Electrocardiographic Interpretation Research Articles

Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review.

Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG. Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.

The Use of Animations Depicting Cardiac Electrical Activity to Improve Confidence in Understanding of Cardiac Pathology and Electrocardiography Traces Among Final-Year Medical Students: Nonrandomized Controlled Trial.

Electrocardiography (ECG) interpretation is a fundamental skill for medical students and practicing medical professionals. Recognizing ECG pathologies promptly allows for quick intervention, especially in acute settings where urgent care is needed. However, many medical students find ECG interpretation and understanding of the underlying pathology challenging, with teaching methods varying greatly. This study involved the development of novel animations demonstrating the passage of electrical activity for well-described cardiac pathologies and showcased them alongside the corresponding live ECG traces during a web-based tutorial for final-year medical students. We aimed to assess whether the animations improved medical students' confidence in visualizing cardiac electrical activity and ECG interpretation, compared to standard ECG teaching methods. Final-year medical students at Imperial College London attended a web-based tutorial demonstrating the 7 animations depicting cardiac electrical activity and the corresponding ECG trace. Another tutorial without the animations was held to act as a control. Students completed a questionnaire assessing their confidence in interpreting ECGs and visualizing cardiovascular electrical transmission before and after the tutorial. Intervention-arm participants were also invited to a web-based focus group to explore their experiences of past ECG teaching and the tutorial, particularly on aspects they found helpful and what could be further improved in the tutorial and animations. Wilcoxon signed-rank tests and Mann-Whitney U tests were used to assess the statistical significance of any changes in confidence. Focus group transcripts were analyzed using inductive thematic analysis. Overall, 19 students attended the intervention arm, with 15 (79%) completing both the pre- and posttutorial questionnaires and 15 (79%) participating in focus groups, whereas 14 students attended the control arm, with 13 (93%) completing both questionnaires. Median confidence in interpreting ECGs in the intervention arm increased after the tutorial (2, IQR 1.5-3.0 vs 3, IQR 3-4.5; P<.001). Improvement was seen in both confidence in reviewing or diagnosing cardiac rhythms and the visualization of cardiac electrical activity. However, there was no significant difference between the intervention and control arms, for all pathologies (all P>.05). The main themes from the thematic analysis were that ECGs are a complex topic and past ECG teaching has focused on memorizing traces; the visualizations enabled deeper understanding of cardiac pathology; and ECG learning requires repetition, and clinical links remain essential. This study highlights the value of providing concise explanations of the meaning and pathophysiology behind ECG traces, both visually and verbally. ECG teaching that incorporates relevant pathophysiology, alongside vignettes with discussions regarding investigations and management options, is likely more helpful to students than practices based solely on pattern recognition. Although the animations supported student learning, the key element was the tutor's explanations. These animations may be more helpful as a supplement to teaching, for instance, as open-access videos.

ECG Interpretation and Clinical Application in Cardiology Nursing Practice

This study is dedicated to elucidating the significance of electrocardiography (ECG) in the management of Acute Coronary Syndrome (ACS), particularly in diagnosing and making treatment decisions for patients with ST-Segment Elevation Myocardial Infarction (STEMI) and Non-ST-Segment Elevation Myocardial Infarction (NSTEMI). Through a detailed analysis of ECG waveform changes, we demonstrate how to quickly identify signs of myocardial infarction, and based on these findings, decide whether coronary angiography or emergency revascularization surgery is necessary.

Factors associated with electrocardiographic left ventricular hypertrophy among patients with hypertension in Thailand

