cardiology Program Committee; stock in General Electric, Ionetix, and Cardinal Health; scientific advisor to Ionetix; received software from INVIA Medical Imaging Solutions. J.N. No relevant relationships. P.F.R.L. Grants or contracts from iTHRIV Scholar, which is supported in part by the National Center for Advancing Translational Sciences of the NIH (award nos. UL1TR003015 and KL2TR003016); patent planned, issued, or pending number 3062/140PCT/US for "Methods, systems, and computer-readable media for utilizing US device to perform MV decalcification." N.R.S. Grant support from Pfizer; stock in General Electric. A.S. No relevant relationships. P.S. Grant funding from Pfizer to author’s institution and from Astellas; royalties from UpToDate; personal consulting fees from Bridgebio (Eidos), Spectrum Dynamics, Pfizer, and Alnylam; member of the American Society of Nuclear Cardiology board of directors. R.C.T. No relevant relationships. D.W. Consultant for Astellas, BridgeBio, Pfizer, and Alnylam advisory boards; payment for speakers bureaus work from Astellas and Pfizer; chair of ASNC Health Policy Committee; member of advisory boards of Bridge Bio and Alynylam, and grant interviewer for Ionis. Y.A.C. No relevant relationships. E.M. No relevant relationships. M.J.R. No relevant relationships. J.L.M. No relevant relationships. P.P. Consulting fees from Medtronic and AstraZeneca. M.S. No relevant relationships. T.N.B.P. No relevant relationships. Y.P. No relevant relationships. M.D. No relevant relationships. D.P. No relevant relationships. A.J.E. Support from the International Atomic Energy Agency to Columbia University; grants from GE HealthCare, Canon Medical Systems, W.L. Gore & Associates, Eidos Therapeutics, Attralus, Pfizer, Roche Medical Systems, and Neovasc to Columbia University; speaker’s fees from Ionetix; consulting fees from W. L. Gore & Associates; support from the Society of Nuclear Medicine and Molecular Imaging for attending meetings and/or travel; patents planned, issued, or pending for Columbia Technology Ventures; leadership or fiduciary role in the American College of Cardiology, American Society of Nuclear Cardiology, and the Society of Nuclear Medicine and Molecular Imaging; and authorship fees from Wolters Kluwer Healthcare–UpToDate., (© 2023 by the Radiological Society of North America, Inc.)

Aims: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA)., Methods: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up., Results: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively., Conclusions: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE., (© 2023 The Authors. Clinical Cardiology published by Wiley Periodicals, LLC.)

Background: As cardiovascular computed tomography (CCT) practice evolves, the demand for specialists continues to increase. However, CCT training remains variable globally with limited contemporaneous data to understand this heterogeneity. We sought to understand the role of CCT globally and the training available to underpin its use., Methods: We performed two consecutive surveys of cardiology and radiology physicians, two years apart, utilizing the Society of Cardiovascular Computed Tomography (SCCT) website, weblinks, social media platforms, and meeting handouts to maximize our response rate. We compared United States (US)-based vs. international responses to understand global similarities and differences in practice and training in the surveys., Results: 235 respondents (37% trainees and 63% educators/non-trainees) initiated the first survey with 174 (74%) completing the core survey, with 205 providing their work location (114 US and 91 international). Eighty-four percent (92/110) of educator respondents stated a need for increased training opportunities to meet growing demand. Dedicated training fellowships are heterogenous, with limited access to structural heart imaging training, despite structural scanning being performed within institutions. The lack of a standardized curriculum was identified as the main obstacle to effective CCT learning, particularly in the US, with web-based learning platforms being the most popular option for improving access to CCT training. 148 trainees initiated the second survey with 107 (72%) completing the core components (51% North America, 49% international). Only 68% said they would be able to meet their required CCT education needs via their training program. Obstacles in obtaining CCT training again included a lack of a developed curriculum (51%), a lack of dedicated training time (35%), and a lack of local faculty expertise (31%). There was regional variability in access to CCT training, and, in contrast to the first survey, most (89%) felt 1:1 live review of cases with trained/expert reader was most useful for improving CCT training alongside formal curriculum/live lectures (72%)., Conclusions: There is a need to expand dedicated CCT training globally to meet the demand for complex CCT practice. Access to CCT education (didactic and 1:1 case-based teaching from expert faculty), implementation of recently published global training curricula, and increased teaching resources (web-based) as an adjunct to existing experiential learning opportunities, are all deemed necessary to address current educational shortfalls., Competing Interests: Declaration of competing interest The authors declare no relevant competing interests. N. Madan: None. D. J. Hur: None. M. P. Gannon: None. S. Gupta: None. J. R. Weir-McCall: None. C. Johns: None. A. Kumar: None. P. Nagpal: None. E. Fentanes: None. J. Lee: None. A. D. Choi: None. M. Ferencik: None. C. D. Maroules: None. T. C. Villines: None. E. D. Nicol: None., (Published by Elsevier Inc.)

