Electrocardiogram Interpretation Research Articles

Implementing mind mapping in small-group learning to promote student engagement in the medical diagnostic curriculum: a pilot study

BackgroundMedical diagnostics is a pivotal bridge curriculum that receives much less attention from undergraduates in non-clinical medicine health profession programs with less student engagement and poor performance. Mind mapping is an active learning strategy for graphically presenting radiant thinking to culture clinical reasoning. The purpose of this study was to explore whether students’ comprehensive diagnostic skills are enhanced through increased student engagement by employing mind mapping.MethodsWe implemented mind mapping in small-grouped workshops with 86 junior undergraduates from preventive medicine program, for physical diagnostic sessions including physical examination (PE) maneuver, electrocardiogram (ECG) interpretation and medical history collection. We also conducted assessments of the above skills, as well as online surveys regarding their expectation on this course, self-evaluation of mind mapping in teaching and the learning process of all the modules.ResultsGroup members employing mind mapping in all PE sessions obtained higher scores in the heart and lung systems during the PE maneuver exam. Similarly, groups that made more in-depth mind maps achieved higher scores on the ECG quiz. In addition, groups displaying mind maps for history taking from normal classes and reformed class exhibited greater completeness of medical history with both standardized patients and real patients, which was consistent with increased collection of accompanying symptoms. Mind mapping was valued by the majority of students for its benefits in terms of acquiring PE maneuver, theoretical knowledge, medical history collection and medical records writing, clinical reasoning, communication skills, sense of teamwork and cooperation, professionalism and humanistic literacy.DiscussionThe visual feature of mind mapping evoked extensive behavioral engagement in all groups, as did cognitive and emotional engagement, as the majority of students expressed their willingness and affective reactions. In the short term, the positive feedbacks encourage growing engagement. The continuous benefits of mind mapping require long-term observation.

Sensitivity of Electrocardiogram on Electrode-Pair Locations for Wearable Devices: Computational Analysis of Amplitude and Waveform Distortion.

An electrocardiogram (ECG) is used to observe the electrical activity of the heart via electrodes on the body surface. Recently, an ECG with fewer electrodes, such as a bipolar ECG in which two electrodes are attached to the chest, has been employed as wearable devices. However, the effect of different geometrical factors and electrode-pair locations on the amplitude and waveform of ECG signals remains unclear. In this study, we computationally evaluated the effects of body morphology, heart size and orientation, and electrode misalignment on ECG signals for 48 scenarios using 35 bipolar electrode pairs (1680 waveforms) with a dynamic time warping (DTW) algorithm. It was observed that the physique of the human body model predominantly affected the amplitude and waveform of the ECG signals. A multivariate analysis indicated that the heart-electrode distance and the solid angle of the heart from the electrode characterized the amplitude and waveform of the ECG signals, respectively. Furthermore, the electrode locations for less individual variability and less waveform distortion were close to the location of electrodes V2 and V3 in the standard 12-lead. These findings will facilitate the placement of ECG electrodes and interpretation of the measured ECG signals for wearable devices.

The use of cognitive load theory to assist in the teaching of electrocardiogram interpretation within paramedical science education.

Paramedics are expected to record electrocardiograms (ECGs) as part of their clinical assessment; however, it is an extremely difficult skill to learn and understand as it has a high intrinsic cognitive load which can also be challenging to teach effectively. This article will explore the use of cognitive load theory to assist in the teaching of ECG interpretation within the context of paramedical education. Cognitive load theory can be useful to aid teaching within complex medical and health science domains including clinical skills teaching. The application of cognitive load theory to the teaching of ECG interpretation can be useful as it allows for the development of understanding, building schemata linking information currently being learned to knowledge already gained within the long-term memory, which can maximise germane load by the appropriate selection of intrinsic load, minimising extraneous load therefore not overloading the working memory.

