Progression of non-obstructive coronary plaque: a practical CCTA-based risk score from the PARADIGM registry.

OBJECTIVES: No clear recommendations are endorsed by the different scientific societies on the clinical use of repeat coronary computed tomography angiography (CCTA) in patients with non-obstructive coronary artery disease (CAD). This study aimed to develop and validate a practical CCTA risk score to predict medium-term disease progression in patients at a low-to-intermediate probability of CAD. METHODS: Patients were part of the Progression of AtheRosclerotic PlAque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry. Specifically, 370 (derivation cohort) and 219 (validation cohort) patients with two repeat, clinically indicated CCTA scans, non-obstructive CAD, and absence of high-risk plaque (≥ 2 high-risk features) at baseline CCTA were included. Disease progression was defined as the new occurrence of ≥ 50% stenosis and/or high-risk plaque at follow-up CCTA. RESULTS: In the derivation cohort, 104 (28%) patients experienced disease progression. The median time interval between the two CCTAs was 3.3 years (2.7-4.8). Odds ratios for disease progression derived from multivariable logistic regression were as follows: 4.59 (95% confidence interval: 1.69-12.48) for the number of plaques with spotty calcification, 3.73 (1.46-9.52) for the number of plaques with low attenuation component, 2.71 (1.62-4.50) for 25-49% stenosis severity, 1.47 (1.17-1.84) for the number of bifurcation plaques, and 1.21 (1.02-1.42) for the time between the two CCTAs. The C-statistics of the model were 0.732 (0.676-0.788) and 0.668 (0.583-0.752) in the derivation and validation cohorts, respectively. CONCLUSIONS: The new CCTA-based risk score is a simple and practical tool that can predict mid-term CAD progression in patients with known non-obstructive CAD. CLINICAL RELEVANCE STATEMENT: The clinical implementation of this new CCTA-based risk score can help promote the management of patients with non-obstructive coronary disease in terms of timing of imaging follow-up and therapeutic strategies. KEY POINTS: • No recommendations are available on the use of repeat CCTA in patients with non-obstructive CAD. • This new CCTA score predicts mid-term CAD progression in patients with non-obstructive stenosis at baseline. • This new CCTA score can help guide the clinical management of patients with non-obstructive CAD.

A comparison of intracardiac echocardiography and transesophageal echocardiography for guiding device closure of ostium secundum atrial septal defect: A 15-year experience.

BACKGROUND AND AIM: Our aim was to evaluate the fluoroscopy time (FT), procedure time (PT) safety and efficacy when using intracardiac echocardiography (ICE) in comparison to transesophageal echocardiography (TEE) guidance for transcatheter closure of Ostium Secundum Atrial Septal Defect (OS-ASD). METHOD: Ninety patients (n = 90) diagnosed with OS-ASD underwent transcatheter closure between March 2006 and October 2021. Fifty-seven patients were treated under ICE guidance, while 33 patients were treated under TEE guidance. RESULTS: Mean age was 43 ± 15 years and 42 ± 10 years in the ICE and TEE groups, respectively. The majority of patients had a centrally placed defect. Median FT was 8.40 min versus 11.70 min (p < .001) in the ICE group compared to the TEE group, respectively. Median PT was 43 min versus 94 min (p < .001) in the ICE group compared to the TEE group, respectively. Both ICE and TEE provided high quality images. All interventions were completed successfully, except for one patient in the ICE group who experienced a device migration, the development of atrial tachycardia in one patient and atrial fibrillation in two patients in the ICE group which spontaneously cardioverted. There were no other complications. CONCLUSION: This study on a consistent cohort of patients with OS-ASD undergoing percutaneous closure suggests that use of ICE is safe and efficacious. Compared to TEE, ICE demonstrated significantly shorter FT and PT, decreasing the entire duration of the procedure and x-ray exposure. No relevant differences were observed in terms of success rate and complications.

AI Cardiac MRI Scar Analysis Aids Prediction of Major Arrhythmic Events in the Multicenter DERIVATE Registry.

