Accuracy Of Electrocardiogram Interpretation Research Articles

Signal-guided multitask learning for myocardial infarction classification using images of electrocardiogram.

The diagnosis of myocardial infarction (MI) needs to be swift and accurate, but definitively diagnosing it based on the first test encountered in clinical practice, the electrocardiogram (ECG), is not an easy task. The purpose of the study is to develop a deep learning (DL) algorithm using multitask learning method to differentiate patients experiencing MI from those without coronary artery disease using image-based ECG data. A DL model was developed based on 11,227 ECG images. We developed a new ECG interpretation algorithm through signal-guided multitask learning, building on a previously published single-task algorithm. The utility of this model was evaluated by testing 51 physicians in interpreting ECGs with and without the assistance of the DL model. The proposed model demonstrated superior performance, achieving 90.56% accuracy, 83.82% sensitivity, 93.02% specificity, 81.44% precision, and an F1 score of 82.61% in discriminating MI ECG. Overall, the median accuracy of ECG interpretation improved from 62% to 68% with the DL algorithm. Trainees and specialists in internal medicine experienced significant accuracy increases (60% to 66% for trainees, 72% to 80% for specialists). In the MI group, NSTEMI accuracy was notably lower than STEMI (33% vs. 80%, p < 0.001), but the DL algorithm improved interpretative capabilities in both NSTEMI and STEMI. Signal-guided multitask DL algorithm demonstrated superior performance compared with previous single-task algorithm. The DL algorithm supports the physicians' decision discriminating MI ECGs from non-MI ECGs. The improvement was consistent in subgroups of STEMI and NSTEMI.

Impact of automatic acquisition of key clinical information on the accuracy of electrocardiogram interpretation: a cross-sectional study

BackgroundThe accuracy of electrocardiogram (ECG) interpretation by doctors are affected by the available clinical information. However, having a complete set of clinical details before making a diagnosis is very difficult in the clinical setting especially in the early stages of the admission process. Therefore, we developed an artificial intelligence-assisted ECG diagnostic system (AI-ECG) using natural language processing to provide screened key clinical information during ECG interpretation.MethodsDoctors with varying levels of training were asked to make diagnoses from 50 ECGs using a common ECG diagnosis system that does not contain clinical information. After a two-week-blanking period, the same set of ECGs was reinterpreted by the same doctors with AI-ECG containing clinical information. Two cardiologists independently provided diagnostic criteria for 50 ECGs, and discrepancies were resolved by consensus or, if necessary, by a third cardiologist. The accuracy of ECG interpretation was assessed, with each response scored as correct/partially correct = 1 or incorrect = 0.ResultsThe mean accuracy of ECG interpretation was 30.2% and 36.2% with the common ECG system and AI-ECG system, respectively. Compared to the unaided ECG system, the accuracy of interpretation was significantly improved with the AI-ECG system (P for paired t-test = 0.002). For senior doctors, no improvement was found in ECG interpretation accuracy, while an AI-ECG system was associated with 27% higher mean scores (24.3 ± 9.4% vs. 30.9 ± 10.6%, P = 0.005) for junior doctors.ConclusionIntelligently screened key clinical information could improve the accuracy of ECG interpretation by doctors, especially for junior doctors.

Integrated ECG Interpretation Course for the Improvement of Medical Student Electrocardiography Literacy.

BackgroundWhile undergraduate medical education curricula typically integrate electrocardiogram (ECG) interpretation during the preclinical phase, the skill can be challenging, and students may lack confidence to interpret ECGs in clinical practice. The purpose of this online curriculum was to provide a supplemental course which provides a practical, systematic approach to ECG reading and increases student confidence in recognizing basic patterns of pathology.MethodsWe used a proprietary online learning content-management system to create a course for 4th-year medical students at a US-based MD program. The course consisted of 12 video modules which reviewed basic ECG interpretation and then provided practice ECG tracings for the students to interpret. Weekly online video calls with faculty were held to address any questions and to emphasize key concepts presented in that week’s material. The students completed a pre- and post-course assessment on ECG pathology and reported their self-confidence in ECG interpretation skills.ResultsThe median score for the pre-test was 76.5/100 with a median self-confidence score of 3/10. After completion of the course, the students’ post-test median score increased to 87.5 (p < 0.0001) with a median self-confidence score of 7.5 (p < 0.0001). As a result of this course, the students showed a significant improvement in grades and self-confidence.DiscussionAn online course consisting of practice ECGs and limited direct faculty interaction helped the students improve ECG interpretation accuracy and self-confidence.