Exploration and Application of Three-stage Strengthening Mode based on Massive Open Online Course to Improve Students’ Competence in Electrocardiogram Interpretation Skills

Electrocardiogram (ECG) is a widely common diagnostic test in clinical practice. However, research has shown that residents and medical students cannot analyze ECG skillfully. In this study, we explore a three-stage strengthening mode based on massive open online course (MOOC) to improve students’ competence in ECG interpretation skills. We carry out the first stage in a diagnostics course, in which instructors adopted online and offline blended mode for the teaching method of ECG with clinical medicine students in grade 2018 based on MOOC. We compare scores of ECG interpretation skills for 91 students in grade 2018 with 81 students in grade 2016 to evaluate the effect. The second stage concerns studying internal medicine, in which we divide students in grade 2018 into two groups with 49 students in group A and 42 students in group B. Group A strengthened their ECG interpretation skills when learning about heart diseases. The third stage pertains to rotation in the internal medicine department in the internship period. Some students in group A continued to receive intensive training. Students in group B had no reinforcement arrangement in the second and the third stage. Students in grade 2018 took graduation tests at the end of their internship. We use the scores on ECG interpretation skills in the graduation examination to evaluate the strengthening effect between group A and group B. The result of the first stage shows that scores on ECG interpretation skills in the final exam in grade 2018 were 19.32 ± 4.62 points, which is higher than those of grade 2016, whose points were 16.89 ± 5.30 (total scores in both grades were 30 points). Difference is statistically significant. Scores on the graduation examination in group A were 38.89 ± 16.81 points, and those of group B were 26.86 ± 15.43 points (total score was 60 points), with statistical difference between these two groups. The three-stage strengthening mode based on ECG MOOC is an effective method to improve students’ ECG interpretation skills. Results show that an online and offline blended teaching strategy is superior to the traditional lecture method. Repeated practice is necessary for improving ECG interpretation competence. Students in the strengthening program group had better grades in the graduation examination.

Drawing for Retention: Using Visual Arts to Teach Cardiovascular Pathophysiology in Athletic Training

Context Sudden cardiac arrest is the leading cause of sport-related deaths in young athletes. Athletic training students must understand cardiovascular anatomy and physiology in addition to pathophysiology to appropriately care for patients with cardiovascular conditions. Objective Describe a teaching technique to actively engage students in lecture about cardiovascular anatomy, physiology, pathophysiology, and electrocardiograms. Background Drawing for retention is an educational technique used in medical education to help students recognize patterns, reinforce knowledge, and retain information. Using drawing for abstract concepts that cannot always be visualized or applied enhances comprehension. Description An educational technique using drawing for retention to teach cardiovascular concepts, conditions, and basic electrocardiogram interpretation was implemented in an emergency care in athletic training course. The instructor drew and lectured on concepts of anatomy, physiology, pathophysiology, electrocardiograms, and interventions while the students drew and took notes on the concepts. Educational Advantage(s) Students describe this activity as a beneficial way to learn and apply cardiovascular anatomy and physiology to cardiovascular conditions and treatment. Conclusions Faculty may consider implementing drawing for retention as an active learning technique to engage students with complex or abstract topics.

Transport decisions: acute coronary syndrome and high-risk electrocardiograms

Patients experiencing a high-risk non-ST-elevation myocardial infarction are likely to benefit from prehospital medical contact, transport to a percutaneous coronary intervention (PCI) centre and early definitive treatment. They may be provisionally diagnosed through the identification of a high-risk electrocardiogram (ECG). Advances in ECG interpretation have led to the identification of a cohort of at-risk patients whose treatment may previously have been delayed. Patients with certain ECG changes are considered high risk and may be experiencing coronary occlusions. Early PCI and direct transport to a PCI-capable hospital are associated with a reduction in mortality and morbidity; this is associated with a lower financial burden on the health service. Early paramedic identification of these high-risk ECGs and appropriate transport decision-making can positively influence patient outcomes. High-risk ECGs include those that are positive for the modified Smith-Sgarbossa criteria, DeWinter T waves, Wellens syndrome, posterior acute myocardial infarction and aVR elevation. Continued education is correlated with more accurate ECG interpretation.