Background Scar burden with late gadolinium enhancement (LGE) cardiac MRI (CMR) predicts arrhythmic events in patients with postinfarction in single-center studies. However, LGE analysis requires experienced human observers, is time consuming, and introduces variability. Purpose To test whether postinfarct scar with LGE CMR can be quantified fully automatically by machines and to compare the ability of LGE CMR scar analyzed by humans and machines to predict arrhythmic events. Materials and Methods This study is a retrospective analysis of the multicenter, multivendor CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry. Patients with chronic heart failure, echocardiographic left ventricular ejection fraction (LVEF) of less than 50%, and LGE CMR were recruited (from January 2015 through December 2020). In the current study, only patients with ischemic cardiomyopathy were included. Quantification of total, dense, and nondense scars was carried out by two experienced readers or a Ternaus network, trained and tested with LGE images of 515 and 246 patients, respectively. Univariable and multivariable Cox analyses were used to assess patient and cardiac characteristics associated with a major adverse cardiac event (MACE). Area under the receiver operating characteristic curve (AUC) was used to compare model performances. Results In 761 patients (mean age, 65 years ± 11, 671 men), 83 MACEs occurred. With use of the testing group, univariable Cox-analysis found New York Heart Association class, left ventricle volume and/or function parameters (by echocardiography or CMR), guideline criterion (LVEF of ≤35% and New York Heart Association class II or III), and LGE scar analyzed by humans or the machine-learning algorithm as predictors of MACE. Machine-based dense or total scar conferred incremental value over the guideline criterion for the association with MACE (AUC: 0.68 vs 0.63, P = .02 and AUC: 0.67 vs 0.63, P = .01, respectively). Modeling with competing risks yielded for dense and total scar (AUC: 0.67 vs 0.61, P = .01 and AUC: 0.66 vs 0.61, P = .005, respectively). Conclusion In this analysis of the multicenter CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry, fully automatic machine learning-based late gadolinium enhancement analysis reliably quantifies myocardial scar mass and improves the current prediction model that uses guideline-based risk criteria for implantable cardioverter defibrillator implantation. ClinicalTrials.gov registration no.: NCT03352648 Published under a CC BY 4.0 license. Supplemental material is available for this article.

Prediction of myocardial blood flow under stress conditions by means of a computational model.

PURPOSE: Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps. METHODS: A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBFCTP) maps. The relative error between the computational MBF under stress conditions (MBFCOMP) and MBFCTP was evaluated to assess the accuracy of the proposed computational model. RESULTS: Applying our method to 9 patients (4 control subjects without ischemia vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBFCOMP and MBFCTP. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%. CONCLUSION: The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e., the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.

Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion.

PURPOSE: To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation. METHODS: One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DLrest) and stress dataset (CTP-DLstress) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DLrest, and CCTA + CTP-DLstress was measured and compared. The time of analysis for CTP stress, CTP-DLrest, and CTP-DLStress was recorded. RESULTS: Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTPStress were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DLrest and CCTA + DLstress were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DLRest, and CCTA + CTP-DLStress significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001). CONCLUSION: Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress..

Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment.

BACKGROUND: Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED). METHODS: In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi42(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability. RESULTS: In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF - 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s). CONCLUSIONS: Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.

T1 mapping and cardiac magnetic resonance feature tracking in mitral valve prolapse.

