Background: Competency in electrocardiogram (ECG) interpretation is central to undergraduate and postgraduate clinical training. Studies have demonstrated ECGs are interpreted sub-optimally. Our study compares the effectiveness of two learning strategies to improve competence and confidence.
Method: A 1-month prospective randomized study compared the strategies in two cohorts: undergraduate third year medical students and postgraduate foundation year one (FY1) doctors. Both had blinded randomization to one of these learning strategies: focused teaching program (FTP) and self-directed learning (SDL). All volunteers completed a confidence questionnaire before and after allocation learning strategy and an ECG recognition multiple choice question (MCQ) paper at the end of the learning period.
Results: The FTP group of undergraduates demonstrated a significant difference in successfully interpreting "ventricular tachycardia" (P = 0.046) and "narrow complex tachycardia" (P = 0.009) than the SDL group. Participant confidence increased in both learning strategies. FTP confidence demonstrated a greater improvement than SDL for both cohorts.
Conclusion: A dedicated teaching program can improve trainee confidence and competence in ECG interpretation. A larger benefit is observed in undergraduates and those undertaking a FTP.
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http://dx.doi.org/10.14740/cr333e | DOI Listing |
Heart Rhythm O2
December 2024
Cardiology Department, Bichat Hospital, Paris, France.
Background: Detection of atrial tachyarrhythmias (ATA) on long-term electrocardiogram (ECG) recordings is a prerequisite to reduce ATA-related adverse events. However, the burden of editing massive ECG data is not sustainable. Deep learning (DL) algorithms provide improved performances on resting ECG databases.
View Article and Find Full Text PDFOpen Heart
January 2025
Department of Molecular and Clinical Medicine, University of Gothenburg Institute of Medicine, Gothenburg, Sweden.
Purpose: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.
Methods: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation.
Heart Rhythm
January 2025
IDOVEN Research, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Myocardial Pathophysiology Area, Madrid, Spain; Centro de Investigación Biomédica en Red. Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain. Electronic address:
Background: Although smartphone-based devices have been developed to record 1-lead ECG, existing solutions for automatic atrial fibrillation (AF) detection often has poor positive predictive value.
Objective: This study aimed to validate a cloud-based deep learning platform for automatic AF detection in a large cohort of patients using 1-lead ECG records.
Methods: We analyzed 8,528 patients with 30-second ECG records from a single-lead handheld ECG device.
NPJ Digit Med
January 2025
Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Cardiac wall motion abnormalities (WMA) are strong predictors of mortality, but current screening methods using Q waves from electrocardiograms (ECGs) have limited accuracy and vary across racial and ethnic groups. This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. We collected ECG and echocardiogram data from 35,210 patients in California and labeled WMA using unstructured language parsing of echocardiographic reports.
View Article and Find Full Text PDFJ Electrocardiol
December 2024
Crown Princess Victoria Children's Hospital, Dept of Biomedical and Clinical Sciences, Dept of Pediatrics, Linköping University, Sweden; Pediatric Heart Centre, Skåne University Hospital and Dept of Clinical Sciences, Lund University, Sweden. Electronic address:
Background: Myocardial fibrosis, expressed as late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR), is an important risk factor for malignant cardiac events in hypertrophic cardiomyopathy (HCM). However, CMR is not easily available, expensive, also needing intravenous access and contrast.
Objective: To determine if derived vectorcardiographic spatial QRS-T angles, an aspect of advanced ECG (A-ECG), can indicate LGE to appropriately prioritize young HCM-patients for CMR.
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