Bridging the Gap: Evaluation of an Electrocardiogram Curriculum for Advanced Practice Clinicians.

Training programs for advanced practice providers (APP) often have significant variability in their curriculum, including electrocardiogram (ECG) education. Despite limitations in formal ECG training, APPs in the emergency department (ED) may be the first practitioner to interpret an ECG. Foundations of Emergency Medicine (FoEM) offers free, open-access curricula that are widely used for resident education. We sought to improve APP ECG interpretation skills by implementing the FoEM ECG I course. This was a single-site, pre- and post-intervention study of 23 APPs at our high-acuity, urban ED. In the fall of 2020, APP learners enrolled in a FoEM ECG I course led by faculty and senior resident instructors. The course consisted of six virtual, small-group, active-learning ECG workshops. Participants completed a 15-question multiple-choice test before, immediately after, and six months post-intervention to quantify knowledge acquisition. Additionally, a pre- and post-intervention knowledge, attitudes, and practices survey was administered on ECG interpretation skills and to evaluate the course. We evaluated change in ECG knowledge scores using a Wilcoxon signed-rank test. Changes in self-assessed knowledge were evaluated using an ordinal logistic mixed-effects regression. A total of 23 APPs enrolled in the course. Knowledge assessments showed APPs improved from the pre-test (median 9/15, interquartile range [IQR] 7-11) to the post-test (median 12/15, IQR 10-13; P = 0.001). Test scores did not significantly change from the post-test to the delayed post-test (median 12/15, IQR 12-13; P = 0.30). Respondents' subjective rating of their skill did not significantly change (P = 0.06). Respondents reported no change in their likelihood of approaching an attending when uncertain of the correct interpretation of an ECG (P = 0.16). Overall, 91% were satisfied with the course and 96% agreed that the course difficulty was appropriate. The FoEM ECG course provided a standardized curriculum that improved APP knowledge for interpreting ECGs. Despite this, the course did not alter APPs' willingness to approach physicians for guidance with interpretation of abnormal ECGs. These findings may inform expansion of this concept for other programs who desire formalized APP ECG education.

Artificial intelligence predicts undiagnosed atrial fibrillation in patients with embolic stroke of undetermined source using sinus rhythm electrocardiograms

BackgroundArtificial intelligence (AI)–enabled sinus rhythm (SR) electrocardiogram (ECG) interpretation can aid in identifying undiagnosed paroxysmal atrial fibrillation (AF) in patients with embolic stroke of undetermined source (ESUS). ObjectiveThe purpose of this study was to assess the efficacy of an AI model in identifying AF based on SR ECGs in patients with ESUS. MethodsA transformer-based vision AI model was developed using 737,815 SR ECGs from patients with and without AF to detect current paroxysmal AF or predict the future development of AF within a 2-year period. Probability of AF was calculated from baseline SR ECGs using this algorithm. Its diagnostic performance was further tested in a cohort of 352 ESUS patients from 4 tertiary hospitals, all of whom were monitored using an insertable cardiac monitor (ICM) for AF surveillance. ResultsOver 25.1-month follow-up, AF episodes lasting ≥1 hour were identified in 58 patients (14.4%) using ICMs. In the receiver operating curve (ROC) analysis, the area under the curve for the AI algorithm to identify AF ≥1 hour was 0.806, which improved to 0.880 after integrating the clinical parameters into the model. The AI algorithm exhibited greater accuracy in identifying longer AF episodes (ROC for AF ≥12 hours: 0.837, for AF ≥24 hours: 0.879) and a temporal trend indicating that the AI-based AF risk score increased as the ECG recording approached the AF onset (P for trend <.0001). ConclusionsOur AI model demonstrated excellent diagnostic performance in predicting AF in patients with ESUS, potentially enhancing patient prognosis through timely intervention and secondary prevention of ischemic stroke in ESUS cohorts.

High Diagnostic Accuracy of Long-Term Electrocardiogram Interpretation by General Practitioners.

