Anomalous coronary arteries originating from the opposite sinus of Valsalva (ACAOS) are a challenge because of their various anatomic and clinical presentation. Although the prevalence is low, the absolute numbers of detected ACAOS are increasing because of the growing use of noninvasive anatomical imaging for ruling out coronary artery disease. As evidence‐based guidelines are lacking, treating physicians are left in uncertainty for the optimal management of such patients. The sole presence of ACAOS does not justify surgical correction, and therefore a thorough anatomic and hemodynamic assessment is warranted. Invasive and noninvasive multimodality imaging provides information to the clinical question whether the presence of ACAOS is an innocent coincidental finding, is responsible for the patient’s symptoms, or even might be a risk for sudden cardiac death. Based on recent clinical data, focusing on the pathophysiology of patients with ACAOS, myocardial ischemia is dependent on both the extent of fixed and dynamic components, represented by anatomic high‐risk features. These varying combinations should be considered individually in the decision making for the different therapeutic options. This state‐of‐the‐art review focuses on the advantages and limitations of the common contemporary surgical, interventional, and medical therapy with regard to the anatomy and pathophysiology of ACAOS. Further, we propose a therapeutic management algorithm based on current evidence on multimodality invasive and noninvasive imaging findings and highlight remaining gaps of knowledge.

The basement membrane (BM) surrounding capillaries in skeletal muscles varies physiologically in thickness according to age, physical fitness, and anatomical site in humans. Furthermore, the pericapillary BM thickness (CBMT) increases pathophysiologically during several common disease states, including peripheral arterial disease and diabetes mellitus. This review on CBM thickening in human skeletal muscles is two pronged. First, it addresses the advantages/disadvantages of grid- and tablet-based measuring and morphometric techniques that are implemented to assess the CBMT on transmission electron micrographs. Second, it deals with the biology of CBM thickening in skeletal muscles, particularly its possible causes, molecular mechanisms, and functional impact. CBM thickening is triggered by several physical factors, including diabetes-associated glycation, hydrostatic pressure, and inflammation. Increased biosynthesis of type IV collagen expression or repetitive cycles in pericyte or endothelial cell degeneration/proliferation appear to be most critical for CBM accumulation. A thickened CBM obviously poses a greater barrier for diffusion, lowers the microvascular elasticity, and impedes transcytosis of inflammatory cells. Our own morphometric data reveal the CBM enlargement to be not accompanied by the pericyte coverage. Owing to an overlap or redundancy in the capillary supply, CBM thickening in skeletal muscles might not be such a devastating occurrence as in organs with endarterial circulation (e.g., kidney and retina). CBM growth in skeletal muscles can be reversed by training or administration of antidiabetic drugs. In conclusion, CBM thickening in skeletal muscles is a microvascular remodeling process by which metabolic, hemodynamic, and inflammatory forces are integrated together and which could play a hitherto underestimated role in etiology/progression of human diseases.

Cardiovascular disease remains the leading global cause of death, and the number of patients with coronary artery disease (CAD) and exhausted therapeutic options (i.e., percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG) and medical treatment) is on the rise. Therefore, the evaluation of new therapeutic approaches to offer an alternative treatment strategy for these patients is necessary. A promising research field is the promotion of the coronary collateral circulation, an arterio-arterial network able to prevent or reduce myocardial ischemia in CAD. This review summarizes the basic principles of the human coronary collateral circulation, its extracardiac anastomoses as well as the different therapeutic approaches, especially that of stimulating the extracardiac collateral circulation via permanent occlusion of the internal mammary arteries.

The electrocardiogram (ECG) is a valuable diagnostic tool for the diagnosis of myocardial ischemia during acute coronary syndrome. Aside from the commonly used ST-segment shift indicative of ischemia, several other ECG parameters are pathophysiologically reasonable. Thus, the goal of this study was to assess the accuracy of different ischemia parameters as obtained by the highly susceptible intracoronary ECG (icECG). This was a retrospective observational study in 100 patients with chronic coronary syndrome. From each patient, a non-ischemic as well as ischemic icECG at the end of a one-minute proximal coronary balloon occlusion was available, and analysed twice by three different physicians, as well as once together for consensual results. The evaluated parameters were icECG ST-segment shift (mV), ST-integral (mV*sec), T-wave-integral (mV*sec), T-peak (mV), T-peak-to-end time (TPE; msec) and QTc-time (msec). All six icECG parameters showed significant differences between the non-ischemic and the ischemic recording. Using the icECG recording during coronary patency or occlusion as criterion for absent or present myocardial ischemia, ROC-analysis of icECG ST-segment shift showed an area under the curve (AUC) of 0.963 ± 0.029 (p < 0.0001). AUC for ST-integral was 0.899 ± 0.044 (p < 0.0001), for T-wave integral 0.791 ± 0.059 (p < 0.0001), for T-peak 0.811 ± 0.057 (p < 0.0001), for TPE 0.667 ± 0.068 (p < 0.0001), and for QTc-time 0.770 ± 0.061 (p < 0.0001). The best cut-off point for the detection of ischemia by icECG ST-segment shift was 0.365 mV (sensitivity 90%, specificity 95%). When tested in a setting with artificially induced absolute myocardial ischemia, icECG ST-segment shift at a threshold of 0.365 mV most accurately distinguishes between absent and present ischemia.

Introduction: The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. Hence, CNN enable an unbiased view on well-known clinical phenomenon, e.g., myocardial ischemia. This study tested a novel, hypothesis-generating approach using pre-trained CNN to determine the optimal ischemic parameter as obtained from the highly susceptible intracoronary ECG (icECG). Method: This was a retrospective observational study in 228 patients with chronic coronary syndrome. Each patient had participated in clinical trials with icECG recording and ST-segment shift measurement at the beginning (i.e., non-ischemic) and the end (i.e., ischemic) of a one-minute proximal coronary artery balloon occlusion establishing the reference. Using these data (893 icECGs in total), two pre-trained, open-access CNN (GoogLeNet/ResNet101) were trained to recognize ischemia. The best performing CNN during training were compared with the icECG ST-segment shift for diagnostic accuracy in the detection of artificially induced myocardial ischemia. Results: Using coronary patency or occlusion as reference for absent or present myocardial ischemia, receiver-operating-characteristics (ROC)-analysis of manually obtained icECG ST-segment shift (mV) showed an area under the ROC-curve (AUC) of 0.903±0.043 (p<0.0001, sensitivity 80%, specificity 92% at a cut-off of 0.279mV). The best performing CNN showed an AUC of 0.924 (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed no significant difference between the AUCs. The underlying morphology responsible for the network prediction differed between the trained networks but was focused on the ST-segment and the T-wave for myocardial ischemia detection. Conclusions: When tested in an experimental setting with artificially induced coronary artery occlusion, quantitative icECG ST-segment shift and CNN using pathophysiologic prediction criteria detect myocardial ischemia with similarly high accuracy.