Background: Clinical reads of coronary computed tomography angiography (CTA), especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. Artificial intelligence (AI)-based solutions applied to coronary CTA may overcome these limitations., Objectives: This study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography (AI-QCT) angiography analyses to core lab-interpreted coronary CTA, core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR)., Methods: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination., Results: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8., Conclusions: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275)., Competing Interests: Funding Support and Author Disclosures Drs Choi and Marques have equity interest in Cleerly Inc. Dr Earls has equity interest in and as of April 1, 2021, is an employee of Cleerly Inc. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: This study evaluates the relationship between atherosclerotic plaque characteristics (APCs) and angiographic stenosis severity in patients with and without diabetes. Whether APCs differ based on lesion severity and diabetes status is unknown., Research Design and Methods: We retrospectively evaluated 303 subjects from the Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia (CREDENCE) trial referred for invasive coronary angiography with coronary computed tomographic angiography (CCTA) and classified lesions as obstructive (≥50% stenosed) or nonobstructive using blinded core laboratory analysis of quantitative coronary angiography. CCTA quantified APCs, including plaque volume (PV), calcified plaque (CP), noncalcified plaque (NCP), low-density NCP (LD-NCP), lesion length, positive remodeling (PR), high-risk plaque (HRP), and percentage of atheroma volume (PAV; PV normalized for vessel volume). The relationship between APCs, stenosis severity, and diabetes status was assessed., Results: Among the 303 patients, 95 (31.4%) had diabetes. There were 117 lesions in the cohort with diabetes, 58.1% of which were obstructive. Patients with diabetes had greater plaque burden (P = 0.004). Patients with diabetes and nonobstructive disease had greater PV (P = 0.02), PAV (P = 0.02), NCP (P = 0.03), PAV NCP (P = 0.02), diseased vessels (P = 0.03), and maximum stenosis (P = 0.02) than patients without diabetes with nonobstructive disease. APCs were similar between patients with diabetes with nonobstructive disease and patients without diabetes with obstructive disease. Diabetes status did not affect HRP or PR. Patients with diabetes had similar APCs in obstructive and nonobstructive lesions., Conclusions: Patients with diabetes and nonobstructive stenosis had an association to similar APCs as patients without diabetes who had obstructive stenosis. Among patients with nonobstructive disease, patients with diabetes had more total PV and NCP., (© 2023 by the American Diabetes Association.)

Competing Interests: Declaration of competing interest K.N. acknowledges support from the NIH (NIH R01- HL141712; NIH R01 - HL146754), and reports unrestricted institutional research support from Siemens Healthineers, Bayer, HeartFlow Inc, Novartis unrelated to this work, consulting for Siemens Medical Solutions USA, and equity in Lumen Therapeutics. AC. reports grant support from GW Heart and Vascular Institute, equity in Cleerly, Inc. and consulting for Siemens. M.M. reports no disclosures.

Cardio-Oncology is a rapidly growing sub-specialty of medicine, however, there is very limited guidance on the use of cardiac CT (CCT) in the care of Cardio-Oncology patients. In order to fill in the existing gaps, this Expert Consensus statement comprised of a multidisciplinary collaboration of experts in Cardiology, Radiology, Cardiovascular Multimodality Imaging, Cardio-Oncology, Oncology and Radiation Oncology aims to summarize current evidence for CCT applications in Cardio-Oncology and provide practice recommendations for clinicians., Competing Interests: Conflict of interest The authors declare that they don't have conflicts of interest related to the contents of this document to declare. Please refer to Appendix 1 for disclosures about relationships with industry for each author., (Copyright © 2022 Society of Cardiovascular Computed Tomography. All rights reserved.)