OBJECTIVES: T1 mapping (T1-map) and cardiac magnetic resonance feature tracking (CMR-FT) techniques have been introduced for the early detection of interstitial myocardial fibrosis and deformation abnormalities. We sought to demonstrate that T1-map and CMR-FT may identify the presence of subclinical myocardial structural changes in patients with mitral valve prolapse (MVP). METHODS: Consecutive MVP patients with moderate-to-severe mitral regurgitation and comparative matched healthy subjects were prospectively enrolled and underwent CMR-FT analysis to calculate 2D global and segmental circumferential (CS) and radial strain (RS) and T1-map to determine global and segmental native T1 (nT1) values. RESULTS: Seventy-three MVP patients (mean age, 57 ± 13 years old; male, 76%; regurgitant volume, 57 ± 21 mL) and 42 matched control subjects (mean age, 56 ± 18 years; male, 74%) were included. MVP patients showed a lower global CS (- 16.3 ± 3.4% vs. - 17.8 ± 1.9%, p = 0.020) and longer global nT1 (1124.9 ± 97.7 ms vs. 1007.4 ± 26.1 ms, p < 0.001) as compared to controls. Moreover, MVP patients showed lower RS and CS in basal (21.6 ± 12.3% vs. 27.6 ± 8.9%, p = 0.008, and - 13.0 ± 6.7% vs. - 14.9 ± 4.1%, p = 0.013) and mid-inferolateral (20.6 ± 10.7% vs. 28.4 ± 8.7%, p < 0.001, and - 12.8 ± 6.3% vs. - 16.5 ± 4.0%, p < 0.001) walls as compared to other myocardial segments. Similarly, MVP patients showed longer nT1 values in basal (1080 ± 68 ms vs. 1043 ± 43 ms, p < 0.001) and mid-inferolateral (1080 ± 77 ms vs. 1034 ± 37 ms, p < 0.001) walls as compared to other myocardial segments. Of note, nT1 values were significantly correlated with CS (r, 0.36; p < 0.001) and RS (r, 0.37; p < 0.001) but not with regurgitant volume. CONCLUSIONS: T1-map and CMR-FT identify subclinical left ventricle tissue changes in patients with MVP. Further studies are required to correlate these subclinical tissue changes with the outcome. KEY POINTS: • T1 mapping (T1-map) and cardiac magnetic resonance feature tracking (CMR-FT) techniques have been introduced for the early detection of interstitial myocardial fibrosis and deformation abnormalities. • In MVP patients, we demonstrated a longer global nT1 with associated reduced global circumferential (CS) and radial strain (RS) as compared to control subjects. • Among MVP patients, the mid-basal left ventricle inferolateral wall showed longer nT1 with reduced CS and RS as compared to other myocardial segments. Further studies are required to correlate these subclinical tissue changes with the outcome.

CarDiac magnEtic Resonance for prophylactic Implantable-cardioVerter defibrillAtor ThErapy in Non-Ischaemic dilated CardioMyopathy: an international Registry.

AIMS: The aim of this registry was to evaluate the additional prognostic value of a composite cardiac magnetic resonance (CMR)-based risk score over standard-of-care (SOC) evaluation in a large cohort of consecutive unselected non-ischaemic cardiomyopathy (NICM) patients. METHODS AND RESULTS: In the DERIVATE registry (www.clinicaltrials.gov/registration: RCT#NCT03352648), 1000 (derivation cohort) and 508 (validation cohort) NICM patients with chronic heart failure (HF) and left ventricular ejection fraction <50% were included. All-cause mortality and major adverse arrhythmic cardiac events (MAACE) were the primary and secondary endpoints, respectively. During a median follow-up of 959 days, all-cause mortality and MAACE occurred in 72 (7%) and 93 (9%) patients, respectively. Age and >3 segments with midwall fibrosis on late gadolinium enhancement (LGE) were the only independent predictors of all-cause mortality (HR: 1.036, 95% CI: 1.0117-1.056, P < 0.001 and HR: 2.077, 95% CI: 1.211-3.562, P = 0.008, respectively). For MAACE, the independent predictors were male gender, left ventricular end-diastolic volume index by CMR (CMR-LVEDVi), and >3 segments with midwall fibrosis on LGE (HR: 2.131, 95% CI: 1.231-3.690, P = 0.007; HR: 3.161, 95% CI: 1.750-5.709, P < 0.001; and HR: 1.693, 95% CI: 1.084-2.644, P = 0.021, respectively). A composite clinical and CMR-based risk score provided a net reclassification improvement of 63.7% (P < 0.001) for MAACE occurrence when added to the model based on SOC evaluation. These findings were confirmed in the validation cohort. CONCLUSION: In a large multicentre, multivendor cohort registry reflecting daily clinical practice in NICM work-up, a composite clinical and CMR-based risk score provides incremental prognostic value beyond SOC evaluation, which may have impact on the indication of implantable cardioverter-defibrillator implantation.

