Aim: This study was conducted to develop an electrocardiogram education program that incorporates an HTML webpage and blended learning methods to enhance electrocardiogram interpretation skills. Through continual and efficient education, the program aims to assist nurses in providing appropriate care and treatment to patients.
Design: Pre-post design study.
Methods: We developed an electrocardiogram interpretation HTML webpage based on an electrocardiogram interpretation algorithm and implemented an 18-week (2023.5.15 ~ 2023.9.22) electrocardiogram education program, which included daily 5-minute training sessions. Twenty-seven ward nurses were provided with the URL ( https://ecgweb.github.io/ECGwebEN ) to the electrocardiogram interpretation HTML webpage and shared one electrocardiogram case daily for self-interpretation. Electrocardiogram interpretation performance and confidence were evaluated through questionnaires at three phases: before the program, after 6 weeks of basic electrocardiogram and arrhythmia education, and after 12 weeks of application of the electrocardiogram interpretation HTML webpage and case-based lecture education. The statistical tests used were repeated-measures ANOVA or the Wilcoxon signed-rank test.
Results: The average score for electrocardiogram interpretation performance before the electrocardiogram education program was 11.89(SD = 3.50), after 6 weeks of basic electrocardiogram and arrhythmia education it was 14.15(SD = 3.68), and after 12 weeks of application of the electrocardiogram interpretation HTML webpage and case-based lecture education, it was 15.56(SD = 3.04). This shows that electrocardiogram interpretation performance significantly improved over time (p < .001). Additionally, post-hoc analysis revealed significant differences in electrocardiogram interpretation performance at each stage, i.e., before, during, and after the application of an electrocardiogram education program. Furthermore, the electrocardiogram interpretation confidence questionnaire score (pre-Median 18, IQR = 5; post-Median 23, IQR = 3) was improved significantly after the completion of the 18-week education program (p < .001).
Conclusions: Based on the results of this study, we believe that an electrocardiogram education program using HTML webpage, and a blended teaching method would be very beneficial for maintaining and improving electrocardiogram interpretation skills of clinical nurses. Such a program can help nurses interpret electrocardiograms more effectively and assist them in making important decisions in patient care.
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http://dx.doi.org/10.1186/s12909-024-05960-8 | 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.
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.
Eur Heart J Digit Health
January 2025
Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, No. 88 West Taishan Road, Zhuzhou 412007, Hunan, China.
Aims: The electrocardiogram (ECG) is the primary method for diagnosing atrial fibrillation (AF), but interpreting ECGs can be time-consuming and labour-intensive, which deserves more exploration.
Methods And Results: We collected ECG data from 6590 patients as YY2023, classified as Normal, AF, and Other. Convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), and Attention construct the AF recognition model CNN BiLSTM Attention-Atrial Fibrillation (CLA-AF).
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