Coronary Plaque Characteristics and Cut-Off Stenosis for Developing Spasm in Patients with Vasospastic Angina

Coronary Artery Spasm: From Physiopathology to Diagnosis

Coronary artery spasm (CAS) is a reversible vasoconstriction of normal or atherosclerotic epicardial coronary arteries with a subsequent reduction in myocardial blood flow, leading to myocardial ischemia, myocardial infarction, severe arrhythmias, or even sudden death. It is an entity that should be recognized based on a particular clinical presentation. Numerous differences exist between CAS and obstructive coronary disease in terms of mechanisms, risk factors, and therapeutic solutions. The gold standard for CAS diagnosis is represented by transitory and reversible occlusion of the coronary arteries at spasm provocation test, which consists of an intracoronary administration of Ach, ergonovine, or methylergonovine during angiography. The pathophysiology of CAS is not fully understood. However, the core of CAS is represented by vascular smooth muscle cell contraction, with a circadian pattern. The initiating event of this contraction may be represented by endothelial dysfunction, inflammation, or autonomic nervous system unbalance. Our study explores the intricate balance of these factors and their clinical relevance in the management of CAS.

Association Between Coronary Artery Spasm and Atherosclerotic Disease

In at least one-half of the patients with angina or ischemia and nonobstructive coronary arteries undergoing coronary function testing, coronary artery spasm (CAS) is detected. CAS is associated with an adverse prognosis regarding recurrent complaints and ischemic events. Current treatment options are mainly focused on the complaints, not on the underlying pathophysiological process. In this review we discuss available evidence regarding the presence, amount, and morphology of atherosclerosis in CAS patients. The reviewed evidence confirmed that atherosclerosis and vulnerable plaque characteristics are often detected in patients with CAS. The amount of atherosclerosis is higher in patients with focal CAS compared with patients with diffuse CAS. Severity of atherosclerosis is associated with the presence of CAS and the prognosis in CAS patients with atherosclerotic stenosis is worse. Therefore, CAS patients with atherosclerosis might benefit from targeted atherosclerotic treatment. Longitudinal studies are needed to elucidate the exact relation between atherosclerosis and CAS.

Machine learning-based prediction of hemodynamic parameters in left coronary artery bifurcation: A CFD approach

Coronary artery disease (CAD) is a leading cause of global mortality, often involving the development of atherosclerotic plaques in coronary arteries, particularly at bifurcation sites. Percutaneous coronary intervention (PCI) of bifurcation lesions presents challenges, necessitating accurate assessment of hemodynamic parameters such as wall shear stress (WSS) and oscillatory shear index (OSI) to predict acute coronary syndrome (ACS) risk. Computational fluid dynamics (CFD) provides valuable insights but is computationally intensive, prompting exploration of machine learning (ML) models for efficient hemodynamics prediction. This study aims to bridge the gap in understanding the influence of stenosis severity and location on hemodynamics in the left coronary artery (LCA) bifurcation by integrating ML algorithms with comprehensive CFD simulations, thereby enhancing non-invasive prediction of complex hemodynamics. An extensive dataset of 6858 synthetic LCA geometries with varying plaque severities and locations was generated for analysis. Hemodynamic parameters (TAWSS and OSI) were computed using CFD simulations and utilized for ML model training. Fourteen ML algorithms were employed for regression analysis, and their performance was evaluated using multiple metrics. The Decision Tree Regressor and K Nearest Neighbors models demonstrated the most effective prediction of TAWSS and OSI parameters, aligning well with CFD simulation results. The Decision Tree Regressor showed minimal prediction discrepancies (TAWSS: R2 = 0.998952, MAE = 0.000587, RMSE = 0.001626; OSI: R2 = 0.961977, MAE = 0.022264, RMSE = 0.041411) offering rapid and reliable assessments of hemodynamic conditions in the LCA bifurcation. Integration of ML algorithms with comprehensive CFD simulations provides a promising approach to enhance the non-invasive prediction of complex hemodynamics in the LCA bifurcation. The ability to efficiently predict hemodynamic parameters could significantly aid medical practitioners in time-sensitive clinical settings, offering valuable insights for coronary artery disease management. Further research is warranted to evaluate the effectiveness of deep learning models and address challenges in patient-specific applications.

Diagnostic value of nocturnal trend changes in a dynamic electrocardiogram for coronary heart disease

OBJECTIVE

To explore the diagnostic value of intermittent changes in the nocturnal ST segment trend graph in a dynamic electrocardiogram (ECG) for coronary heart disease (CHD).

METHODS

A total of 205 patients who underwent coronary angiography were included in this retrospective study. The study sample was determined through a power analysis aimed at achieving power of 80% with a significance level of 0.05. The participants were divided into the CHD (n = 101) and the non-CHD (n = 104) group, based on the degree of coronary artery diameter stenosis. The morphological changes in the ST segment trend graph were observed and divided into two categories: 'wall-shaped' and 'peak-shaped' changes.

RESULTS

Among the 205 patients, 94 had nocturnal ST segment dynamic changes and 111 did not. The detection rate of CHD without nocturnal ST segment dynamic changes was 21.59%, significantly lower than the detection rate of 93.18% in those with nocturnal ST segment changes, reflecting a statistically significant difference (P < 0.05). The positive rate of ST segment in patients with single-vessel disease (71.88%) was lower than in patients with multi-vessel disease (78.57%), and both differences were statistically significant (P < 0.05). The duration of ST segment trend graph changes in 94 cases in the CHD group with intermittent changes in the nocturnal ST segment trend graph was higher than in the non-CHD group, but no significant difference was observed (P > 0.05). The detection rate of CHD in the peak-shaped dynamic change group of the nocturnal ST segment trend graph was significantly higher (76/82) than in the wall-shaped (6/82) dynamic change group (P < 0.05).

CONCLUSION

Peak-shaped changes in the nocturnal ST segment trend graph indicate coronary artery lesions. Nocturnal ST segment changes observed through dynamic ECG monitoring can serve as a valuable non-invasive predictor for CHD, providing a feasible method for early diagnosis and intervention in clinical practice.

An Unusual ST-Segment Elevation in Unexplained Syncope

This case report describes a patient in their 70s with a lambda wave pattern on electrocardiography.