CT coronary angiography-guided cardiovascular risk screening in asymptomatic patients: is it time?

A sensitivity indicator screening and intelligent classification method for the diagnosis of T2D-CHD

Background

The prevalence of Type 2 Diabetes Mellitus (T2D) and its significant role in increasing Coronary Heart Disease (CHD) risk highlights the urgent need for effective CHD screening within this population. Despite current advancements in T2D management, the complexity of cardiovascular complications persists. Our study aims to develop a comprehensive CHD screening model for T2D patients, employing multimodal data to improve early detection and management, addressing a critical gap in clinical practice.

Methods

We analyzed data from 699 patients, including 471 with CHD (221 of these also had T2D) and a control group of 228 without CHD. Employing strict diagnostic criteria, we conducted significance testing and multivariate analysis to identify key indicators for T2D-CHD diagnosis. This led to the creation of a neural network model using 21 indicators and a logistic regression model based on an 8-indicator subset. External validation was performed with an independent dataset from an additional 212 patients to confirm the models' generalizability.

Results

The neural network model achieved an accuracy of 90.7%, recall of 90.78%, precision of 90.83%, and an F-1 score of 0.908. The logistic regression model demonstrated an accuracy of 90.13%, recall of 90.1%, precision of 90.22%, and an F-1 score of 0.9016. External validation reinforced the models' reliability and effectiveness in broader clinical settings.

Conclusion

Our AI-driven diagnostic models significantly enhance early CHD detection and management in T2D patients, offering a novel, efficient approach to addressing the complex interplay between these conditions. By leveraging advanced analytics and comprehensive patient data, we present a scalable solution for improving clinical outcomes in this high-risk population, potentially setting a new standard in personalized care and preventative medicine.

Dual-Energy CT in Cardiothoracic Imaging: Current Developments

This article describes the technical principles and clinical applications of dual-energy computed tomography (DECT) in the context of cardiothoracic imaging with a focus on current developments and techniques. Since the introduction of DECT, different vendors developed distinct hard and software approaches for generating multi-energy datasets and multiple DECT applications that were developed and clinically investigated for different fields of interest. Benefits for various clinical settings, such as oncology, trauma and emergency radiology, as well as musculoskeletal and cardiovascular imaging, were recently reported in the literature. State-of-the-art applications, such as virtual monoenergetic imaging (VMI), material decomposition, perfused blood volume imaging, virtual non-contrast imaging (VNC), plaque removal, and virtual non-calcium (VNCa) imaging, can significantly improve cardiothoracic CT image workflows and have a high potential for improvement of diagnostic accuracy and patient safety.

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.

Association Between Premorbid Metabolic Syndrome and Coronary Arterial Stenosis: Results from One Medical Center in Taiwan

Background: Although the relationship between metabolic syndrome (MetS) and cardiovascular disease is already well-established, there is limited evidence as to whether individuals are at risk for cardiovascular disease during the premorbid state of MetS. The aim of this study is to explore the relationship between coronary arterial stenosis and MetS in a nonhypertensive and nondiabetic population. Methods: In this cross-sectional study, we analyzed the data of participants who underwent annual health checkups in a medical center. These data were collected from physical examination, blood tests, cardiac computed tomography examinations, and medical charts. We excluded those with established hypertension or diabetes and age of <50 or >75 years. Results: This study recruited 700 participants with a mean age of 59.5 years. More than 31% had MetS, and the overall prevalence of coronary arterial stenosis was 48.0% (grade 1, 2, 3, 4: 16.3%, 20.9%, 8.4%, 2.4%, respectively). In univariate analysis, older age, male sex, smoking, body mass index, elevated fasting plasma glucose (FPG), elevated triglyceride, lower level of high-density lipoprotein cholesterol, and presence of MetS were all positively correlated with coronary arterial stenosis. After adjustment for confounding factors, MetS still showed strong association with high grades of coronary arterial stenosis [odds ratio (OR) 2.86, confidence interval (95% CI) 1.30-4.01]. Specific components of MetS, such as elevated triglyceride [OR 2.02, 95% CI 1.14-3.57] and elevated FPG [OR 2.25, 95% CI 1.31-3.88], were also associated with coronary arterial stenosis. Conclusion: Our study concluded that premorbid MetS was significantly associated with coronary arterial stenosis. As for the individual components, elevated triglyceride and elevated FPG were both correlated with coronary arterial stenosis. Early preventive measures would be suggested at this stage of MetS to prevent future cardiovascular events.