Role of Cardiac Magnetic Resonance Imaging in Myocardial Infarction.

PURPOSE OF REVIEW: The purpose of the present paper is to analytically review the diagnostic and prognostic role of CMR in ST-segment elevation myocardial infarction (STEMI) survivors. Percutaneous coronary intervention (PCI) is the treatment of choice in patients STEMI. However, risk of future events remains substantial. Assessment of the extent of myocardial infarction (MI), cardiac function and ventricular remodelling has become the focus of recent studies. Electrocardiography, angiography and echocardiography parameters, as well as risk scores, lack sensitivity and reproducibility in predicting future cardiovascular events. A major advantage of cardiac magnetic resonance imaging (CMR) is that it provides myocardial tissue characterization. RECENT FINDINGS: CMR is able to quantify both reversible and irreversible myocardial injury and correlates with future events. This review will illustrate how microvascular function indices (myocardial salvage index, presence and amount of microvascular obstruction and intramyocardial haemorrhage) detectable by CMR add prognostic information and could impact on future strategies to improve outcomes in revascularized patients.

Artificial Intelligence in Transcatheter Aortic Valve Replacement: Its Current Role and Ongoing Challenges.

Transcatheter aortic valve replacement (TAVR) has emerged as a viable alternative to surgical aortic valve replacement, as accumulating clinical evidence has demonstrated its safety and efficacy. TAVR indications have expanded beyond high-risk or inoperable patients to include intermediate and low-risk patients with severe aortic stenosis. Artificial intelligence (AI) is revolutionizing the field of cardiology, aiding in the interpretation of medical imaging and developing risk models for at-risk individuals and those with cardiac disease. This article explores the growing role of AI in TAVR procedures and assesses its potential impact, with particular focus on its ability to improve patient selection, procedural planning, post-implantation monitoring and contribute to optimized patient outcomes. In addition, current challenges and future directions in AI implementation are highlighted.

SCCT Health Policy and Advocacy Efforts.

Cardiac computed tomography has a growing presence in multiple guidelines supported by a growing evidence base as to its accuracy and impact on clinical outcomes. Despite this, dissemination into widespread routine clinical practice has been slow, largely restricted to large academic centers and urban settings. The reasons of this are multifactorial, but one of the most impactful of these reasons is undeniably reimbursement. Currently, there is marked discrepancy between the costs of performing cardiac CT and the renumeration provided for this. Until this is addressed, cardiac CT will not reach its potential for the benefit of patients. It is for this reason that the SCCT continues to dedicate significant efforts to represent the need of the cardiology and radiology communities in bringing about changes in policy and billing. Significant momentum has been gained in recent years with the engagement of both congress and Medicare in moving towards a system of payment that recognizes the time and expertise required to acquire high quality cardiac CT. In this article we cover these recent efforts, and the next steps in this continued effort over the coming years.

Clinical Validation of a Machine-Learned, Point-of-Care System to IDENTIFY Functionally Significant Coronary Artery Disease.