BackgroundLeft ventricular hypertrophy (LVH) strongly predicts cardiovascular diseases (CVD) and death. One-fourth of Thai adults suffer from hypertension. Nevertheless, the information on LVH among Thai patients with hypertension is not well characterized. We aimed to identify the prevalence and factors associated with electrocardiographic LVH (ECG-LVH) among patients with hypertension in Thailand.MethodsThe present study obtained the dataset from the Thailand Diabetes Mellitus/Hypertension study, which included hypertension patients aged 20 years and older receiving continuous care at outpatient clinics in hospitals nationwide in 2011–2015 and 2018. Meanwhile, those without a record of 12-lead electrocardiography (ECG) were excluded from the analysis. ECG-LVH was defined as the LVH noted regarding ECG interpretation in the medical records. Multivariable logistic regression analysis was utilized for determining factors associated with ECG-LVH and presented as the adjusted odds ratio (AOR) and 95% confidence interval (CI).ResultsFrom 226,420 hypertensive patients in the Thailand Diabetes Mellitus/Hypertension study, 38,807 individuals (17.1%) with ECG data recorded were included in the analysis. The mean age was 64.8 ± 11.5 years, and 62.2% were women. Overall, 1,557 study participants had ECG-LVH, with an estimated prevalence of 4.0% (95% CI, 3.8–4.2%). Age-adjusted ECG-LVH prevalence among women and men was 3.4 and 5.1%, respectively (P < 0.001). Multivariable analysis determined factors associated with ECG-LVH, including being men (AOR, 1.49; 95% CI, 1.31–1.69), individuals aged 70 to 79 years (AOR, 1.56; 95% CI, 1.20–2.02) and ≥ 80 years (AOR, 2.10; 95% CI, 1.58–2.78) compared to individuals aged less than 50 years, current smokers (AOR, 1.26; 95% CI, 1.09–1.46) compared to those who never smoked, systolic blood pressure ≥ 140 mmHg and diastolic blood pressure ≥ 90 mmHg (AOR, 1.58; 95% CI, 1.30–1.92) compared to systolic blood pressure < 140 mmHg and diastolic blood pressure < 90 mmHg.ConclusionsThe current study illustrated the prevalence of ECG-LVH among Thai patients with hypertension who had ECG recorded and identified high-risk groups who tended to have ECG-LVH. The findings underscore the need for targeted interventions, particularly among high-risk groups such as older individuals, men, and current smokers, to address modifiable factors associated with ECG-LVH.

Historical developments in the diagnosis and treatment of pre-excitation syndromes (WPW)

In 1930, Wolff, Parkinson and White described the syndrome that bears their names. The mechanisms of supraventricular tachycardias were analyzed by brilliant electrocardiography interpretation by Pick and Langendorf. Wellens and Durrer using electrophysiologic studies analyzed the tachycardia mechanism invasively. In Germany the group by Seipel and Breithardt as well as Neuss and Schlepper studied the tachycardia mechanisms and response to antiarrhythmic drugs invasively by electrophysiological studies. Following the first successful interruption of an accessory pathway by Sealy in 1967, surgeons and electrophysiologists cooperated in Germany. Two centers, Hannover and Düsseldorf were established. Direct current (DC) ablation of accessory pathways was introduced by Morady and Scheinman. Because of side effects induced by barotrauma of DC, alternative strategies were studied. In 1987, radiofrequency ablation was introduced and thereafter established as curative therapy of accessory pathways in all locations.

Need assessment of medical school curriculum for MOOCs: perspectives of instructors and students of Shiraz University of Medical Sciences

IntroductionDesigning, developing, and implementing a course without assessing and prioritizing instructional needs may result in inefficiency due to the disregard for the actual needs of the target population. The present study aimed to determine and prioritize medical students’ instructional needs regarding Massive Open Online Courses (MOOCs) at Shiraz University of Medical Sciences.MethodsThis survey study was carried out in three stages (2020–2021) using the Delphi technique. Purposive and snowball sampling methods were used to select the instructors. The students were selected through simple random sampling. The first round of the Delphi technique involved a questionnaire consisting of one open-ended question, completed by 49 basic/clinical faculty members and 47 senior medical students. In the second round, a 5-point Likert scale-based questionnaire was used to prioritize the instructional needs. The reliability of the questionnaire was verified by Cronbach’s alpha coefficient. In the third round, a focus group was used. A total of six expert faculty members and one senior medical student were invited to the focus group session to prioritize the needs. Data were analyzed using Friedman’s non-parametric ranking test in SPSS version 26.ResultsTen instructional needs priorities were extracted, including common pharmacotherapies (antibiotics and narcotics), prescriptions, physiology, anatomy, physical examination, electrocardiography interpretation, radiography, computed tomography scans, serum electrolyte disorders, and cardiovascular and internal (endocrine and metabolic) diseases. The chi-squared calculated value (715.584) indicated a significant difference in the importance of the questionnaire’s questions (P < 0.001). These questions did not have equal value, and the importance, from the respondent’s point of view and the observed distribution of ranks, was not the output of a random factor.ConclusionsThe findings of this study can be used to design MOOCs, revise instructional programs, and adapt the curriculum to meet the needs of general practitioners, which will, in turn, help meet the medical needs of the general population.

