BACKGROUND: The best management of symptomatic patients with low-gradient (LG) severe aortic stenosis (AS) and preserved left ventricular ejection fraction (LVEF) has not been established. The Randomised study for the Optimal Treatment of symptomatic patients with low-gradient severe Aortic valve Stenosis (ROTAS) trial aimed to assess the superiority of aortic valve replacement (AVR) versus medical treatment (MT) in this specific group of AS patients.
METHODS: Patients with symptomatic LG severe AS and preserved LVEF (>50%) underwent dobutamine stress echocardiography and/or CT-aortic calcium score to confirm AS severity and were then randomised 1:1 to AVR or MT. The primary endpoint was a composite of overall death and/or cardiovascular hospitalisation.
RESULTS: The ROTAS study was stopped early because of insufficient recruitment. In the end, only 52 patients (age 79±7 years; women 54%; NYHA III-IV 27%; median STS score 3.3%) were included in the study. During follow-up (mean: 14±7 months), the primary endpoint occurred in 12 (23%) patients. Compared with MT, AVR was not associated with a significant prognostic benefit (events: 5/26 (19%) vs 7/26 (27%) (HR 0.76, 95% CI 0.24 to 2.39, p=0.63). During follow-up, 11 (42%) patients in the MT group developed class I criteria for AVR or severe symptoms justifying a cross-over to the AVR group.
CONCLUSIONS: Because of the small number of included patients and short follow-up the ROTAS trial was underpowered and unable to demonstrate a difference in the study endpoint between treatment arms. In patients in the MT arm, a regular echocardiographic and clinical assessment might be useful to disclose those developing class I indications of AVR or severe AS-related symptoms.
BACKGROUND: NCT01835028.

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Cardiovascular imaging is exponentially increasing in the diagnosis, risk stratification, and therapeutic management of patients with cardiovascular disease. The European Society of Cardiovascular Radiology (ESCR) is a non-profit scientific medical society dedicated to promoting and coordinating activities in cardiovascular imaging. The purpose of this paper, written by ESCR committees and Executive board members and approved by the ESCR Executive Board and Guidelines committee, is to codify a standardized approach to creating ESCR scientific documents. Indeed, consensus development methods must be adopted to ensure transparent decision-making that optimizes national and global health and reaches a certain scientific credibility. ESCR consensus documents developed based on a rigorous methodology will improve their scientific impact on the management of patients with cardiac involvement. CRITICAL RELEVANCE STATEMENT: This document aims to codify the methodology for producing consensus documents of the ESCR. These ESCR indications will broaden the scientific quality and credibility of further publications and, consequently, the impact on the diagnostic management of patients with cardiac involvement. KEY POINTS: Cardiovascular imaging is exponentially increasing for diagnosis, risk stratification, and therapeutic management. The ESCR is committed to promoting cardiovascular imaging. A rigorous methodology for ESCR consensus documents will improve their scientific impact.

Pericardial diseases have gained renewed clinical interest, leading to a renaissance in the field. There have been many recent advances in pericardial diseases in both multimodality cardiac imaging of diagnoses, such as recurrent, transient constrictive and effusive-constrictive pericarditis, and targeted therapeutics, especially anti-interleukin (IL)-1 agents that affect the inflammasome as part of autoinflammatory pathophysiology. There remains a large educational gap for clinicians, leading to variability in evaluation and management of these patients. The latest pericardial imaging (American Society of Echocardiography, European Association of Cardiovascular Imaging) and clinical guidelines (European Society of Cardiology) are >8-10 years of age and may not reflect current practice. Recent clinical trials involving anti-IL-1 agents in recurrent pericarditis, including anakinra (AIRTRIP), rilonacept (RHAPSODY), and goflikicept have demonstrated their efficacy. The present document represents an international position statement from world leaders in the pericardial field, focusing on novel concepts and emphasizing the role of multimodality cardiac imaging as well as new therapeutics in pericardial diseases.

UNASSIGNED: Indiscriminate coronary computed tomography angiography (CCTA) referrals for suspected coronary artery disease could result in a higher rate of equivocal and non-diagnostic studies, leading to inappropriate downstream resource utilization or delayed time to diagnosis. We sought to develop a simple clinical tool for predicting the likelihood of a non-diagnostic CCTA to help identify patients who might be better served with a different test.
UNASSIGNED: We developed a clinical scoring system from a cohort of 21 492 consecutive patients who underwent CCTA between February 2006 and May 2021. Coronary computed tomography angiography study results were categorized as normal, abnormal, or non-diagnostic. Multivariable logistic regression analysis was conducted to produce a model that predicted the likelihood of a non-diagnostic test. Machine learning (ML) models were utilized to validate the predictor selection and prediction performance. Both logistic regression and ML models achieved fair discriminate ability with an area under the curve of 0.630 [95% confidence interval (CI) 0.618-0.641] and 0.634 (95% CI 0.612-0.656), respectively. The presence of a cardiac implant and weight >100 kg were among the most influential predictors of a non-diagnostic study.
UNASSIGNED: We developed a model that could be implemented at the \'point-of-scheduling\' to identify patients who would be best served by another non-invasive diagnostic test.

