Noninvasive assessment of coronary atherosclerosis by cardiac computed tomography for risk stratifying patients with suspected coronary heart disease

Estimating cardiac output from coronary CT angiography: an individualized compartment model in comparison to the Stewart-Hamilton method

Background

Attenuation is correlated with the concentration of contrast medium (CM) in the arteries. The cardiac output (CO) affects the concentration of CM in the circulatory system; therefore, CO affects the time-density curve (TDC). Thus, estimating CO using TDC from test-bolus images acquired in computed tomography (CT) is possible. In this study, we compare two methods of estimating CO, namely, an individualized mathematical compartment model, integrating patient, contrast, and scanning factors with TDC, and the Stewart-Hamilton method based on the area under the curve of the TDC.

Materials and methods

Attenuation in the aorta was measured during test-bolus in 40 consecutive patients with a clinical indication for coronary CT angiography (CCTA). Each participant underwent cardiac magnetic resonance imaging following CCTA to validate the estimated CO. The individual compartment model used TDC in conjunction with scanning and patient-specific parameters to estimate the concentration of CM and CO over time. This was compared to the CO calculated from the area under the curve using the Stewart-Hamilton method.

Results

Both CO estimated with our individualized compartment model (r = 0.66, p < 0.01) and the Stewart-Hamilton method (r = 0.53, p < 0.01) were moderately correlated with CO measured with cardiac MRI. Body surface area (BSA) and time to peak (TTP) affected the accuracy of our model. Lower BSA resulted in overestimation, and lower TTP resulted in CO underestimation, respectively. We found no gender-specific difference in the accuracy of our model when correcting for BSA. The Stewart-Hamilton method performed better with a more complete TDC, whereas the compartment model performed better overall with a partial TDC.

Conclusion

The TDC acquired in CCTA allows for CO estimation. Both the Stewart-Hamilton method and our mathematical compartment model show moderate correlation when applied to our data, although each method has its strengths and limitations. If the majority of the TDC is known, the Stewart-Hamilton method may be more reliable, but an individual compartment model is preferable when there are insufficient data points in the TDC. Regardless, both methods can potentially increase the diagnostic information acquired from a CCTA, which is increasingly recommended in clinical guidelines.

A machine learning model for non-invasive detection of atherosclerotic coronary artery aneurysm

PURPOSE

Atherosclerosis plays a significant role in the initiation of coronary artery aneurysms (CAA). Although the treatment options for this kind of vascular disease are developing, there are challenges and limitations in both selecting and applying sufficient medical solutions. For surgical interventions, that are novel therapies, non-invasive specific patient-based studies could lead to obtaining more promising results. Despite medical and pathological tests, these pre-surgical investigations require special biomedical and computer-aided engineering techniques. In this study, a machine learning (ML) model is proposed for the non-invasive detection of atherosclerotic CAA for the first time.

METHODS

The database for study was collected from hemodynamic analysis and computed tomography angiography (CTA) of 80 CAAs from 61 patients, approved by the Institutional Review Board (IRB). The proposed ML model is formulated for learning by a one-class support vector machine (1SVM) that is a field of ML to provide techniques for outlier and anomaly detection.

RESULTS

The applied ML algorithms yield reasonable results with high and significant accuracy in designing a procedure for the non-invasive diagnosis of atherosclerotic aneurysms. This proposed method could be employed as a unique artificial intelligence (AI) tool for assurance in clinical decision-making procedures for surgical intervention treatment methods in the future.

CONCLUSIONS

The non-invasive diagnosis of the atherosclerotic CAAs, which is one of the vital factors in the accomplishment of endovascular surgeries, is important due to some clinical decisions. Although there is no accurate tool for managing this kind of diagnosis, an ML model that can decrease the probability of endovascular surgical failures, death risk, and post-operational complications is proposed in this study. The model is able to increase the clinical decision accuracy for low-risk selection of treatment options.

Cardiac CT angiography in current practice: An American society for preventive cardiology clinical practice statement✰

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.

Natural Drugs as a Treatment Strategy for Cardiovascular Disease through the Regulation of Oxidative Stress

Oxidative stress (OS) refers to the physiological imbalance between oxidative and antioxidative processes leading to increased oxidation, which then results in the inflammatory infiltration of neutrophils, increased protease secretion, and the production of a large number of oxidative intermediates. Oxidative stress is considered an important factor in the pathogenesis of cardiovascular disease (CVD). At present, active components of Chinese herbal medicines (CHMs) have been widely used for the treatment of CVD, including coronary heart disease and hypertension. Since the discovery of artemisinin for the treatment of malaria by Nobel laureate Youyou Tu, the therapeutic effects of active components of CHM on various diseases have been widely investigated by the medical community. It has been found that various active CHM components can regulate oxidative stress and the circulatory system, including ginsenoside, astragaloside, and resveratrol. This paper reviews advances in the use of active CHM components that modulate oxidative stress, suggesting potential drugs for the treatment of various CVDs.