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.

Clinical and electrocardiographic interpretation of T-wave abnormalities in adult patients

Clinical and electrocardiographic interpretation of T-wave abnormalities in adult patients

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.

English

Objective: The objective of this study is to determine the prevalence of electrocardiographic abnormalities that could endanger the lives of elite and sub-elite professional athletes based on normal, borderline, and abnormal findings described in international recommendations. Material and method: This retrospective observational study was performed only on men elite football players, men elite baseball players, men elite basketball players, and men sub-elite football players (second division, third division, U-15, U-17, and U-20). Data were collected from pre-competition ECGs performed by team-affiliated physicians in the 2012 – 2019 preseasons of active-roster athletes and sub-elite players. The qualitative characteristics of each ECG were analyzed using the international recommendations for electrocardiographic interpretation in athletes to detect accepted training-related ECG findings and findings classified as borderline and abnormal. Results: A total of 716 ECGs were included. Common training-related ECG changes were found in 63.1%; sinus bradycardia was the most prevalent training-related ECG change (47.2%). The prevalence of borderline ECG readings among all the participants was 3.9%; the most frequent change was right axis deviation. The prevalence of abnormal ECG findings overall was 4.2%. Conclusion: Electrocardiographic changes in athletes are frequently seen; however, a constant review of changes within abnormal or borderline parameters is recommended. It is suggested that further research studies should study the electrocardiographic changes in elite and sub-elite athletes and compare these changes considering the biological sex of the athletes to see if there are any differences.

Bridging The Knowledge, Practice Gap Among Pediatric Nurses Regarding Electrocardiograph Implementation and Interpretation Skills for Pediatric Patients

Bridging The Knowledge, Practice Gap Among Pediatric Nurses Regarding Electrocardiograph Implementation and Interpretation Skills for Pediatric Patients

Assessing the Inter-observer Agreement of Electrocardiography Interpretation in the Elderly Surgical Patients: A Cross-Sectional Study

Background: Electrocardiography (ECG) is an essential objective diagnosis tool, specifically for the elderly who are about to undergo surgery. From the examination results, it is possible to determine the presence of a heart condition that could impair the surgical outcome. Furthermore, the examination is slightly influenced by the subjectivity of observers. Purpose: To evaluate the inter-observer agreement on the reader of a 12-lead ECG on elderly patients subjected to elective surgery. Method: A consequential ECG examination was conducted on elderly patients who underwent elective surgery at Dr. Kariadi Hospital Semarang between July and November 2021. Two junior internist observers were independently involved in reading the ECG results in different places. The inter-observer reliability analysis used kappa statistics to determine consistency between observers. Result: Analysis was carried out on 193 patients aged 60-87 years old, with a prevalence of abnormal ECG was 33.7%. Kappa Conformity Value was 0.864 (95 % CI: 0.790-0.938, p&lt;0.001). Conclusion: The value of agreement between junior internists in ECG interpretation among elderly surgical patients is good (kappa value &gt;0.8). Even though the observers are junior internist, the result of the interpretation with a high kappa is considered to have reliable validity.