Background: The difference between expert level (L3) reader and artificial intelligence (AI) performance for quantifying coronary plaque and plaque components is unknown., Objective: This study evaluates the interobserver variability among expert readers for quantifying the volume of coronary plaque and plaque components on coronary computed tomographic angiography (CCTA) using an artificial intelligence enabled quantitative CCTA analysis software as a reference (AI-QCT)., Methods: This study uses CCTA imaging obtained from 232 patients enrolled in the CLARIFY (CT EvaLuation by ARtificial Intelligence For Atherosclerosis, Stenosis and Vascular MorphologY) study. Readers quantified overall plaque volume and the % breakdown of noncalcified plaque (NCP) and calcified plaque (CP) on a per vessel basis. Readers categorized high risk plaque (HRP) based on the presence of low-attenuation-noncalcified plaque (LA-NCP) and positive remodeling (PR; ≥1.10). All CCTAs were analyzed by an FDA-cleared software service that performs AI-driven plaque characterization and quantification (AI-QCT) for comparison to L3 readers. Reader generated analyses were compared among readers and to AI-QCT generated analyses., Results: When evaluating plaque volume on a per vessel basis, expert readers achieved moderate to high interobserver consistency with an intra-class correlation coefficient of 0.78 for a single reader score and 0.91 for mean scores. There was a moderate trend between readers 1, 2, and 3 and AI with spearman coefficients of 0.70, 0.68 and 0.74, respectively. There was high discordance between readers and AI plaque component analyses. When quantifying %NCP v. %CP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.23, 0.34 and 0.24, respectively, compared to AI with a spearman coefficient of 0.38, 0.51, and 0.60, respectively. The intra-class correlation coefficient among readers for plaque composition assessment was 0.68. With respect to HRP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.22, 0.26, and 0.17, respectively, and a spearman coefficient of 0.36, 0.35, and 0.44, respectively., Conclusion: Expert readers performed moderately well quantifying total plaque volumes with high consistency. However, there was both significant interobserver variability and high discordance with AI-QCT when quantifying plaque composition., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hugo Marques reports a relationship with Cleerly Inc. that includes: consulting or advisory and equity or stocks. Andrew D. Choi reports a relationship with Cleerly Inc. that includes: equity or stocks. James P. Earls reports a relationship with Cleerly Inc. that includes: board membership, employment, and equity or stocks. Robert S. Jennings reports a relationship with Cleerly Inc. that includes: employment. Tami R. Crabtree reports a relationship with Cleerly Inc. that includes: employment., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Coronary Artery Disease Reporting and Data System (CAD-RADS) was created to standardize reporting system for patients undergoing coronary CT angiography (CCTA) and to guide possible next steps in patient management. The goal of this updated 2022 CAD-RADS 2.0 is to improve the initial reporting system for CCTA by considering new technical developments in Cardiac CT, including data from recent clinical trials and new clinical guidelines. The updated CAD-RADS classification will follow an established framework of stenosis, plaque burden, and modifiers, which will include assessment of lesion-specific ischemia using CT fractional-flow-reserve (CT-FFR) or myocardial CT perfusion (CTP), when performed. Similar to the method used in the original CAD-RADS version, the determinant for stenosis severity classification will be the most severe coronary artery luminal stenosis on a per-patient basis, ranging from CAD-RADS 0 (zero) for absence of any plaque or stenosis to CAD-RADS 5 indicating the presence of at least one totally occluded coronary artery. Given the increasing data supporting the prognostic relevance of coronary plaque burden, this document will provide various methods to estimate and report total plaque burden. The addition of P1 to P4 descriptors are used to denote increasing categories of plaque burden. The main goal of CAD-RADS, which should always be interpreted together with the impression found in the report, remains to facilitate communication of test results with referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will continue to provide a framework of standardization that may benefit education, research, peer-review, artificial intelligence development, clinical trial design, population health and quality assurance with the ultimate goal of improving patient care., (Copyright © 2022 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Background: Adverse cardiovascular events are a significant cause of mortality in end-stage renal disease (ESRD) patients. High-risk plaque anatomy may be a significant contributor. However, their atherosclerotic phenotypes have not been described. We sought to define atherosclerotic plaque characteristics (APC) in dialysis patients using artificial-intelligence augmented CCTA., Methods: We retrospectively analyzed ESRD patients referred for CCTA using an FDA approved artificial-intelligence augmented-CCTA program (Cleerly). Coronary lesions were evaluated for APCs by CCTA. APCs included percent atheroma volume(PAV), low-density non-calcified-plaque (LD-NCP), non-calcified-plaque (NCP), calcified-plaque (CP), length, and high-risk-plaque (HRP), defined by LD-NCP and positive arterial remodeling >1.10 (PR)., Results: 79 ESRD patients were enrolled, mean age 65.3 years, 32.9% female. Disease distribution was non-obstructive (65.8%), 1-vessel disease (21.5%), 2-vessel disease (7.6%), and 3-vessel disease (5.1%). Mean total plaque volume (TPV) was 810.0 mm 3 , LD-NCP 16.8 mm 3 , NCP 403.1 mm 3 , and CP 390.1 mm 3 . HRP was present in 81.0% patients. Patients with at least one >50% stenosis, or obstructive lesions, had significantly higher TPV, LD-NCP, NCP, and CP. Patients >65 years had more CP and higher PAV., Conclusion: Our study provides novel insight into ESRD plaque phenotypes and demonstrates that artificial-intelligence augmented CCTA analysis is feasible for CAD characterization despite severe calcification. We demonstrate elevated plaque burden and stenosis caused by predominantly non-calcified-plaque. Furthermore, the quantity of calcified-plaques increased with age, with men exhibiting increased number of 2-feature plaques and higher plaque volumes. Artificial-intelligence augmented CCTA analysis of APCs may be a promising metric for cardiac risk stratification and warrants further prospective investigation., (Copyright © 2022. Published by Elsevier Inc.)