Traditional long-term ECG monitoring systems have primarily been used by cardiologist. New remote and wearable easy-to-use devices have led to increased use of ECG recordings also outside cardiology clinics. The aims of this study were to assess the feasibility and diagnostic accuracy of interpretation of the one-lead ECG recordings from a patch ECG device (ECG247 Smart Heart Sensor system) by general practitioners (GP). Norwegian GPs were invited to digitally assess 10 long-term ECG recordings with different arrhythmias performed by the ECG247 Smart Heart Sensor system. For all ECG examinations, the presence/absence of different arrhythmias was registered. A total of 40 GPs accepted the invitation and assessed all the 10 long-term ECG recordings. All the tests were assessed as interpretable by all the GPs. Arrhythmias (atrial fibrillation/flutter, supraventricular tachycardia, and ventricular tachycardia) were correctly identified in most cases, with sensitivity of 98% (95% CI 95-99%), specificity of 75% (95% CI 68-82%), and diagnostic accuracy of 89% (85-92%). Incorrect automatic system algorithm interpretations were rarely corrected by the GPs. GPs interpreted one-lead recordings by the ECG247 Smart Heart Sensor system with high diagnostic accuracy for common arrhythmias. However, in cases with rare arrhythmias, we recommend consulting a cardiologist to confirm the diagnosis before treatment is initiated. This trial is registered with NCT04700865.

An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review.

Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.

A Case of a Presurgical Abnormal Rhythm.

Editor's note: This is the next installment in a series on electrocardiogram (ECG) interpretation. Nurses in all settings should know the basics, as medications and physiological changes can cause cardiac arrhythmias. Each article will start with a brief case scenario and an ECG strip and then take you step by step through analyzing the heart rhythm.

Developing an interactive online curriculum directed at pediatric trainees for instruction on pediatric electrocardiogram interpretation

BackgroundInterpretation of pediatric ECG is a specific skill set that requires familiarity with normal electrographic changes in the developing heart, common pediatric dysrhythmias, and conduction alterations secondary to structural lesions. Previous studies showed that pediatric trainees are neither comfortable nor proficient in ECG reading. ObjectivesWe aimed to develop an interactive self-learning curriculum for teaching pediatric ECG interpretation. MethodsIn a collaboration between pediatric residents and cardiologists, we identified high-yield topics, ‘red flags,’ and age-related normal variances that are important to recognize. We built a 5–10-minute teaching module for each topic using Adobe Captivate. The modules were available online on a shared server. Participants were asked to take pre- and post-participation surveys and interpretation skill tests to define knowledge gaps and to focus our curricular development. ResultsThe workgroup defined 14 topics for the teaching modules. The pre-participation survey and test were completed by 74 learners (53 % pediatric residents, 29% pediatric advanced care provider fellows, and others). Only 22 % and 26 % reported comfort with distinguishing normal from abnormal ECG or identifying common arrhythmias, respectively; only one learner reported feeling comfortable identifying signs of structural abnormalities. Of this group, 16 participants completed all 14 modules and took the post-test. They reported improved confidence and scored higher on performance test (p = 0.02). ConclusionWe present a newly developed educational program for ECG interpretation that centers on visual learning and pattern recognition of high-yield findings in pediatrics. Initial surveys confirm the need for an accessible teaching tool that is asynchronous and can answer the specific topics of interest of each learner. We intend to share our education material for broader use outside of our institution.

Validity of Computer-interpreted "Normal" and "Otherwise Normal" ECG in Emergency Department Triage Patients.