Artificial Intelligence Enabled ECG Interpretation Informs Outcomes after Autologous Transplantation for Multiple Myeloma

High dose chemotherapy followed by autologous hematopoietic cell transplantation (auto HCT) is a standard therapy for patients with plasma cell disorders (PCD). A barrier to improving candidate selection for auto HCT is appraisal of risk for transplant related complications. Electrocardiography (ECG) is a standard pre-HCT test to evaluate the potential for arrhythmia and structural heart disease. We hypothesized that an artificial intelligence (AI) based interpretation of ECG waveform data (AI-ECG) could enhance risk prediction for cardiac-specific and other outcomes following auto HCT for patients with PCD. To test this hypothesis, we used several AI-ECG tools developed using a deep-learning based approach that were trained to predict comorbidity (Attia et al, Lancet 2019 &amp; Grogan et al, Mayo Clin Proc 2021). Model outputs include a scaled prediction value indicating increasing risk for atrial fibrillation/flutter (AF), reduced ejection fraction (EF), or cardiac amyloidosis. We evaluated this tool in 9,225 ECGs obtained from 849 patients (median 6 ECGs/patient, inter-quartile range: 3-12) who underwent first or subsequent auto HCT at Memorial Sloan Kettering Cancer Center between 2016-2022 and who had a diagnosis of multiple myeloma (N = 810), amyloidosis (N = 37), or other PCD (N = 2). The endpoints were to determine whether baseline AI-ECG prediction of AF risk correlated with peri-transplant AF events, and to explore whether model outputs correlated with survival and other cardiac clinical outcomes. The mean age of patients was 61.0 years (range 23.7 - 81.2) and median follow up was 2.7 years post auto HCT. Contributing cardiovascular risk factors included a prior history of AF (N = 101, 11.9%), coronary or cerebrovascular disease (N = 95, 11.2%), and diabetes mellitus (N = 98, 11.5%). Reduced dose melphalan (&amp;lt; 200 mg/m 2) was administered in 26%. The HCT-comorbidity index (HCT-CI) was high risk (≥3) in 52.7%. The one-year overall survival and progression-free survival in the whole cohort was 94% (95% confidence interval (95% CI): 92 - 95%) and 86% (84 - 89%), respectively. Among patients not in AF at pre HCT evaluation, the 90-day cumulative incidence of AF was 5.6% (4.2 - 7.3%). In univariable competing risk regression, baseline pre HCT AI-ECG appraisal of AF risk was strongly correlated with incidence AF in patients who were in sinus rhythm before HCT (hazard ratio (HR): 25.1, 95% CI: 11.7 - 53.7, p &amp;lt; 0.001). In a multivariable model adjusting for patient age and HCT-CI, AI-ECG AF predicted risk remained associated with incidence of post HCT AF (HR: 13.4, 95% CI: 5.9 - 30.4, p &amp;lt; 0.001). Considering a decision threshold identified during original model training, AI-ECG predicted AF events with an accuracy of 75% (Mcnemar's p &amp;lt; 2 x 10 -16). The precision, recall, and F1-score were 0.94, 0.77, and 0.85, respectively. AI-ECG predicted age correlated highly with actual patient age (Spearman rho = 0.59, p &amp;lt; 2 x 10 -16). Amyloid risk scores were highly correlated with a known clinical history of amyloidosis (median 0.45 versus 0.05, Wilcoxon rank-sum p &amp;lt; 0.001). Patients with EF &amp;lt; 50% had greater prediction scores for cardiomyopathy compared to patients with normal EF (median 0.23 versus 0.01, Wilcoxon rank-sum p &amp;lt;0.001). In univariable analysis, increasing AI-ECG predicted age (HR 1.02, CI: 1.003 - 1.04, p = 0.02) and predicted AF risk score (HR: 3.2, CI: 1.4 - 7.2, p = 0.006) were associated with risk for death, whereas predicted amyloid score and predicted low EF were not associated with decreased survival. In multivariable analysis of all patients that considered patient age, hematopoietic comorbidity index, and remission status at HCT, increasing predicted AF score trended towards association with increased risk for death (HR: 2.3, 95%CI: 1.0 - 5.5, p = 0.059). Predicted AF score correlated highly with the HCT-CI and patient age. Finally, we examined whether risk prediction for low EF correlated with peri-HCT fluid overload (FO) defined by absolute weight gain. The incidence of ≥ 10% weight gain (grade 2 FO) and ≥ 20% weight gain (grade 3 FO) at 180 days post HCT was 7.4% (95% CI: 5.8%, 9.3%) and 1.0% (95% CI: 0.5%, 1.8%), respectively. Predicted low EF was not associated with development of either grade 2 or grade 3 FO in this population. In summary, AI-ECG predicts episodes of AF in patients undergoing auto HCT for PCD and may augment transplant risk appraisal when considered in conjunction with other measures of comorbidity.

