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| http://dx.doi.org/10.1080/08998280.2021.2021762 | DOI Listing |
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Machine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis.
Biomedicines
December 2024
World-Class Research Center «Digital Biodesign and Personalized Healthcare», I.M. Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya Street, 119991 Moscow, Russia.
Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging.
View Article and Find Full Text PDFThe association of workforce configurations with length of stay and charges in hospitalized patients with congestive heart failure.
Front Health Serv
December 2024
Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States.
Introduction: Clinicians are the conduits of high-quality care delivery. Clinicians have driven advancements in pharmacotherapeutics, devices, and related interventions and improved morbidity and mortality in patients with congestive heart failure over the past decade. Yet, the management of congestive heart failure has become extraordinarily complex and has fueled recommendations from the American Heart Association and the American College of Cardiology to optimize the composition of the care team to reduce the health, economic, and the health system burden of high lengths of stay and hospital charges.
View Article and Find Full Text PDFComparative analysis of diagnostic accuracy in adult congenital heart disease: A study of three physician groups and ChatGPT.
Int J Cardiol Congenit Heart Dis
December 2024
Cardiology Department, Tel Aviv Sourasky Medical Center, Israel.
This research letter evaluates the diagnostic accuracy of three physician groups-pediatric cardiologists, adult congenital heart disease (ACHD) specialists, and general cardiologists-and compares it to the performance of an AI (Artificial Intelligence) LLM model. Based on eight targeted questions in adult congenital heart disease, the analysis highlights significant disparities in correct answer rates, with congenital specialists achieving the highest accuracy and the AI model - the lowest.
View Article and Find Full Text PDFLipid-lowering agent preferences among patients with hypercholesterolemia: a focus group study.
J Pharm Policy Pract
December 2024
Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China.
Background: Non-adherence to lipid-lowering agents poses significant risks to patients and diminishes treatment effectiveness. Current understanding of patients' preferences regarding the characteristics of these agents is limited. This study aims to qualitatively identify the barriers to lipid-lowering medication adherence and the factors considered by patients with hypercholesterolemia when choosing lipid-lowering agents, and to inform the design of a medication preference study.
View Article and Find Full Text PDFAthlete's ECG Made Easy: A Practical Guide to Surviving Everyday Clinical Practice.
J Cardiovasc Dev Dis
October 2024
Cardiology Unit, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy.
Article Synopsis
- Athletes often show changes in their electrocardiograms (ECGs) due to heart adaptations from regular training, known as the athlete's heart, but these changes can sometimes mimic heart disease signs.
- Misdiagnosis can lead to serious consequences, including sudden cardiac death, which is a major risk for young athletes.
- This review aims to help cardiologists and sports doctors differentiate between normal exercise-related ECG changes and those indicating potential heart problems, guiding further investigations when needed.
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