Holter Monitoring and Cardiac Biomarkers in Screening for Cardiac Sarcoidosis.

Introduction: Early detection of cardiac sarcoidosis (CS) is crucial due to its association with severe complications such as ventricular arrhythmias, heart failure, and sudden cardiac death. Advanced imaging techniques like cardiac magnetic resonance imaging (CMR) and 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG-PET/CT) are effective in detecting CS but not easily accessible. The optimal method for selecting patients for advanced screening remains uncertain.

Sequential Grafting of the Left Internal Thoracic Artery to the Left Anterior Descending Artery and Graft Failure.

Background: There is concern that left internal thoracic artery (LITA)-to diagonal (D)-to left anterior descending artery (LAD) grafts may be more susceptible to failure compared with single LITA-LAD grafts.

Methods: Pooled individual patient data from 8 clinical trials with systematic graft imaging were analyzed to assess the incidence of sequential LITA-D-LAD vs single LITA-LAD grafts. Mixed-effects multivariable logistic regression, adjusting for patient characteristics and clustering within trials, was used.

Non-invasive physiological assessment of coronary artery obstruction on coronary computed tomography angiography.

Computed tomography-derived fractional flow reserve (CT-FFR) enhances the specificity of coronary computed tomography angiography (CCTA) to that of the most specific non-invasive imaging techniques, while maintaining high sensitivity in stable coronary artery disease (CAD). As gatekeeper for invasive coronary angiography (ICA), use of CT-FFR results in a significant reduction of negative ICA procedures and associated costs and complications, without increasing cardiovascular events. It is expected that CT-FFR algorithms will continue to improve, regarding accuracy and generalisability, and that introduction of new features will allow further treatment guidance and reduced invasive diagnostic testing.

Redefining Spectral Data Analysis with Immersive Analytics: Exploring Domain-Shifted Model Spaces for Optimal Model Selection.

Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g.