COMMUNITY-TO-INSTITUTION ATHLETIC CARDIOVASCULAR SCREENING: VALIDATION OF AN ELECTROCARDIOGRAM WORKFLOW MODEL

COMMUNITY-TO-INSTITUTION ATHLETIC CARDIOVASCULAR SCREENING: VALIDATION OF AN ELECTROCARDIOGRAM WORKFLOW MODEL

Utilizing Supplemental Online Modules for Physician Assistant Student Electrocardiogram Interpretation Training.

The literature suggests that graduating medical and physician assistant (PA) students lack competency in electrocardiogram (ECG) interpretation. This project aimed to determine whether use of perceptual adaptive learning modules (PALMs) would improve PA students' ECG interpretation, alter self-perceptions of their ECG education, or both. PALMs were incorporated into the PA curriculum after lecture-based ECG learning. Students' pretest, posttest, and delayed-posttest scores were then compared. Students' ability to correctly interpret ECGs (accuracy) and the percentage of ECGs accurately interpreted within 15 seconds or less (fluency) also were evaluated. Finally, students' perceptions of PALMs and overall ECG training were assessed. PALM training improved ECG interpretation accuracy and fluency (p < .0001), as well as delayed-posttest accuracy and fluency (p < .0001). The majority of student respondents felt supplemental training enhanced their learning. These perception results combined with data on ECG interpretation improvement supports continued use of supplemental PALMs in ECG interpretation training.

Getting to the heart of the issue: senior emergency resident electrocardiogram interpretation and its impact on quality assurance events.

ObjectiveElectrocardiogram (ECG) interpretation skills are of critical importance for diagnostic accuracy and patient safety. In our emergency department (ED), senior third-year emergency medicine residents (EM3s) are the initial interpreters of all ED ECGs. While this is an integral part of emergency medicine education, the accuracy of ECG interpretation is unknown. We aimed to review the adverse quality assurance (QA) events associated with ECG interpretation by EM3s.MethodsWe conducted a retrospective study of all ED ECGs performed between October 2015 and October 2018, which were read primarily by EM3s, at an urban tertiary care medical center treating 56,000 patients per year. All cases referred to the ED QA committee during this time were reviewed. Cases involving a perceived error were referred to a 20-member committee of ED leadership staff, attendings, residents, and nurses for further consensus review. Ninety-five percent confidence intervals (CIs) were calculated.ResultsEM3s read 92,928 ECGs during the study period. Of the 3,983 total ED QA cases reviewed, errors were identified in 268 (6.7%; 95% CI, 6.0%–7.6%). Four of the 268 errors involved ECG misinterpretation or failure to act on an ECG abnormality by a resident (1.5%; 95% CI, 0.0%–2.9%).ConclusionA small percentage of the cases referred to the QA committee were a result of EM3 misinterpretation of ECGs. The majority of emergency medicine residencies do not include the senior resident as a primary interpreter of ECGs. These findings support the use of EM3s as initial ED ECG interpreters to increase their clinical exposure.

Accuracy of Physicians’ Electrocardiogram Interpretations

The electrocardiogram (ECG) is the most common cardiovascular diagnostic test. Physicians' skill in ECG interpretation is incompletely understood. To identify and summarize published research on the accuracy of physicians' ECG interpretations. A search of PubMed/MEDLINE, Embase, Cochrane CENTRAL (Central Register of Controlled Trials), PsycINFO, CINAHL (Cumulative Index to Nursing and Allied Health), ERIC (Education Resources Information Center), and Web of Science was conducted for articles published from database inception to February 21, 2020. Of 1138 articles initially identified, 78 studies that assessed the accuracy of physicians' or medical students' ECG interpretations in a test setting were selected. Data on study purpose, participants, assessment features, and outcomes were abstracted, and methodological quality was appraised with the Medical Education Research Study Quality Instrument. Results were pooled using random-effects meta-analysis. Accuracy of ECG interpretation. Of 1138 studies initially identified, 78 assessed the accuracy of ECG interpretation. Across all training levels, the median accuracy was 54% (interquartile range [IQR], 40%-66%; n = 62 studies) on pretraining assessments and 67% (IQR, 55%-77%; n = 47 studies) on posttraining assessments. Accuracy varied widely across studies. The pooled accuracy for pretraining assessments was 42.0% (95% CI, 34.3%-49.6%; n = 24 studies; I2 = 99%) for medical students, 55.8% (95% CI, 48.1%-63.6%; n = 37 studies; I2 = 96%) for residents, 68.5% (95% CI, 57.6%-79.5%; n = 10 studies; I2 = 86%) for practicing physicians, and 74.9% (95% CI, 63.2%-86.7%; n = 8 studies; I2 = 22%) for cardiologists. Physicians at all training levels had deficiencies in ECG interpretation, even after educational interventions. Improved education across the practice continuum appears warranted. Wide variation in outcomes could reflect real differences in training or skill or differences in assessment design.

