Study Objective: Routine ECG testing is recommended in the evaluation of syncope, although the value of such testing in young patients is unclear. For ECG testing, we assess the diagnostic yield (frequency that ECG identified the reason for syncope) and predictive accuracy for 14-day cardiac events after an episode of syncope as a function of age.
Methods: Adult patients with syncope or near-syncope were prospectively enrolled for 1 year at a single academic emergency department (ED). A 3-physician panel reviewed ED charts, hospital records, and telephone interview forms to identify predefined cardiac events. The primary outcome included all 14-day, predefined cardiac events including arrhythmia, myocardial ischemia, and structural heart disease.
Results: Of 592 eligible patients, 477 (81%) provided informed consent. Direct telephone contact or admission/outpatient records were successfully obtained for 461 (97%) patients, who comprised the analytic cohort. There were 44 (10%) patients who experienced a 14-day cardiac event. Overall diagnostic yield of ECG testing was 4% (95% confidence interval 2% to 6%). For patients younger than 40 years, ECG testing had a diagnostic yield of 0% (95% confidence interval 0% to 3%) and was associated with a 10% frequency of abnormal findings.
Conclusion: ECG testing in patients younger than 40 years did not reveal a cardiac cause of syncope and was associated with a significant frequency of abnormal ECG findings unrelated to syncope. Although our findings should be verified in larger studies, it may be reasonable to defer ECG testing in younger patients who have a presentation consistent with a benign cause of syncope.
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http://dx.doi.org/10.1016/j.annemergmed.2007.04.006 | DOI Listing |
Front Cardiovasc Med
January 2025
Department of Cardiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
Introduction: The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes.
Methods: The present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet).
Heliyon
January 2025
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, Ancona, 60131, Italy.
Background: Deep-learning applications in cardiology typically perform trivial binary classification and are able to discriminate between subjects affected or not affected by a specific cardiac disease. However, this working scenario is very different from the real one, where clinicians are required to recognize the occurrence of one cardiac disease among the several possible ones, performing a multiclass classification. The present work aims to create a new interpretable deep-learning tool able to perform a multiclass classification and, thus, discriminate among several different cardiac diseases.
View Article and Find Full Text PDFCurr Cardiol Rep
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Purpose Of Review: Artificial Intelligence (AI) technology will significantly alter critical care cardiology, from our understanding of diseases to the way in which we communicate with patients and colleagues. We summarize the potential applications of AI in the cardiac intensive care unit (CICU) by reviewing current evidence, future developments and possible challenges.
Recent Findings: Machine Learning (ML) methods have been leveraged to improve interpretation and discover novel uses for diagnostic tests such as the ECG and echocardiograms.
Background And Aims: Bendopnea is a symptom found in patients with heart failure (HF) defined as shortness of breath when bending forward. The present study examined the correlation between bendopnea with other cardiac symptoms, echocardiographic findings, and cardiac function parameters.
Methods: This was a single-center prospective cross-sectional study of patients diagnosed with systolic HF.
Cureus
December 2024
Business Development Hospitals, Wockhardt Hospitals Ltd., Mumbai, IND.
Background and objectives The persistent nature of diabetic foot ulcers (DFUs) is mainly attributable to compromised wound healing mechanisms, which are aggravated due to poor blood flow, neuropathy, and infection. Growth factors have become essential agents in the treatment of DFUs, serving as primary mediators that enhance wound healing through the stimulation of cell proliferation, migration, and angiogenesis. This prospective open-label, randomised, comparative, multi-centre, investigator-initiated study compared the safety and effectiveness of adjuvant therapy with topical application of autologous growth factor concentrate (AGFC) using the Healrex therapy kit (Wockhardt, India) versus standard of care (SoC) in DFUs.
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