Many clinical studies have shown wide performance variation in tests to identify coronary artery disease (CAD). Coronary computed tomography angiography (CCTA) has been identified as an effective rule-out test but is not widely available in the USA, particularly so in rural areas. Patients in rural areas are underserved in the healthcare system as compared to urban areas, rendering it a priority population to target with highly accessible diagnostics. We previously developed a machine-learned algorithm to identify the presence of CAD (defined by functional significance) in patients with symptoms without the use of radiation or stress. The algorithm requires 215 s temporally synchronized photoplethysmographic and orthogonal voltage gradient signals acquired at rest. The purpose of the present work is to validate the performance of the algorithm in a frozen state (i.e., no retraining) in a large, blinded dataset from the IDENTIFY trial. IDENTIFY is a multicenter, selectively blinded, non-randomized, prospective, repository study to acquire signals with paired metadata from subjects with symptoms indicative of CAD within seven days prior to either left heart catheterization or CCTA. The algorithm's sensitivity and specificity were validated using a set of unseen patient signals (n = 1816). Pre-specified endpoints were chosen to demonstrate a rule-out performance comparable to CCTA. The ROC-AUC in the validation set was 0.80 (95% CI: 0.78-0.82). This performance was maintained in both male and female subgroups. At the pre-specified cut point, the sensitivity was 0.85 (95% CI: 0.82-0.88), and the specificity was 0.58 (95% CI: 0.54-0.62), passing the pre-specified endpoints. Assuming a 4% disease prevalence, the NPV was 0.99. Algorithm performance is comparable to tertiary center testing using CCTA. Selection of a suitable cut-point results in the same sensitivity and specificity performance in females as in males. Therefore, a medical device embedding this algorithm may address an unmet need for a non-invasive, front-line point-of-care test for CAD (without any radiation or stress), thus offering significant benefits to the patient, physician, and healthcare system.

PROGnostic RolE of strain measurements in stress cardiac MRI in predicting major adverse cardiac events.

OBJECTIVES: We aimed to investigate the role of feature-tracking (FT) strain in long-term risk stratification of patients with known or suspected coronary artery disease (CAD) who underwent stress cardiac MRI with dipyridamole; to determine if contrast-free stress cardiac MRI with strain measurements could provide comparable prognostic value to myocardial perfusion. MATERIALS AND METHODS: This retrospective study included consecutive patients with stable symptoms suggesting possible cardiac ischemia who underwent stress cardiac MRI with dipyridamole. The mean follow-up period was 5.8 years ±1.2 [SD]. FT cardiac MRI analysis was performed for each patient to obtain 2D global peak circumferential strain (GCS). The primary outcome measure was major adverse cardiac events (MACE), defined as nonfatal myocardial infarction and cardiac death. RESULTS: A total of 729 patients (mean age, 63 years ±10 [SD]; 616 males) were included. MACE occurred in 70 (9.6%) patients. The presence of late gadolinium enhancement (LGE) ([HR] 2.74, [95% CI: 1.53, 4.88]; P < .001) and stress GCS (HR, 1.06 [95% CI: 1.01, 1.12]; P = .016) were independently associated with MACE. A model based on contrast-free assessment of LVEF and stress GCS showed similar performance for predicting MACE than LVEF and perfusion (P = .056). CONCLUSIONS: In patients with known or suspected CAD undergoing stress cardiac MRI with dipyridamole, GCS and LGE presence were independent predictors of MACE. Contrast-free stress cardiac MRI with stress GCS measurement offered prognostic value akin to myocardial perfusion assessment. CLINICAL RELEVANCE STATEMENT: Stress global circumferential strain represented an additional method to predict major adverse cardiac events in patients undergoing stress cardiac MRI, even without the use of contrast agents. This would be of particular significance in patients with severe renal impairment.

STress computed tomogRaphy perfusion and stress cArdiac magnetic resonance for ThE manaGement of suspected or known coronarY artery disease: resources and outcomes impact.

