Early diagnosis of coronary microvascular dysfunction by myocardial contrast stress echocardiography

Epicardial adipose tissue volume, plaque vulnerability and myocardial ischemia in non-obstructive coronary artery disease

Background

Epicardial adipose tissue (EAT) accumulation has been associated with inflammation, atherosclerosis and microvascular dysfunction. Whether increased EAT volume is associated with coronary plaque vulnerability and demand myocardial ischemia in patients with non-obstructive coronary artery disease (CAD) is less explored.

Methods

In 125 patients (median age 63[58, 69] years and 58% women) with chest pain and non-obstructive CAD, EAT volume was quantified on non-contrast cardiac CT images. EAT volume in the highest tertile (>125 ml) was defined as high EAT volume. Total coronary plaque volume and plaque vulnerability were quantified by coronary CT angiography (CCTA). Demand myocardial ischemia was detected by contrast dobutamine stress echocardiography.

Results

High EAT volume was more common in men and associated with higher BMI, hypertension, increased left ventricular mass index (LVMi), C-reactive protein (CRP) and positive remodelling (all p < 0.05). There was no difference in age, coronary calcium score, total and non-calcified plaque volume or presence of demand myocardial ischemia between groups (all p ≥ 0.34). In a multivariable model, obesity (p = 0.006), hypertension (p = 0.007) and LVMi (p = 0.016) were independently associated with high EAT volume. Including plaque vulnerability in an alternative model, positive remodelling (p = 0.038) was independently associated with high EAT volume.

Conclusion

In non-obstructive CAD, high EAT volume was associated with cardiometabolic risk factors, inflammation and plaque vulnerability, while there was no association with demand myocardial ischemia or coronary plaque volume. Following our results, the role of EAT volume as a biomarker in non-obstructive CAD remains unclear.

The Association of Severe Coronary Tortuosity and Non-Obstructive Coronary Artery Disease

Background and Objectives: There is an increasing interest in the coronary tortuosity as a novel pathophysiological mechanism of ischemia in coronary artery disease without significant obstruction, but there are a lack of studies to confirm this relationship in the clinical setting. The aim of our study was to evaluate the association of severe coronary tortuosity and the potential role of coronary blood supply dominance in the appearance of myocardial ischemia in patients with non-obstructive coronary artery disease (non-CAD), compared to patients with obstructive coronary artery disease (CAD). Materials and Methods: The study enrolled 131 participants (71 male and 60 female), recruited among patients referred to cardiologists due to angina symptoms with ischemic alterations established by cardiac stress tests, as well as those admitted to the hospital for acute coronary syndrome. Results: Mean age of recruited patients was 61.6 (±10.1) years. According to the coronary angiography, they were divided into two groups: non-obstructive and obstructive CAD (77 and 54, respectively). There were significantly more women (61% vs. 24%, p < 0.001) in the non-CAD group. Both tortuous coronary arteries (50.6% vs. 14.8%, p < 0.001) and left coronary dominance (37.7% vs. 16.7%, p = 0.006) were more frequent in the non-CAD group compared to the CAD group. Female sex (OR = 17.516, p = 0.001), tortuous coronary arteries (OR = 7.962, p = 0.006) and left dominance of blood supply were significant predictors for non-CAD. Conclusions: Non-obstructive CAD is common among patients, especially women, who are referred for coronary angiography. Severe coronary artery tortuosity is the strongest independent predictor of non-obstructive CAD, followed by female gender and left coronary dominance.

Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm

PURPOSE

Coronary microvascular dysfunction (CMD) is emerging as an important cause of myocardial ischemia, but there is a lack of a non-invasive method for reliable early detection of CMD.

AIM

To develop an electrocardiogram (ECG)-based machine learning algorithm for CMD detection that will lay the groundwork for patient-specific non-invasive early detection of CMD.

METHODS

Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls. Sample entropy (SampEn), approximate entropy (ApEn), and complexity index (CI) derived from multiscale entropy were extracted from ST-T segments of each lead in ECGs and VCGs. The most effective entropy subset was determined using the sequential backward selection algorithm under the intra-patient and inter-patient schemes, separately. Then, the corresponding optimal model was selected from eight machine learning models for each entropy feature based on five-fold cross-validations. Finally, the classification performance of SampEn-based, ApEn-based, and CI-based models was comprehensively evaluated and tested on a testing dataset to investigate the best one under each scheme.

RESULTS

ApEn-based SVM model was validated as the optimal one under the intra-patient scheme, with all testing evaluation metrics over 0.8. Similarly, ApEn-based SVM model was selected as the best one under the intra-patient scheme, with major evaluation metrics over 0.8.

CONCLUSIONS

Entropies derived from ECGs and VCGs can effectively detect CMD under both intra-patient and inter-patient schemes. Our proposed models may provide the possibility of an ECG-based tool for non-invasive detection of CMD.