Background: Electrocardiogram (ECG) testing in pre-participation screening (PPS) remains controversial due to its cost, resource dependency, and the potential for inaccurate interpretations. At most centres, ECGs are conducted internally by providers trained in athletic ECG interpretation. Outsourcing ECG requisitions to an athlete's primary care network (PCN) may reduce institutional demands. This study compared PCN-conducted athletic ECG interpretation to expert sports cardiology interpretation.
Methods: This was a retrospective, single-centre chart-review study of all athletes who underwent cardiovascular PPS between 2017 and 2021. All athletes submitted an ECG with their screening package, which was conducted and interpreted within their PCN. All ECGs were reinterpreted by a sports cardiologist using the International Criteria (IC) for electrocardiographic interpretation in athletes. Overall, positive, and negative percent agreement were used to compare PCN-conducted ECG interpretation with IC interpretation.
Results: A total of 740 athletes submitted a screening package with a valid ECG (mean age: 18.5 years, 39.6% female). PCN-conducted ECGs were interpreted by 181 unique physicians. Among 41 (5.5%) PCN-conducted ECGs that were initially interpreted as abnormal, only 5 (0.7%) were classified as abnormal according to the IC. All PCN-conducted ECGs reported as normal were also classified as normal according to the IC. The overall agreement between PCN-conducted and IC ECG interpretation was 95.1% (positive percent agreement: 100%, negative percent agreement: 95.1%).
Conclusions: Normal PCN-conducted athletic ECGs are interpreted with high agreement to the IC. Majority of PCN-conducted ECGs interpreted as abnormal are indeed normal as per the IC. These findings suggest that a PPS workflow model that outsources ECG requisitions to a PCN may be a reliable approach to PPS, all while reducing screening-related institutional costs and resource requirements.
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http://dx.doi.org/10.1016/j.jelectrocard.2023.07.007 | DOI Listing |
Children (Basel)
December 2024
School of Medicine, University of Crete, 71 003 Heraklion, Crete, Greece.
Background: Screening for cardiovascular disease (CVD) and its associated risk factors in childhood facilitates early detection and timely preventive interventions. However, limited data are available regarding screening tools and their diagnostic yield when applied in unselected pediatric populations.
Aims: To evaluate the performance of a CVD screening program, based on history, 12-lead ECG and phonocardiography, applied in primary school children.
Children (Basel)
December 2024
Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL 32224, USA.
Artificial intelligence (AI) is revolutionizing healthcare by offering innovative solutions for diagnosis, treatment, and patient management. Only recently has the field of pediatric cardiology begun to explore the use of deep learning methods to analyze electrocardiogram (ECG) data, aiming to enhance diagnostic accuracy, expedite workflows, and improve patient outcomes. This review examines the current state of AI-enhanced ECG interpretation in pediatric cardiology applications, drawing insights from adult AI-ECG research given the progress in this field.
View Article and Find Full Text PDFBMC Cardiovasc Disord
January 2025
Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Objectives: This study aimed to evaluate the feasibility and accuracy of non-electrocardiogram (ECG)-triggered chest low-dose computed tomography (LDCT) with a kV-independent reconstruction algorithm in assessing coronary artery calcification (CAC) degree and cardiovascular disease risk in patients receiving maintenance hemodialysis (MHD).
Methods: In total, 181 patients receiving MHD who needed chest CT and coronary artery calcium score (CACS) scannings sequentially underwent non-ECG-triggered, automated tube voltage selection, high-pitch chest LDCT with a kV-independent reconstruction algorithm and ECG-triggered standard CACS scannings. Then, the image quality, radiation doses, Agatston scores (ASs), and cardiac risk classifications of the two scans were compared.
Am J Emerg Med
January 2025
Department of Emergency Medicine, Aksaray Education and Research Hospital, Aksaray, Turkey.
Eur Heart J Digit Health
January 2025
Cardiovascular Center, Tufts Medical Center, 800 Washington Street, Boston, MA 02111, USA.
Aims: This study evaluates the performance of OpenAI's latest large language model (LLM), Chat Generative Pre-trained Transformer-4o, on the Adult Clinical Cardiology Self-Assessment Program (ACCSAP).
Methods And Results: Chat Generative Pre-trained Transformer-4o was tested on 639 ACCSAP questions, excluding 45 questions containing video clips, resulting in 594 questions for analysis. The questions included a mix of text-based and static image-based [electrocardiogram (ECG), angiogram, computed tomography (CT) scan, and echocardiogram] formats.
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