BACKGROUND. Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. OBJECTIVE. The purpose of our study was to compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography and to assess the downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. METHODS. This study entailed a retrospective post hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The study included 301 patients (88 women and 213 men; mean age, 64.4 ± 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. RESULTS. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥ 50%) stenosis in 54% of patients, including severe (≥ 70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified nonobstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p < .001) than MPI for predicting ≥ 50% stenosis by QCA (0.88 vs 0.66), ≥ 70% stenosis by QCA (0.92 vs 0.81), and FFR < 0.80 (0.90 vs 0.71). An AI-QCT result of ≥ 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥ 50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AIQCT in all patients and those showing ≥ 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥ 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. CONCLUSION. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. CLINICAL IMPACT. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs. TRIAL REGISTRATION. Clinicaltrials.gov NCT02173275.

Objectives: This study aims to compare veterans and non-veterans undergoing transcatheter aortic valve replacement (TAVR) using data from the Society for Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy (STS/ACC TVT) registry., Methods: Patients undergoing TAVR at George Washington University (GWU) and veterans treated at Washington DC Veterans Affairs Medical Center (VAMC) who underwent TAVR at GWU from 2014-2020 were included. All patients were reported in the TVT registry. Emergency and valve-in-valve TAVR were excluded. Cohorts were divided based on veteran status. Operators were the same for both groups. Outcomes were compared at 30 days and 1 year. The primary outcome was mortality and secondary outcomes were morbidity metrics., Results: A total of 299 patients (91 veterans, 208 non-veterans) were included. Veterans had higher rates of hypertension (87.9% vs 77.9%; P=.04), diabetes (46.7% vs 28.9%; P<.01), and lung disease (2.4% vs 11.0%; P<.001). Outcomes were not significantly different between veterans and non-veterans, including 30-day mortality (0% vs 2.9%, respectively; P=.18), 1-year mortality (9.8% vs 10.7%, respectively; P=.61), stroke incidence (0% vs 2.5%, respectively; P=.73), median intensive care unit stay (24 hours in both groups), and overall hospital stay (2 days in both groups)., Conclusions: The affiliation between a VAMC and an academic medical center allowed for direct comparison between veterans and non-veterans undergoing TAVR by the same operators using the TVT registry. Despite significantly higher rates of comorbidities, veterans had equivalent outcomes compared with non-veterans. This may be in part due to the comprehensive care that veterans receive in the VAMC and this institution's integrated heart center team.