Chest pain is the second most common chief complaint for patients undergoing evaluation in emergency departments (ED) in the United States. The American Heart Association recommends immediate physician interpretation of all electrocardiograms (ECG) performed for adults with chest pain within 10 minutes to evaluate for the finding of ST-elevation myocardial infarction (STEMI). The ECG machines provide computerized interpretation of each ECG, potentially obviating the need for immediate physician analysis; however, the reliability of computer-interpreted findings of "normal" or "otherwise normal" ECG to rule out STEMI requiring immediate intervention in the ED is unknown. We performed a prospective cohort analysis of 2,275 ECGs performed in triage in the adult ED of a single academic medical center, comparing the computerized interpretations of "normal" and "otherwise normal" ECGs to those of attending cardiologists. ECGs were obtained with a GE MAC 5500 machine and interpreted using Marquette 12SL. In our study population, a triage ECG with a computerized interpretation of "normal" or "otherwise normal" ECG had a negative predictive value of 100% for STEMI (one-sided, lower 97.5% confidence interval 99.6%). None of the studied patients with these ECG interpretations had a final diagnosis of STEMI, acute coronary syndrome, or other diagnosis requiring emergent cardiac catheterization. In our study population, ECG machine interpretations of "normal" or "otherwise normal" ECG excluded findings of STEMI. The ECGs with these computerized interpretations could safely wait for physician interpretation until the time of patient evaluation without delaying an acute STEMI diagnosis.

Educating correct ECG interpretation is a way to promote the satisfaction and competency the diagnostic service providers.

Given electrocardiogram (ECG) interpretation as one of the diagnostical challenges for medical students and health professionals, this research was carried out to present an experience of web-based teaching method and novel approaches used for training of ECG interpretation. This online program was conducted in three days. The main content of the class was taught during one hour, and after that, the teacher spent enough time for responding the asked questions. The components of a normal ECG and different changes that can occur in these waves were taught through clinical case-based scenarios using the web platform and Adobe Connect software. The participants' satisfaction was assessed with a 12-item questionnaire, and the short-term retention of ECG interpretation skill was examined by comparing the posttest scores with pretest. A total of 224 individuals completed the course. Total satisfaction score was 53.05±6.98 (out of the maximum score of 60). Based on the results of the paired t test, the interpretation skill scores of the participants increased significantly from 2.5 ± 1.57 to 6.96 ± 1.89. (p<0.001, CI = -4.8 to- 4.11). This web-based nationwide training program provided a supplementary resource for ECG learning among medical students and health-care providers.

Assessing the Level of Electrocardiographic Interpretation Competency among Emergency Nurses in Palestine.

The use of electrocardiograms (ECGs) is widespread among emergency room (ER) nurses for diagnosis and triage, making it crucial for them to have the appropriate level of competency in interpreting ECGs. This can lead to better healthcare and patient outcomes. This study aims to assess the competency level of emergency nurses in Palestine in interpreting normal ECG and certain cardiac arrhythmias, and to explore the association between socio-demographic characteristics and their ECG interpretation competency. The study used a cross-sectional descriptive design, and 196 ER nurses were conveniently recruited to fill out a self-administered questionnaire based on previous literature. The data collected were analyzed using SPSS with strict adherence to ethical considerations. The results showed that 70.9% of the nurses were males, 65.3% held a bachelor's degree in nursing, 46.9% had 1-5 years of experience, and 60.7% had received previous courses on ECG interpretation. The mean competency level of the ER nurses was satisfactory at 60.714%, but 38.8% had a poor level of competency. This was significantly higher among nurses with higher educational levels (p-value < 0.001), those who had taken previous ECG courses (p-value = 0.045) and life support (p-value < 0.05), and those who were exposed to more ECG interpretations per day (p-value = 0.001). There is a need to focus more on ECG competency levels in Palestinian literature and compare them between different departments. It is also essential to evaluate nurses' needs for continuous education.

