Purpose: Defibrillation in shockable rhythm is a well-known key intervention in cardiopulmonary resuscitation (CPR). The aim of this study was to analyze accuracy (the sum of the numbers of true positive results and true negative results, divided by the number of total results) of deciding by paramedics whether the rhythm was shockable or non-shockable.
Methods: In this study 103 paramedics from various regions of Poland participated voluntarily. Study participants were presented with 22 simulated various electrocardiogram (ECG) recordings based on 10-s videos. These rhythms were also assessed using a manual defibrillator with shock-advisory mode known as automated external defibrillator (AED) mode.
Results: Among the 103 participants, the mean of correct answers (correct decision to defibrillate or correct decision not to defibrillate) was 18/22 (83.7 %). The highest possible score was achieved by the participant with 22/22 (100 %) correct answers, while the lowest was 10/22 (45.5 %). The highest score obtained for single rhythm was 97.1 % and the lowest was 32 %. Mean accuracy of shock-advisory mode was 77.3 %.
Conclusions: Improving the quality of paramedic training and continuous quality monitoring (e.g., by analyzing ECG recordings from resuscitations) is essential to improve the accuracy of defibrillation rhythm recognition. The role of the AED mode can be advisory, but is not a substitute for assessment by medical professionals in Emergency Medical Service.
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http://dx.doi.org/10.1016/j.advms.2024.12.002 | DOI Listing |
J Physiol
January 2025
Department of Biological Sciences, Southern Methodist University, Dallas, TX, USA.
Sudden unexpected death in epilepsy (SUDEP) is a devastating complication of epilepsy with possible sex-specific risk factors, although the exact relationship between sex and SUDEP remains unclear. To investigate this, we studied Kcna1 knockout (Kcna1) mice, which lack voltage-gated Kv1.1 channel subunits and are widely used as a SUDEP model that mirrors key features in humans.
View Article and Find Full Text PDFJ Interv Card Electrophysiol
January 2025
Department of Cardiology, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, No.180, Feng-Lin Road, Shanghai, 200032, P.R. China.
Background: Ventricular arrhythmia (VA) originating from the left ventricular summit (LVS) poses particular challenges, with higher rates of ablation failure.
Objective: To further evaluate the anatomical ablation approach from the subaortic region for LVS VAs and their electrophysiological characteristics.
Method: The study enrolled 27 consecutive patients with sympatomatic VAs originating from LVS and who received an anatomical ablation approach from R-L ILT in our center.
Biomed Phys Eng Express
January 2025
Department of Health Science and Technology, Aalborg University, Selma Lagerløfs Vej 249, Aalborg, 9260, DENMARK.
Unlabelled: Fetal phonocardiography is a well-known auscultation technique for evaluation of fetal health. However, murmurs that are synchronous with the maternal heartbeat can often be heard while listening to fetal heart sounds. Maternal placental murmurs (MPM) could be used to detect maternal cardiovascular and placental abnormalities, but the recorded MPMs are often contaminated by ambient interference and noise.
View Article and Find Full Text PDFBMJ Case Rep
January 2025
Cardiology, Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai, Maharashtra, India.
A man in his early 50s presented to the emergency department (ED) with sudden onset of palpitation and presyncope. The 12-lead electrocardiogram (ECG) recorded in ED showed monomorphic ventricular tachycardia requiring cardioversion in view of haemodynamic instability. The patient was subsequently detected to have an anomalous left coronary artery origin from the pulmonary artery.
View Article and Find Full Text PDFBMJ Open
January 2025
Newcastle University Translational and Clinical Research Institute, Newcastle upon Tyne, UK
Introduction: Heart failure (HF) is a complex clinical syndrome. Accurate risk stratification and early diagnosis of HF are challenging as its signs and symptoms are non-specific. We propose to address this global challenge by developing the STRATIFYHF artificial intelligence-driven decision support system (DSS), which uses novel analytical methods in determining the risk, diagnosis and prognosis of HF.
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