Smartphone-based six-lead ECG: A new device for electrocardiographic recording in dogs

Smartphone-based technology for electrocardiographic recording is now part of the new concept of mobile health in both human and veterinary medicine. Although smartphone-based ECG for electrocardiographic screening in dogs is reliable, one-lead ECG devices have mainly been evaluated. This prospective study assessed the feasibility and the diagnostic reliability of a new smartphone-based six-lead electrocardiograph (smECG) in dogs, in comparison to a standard six-lead electrocardiograph (stECG). All ECG tracings were blindly reviewed by an expert operator, who judged whether tracings were acceptable for interpretation, performed the electrocardiographic measurements, and assigned a diagnosis. The agreement in the electrocardiographic interpretation and diagnosis between smECG and stECG was assessed using the Bland-Altman test and Cohen's k test.The study included 108 client-owned dogs. The tracings obtained by the smECG were interpretable in 100 % of cases. No clinically relevant differences between smECG and stECG were found in the assessment of heart rate, interval duration, and QRS mean electrical axis. The smECG tended to underestimate the amplitude of the P and R waves. Perfect agreement was found in the detection of sinus rhythm, atrial fibrillation, ventricular arrhythmias, atrioventricular blocks, and bundle branch blocks. Our study suggests that the tested smartphone-based six-lead ECG is a clinically reliable device for the assessment of heart rate and heart rhythm in dogs, and thus could be used in a clinical setting in dogs and for telemedicine.

Pilot study of massive open online course (MOOC) implementation in electrocardiography (ECG) learning: perspectives from general practitioners

Link of Video Abstract: https://youtu.be/N3kTT9WiLNw Background: Cardiovascular disease (CVD) is Indonesia's primary cause of morbidity and mortality. Rapid detection and appropriate treatment of CVD cases are needed to reduce mortality. Electrocardiography (ECG) is a simple examination modality that can detect many cardiac abnormalities. General practitioners are expected to master the interpretation of basic ECG. Massive open online course (MOOC) is a breakthrough that can facilitate profound learning without temporal and physical barriers and support continuing medical education. This pilot study aimed to evaluate the implementation of MOOC in ECG learning among a limited number of general practitioners. Methods: Consecutive sampling was used to recruit the subjects. Subjects were given ten weeks to finish all modules and quizzes. Pre-test and post-test were taken. User satisfaction was evaluated using a questionnaire. A focused group discussion (FGD) was conducted to explore subjects' learning experiences and perspectives toward the learning system using MOOC. Results: 17 general practitioners participated in the study, but only seven could finish all the modules and quizzes. All subjects were aged under 30 years old. Those who could complete the course took 10-39 days. Post-test scores were significantly higher than pre-test scores (p=0.038). Although subjects agreed that MOOC was convenient, attractive, and suitable for general practitioners in clinical practice, they demanded an explanation of the quiz answers. They suggested that some modules should be optional. Conclusion: MOOC learning was suitable for general practitioners to improve their ECG interpretation skills. Some features should be added to increase user satisfaction. A study with extensive subjects is needed to analyze the effectiveness of this ECG course.

Electrocardiographic interpretation by emergency medical services professionals in Saudi Arabia: A cross sectional study.