SPICED-ACS: Study of the potential impact of a computer-generated ECG diagnostic algorithmic certainty index in STEMI diagnosis: Towards transparent AI

BackgroundComputerised electrocardiogram (ECG) interpretation diagnostic algorithms have been developed to guide clinical decisions like with ST segment elevation myocardial infarction (STEMI) where time in decision making is critical. These computer-generated diagnoses have been proven to strongly influence the final ECG diagnosis by the clinician; often called automation bias. However, the computerised diagnosis may be inaccurate and could result in a wrong or delayed treatment harm to the patient. We hypothesise that an algorithmic certainty index alongside a computer-generated diagnosis might mitigate automation bias. The impact of reporting a certainty index on the final diagnosis is not known. PurposeTo ascertain whether knowledge of the computer-generated ECG algorithmic certainty index influences operator diagnostic accuracy. MethodologyClinicians who regularly analyse ECGs such as cardiology or acute care doctors, cardiac nurses and ambulance staff were invited to complete an online anonymous survey between March and April 2019. The survey had 36 ECGs with a clinical vignette of a typical chest pain and which were either a STEMI, normal, or borderline (but do not fit the STEMI criteria) along with an artificially created certainty index that was either high, medium, low or none. Participants were asked whether the ECG showed a STEMI and their confidence in the diagnosis. The primary outcomes were whether a computer-generated certainty index influenced interpreter's diagnostic decisions and improved their diagnostic accuracy. Secondary outcomes were influence of certainty index between different types of clinicians and influence of certainty index on user's own-diagnostic confidence. ResultsA total of 91 participants undertook the survey and submitted 3262 ECG interpretations of which 75% of ECG interpretations were correct. Presence of a certainty index significantly increased the odds ratio of a correct ECG interpretation (OR 1.063, 95% CI 1.022–1.106, p = 0.004) but there was no significant difference between correct certainty index and incorrect certainty index (OR 1.028, 95% CI 0.923–1.145, p = 0.615). There was a trend for low certainty index to increase odds ratio compared to no certainty index (OR 1.153, 95% CI 0.898–1.482, p = 0.264) but a high certainty index significantly decreased the odds ratio of a correct ECG interpretation (OR 0.492, 95% CI 0.391–0.619, p < 0.001). There was no impact of presence of a certainty index (p = 0.528) or correct certainty index (p = 0.812) on interpreters' confidence in their ECG interpretation. ConclusionsOur results show that the presence of an ECG certainty index improves the users ECG interpretation accuracy. This effect is not seen with differing levels of confidence within a certainty index, with reduced ECG interpretation success with a high certainty index compared with a trend for increased success with a low certainty index. This suggests that a certainty index improves interpretation when there is an increased element of doubt, possibly forcing the ECG user to spend more time and effort analysing the ECG. Further research is needed looking at time spent analysing differing certainty indices with alternate ECG diagnoses.

Accuracy Screening for ST Elevation Myocardial Infarction in a Task-switching Simulation.

IntroductionInterruptions in the emergency department (ED) are associated with clinical errors, yet are important when providing care to multiple patients. Screening triage electrocardiograms (ECG) for ST-segment elevation myocardial infarction (STEMI) represent a critical interrupting task that emergency physicians (EP) frequently encounter. To address interruptions such as ECG interpretation, many EPs engage in task switching, pausing their primary task to address an interrupting task. The impact of task switching on clinical errors in interpreting screening ECGs for STEMI remains unknown.MethodsResident and attending EPs were invited to participate in a crossover simulation trial. Physicians first completed a task-switching simulation in which they viewed patient presentations interrupted by clinical tasks, including screening ECGs requiring immediate interpretation before resuming the patient presentation. Participants then completed an uninterrupted simulation in which patient presentations and clinical tasks were completed sequentially without interruption. The primary outcome was accuracy of ECG interpretation for STEMI during task switching and uninterrupted simulations.ResultsThirty-five participants completed the study. We found no significant difference in accuracy of ECG interpretation for STEMI (task switching 0.89, uninterrupted 0.91, paired t-test p=0.21). Attending physician status (odds ratio [OR] [2.56], confidence interval [CI] [1.66–3.94], p<0.01) and inferior STEMI (OR [0.08], CI [0.04–0.14], p<0.01) were associated with increased and decreased odds of correct interpretation, respectively. Low self-reported confidence in interpretation was associated with decreased odds of correct interpretation in the task-switching simulation, but not in the uninterrupted simulation (interaction p=0.02).ConclusionIn our simulation, task switching was not associated with overall accuracy of ECG interpretation for STEMI. However, odds of correct interpretation decreased with inferior STEMI ECGs and when participants self-reported low confidence when interrupted. Our study highlights opportunities to improve through focused ECG training, as well as self-identification of “high-risk” screening ECGs prone to error during interrupted clinical workflow.

