Background: Electrocardiogram (ECG) interpretation is widely performed by emergency physicians. We aimed to determine the accuracy of interpretation of potential ST-segment elevation myocardial infarction (STEMI) ECGs by emergency physicians.
Methods: Thirty-six ECGs resulted in putative STEMI diagnoses were selected. Participants were asked to focus on whether or not the ECG in question met the diagnostic criteria for an acutely blocked coronary artery causing a STEMI. Based on the coronary angiogram, a binary outcome of accurate versus inaccurate ECG interpretation was defined. We computed the overall sensitivity, specificity, accuracy and 95% confidence intervals (95%CIs) for ECG interpretation. Data on participant training level, working experience and place were collected.
Results: 135 participants interpreted 4603 ECGs. Overall sensitivity to identify 'true' STEMI ECGs was 64.5% (95%CI: 62.8-66.3); specificity in determining 'false' ECGs was 78% (95%CI: 76-80.1). Overall accuracy was modest (69.1, 95%CI: 67.8-70.4). Higher accuracy in ECG interpretation was observed for attending physicians, participants working in tertiary care hospitals and those more experienced.
Conclusion: The accuracy of interpretation of potential STEMI ECGs was modest among emergency physicians. The study supports the notion that ECG interpretation for establishing a STEMI diagnosis lacks the necessary sensitivity and specificity to be considered a reliable 'stand-alone' diagnostic test.
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http://dx.doi.org/10.1080/17482941.2016.1234058 | DOI Listing |
Rev Cardiovasc Med
January 2025
Department of Cardiovasculair Sciences, KU Leuven, 3000 Leuven, Belgium.
Ventricular depolarization refers to the electrical activation and subsequent contraction of the ventricles, visible as the QRS complex on a 12-lead electrocardiogram (ECG). A well-organized and efficient depolarization is critical for cardiac function. Abnormalities in ventricular depolarization may indicate various pathologies and can be present in all leads if the condition is general, or in a subgroup of anatomically contiguous leads if the condition is limited to the corresponding anatomic location of the heart.
View Article and Find Full Text PDFAm J Emerg Med
January 2025
UniCamillus-Saint Camillus International University of Health Science, Rome, Italy; Department of Internal Medicine, Intermediate Care Unit, Hospital Alto Vicentino (AULSS-7), Santorso, Italy.
Sensors (Basel)
January 2025
Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.
View Article and Find Full Text PDFChildren (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.
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