Management of acute myocardial infarction (AMI) and cardiac arrhythmias in prehospital settings is largely determined by providers of emergency medical services (EMS) who can proficiently interpret the electrocardiography (ECG). The aim of this study was to assess the ECG competency of EMS providers in Saudi Arabia. Between Aug and Sep 2022, we invited all EMS providers working for the Saudi Red Crescent Authority in Makkah, Riyadh, and Sharqiyah regions to complete a cross-sectional survey. The survey was used to assess the ability of EMS providers to interpret 12 ECG strips. Characteristics and ECG competency were summarized using descriptive statistics. Differences in ECG competency across paramedics with lower and higher qualifications were assessed. During the study period, 231 participants completed the survey, and all were included. The overall mean age was 33.4, and most participants were male (94.8%). Nearly half of the participants were paramedics with an associate degree and 46.4% were paramedics with higher degrees. The average rate of correct answers to the 12 ECG strips was 43.3% (95% CI: 35.4%, 51.3%). Atrial flutter, ventricular fibrillation, atrial fibrillation, 3rd degree heart block, and ventricular tachycardia were identified by 52.8%, 60.2%, 42.0%, 40.7%, and 49.4% of the participants, respectively. The strip with an AMI was identified by 41.1%, while a pathological Q wave and ventricular extrasystole were identified by 19.1% and 24.7%, respectively. Paramedics with higher qualifications were as 28.0%-61.0% more likely to correctly interpret the 12 ECG strips compared to those with an associate degree (p-value across all variables was ≤ 0.001). While the majority of participants in our region were unable to correctly answer the 12 ECG questionnaire, paramedics with higher qualifications were. Our study indicates that there is a need for evidenced-based ECG curricula targeting different levels of EMS professionals.

12-Leads Electrocardiogram Interpretation Competency among Nurses working in Critical Care Areas at Dow University Hospital, Karachi

OBJECTIVE: To assess the 12-lead Electrocardiograph (ECG) interpretation competency level of nurses working in the critical care units of Dow University Hospital, Karachi. METHODOLOGY: The descriptive cross-sectional design study was conducted from October to December 2019. The purposive sampling technique was used to include the participants. The study sample constitutes 120 registered nurses from the critical care units of Dow University Hospital (DUH), including the intensive care unit (ICU), coronary care unit (CCU), and emergency department(ED). Full-time Registered nurses with more than one year of professional experience and currently working in critical care units of Dow University Hospital were included, and those with post-basic-cardiology specialization were excluded. Data were collected through a 12-item validated study questionnaire and analyzed through descriptive statistics using SPSS version 21.0. RESULTS: Most of the participants, 84 (70.0%) in this study, were male, and 46(38.3%) of the nurses have completed Post RN-BS Nursing (post-diploma two years undergraduate nursing degree program) as the highest professional qualification. This study determined ECG interpretation competence as a score of ≥ 9 out of 12. The findings of this study revealed that 38(31.66%) nurses scored ≥ 9, whereas 82 (69.34%) nurses scored eight or less. CONCLUSION: The study found that most nurses performing their duties in critical hospital areas have suboptimal ECG interpretation competency levels. A relatively higher level of competency was found among nurses working in CCU. KEYWORDS: Nurse, Interpretation, competency level, Electrocardiogram, Tertiary care hospital

Transforming the Heart of Student Learning and Engagement

Purpose: Entry-level physical therapists must effectively learn cardiovascular and physiological monitoring, including electrocardiography (ECG) interpretation, to fulfill the demands of acute care hospital settings. Preliminary evidence among medical students and young physicians suggests that proficiency in basic ECG interpretation may be insufficient to meet the needs of a society with increasing levels of cardiovascular disease. In addition, as a new generation of students (Generation Z) enters health care education programs, educators may need to adapt their instructional approaches and technology. The purpose of this study was to evaluate whether a novel virtual reality (VR) learning module resulted in more accurate ECG interpretation than a conventional written learning activity for predominantly Generation Z Doctor of Physical Therapy (DPT) students. The study also assessed whether a preference existed for either of the 2 activities. Methods: A blocked-randomized controlled crossover trial was integrated within a second-year cardiopulmonary course for DPT students. Students were blocked into 2 groups, VR-Conventional or Conventional-VR, based on the order in which they participated in the activities. Twenty-item multiple-choice knowledge assessments and a satisfaction survey were the outcomes. Results: Eighteen DPT students participated. Median pretest scores for the VR-Conventional group and the Conventional-VR group were 60% (interquartile range [IQR]: 50-75) and 65% (IQR: 55-75), respectively; the distributions were not different (Mann-Whitney U test =36.00, n1 = n2 = 9, P = .688, 2-tailed). Median posttest 1 scores for the VR-Conventional group and the Conventional-VR group were 70% (IQR: 62.50-85) and 75% (IQR: 67.50-85), respectively; the distributions were not different (Mann-Whitney U test = 39.00, n1 = n2 = 9, P = .893, 2-tailed). After crossing over, the VR-Conventional group scored 70% (IQR: 62.50-80) on posttest 2 while the Conventional-VR group scored 70% (IQR: 65-77.50); the distributions were not different (Mann-Whitney U test = 38.50, n1 = n2 = 9, P = .858, 2-tailed). Fifty percent of participants preferred the VR activity, 33% preferred the conventional written activity, and 17% had no preference. All participants reported wanting to see VR used more in education. Conclusions: VR was well received and may aid student engagement and motivation for learning complex topics such as ECG interpretation. In this study, VR was not superior to conventional learning activities for knowledge acquisition. Educators should additionally consider an evidence-based instructional design when choosing to integrate VR technologies in the classroom.

