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Procedure: Heart Transplant
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Total 644 results found since Jan 2013.
Risk of hospital admission or emergency department presentation due to diabetes complications: a retrospective cohort study in Tasmania, Australia
ConclusionsOur results demonstrated the high demand on hospital services due to diabetes complications (especially macrovascular complications) and highlighted the importance of preventing and properly managing microvascular complications. These findings will support future resource allocation to reduce the increasing burden of diabetes in Australia.PMID:37137728 | DOI:10.1071/AH22271
Source: Australian Health Review - May 3, 2023 Category: Hospital Management Authors: Ngan T T Dinh Barbara de Graaff Julie A Campbell Matthew D Jose John Burgess Timothy Saunder Alex Kitsos Petr Otahal Andrew J Palmer Source Type: research
Dh-452784-2 identifying atrial fibrillation with sinus rhythm electrocardiogram using artificial intelligence in embolic stroke with undetermined source
Previous studies have demonstrated acceptable ranges of accuracy of artificial intelligence (AI) algorithms for identifying patients with paroxysmal atrial fibrillation (AF) based on their sinus rhythm electrocardiograms (ECGs). However, none of them has been validated in patients with embolic stroke with undetermined source (ESUS) in which thorough AF screening is required.
Source: Heart Rhythm - May 1, 2023 Category: Cardiology Authors: Ji Hyun Lee, Youngjin Cho, Joonghee Kim Source Type: research
Overset meshing in combination with novel blended weak-strong fluid-structure interactions for simulations of a translating valve in series with a second valve
This study developed a combined computational fluid dynamics and fluid-structure interaction (FSI) methodology for simulating positive displacement bileaflet valves. Overset meshing discretised the fluid domain, and a blended weak-strong coupling FSI algorithm was combined with variable time-stepping. Four operating conditions of relevant stroke lengths and rates were assessed. The results demonstrated this modelling strategy is stable and efficient for modelling positive-displacement artificial hearts.PMID:37071538 | DOI:10.1080/10255842.2023.2199903
Source: Computer Methods in Biomechanics and Biomedical Engineering - April 18, 2023 Category: Biomedical Engineering Authors: J Bornoff H S Gill A Najar I L Perkins A N Cookson K H Fraser Source Type: research
Diagnosis of Coronary Artery Disease based on Machine Learning algorithms Support Vector Machine, Artificial Neural Network, and Random Forest
CONCLUSION: In this study, it was shown that machine learning algorithms can be used with high accuracy to detect CAD. Thus, it allows physicians to perform timely preventive treatment in patients with CAD.PMID:37057235 | PMC:PMC10086656 | DOI:10.4103/abr.abr_383_21
Source: Adv Data - April 14, 2023 Category: Epidemiology Authors: Saeed Saeedbakhsh Mohammad Sattari Maryam Mohammadi Jamshid Najafian Farzaneh Mohammadi Source Type: research
Detection of inflow obstruction in left ventricular assist devices by accelerometer: A porcine model study
Left ventricular assist devices (LVAD) provide circulatory blood pump support for severe heart failure patients. Pump inflow obstructions may lead to stroke and pump malfunction. We aimed to verify in vivo that gradual inflow obstructions, representing prepump thrombosis, are detectable by a pump-attached accelerometer, where the routine use of pump power (PLVAD) is deficient.
Source: The Journal of Heart and Lung Transplantation - April 4, 2023 Category: Transplant Surgery Authors: Didrik Lilja, Itai Schalit, Andreas Espinoza, Arnt Eltvedt Fiane, Gry Dahle, Helen Littorin-Sandbu, Fred-Johan Pettersen, Kristoffer Engh Russell, Amrit Paul Singh Thiara, Ole Jakob Elle, Per Steinar Halvorsen Tags: Original Pre-Clinical Science Source Type: research
Estimation of Stroke Risk in Patients with Fabry Disease Using a Machine Learning Model
In this study, the performance of a machine learning platform in estimating FD patients’ 12-month stroke risk was assessed.
Source: The Journal of Heart and Lung Transplantation - April 1, 2023 Category: Transplant Surgery Authors: J. Jefferies, S. Kallish, G. Biondetti, P. Aguiar, M. Nelson, J. Giuliano, J. Zabinksi, C. Boussios, G. Curhan, J. Bandaria, R. Gliklich, D. Warnock Tags: (753) Source Type: research