Evaluation of 12-lead electrocardiogram at 0.55T for improved cardiac monitoring in magnetic resonance imaging

The 12-lead electrocardiogram (ECG) is a standard diagnostic tool for monitoring cardiac ischemia and heart rhythm during cardiac interventional procedures and stress testing. These procedures can benefit from magnetic resonance imaging (MRI) information; however, the MRI scanner magnetic field leads to ECG distortion that limits ECG interpretation. This study evaluated the potential for improved ECG interpretation in a "low field" 0.55T MRI scanner. The 12-lead ECGs were recorded inside 0.55T, 1.5T, and 3T MRI scanners, as well as at scanner table "home" position in the fringe field and outside the scanner room (seven pigs). To assess interpretation of ischemic ECG changes in a 0.55T MRI scanner, ECGs were recorded before and after coronary artery occlusion (seven pigs). ECGs was also recorded for five healthy human volunteers in the 0.55T scanner. ECG error and variation were assessed over 2-minute recordings for ECG features relevant to clinical interpretation: the PR interval, QRS interval, J point, and ST segment. ECG error was lower at 0.55T compared to higher field scanners. Only at 0.55T table home position, did the error approach the guideline recommended 0.025mV ceiling for ECG distortion (median 0.03mV). At scanner isocenter, only in the 0.55T scanner did J point error fall within the 0.1mV threshold for detecting myocardial ischemia (median 0.03mV in pigs and 0.06mV in healthy volunteers). Correlation of J point deviation inside versus outside the 0.55T scanner following coronary artery occlusion was excellent at scanner table home position (r2 =0.97), and strong at scanner isocenter (r2 =0.92). ECG distortion is improved in 0.55T compared to 1.5T and 3T MRI scanners. At scanner home position, ECG distortion at 0.55T is low enough that clinical interpretation appears feasible without need for more cumbersome patient repositioning. At 0.55T scanner isocenter, ST segment changes during coronary artery occlusion appear detectable but distortion is enough to obscure subtle ST segment changes that could be clinically relevant. Reduced ECG distortion in 0.55T scanners may simplify the problem of suppressing residual distortion by ECG cable positioning, averaging, and filtering and could reduce current restrictions on ECG monitoring during interventional MRI procedures.

Multifocal Atrial Tachycardia Transitioning to Atrial Fibrillation in an Octogenarian

Abstract Multifocal atrial tachycardia (MAT) is an arrhythmia frequently seen in the elderly whose prevalence increases with aging. These arrhythmias may eventually lead to more chaotic arrhythmias such as atrial fibrillation (AF) and flutter. MAT is frequently mistaken for AF in electrocardiogram (ECG). The importance of differentiating MAT from AF is that the former requires no anticoagulation and, therefore, avoids the risks associated with their administration. It is also important to realize that the elderly have multiple conditions such as senile tremors and Parkinson’s disease which may cause mechanical movement artifacts in resting ECGs mimicking cardiac arrhythmias leading to incorrect interpretation of ECGs. Educating physicians about the electrocardiographic appearance of atrial dysrhythmias and the recognition of the confounding artifacts can prevent unnecessary use of treatment which could potentially cause adverse effects in the patient.

COMPUTATIONAL FRAMEWORK FOR PREDICTION OF CARDIAC DISORDERS BY ANALYZING ECG SIGNALS USING MACHINE LEARNING TECHNIQUE

The clinical diagnosis of heart disorders relies heavily on electrocardiograms (ECGs). Numerous abnormalities in heart are being identified with a record of heart signal throughout intervals. Monitoring and diagnosing ECGs signals in daily life are appearing recently due to an increase in healthcare equipment. This paper presents a novel computational framework for detecting heart disorders by analyzing the ECG signals using machine learning technology. The manual interpretation of ECGs by medical experts is time-consuming and subject to human error. This study presents a comprehensive review and implementation of an automated ECG classification system utilizing artificial neural networks (ANNs) for efficient and accurate cardiac condition identification. The proposed ECG classifier employs a multilayered ANN architecture, trained and validated using a diverse dataset of annotated ECG recordings. Preprocessing techniques, including signal denoising, feature extraction, and data augmentation, enhance the quality and relevance of input data for the ANN. The selected features encompass morphological and temporal attributes, essential for distinguishing normal and abnormal ECG patterns. In this paper, noise removal from input ECG dataset is performed by adaptive filter technique and baseline wander is also removed. Machine learning in ECG classification is done by ANN that allows to use less energy while still providing accurate classification. MATLAB software is employed to carry out this work and corresponding outputs are obtained for ECG classification.