BACKGROUND: The aim of this study is to describe resources and outcomes of coronary computed tomography angiography plus Stress CT perfusion (CCTA â€‹+ â€‹Stress-CTP) and stress cardiovascular magnetic resonance (Stress-CMR) in symptomatic patients with suspected or known CAD. METHODS: Six hundred and twenty-four consecutive symptomatic patients with intermediate to high-risk pretest likelihood for CAD or previous history of revascularization referred to our hospital for clinically indicated CCTA â€‹+ â€‹Stress-CTP or Stress-CMR were enrolled. Stress-CTP scans were performed in 223 patients while 401 patients performed Stress-CMR. Patient follow-up was performed at 1 year after index test performance. Endpoints were all cardiac events, as a combined endpoint of revascularization, non-fatal MI and death, and hard cardiac events, as combined endpoint of non-fatal MI and death. RESULTS: Twenty-nine percent of patients who underwent CCTA â€‹+ â€‹Stress-CTP received revascularization, 7% of subjects assessed with Stress-CMR were treated invasively, and a low number of non-fatal MI and death was observed with both strategies (hard events in 0.4% of patients that had CCTA â€‹+ â€‹Stress-CTP as index test, and in 3% of patients evaluated with Stress-CMR). According to the predefined endpoints, CCTA â€‹+ â€‹Stress-CTP group showed high rate of all cardiac events and low rate of hard cardiac events, respectively. The cumulative costs were 1970 â€‹± â€‹2506 Euro and 733 â€‹± â€‹1418 Euro for the CCTA â€‹+ â€‹Stress-CTP group and Stress-CMR group, respectively. CONCLUSIONS: The use of CCTA â€‹+ â€‹Stress-CTP strategy was associated with high referral to revascularization but with a favourable trend in terms of hard cardiac events and diagnostic yield in identifying individuals at lower risk of adverse events despite the presence of CAD.

Impact of Smoking on Coronary Volume-to-Myocardial Mass Ratio: An ADVANCE Registry Substudy.

Purpose To examine the relationship between smoking status and coronary volume-to-myocardial mass ratio (V/M) among individuals with coronary artery disease (CAD) undergoing CT fractional flow reserve (CT-FFR) analysis. Materials and Methods In this secondary analysis, participants from the ADVANCE registry evaluated for suspected CAD from July 15, 2015, to October 20, 2017, who were found to have coronary stenosis of 30% or greater at coronary CT angiography (CCTA) were included if they had known smoking status and underwent CT-FFR and V/M analysis. CCTA images were segmented to calculate coronary volume and myocardial mass. V/M was compared between smoking groups, and predictors of low V/M were determined. Results The sample for analysis included 503 current smokers, 1060 former smokers, and 1311 never-smokers (2874 participants; 1906 male participants). After adjustment for demographic and clinical factors, former smokers had greater coronary volume than never-smokers (former smokers, 3021.7 mm3 ± 934.0 [SD]; never-smokers, 2967.6 mm3 ± 978.0; P = .002), while current smokers had increased myocardial mass compared with never-smokers (current smokers, 127.8 g ± 32.9; never-smokers, 118.0 g ± 32.5; P = .02). However, both current and former smokers had lower V/M than never-smokers (current smokers, 24.1 mm3/g ± 7.9; former smokers, 24.9 mm3/g ± 7.1; never-smokers, 25.8 mm3/g ± 7.4; P < .001 [unadjusted] and P = .002 [unadjusted], respectively). Current smoking status (odds ratio [OR], 0.74 [95% CI: 0.59, 0.93]; P = .009), former smoking status (OR, 0.81 [95% CI: 0.68, 0.97]; P = .02), stenosis of 50% or greater (OR, 0.62 [95% CI: 0.52, 0.74]; P < .001), and diabetes (OR, 0.67 [95% CI: 0.56, 0.82]; P < .001) were independent predictors of low V/M. Conclusion Both current and former smoking status were independently associated with low V/M. Keywords: CT Angiography, Cardiac, Heart, Ischemia/Infarction Clinical trial registration no. NCT02499679 Supplemental material is available for this article. © RSNA, 2024.

DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR.

