In the past 20 years, Cardiac Computed Tomography (CCT) has become a pivotal technique for the noninvasive diagnostic work-up of coronary and cardiac diseases. Continuous technical and methodological improvements, combined with fast growing scientific evidence, have progressively expanded the clinical role of CCT. Recent large multicenter randomized clinical trials documented the high prognostic value of CCT and its capability to increase the cost-effectiveness of the management of patients with suspected CAD. In the meantime, CCT, initially perceived as a simple non-invasive technique for studying coronary anatomy, has transformed into a multiparametric "one-stop-shop" approach able to investigate the heart in a comprehensive way, including functional, structural and pathophysiological biomarkers. In this complex and revolutionary scenario, it is urgently needed to provide an updated guide for the appropriate use of CCT in different clinical settings. This manuscript, endorsed by the Italian Society of Medical and Interventional Radiology (SIRM) and by the Italian Society of Cardiology (SIC), represents the first of two consensus documents collecting the expert opinion of Radiologists and Cardiologists about current appropriate use of CCT.
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http://dx.doi.org/10.1007/s11547-021-01378-0 | DOI Listing |
Trials
December 2024
Department of Cardiology, The Heart Centre, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.
Background: Intermediate-high risk pulmonary embolism (PE) carries a significant risk of hemodynamic deterioration or death. Treatment should balance efficacy in reducing clot burden with the risk of complications, particularly bleeding. Previous studies on high-dose, short-term thrombolysis with alteplase (rtPA) showed a reduced risk of hemodynamic deterioration but no change in mortality and increased bleeding complications.
View Article and Find Full Text PDFBMJ Open
December 2024
Department of Exercise and Sport Science, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Background: Sedentary behaviour (SB) is detrimental to cardiometabolic disease (CMD) risk, which can begin in young adulthood. To devise effective SB-CMD interventions in young adults, it is important to understand which context-specific SB (CS-SB) are most detrimental for CMD risk, the lifestyle behaviours that cluster with CS-SBs and the socioecological predictors of CS-SB.
Methods And Analysis: This longitudinal observational study will recruit 500 college-aged (18-24 years) individuals.
Am J Med
December 2024
Department of Medicine, University of Toronto, Toronto, ON, Canada; HoPingKong Centre for Excellence in Education and Practice, University Health Network, Toronto, ON, Canada; Division of General Internal Medicine and Geriatrics, University Health Network, Toronto, ON, Canada.
Background: Few GIM-specific heart failure transition of care (TOC) programs exist. We thus piloted a TOC program for heart failure patients discharged from GIM that incorporates a remote patient management program, Medly.
Methods: This single-centre, prospective proof-of-concept study described sociodemographic and medical characteristics of included patients, and computed summary statistics to describe clinical and workload outcomes.
J Am Med Inform Assoc
December 2024
AI for Health Institute, Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.
View Article and Find Full Text PDFJ Med Eng Technol
December 2024
Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran.
Nowadays, photoplethysmograph (PPG) technology is being used more often in smart devices and mobile phones due to advancements in information and communication technology in the health field, particularly in monitoring cardiac activities. Developing generative models to generate synthetic PPG signals requires overcoming challenges like data diversity and limited data available for training deep learning models. This paper proposes a generative model by adopting a genetic programming (GP) approach to generate increasingly diversified and accurate data using an initial PPG signal sample.
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