Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Prehospital and disaster medicine
Evaluating Nursing Students' Performance and Retention of BLS/AED Knowledge and Skills One Year After Training: A Randomized Controlled Trial
INTRODUCTION: Nurses often serve as the initial responders in cases of cardiac arrest, making cardiopulmonary resuscitation (CPR) competence essential to effective resuscitation. It is therefore important for nursing students to possess sufficient CPR knowledge and skills for future clinical practice. OBJECTIVE: The aim of this study was to examine whether CPR post-qualification training is needed in maintaining knowledge and skills and if face-to-face or online training is more effective as an educational method in preserving CPR knowledge and skills in nursing students. METHODS: A three-group, parallel randomized-controlled, single-blind design was used. The participants were 133 nursing students, randomly assigned to the face-to-face group (n = 43), the e-learning group (n = 43), and the control group (n = 47). Before randomization, all participants (n = 133) successfully completed the European Resuscitation Council (ERC) standard five-hour Basic Life Support/Automated External Defibrillation (BLS/AED) course (t0) and a questionnaire based on the 2021 ERC Guidelines to assess their knowledge. Six months later, Group A received face-to-face training, Group B received e-learning training, and Group C had no intervention. Six months after interventions, all participants (n = 133) were re-assessed on the same questionnaire, and they were given the same scenario of cardiopulmonary arrest that was used during the initial BLS course to assess the skills that they retained (t1) with the use of the ERC CPR/AED 11-item checklist. The collected data were analyzed with inferential "among group" analysis involving Kruskal-Wallis's and Pearson's Chi-Squared test and inferential "within group" analysis including Wilcoxon's Signed-Rank and McNemar's test. Multiple linear regression was used for adjusting demographic characteristics. RESULTS: Based on pairwise differences between independent groups, both intervention groups presented higher scores in knowledge at t1 (P < 0.001) and in skills at t1 (P < 0.001) compared to the control group. Moreover, both training methods demonstrated comparable effectiveness. Based on comparisons between paired groups, there was also a statistically significant decrease in the Skill Score in the control group after one year (P < 0.001). Most skills were notably higher, primarily in the e-learning group, and to a lesser extent in the face-to-face group. In specific, intervention groups demonstrated statistically significant improvement in nine of the eleven assessed skills (all P < 0.05). CONCLUSION: The current study showed that post-qualification training is needed to retain CPR knowledge and skills of nursing students, and that face-to-face and e-learning training had similar outcomes.
23 May 2026
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Deep learning-based ROSC prediction and ECG phenotyping in out-of-hospital cardiac arrest
INTRODUCTION: Electrocardiogram (ECG) signals during cardiac arrest contain detailed information on cardiac rhythm characteristics and have been associated with resuscitation outcomes. We aimed to develop a convolutional neural network (CNN) model to predict return of spontaneous circulation (ROSC) and identify novel ECG phenotypes in patients with out-of-hospital cardiac arrest (OHCA). METHODS: This retrospective cohort study used Korean OHCA Registry and ECG data from Seoul emergency medical services between July 2021 and December 2023. Adult patients with nontraumatic OHCA who had prehospital ECG signals were included. Five-second ECG segments obtained during resuscitation were analyzed. A one-dimensional CNN was developed to simultaneously predict the probability of ROSC within 2 min and to classify the concurrent ECG rhythm as shockable or non-shockable. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). ECG phenotypes were identified by applying K-means clustering to feature representations extracted from the trained CNN. RESULTS: 3452 patients (median age, 73 years [interquartile range, 61-81]; 34.4% female) were included. The AUCs for predicting ROSC within 2 min and shockable rhythm in the test set were 0.921 (95% confidence interval [CI], 0.897-0.945) and 0.983 (95% CI, 0.979-0.988), respectively. Five ECG phenotype categories with varying morphologies and ROSC probabilities were identified. Phenotype 1 consisted of shockable rhythms with high ROSC probability (30.4%), whereas Phenotype 2 showed shockable rhythms with low ROSC probability (4.8%). Phenotypes 3 and 4 included pulseless electrical activities with relatively high (5.2%) and lower (0.5%) ROSC probabilities, respectively. Phenotype 5 primarily consisted of asystole or near-asystole rhythms. Transitions between ECG phenotypes were associated with CPR quality. CONCLUSIONS: The CNN-based model accurately predicted ROSC and shockable rhythm from ECG signals and identified five novel ECG phenotypes in OHCA. These findings can enable accurate ROSC prediction and individualized prehospital resuscitation for patients with OHCA.
4 May 2026
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