BACKGROUND: Semi-automated software is essential for planning and prosthesis selection prior transcatheter aortic valve replacement (TAVR). Reliable data on the usability of software programs for planning a TAVR is missing. The aim of this study was to compare software programs \'Valve Assist 2\' (GE Healthcare) and 3mensio \'Structural Heart\' (Pie Medical Imaging) regarding usability and accuracy of prosthesis size selection in program-inexperienced users.
METHODS: Thirty-one participants (n = 31) were recruited and divided into program-inexperienced users (beginners) (n = 22) and experts (n = 9). After software training, beginners evaluated 3 patient cases in 129 measurements (n = 129) using either Valve Assist 2 (n = 11) or Structural Heart (n = 11) on 2 test days (T1, T2). System Usability Scale (SUS) and ISONORM 9241/110-S (ISONORM) questionnaire were used after the test. The valve size selected by each beginner was compared with the valve size selected from expert group.
RESULTS: Valve Assist 2 had higher SUS Score: median 78.75 (25th, 75th percentile: 67.50, 85.00) compared to Structural Heart: median 65.00 (25th, 75th percentile: 47.50, 73.75), (p < 0,001, r = 0.557). Also, Valve Assist 2 showed a higher ISONORM score: median 1.05 (25th, 75th percentile: - 0.19, 1.71) compared to Structural Heart with a median 0.05 (25th, 75th percentile: - 0.49, 0.13), (p = 0.036, r = 0.454). Correctly selected valve sizes were stable over time using Valve Assist 2: 72.73% to 69.70% compared to Structural Heart program: 93.94% to 40% (χ2 (1) = 21.10, p < 0.001, φ = 0.579).
CONCLUSIONS: The study shows significant better usability scores for Valve Assist 2 compared to 3mensio Structural Heart in program-inexperienced users.

Environmental exposures including poor air quality and extreme temperatures are exacerbated by climate change and are associated with adverse cardiovascular outcomes. Concomitantly, the delivery of health care generates substantial atmospheric greenhouse gas (GHG) emissions contributing to the climate crisis. Therefore, cardiac imaging teams must be aware not only of the adverse cardiovascular health effects of climate change, but also the downstream environmental ramifications of cardiovascular imaging. The purpose of this review is to highlight the impact of climate change on cardiovascular health, discuss the environmental impact of cardiovascular imaging, and describe opportunities to improve environmental sustainability of cardiac MRI, cardiac CT, echocardiography, cardiac nuclear imaging, and invasive cardiovascular imaging. Overarching strategies to improve environmental sustainability in cardiovascular imaging include prioritizing imaging tests with lower GHG emissions when more than one test is appropriate, reducing low-value imaging, and turning equipment off when not in use. Modality-specific opportunities include focused MRI protocols and low-field-strength applications, iodine contrast media recycling programs in cardiac CT, judicious use of US-enhancing agents in echocardiography, improved radiopharmaceutical procurement and waste management in nuclear cardiology, and use of reusable supplies in interventional suites. Finally, future directions and research are highlighted, including life cycle assessments over the lifespan of cardiac imaging equipment and the impact of artificial intelligence tools. Keywords: Heart, Safety, Sustainability, Cardiovascular Imaging Supplemental material is available for this article. © RSNA, 2024.

Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.

Fractional flow reserve computed tomography (FFRCT) is a novel imaging modality. It utilizes computational fluid dynamics analysis of coronary blood flow obtained from CCTA images to estimate the decrease in pressure across coronary stenosis during the maximum hyperemia. The FFRCT can serve as a valuable tool in the assessment of coronary artery disease (CAD). This non-invasive option can be used as an alternative to the invasive fractional Flow Reserve (FFR) evaluation, which is presently considered the gold standard for evaluating the physiological significance of coronary stenoses. It can help in several clinical situations, including Assessment of Acute and stable chest pain, virtual planning for coronary stenting, and treatment decision-making. Although FFRCT has demonstrated potential clinical applications as a non-invasive imaging technique, it is also crucial to acknowledge its limitations in clinical practice. As a result, it is imperative to meticulously evaluate the advantages and drawbacks of FFRCT individually and contemplate its application in combination with other diagnostic examinations and clinical data.

OBJECTIVE: To assess ChatGPT\'s ability as a resource for educating patients on various aspects of cardiac imaging, including diagnosis, imaging modalities, indications, interpretation of radiology reports, and management.
METHODS: 30 questions were posed to ChatGPT-3.5 and ChatGPT-4 three times in three separate chat sessions. Responses were scored as correct, incorrect, or clinically misleading categories by three observers-two board certified cardiologists and one board certified radiologist with cardiac imaging subspecialization. Consistency of responses across the three sessions was also evaluated. Final categorization was based on majority vote between at least two of the three observers.
RESULTS: ChatGPT-3.5 answered seventeen of twenty eight questions correctly (61 %) by majority vote. Twenty one of twenty eight questions were answered correctly (75 %) by ChatGPT-4 by majority vote. Majority vote for correctness was not achieved for two questions. Twenty six of thirty questions were answered consistently by ChatGPT-3.5 (87 %). Twenty nine of thirty questions were answered consistently by ChatGPT-4 (97 %). ChatGPT-3.5 had both consistent and correct responses to seventeen of twenty eight questions (61 %). ChatGPT-4 had both consistent and correct responses to twenty of twenty eight questions (71 %).
CONCLUSIONS: ChatGPT-4 had overall better performance than ChatGTP-3.5 when answering cardiac imaging questions with regard to correctness and consistency of responses. While both ChatGPT-3.5 and ChatGPT-4 answers over half of cardiac imaging questions correctly, inaccurate, clinically misleading and inconsistent responses suggest the need for further refinement before its application for educating patients about cardiac imaging.