BACKGROUND AND AIMS: This study investigated the additional prognostic value of epicardial adipose tissue (EAT) volume for major adverse cardiovascular events (MACE) in patients undergoing stress cardiac magnetic resonance (CMR) imaging. METHODS: 730 consecutive patients [mean age: 63 ± 10 years; 616 men] who underwent stress CMR for known or suspected coronary artery disease were randomly divided into derivation (n = 365) and validation (n = 365) cohorts. MACE was defined as non-fatal myocardial infarction and cardiac deaths. A deep learning algorithm was developed and trained to quantify EAT volume from CMR. EAT volume was adjusted for height (EAT volume index). A composite CMR-based risk score by Cox analysis of the risk of MACE was created. RESULTS: In the derivation cohort, 32 patients (8.7 %) developed MACE during a follow-up of 2103 days. Left ventricular ejection fraction (LVEF) < 35 % (HR 4.407 [95 % CI 1.903-10.202]; p<0.001), stress perfusion defect (HR 3.550 [95 % CI 1.765-7.138]; p<0.001), late gadolinium enhancement (LGE) (HR 4.428 [95%CI 1.822-10.759]; p = 0.001) and EAT volume index (HR 1.082 [95 % CI 1.045-1.120]; p<0.001) were independent predictors of MACE. In a multivariate Cox regression analysis, adding EAT volume index to a composite risk score including LVEF, stress perfusion defect and LGE provided additional value in MACE prediction, with a net reclassification improvement of 0.683 (95%CI, 0.336-1.03; p<0.001). The combined evaluation of risk score and EAT volume index showed a higher Harrel C statistic as compared to risk score (0.85 vs. 0.76; p<0.001) and EAT volume index alone (0.85 vs.0.74; p<0.001). These findings were confirmed in the validation cohort. CONCLUSIONS: In patients with clinically indicated stress CMR, fully automated EAT volume measured by deep learning can provide additional prognostic information on top of standard clinical and imaging parameters.

Artificial Intelligence-Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome.

BACKGROUND: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization. OBJECTIVES: This study sought to investigate the additive value of artificial intelligence-enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA). METHODS: Among ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort. RESULTS: Among 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA. CONCLUSIONS: AI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328).

Benefit of icosapent ethyl on coronary physiology assessed by computed tomography angiography fractional flow reserve: EVAPORATE-FFRCT.

AIMS: Icosapent ethyl (IPE) significantly reduced ischaemic events in statin-treated patients with atherosclerosis or diabetes and elevated triglycerides in REDUCE-IT, including large reductions in myocardial infarction and elective, urgent, and emergent coronary revascularization. However, the mechanisms driving this clinical benefit are not fully known. The EVAPORATE trial demonstrated that IPE significantly reduced plaque burden. No study to date has assessed the impact of IPE on coronary physiology. Fractional flow reserve (FFR) derived from coronary computed tomography angiography (CTA) data sets (FFRCT) applies computational fluid dynamics to calculate FFR values in epicardial coronary arteries. Our objective was to assess the impact of IPE on coronary physiology assessed by FFRCT using imaging data from EVAPORATE. METHODS AND RESULTS: A total of 47 patients and of 507 coronary lesions at baseline, 9 months, and 18 months with coronary CTA and FFRCT were studied in a blinded core lab. The pre-specified primary endpoint was the FFRCT value in the distal coronary segment from baseline to follow-up in the most diseased vessel per patient using IPE compared with placebo. The pre-specified secondary endpoint was the change in translesional FFRCT (ΔFFRCT) across the most severe (minimum 30% diameter stenosis) coronary lesion per vessel. Baseline FFRCT was similar for IPE compared with placebo (0.83 ± 0.08 vs. 0.84 ± 0.08, P = 0.55). There was significant improvement in the primary endpoint, as IPE improved mean distal segment FFRCT at 9- and 18-month follow-up compared with placebo (0.01 ± 0.05 vs. -0.05 ± 0.09, P = 0.02, and -0.01 ± 0.09 vs. -0.09 ± 0.12, P = 0.03, respectively). ΔFFRCT in 140 coronary lesions was improved, although not statistically significant, with IPE compared with placebo (-0.06 ± 0.08 vs. -0.09 ± 0.1, P = 0.054). CONCLUSION: Icosapent ethyl demonstrated significant benefits in coronary physiology compared with placebo. This early and sustained improvement in FFRCT at 9- and 18-month follow-up provides mechanistic insight into the clinical benefit observed in the REDUCE-IT trial. Furthermore, this is the first assessment of FFRCT to determine drug effect.