Background: The extent to which health care systems have adapted to the COVID-19 pandemic to provide necessary cardiac diagnostic services is unknown., Objectives: The aim of this study was to determine the impact of the pandemic on cardiac testing practices, volumes and types of diagnostic services, and perceived psychological stress to health care providers worldwide., Methods: The International Atomic Energy Agency conducted a worldwide survey assessing alterations from baseline in cardiovascular diagnostic care at the pandemic's onset and 1 year later. Multivariable regression was used to determine factors associated with procedure volume recovery., Results: Surveys were submitted from 669 centers in 107 countries. Worldwide reduction in cardiac procedure volumes of 64% from March 2019 to April 2020 recovered by April 2021 in high- and upper middle-income countries (recovery rates of 108% and 99%) but remained depressed in lower middle- and low-income countries (46% and 30% recovery). Although stress testing was used 12% less frequently in 2021 than in 2019, coronary computed tomographic angiography was used 14% more, a trend also seen for other advanced cardiac imaging modalities (positron emission tomography and magnetic resonance; 22%-25% increases). Pandemic-related psychological stress was estimated to have affected nearly 40% of staff, impacting patient care at 78% of sites. In multivariable regression, only lower-income status and physicians' psychological stress were significant in predicting recovery of cardiac testing., Conclusions: Cardiac diagnostic testing has yet to recover to prepandemic levels in lower-income countries. Worldwide, the decrease in standard stress testing is offset by greater use of advanced cardiac imaging modalities. Pandemic-related psychological stress among providers is widespread and associated with poor recovery of cardiac testing., Competing Interests: Funding Support and Author Disclosures Dr Williams is supported by the British Heart Foundation (FS/ICRF/20/26002). Dr Einstein has received speaker fees from Ionetix; has received consulting fees from W. L. Gore & Associates; has received authorship fees from Wolters Kluwer Healthcare – UpToDate; and has received grants or grants pending to his institution from Attralus, Canon Medical Systems, Eidos Therapeutics, GE Healthcare, Pfizer, Roche Medical Systems, W. L. Gore & Associates, and XyloCor Therapeutics. Dr Williams has received speaker fees from Canon Medical Systems. Dr Dorbala has received honoraria from Pfizer and GE Healthcare; and has received grants to her institution from Pfizer and GE Healthcare. Dr Sinitsyn has received congress speaker honoraria from Bayer, GE Healthcare, Siemens, and Philips. Dr Kudo has received research grants from Nihon Medi-physics and FUJIFILM Toyama Chemical. Dr Bucciarelli-Ducci is CEO (part-time) of the Society for Cardiovascular Magnetic Resonance; and has received speaker fees from Circle Cardiovascular Imaging, Bayer, and Siemens Healthineers. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis., Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters., Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI)., Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had <50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (<350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370-400 mg/ml 90.0%, >400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters., Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables., Condensed Abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Coronary artery disease is a leading cause of death worldwide. There has been a myriad of advancements in the field of cardiovascular imaging to aid in diagnosis, treatment, and prevention of coronary artery disease. The application of artificial intelligence in medicine, particularly in cardiovascular medicine has erupted in the past decade. This article serves to highlight the highest yield articles within cardiovascular imaging with an emphasis on coronary CT angiography methods for % stenosis evaluation and atherosclerosis quantification for the general cardiologist. The paper finally discusses the evolving paradigm of implementation of artificial intelligence in real world practice., Competing Interests: JE is an employee and retains equity in Cleerly Inc. AC reports grant funding from GW Heart and Vascular Institute, and equity in Cleerly. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Covas, De Guzman, Barrows, Bradley, Choi, Krepp, Lewis, Katz, Tracy, Zeman, Earls and Choi.)

In this clinical practice statement, we represent a summary of the current evidence and clinical applications of cardiac computed tomography (CT) in evaluation of coronary artery disease (CAD), from an expert panel organized by the American Society for Preventive Cardiology (ASPC), and appraises the current use and indications of cardiac CT in clinical practice. Cardiac CT is emerging as a front line non-invasive diagnostic test for CAD, with evidence supporting the clinical utility of cardiac CT in diagnosis and prevention. CCTA offers several advantages beyond other testing modalities, due to its ability to identify and characterize coronary stenosis severity and pathophysiological changes in coronary atherosclerosis and stenosis, aiding in early diagnosis, prognosis and management of CAD. This document further explores the emerging applications of CCTA based on functional assessment using CT derived fractional flow reserve, peri‑coronary inflammation and artificial intelligence (AI) that can provide personalized risk assessment and guide targeted treatment. We sought to provide an expert consensus based on the latest evidence and best available clinical practice guidelines regarding the role of CCTA as an essential tool in cardiovascular prevention - applicable to risk assessment and early diagnosis and management, noting potential areas for future investigation., Competing Interests: No funding was received for this manuscript. MB has grant support from General Electric, AC reports grant support from the GW Heart and Vascular Institute and equity in Cleerly, Inc. Dr. Michos reports medical advisory boards for Astra Zeneca, Amarin, Bayer, Boehringer Ingelheim, Esperion, Novartis, and Novo Nordisk. JM is on the Scientific Advisory Board: Arineta, Upside Foods, and is employed and has equity in Cleerly, Inc. All other authors report no conflicts., (© 2022 The Authors. Published by Elsevier B.V.)