Pediatric electrocardiogram interpretation improvement and confidence assessment for resident physicians via training skills workshop

BackgroundPediatric electrocardiogram (P-ECG) interpretation proficiency has been identified as an area of improvement for physicians to decrease rates of incorrect diagnosis and prevent patient adverse events. ObjectivesThe aim of this project was to assess the residents' perception of P-ECG training and skills, improve P-ECG interpretation via a training workshop, and assess confidence of residents in P-ECG interpretation. MethodsThis was an IRB approved prospective cohort study of pediatric, medicine-pediatric, and family medicine residents from 2020 to 2022. Residents were asked to complete a survey to assess their perception of P-ECG education and experience. A P-ECG training skills workshop was then held with pre- and post-tests completed by each resident. While answering questions for the pre- and post-tests, residents marked their degree of confidence via a 5-point Likert scale. ResultsA total of 74 residents completed the P-ECG education and experience survey, and 58 completed the workshop pre- and post-tests along with their interpretation confidence. Based on the survey, only 16.7 % of the residents felt that P-ECG training was sufficient, and most residents ordered 0–5 P-ECGs per month (98.6 %). Post-test scores improved significantly in total for all resident years combined and for first year residents (mean percent increases of 22.4 %; p < 0.001, and 22.9 %; p = 0.01, respectively). Despite increase in post-test scores, confidence scores did not significantly improve. ConclusionsResident survey revealed that P-ECG education opportunities at our center are infrequent. Training skills workshop increased the accuracy for P-ECG interpretation, but not confidence. This study provides a framework for similar assessment of P-ECG education at other institutions that do not have a dedicated inpatient pediatric cardiology service. It further increases targeted P-ECG education to improve trainee/resident interpretation skills and level of confidence.

Registered nurses' knowledge and interpretation of ECG rhythms: A cross-sectional study.

Electrocardiographic (ECG) monitoring and recording are seen as the most commonly used non-invasive diagnostic tool to identify cardiac arrhythmia and myocardial damage in the clinical setting. There is an expectation that critical care nurses are ideally trained to interpret abnormalities and morphology in the ECG more proficiently than nurses from general ward areas. However, the ability to interpret and recognise ECG abnormalities is dependent on which critical care area nurses are currently working in and their level of experience. The aim of this study was to investigate registered nurses' knowledge in being able to identify and interpret select electrocardiographic rhythms. This was a cross-sectional study that evaluated registered nurses' knowledge of electrocardiogram rhythm identification and interpretation. A convenience sample of 105 registered nurses currently enrolled in a 2-year Master's programme leading to critical care specialism and advanced practice nurse award were recruited. A 20-item multiple choice questionnaire that provided examples of electrocardiogram rhythm (n=14) abnormalities and rhythm abnormalities caused by electrolyte disturbances (n=6) RESULTS: The study included registered nurses from critical care and general ward areas. The overall results were poor with only 55% of questions answered correctly. Coronary care nurses scored the highest in identifying ECG rhythms (12/20 ± 1.58; p < .001). When ECG abnormalities associated with electrolyte imbalances were analysed, both groups were unable to identify the effects of hypokalaemia and hypomagnesaemia effectively (p = .748). Length of time as a registered nurse (r = -0.304, p = .002) and length of time in current work environment were weakly correlated (r = -0.328, p = .001). Having a critical care background showed a positive relationship with nursing knowledge of ECG rhythm identification (r = 0.614, p < .001). The results of this study demonstrate that nurses have a poor knowledge of ECG rhythm identification and interpretation, a consistent finding from other work. A possible solution is a revamp of education and training associated with ECG recognition and morphology. Monitoring and assessing ECG morphology provide important details about cardio-electroconductive stability, especially with fluctuations in serum electrolyte levels seen in critical illness or trauma. For this, critical nurses must improve their proficiency through education/training or internal quality improvement activities in detecting abnormalities associated with ECG changes beyond those most easily recognizable rhythms such as atrial fibrillation or ventricular tachycardia.