Compared with physician overread, computer is less accurate but helpful in interpretation of electrocardiography for ST-segment elevation myocardial infarction

IntroductionPrevious studies have demonstrated varying sensitivity and specificity of computer-interpreted electrocardiography (CIE) in identifying ST-segment elevation myocardial infarction (STEMI). This study aims to evaluate the accuracy of contemporary computer software in recognizing electrocardiography (ECG) signs characteristic of STEMI compared to emergency physician overread in clinical practice. Material and methodsIn this retrospective observational single-center study, we reviewed the records of patients in the emergency department (ED) who underwent ECGs and troponin tests. Both the Philips DXL 16-Lead ECG.Algorithm and on-duty emergency physicians interpreted each standard 12‑lead ECG. The sensitivity and specificity of computer interpretation and physician overread ECGs for the definite diagnosis of STEMI were calculated and compared. ResultsAmong the 9340 patients included in the final analysis, 133 were definitively diagnosed with STEMI. When “computer-reported infarct or injury” was used as the indicator, the sensitivity was 87.2% (95% CI 80.3% to 92.4%) and the specificity was 86.2% (95% CI 85.5% to 86.9%). When “physician-overread STEMI” was used as the indicator, the sensitivity was 88.0% (95% CI 81.2% to 93.0%) and the specificity was 99.9% (95% CI 99.8% to 99.9%). The area under the receiver operating characteristic curve for physician-overread STEMI and computer-reported infarct or injury were 0.939 (95% CI 0.907 to 0.972) and 0.867 (95% CI 0.834 to 0.900), respectively. ConclusionsThis study reveals that while the sensitivity of the computer in recognizing ECG signs of STEMI is similar to that of physicians, physician overread of ECGs is more specific and, therefore, more accurate than CIE.

The significant value of educated nurse in ECG Holter monitoring interpretation

Abstract Funding Acknowledgements Type of funding sources: None. Introduction ECG Holter monitoring is a non-invasive diagnostic procedure that continuously records electrocardiography during 24 hours daily routine. The primary role of the nurse is to secure that the electrodes are placed correctly, and to advise the patient. Purpose The aim of our study is to emphasize the great importance of nurses by involving them into the interpretation of ECG Holter monitoring. Materials and Methods Senior nurses in our hospital, R. Macedonia were educated, by cardiologists, in electrocardiography interpretation. The nurses proactively ensured the technical quality of the monitoring and the data processing. We analyzed the time from the setting up the 24 hours ECG Holter monitoring to the time when the results were issued to the patient in the period from January 2020 to December 2022. All data used for the study were taken from the Hospital Information System. Our Hospital Information System records the date and exact time (hours and minutes) of the opening and closing of the report. Only the referred cardiologist is authorized to close the report. Results Three thousand twenty-three 24 hours ECG Holter monitoring reports were processed. The average time of closing the report was 1680 minutes or 28 hours. If we exclude in average the first 22 hours of wearing the device, then we can conclude that the time from when the device is disconnected from the patient to the completion of the report, including the physician examination is in average 6 hours. Conclusion ECG educated nurses can be actively involved in the interpretation of ECG Holter monitoring which reduces the time for obtaining the results to less than 24 hours and enables rapid diagnosis and treatment by a cardiologist/electrophysiologist.