The effect of icosapent ethyl on left atrial and left ventricular morphology.

Atrial fibrillation (AF) is a common arrhythmia associated with poor outcomes. N-3 fatty acids have been shown to provide significant cardiovascular risk reduction, but they may exacerbate the risk of AF. The pathway by which N-3 fatty acids may be arrhythmogenic is unknown. One possible mechanism involves cardiac chamber morphology alteration. The purpose of this study was to investigate the effect of icosapent ethyl (IPE) on left atrial (LA) size and left ventricular (LV) mass. This study used coronary computed tomographic angiography images gathered from the Effect of Icosapent Ethyl on Progression of Coronary Atherosclerosis (EVAPORATE) trial. EVAPORATE was a randomised, double-blind, placebo-controlled study finding a significant reduction in coronary atherosclerosis progression in patients with residually elevated triglycerides despite statin therapy on 4 g IPE daily versus 4 g placebo daily. Computed tomography images were used to measure LA size and LV mass at 0 and 18 months. Of 80 enrolled patients, 68 were included in the final analysis. Baseline demographics and risk factors were similar between IPE and placebo cohorts. LA anterior- posterior diameter measured on axial (p=0.51) and sagittal (p=0.52) orientations were not different over time. Also, there was no difference between groups in the change in LA volume (p=0.84). Change in LV mass was similar between groups (p=0.13). In conclusion, this study did not detect differences in LA size or LV mass over 18 months between patients on 4 g daily IPE versus placebo.

Epicardial and Pericoronary Adipose Tissue, Coronary Inflammation, and Acute Coronary Syndromes.

Vascular inflammation is recognized as the primary trigger of acute coronary syndrome (ACS). However, current noninvasive methods are not capable of accurately detecting coronary inflammation. Epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT), in addition to their role as an energy reserve system, have been found to contribute to the development and progression of coronary artery calcification, inflammation, and plaque vulnerability. They also participate in the vascular response during ischemia, sympathetic stimuli, and arrhythmia. As a result, the evaluation of EAT and PCAT using imaging techniques such as computed tomography (CT), cardiac magnetic resonance (CMR), and nuclear imaging has gained significant attention. PCAT-CT attenuation, which measures the average CT attenuation in Hounsfield units (HU) of the adipose tissue, reflects adipocyte differentiation/size and leukocyte infiltration. It is emerging as a marker of tissue inflammation and has shown prognostic value in coronary artery disease (CAD), being associated with plaque development, vulnerability, and rupture. In patients with acute myocardial infarction (AMI), an inflammatory pericoronary microenvironment promoted by dysfunctional EAT/PCAT has been demonstrated, and more recently, it has been associated with plaque rupture in non-ST-segment elevation myocardial infarction (NSTEMI). Endothelial dysfunction, known for its detrimental effects on coronary vessels and its association with plaque progression, is bidirectionally linked to PCAT. PCAT modulates the secretory profile of endothelial cells in response to inflammation and also plays a crucial role in regulating vascular tone in the coronary district. Consequently, dysregulated PCAT has been hypothesized to contribute to type 2 myocardial infarction with non-obstructive coronary arteries (MINOCA) and coronary vasculitis. Recently, quantitative measures of EAT derived from coronary CT angiography (CCTA) have been included in artificial intelligence (AI) models for cardiovascular risk stratification. These models have shown incremental utility in predicting major adverse cardiovascular events (MACEs) compared to plaque characteristics alone. Therefore, the analysis of PCAT and EAT, particularly through PCAT-CT attenuation, appears to be a safe, valuable, and sufficiently specific noninvasive method for accurately identifying coronary inflammation and subsequent high-risk plaque. These findings are supported by biopsy and in vivo evidence. Although speculative, these pieces of evidence open the door for a fascinating new strategy in cardiovascular risk stratification. The incorporation of PCAT and EAT analysis, mainly through PCAT-CT attenuation, could potentially lead to improved risk stratification and guide early targeted primary prevention and intensive secondary prevention in patients at higher risk of cardiac events.