Twenty-five-year trends in incidence, angiographic appearance, and management of spontaneous coronary artery dissection.

BACKGROUND: Spontaneous coronary artery dissection (SCAD) has been described as an infrequent cause of acute coronary syndrome (ACS). Knowledge about the disease is still limited and SCAD might still be underdiagnosed. OBJECTIVES: Trends in incidence, presentation, angiographic appearance, management, and outcomes of SCAD over 25 years were analyzed. METHODS: Patients with SCAD between 1997 and 2021 at the University Hospital Zurich, Switzerland, were included. Incidences were assessed as total numbers and proportions of ACS cases. Clinical data were collected from medical records and angiographic findings were reviewed. Major adverse cardiac events (MACE) were defined as the composite of all-cause death, cardiac arrest, SCAD recurrence or progression, other myocardial infarction, and stroke. RESULTS: One hundred fifty-six SCAD cases were included in this study. The incidence increased significantly in total (p < 0.001) and relative to ACS cases (p < 0.001). This was based on an increase of shorter lesions (p = 0.004), SCAD type 2 (p < 0.001), and lesions in side branches (p = 0.014), whereas lesions in the left main coronary artery and proximal segments were decreasing (p-values 0.029 and < 0.001, respectively). There was an increase in conservative therapy (p < 0.001). The rate of MACE (24%) was stable, however, there was a reduced proportion of patients with a need for intensive care treatment (p = 0.017). CONCLUSIONS: SCAD represents an important entity of ACS that still might be underappreciated. The increasing incidence of SCAD is likely based on better awareness and familiarity with the disease. A lower need for intensive care treatment suggests positive effects of the increasing implementation of conservative management.

Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction.

Importance: Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied. Objectives: To assess the utility of machine learning systems for automatic discrimination of TTS and AMI. Design, Settings, and Participants: This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021. Exposure: Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis. Results: In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%). Conclusions and Relevance: In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application.

[MINOCA and the invasive assessment of the coronary microvascular function]. / MINOCA und die invasive Messung der kardialen Mikrozirkulation.

MINOCA and the invasive assessment of the coronary microvascular function Abstract. Around 10 % of patients undergoing coronary catheterization for acute myocardial infarction show no obstructive lesion of the epicardial vessels. In these cases, for a proper clinical management further anatomical and functional assessments are recommended, so that Myocardial infarctions with non-obstructive coronary arteries (MINOCAs) could be detected according to the current guidelines. On the other hand, investigations could lead to the diagnosis of Takotsubo Syndrome, the pathophysiologic understanding of which remains largely unclear. Since microvascular dysfunction was shown to play a major role in the origin of the disease, intracoronary flow measurements promise to deliver new meaningful pathophysiologic insights in the matter.

Transplantation and tracking of human-induced pluripotent stem cells in a pig model of myocardial infarction: assessment of cell survival, engraftment, and distribution by hybrid single photon emission computed tomography/computed tomography of sodium iodide symporter transgene expression.

BACKGROUND: Evaluation of novel cellular therapies in large-animal models and patients is currently hampered by the lack of imaging approaches that allow for long-term monitoring of viable transplanted cells. In this study, sodium iodide symporter (NIS) transgene imaging was evaluated as an approach to follow in vivo survival, engraftment, and distribution of human-induced pluripotent stem cell (hiPSC) derivatives in a pig model of myocardial infarction. METHODS AND RESULTS: Transgenic hiPSC lines stably expressing a fluorescent reporter and NIS (NIS(pos)-hiPSCs) were established. Iodide uptake, efflux, and viability of NIS(pos)-hiPSCs were assessed in vitro. Ten (±2) days after induction of myocardial infarction by transient occlusion of the left anterior descending artery, catheter-based intramyocardial injection of NIS(pos)-hiPSCs guided by 3-dimensional NOGA mapping was performed. Dual-isotope single photon emission computed tomographic/computed tomographic imaging was applied with the use of (123)I to follow donor cell survival and distribution and with the use of (99m)TC-tetrofosmin for perfusion imaging. In vitro, iodide uptake in NIS(pos)-hiPSCs was increased 100-fold above that of nontransgenic controls. In vivo, viable NIS(pos)-hiPSCs could be visualized for up to 15 weeks. Immunohistochemistry demonstrated that hiPSC-derived endothelial cells contributed to vascularization. Up to 12 to 15 weeks after transplantation, no teratomas were detected. CONCLUSIONS: This study describes for the first time the feasibility of repeated long-term in vivo imaging of viability and tissue distribution of cellular grafts in large animals. Moreover, this is the first report demonstrating vascular differentiation and long-term engraftment of hiPSCs in a large-animal model of myocardial infarction. NIS(pos)-hiPSCs represent a valuable tool to monitor and improve current cellular treatment strategies in clinically relevant animal models.