Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam—a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.

Exercise stress testing (EST) is commonly used to evaluate chest pain, with some labs using 85% of age-predicted maximum heart rate (APMHR) as an endpoint for EST. The APMHR is often calculated using the formula 220-age. However, the accuracy of this formula and 85% APMHR as an endpoint may be questioned. Moreover, failing to reach 85% APMHR (known as chronotropic insufficiency) may also indicate poor cardiovascular prognosis, but measurements, such as percentage heart rate reserve (%HRR), maximum rate pressure product (MRPP), and the maximum metabolic equivalent of tasks (METs) reached during EST may provide better prediction of cardiovascular outcomes than not reaching 85% of APMHR. There is a need to incorporate comprehensive measurements to improve the diagnostic and prognostic capabilities of EST.

Speckle tracking echocardiography in early detection of myocardial injury in a rat model with stress cardiomyopathy

(1) Background: Recent studies suggest an association between pancreatitis and cardiovascular disease. This article aims to review the available evidence linking cardiovascular disease with acute and chronic pancreatitis. (2) Methods: A comprehensive search was conducted on the PubMed/MEDLINE database from inception to April 2022 using Medical Subject Heading and keywords related to pancreatitis and cardiovascular disease. The search was limited to English-language literature involving human subjects, and various study types, including observational studies, case–control studies, cohort studies, and clinical trials, were screened for eligibility. Following data extraction, the authors conducted a narrative synthesis of the studies. (3) Results: Longitudinal studies indicate that a history of acute pancreatitis is associated with an increased risk of acute atherosclerotic cardiovascular disease and acute coronary syndrome. Elevated triglyceride levels (>2000 mg/dL) have a temporal relationship with acute pancreatitis. Cross-sectional studies have shown that acute pancreatitis is associated with cardiac injury during the acute phase. Based on longitudinal studies, chronic pancreatitis is associated with an increased risk of cerebrovascular diseases. However, data regarding the relationship between chronic pancreatitis and myocardial infarction are conflicting. (4) Conclusions: Based on the available evidence, having a history of acute pancreatitis appears to increase the risk of acute atherosclerotic cardiovascular disease. However, there is insufficient evidence to conclude whether chronic pancreatitis is associated with cardiovascular disease, and no definitive studies have yielded conflicting results.

Bifurcation lesions account for 20% of all percutaneous coronary interventions and represent a complex subset which are associated with lower procedural success and higher rates of restenosis

The utilization of transcatheter aortic valve implantation (TAVI) has become the treatment of choice in patients with severe aortic stenosis (AS) with intermediate-to-high surgical risk for surgical aortic valve replacement (SAVR)