Electrocardiography misreading among sixth-year medical students and interns in Taif University

Background: The electrocardiogram (ECG) is the most important procedure to examine the cardiac rhythm and conduction system abnormalities. The common problem of ECG misinterpretation can lead to inappropriate diagnoses and clinical decisions. There is a limited number of studies identifying the size of the problem in Saudi Arabia. We aimed to assess the frequency of ECG misreading, address the nature of errors, and analyze the determinants of ECG interpretation accuracy among sixth-year medical students and interns of Taif University, Taif, Saudi Arabia.Methods: Participants were given a quiz of 10 multiple-choice questions of commonly encountered ECG interpretations. Each correct answer was given a 1-point score. The overall score for each participant was calculated out of 10 points.Results: A total of 297 participants took part in this study and achieved a median (IQR) overall score of 6.0 (4). The most frequent score was 9 (13.5%), followed by 8 (12.8%) and 4 (12.5%). Second degree AV block, type II was misread by 60.6% of the participants, 2nd-degree AV block, type I was misread by 50.2% while other abnormalities were less frequently misread. Academic year and gender showed no significant influence on ECG interpretation. On the other hand, interest in ECG reading and previous participation in ECG courses were associated with significantly better performance (p&lt;0.001 and p=0.001, respectively).Conclusions: Overall performance of medical students and interns was moderate. Self-learning and providing organized educational courses on ECG can positively improve ECG interpretation and allow adequate diagnosis and management of heart diseases.

Exploring the Relationship Between Eye Movements and Electrocardiogram Interpretation Accuracy.

Interpretation of electrocardiograms (ECGs) is a complex task involving visual inspection. This paper aims to improve understanding of how practitioners perceive ECGs, and determine whether visual behaviour can indicate differences in interpretation accuracy. A group of healthcare practitioners (n = 31) who interpret ECGs as part of their clinical role were shown 11 commonly encountered ECGs on a computer screen. The participants’ eye movement data were recorded as they viewed the ECGs and attempted interpretation. The Jensen-Shannon distance was computed for the distance between two Markov chains, constructed from the transition matrices (visual shifts from and to ECG leads) of the correct and incorrect interpretation groups for each ECG. A permutation test was then used to compare this distance against 10,000 randomly shuffled groups made up of the same participants. The results demonstrated a statistically significant (α 0.05) result in 5 of the 11 stimuli demonstrating that the gaze shift between the ECG leads is different between the groups making correct and incorrect interpretations and therefore a factor in interpretation accuracy. The results shed further light on the relationship between visual behaviour and ECG interpretation accuracy, providing information that can be used to improve both human and automated interpretation approaches.

A computer-human interaction model to improve the diagnostic accuracy and clinical decision-making during 12-lead electrocardiogram interpretation

IntroductionThe 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter. MethodsAn interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model ‘Interactive Progressive based Interpretation’ (IPI) as the user cannot ‘progress’ unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort). ResultsFor the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45%; SD=18.1%; CI=42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85% (SD=42.4%; CI=49.12, 68.58), which indicates a positive mean difference of 13.4%. (CI=4.45, 22.35) An N−1 Chi-square test of independence indicated a 92% chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p=0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort. ConclusionsWe have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice.

ECG COMPUTER INTERPRETATION AND CARDIOLOGY TRAINEES: HELP OR HINDERANCE?

Interpretation of electrocardiograms (ECGs) is a fundamental skill for cardiologists. Prior data has suggested that ECG-computer interpretation (ECG-CI) may increase accuracy. In current clinical practice, ECGs are often presented with ECG-CI, which may or may not be accurate. Although Cardiology trainees are taught to interpret ECGs independent of ECG-CI, the purpose of this study was to determine if ECG-CI affected the accuracy of ECG interpretation by Cardiology trainees.

A Module-Based Pediatric ECG Curriculum to Improving Accuracy of ECG Interpretation by Pediatric Emergency Medicine Providers

OPEN ACCESSApril 24, 2014A Module-Based Pediatric ECG Curriculum to Improving Accuracy of ECG Interpretation by Pediatric Emergency Medicine Providers Priya Gopwani, MD, Shilpa Patel, E. Anne Greene, Jennifer Chapman Priya Gopwani, MD Children's National Medical Center Google Scholar More articles by this author , Shilpa Patel Children's National Medical Center Google Scholar More articles by this author , E. Anne Greene Children's National Medical Center Google Scholar More articles by this author , Jennifer Chapman Children's National Medical Center Google Scholar More articles by this author https://doi.org/10.15766/mep_2374-8265.9779 SectionsAbout ToolsDownload Citations ShareFacebookTwitterEmail AbstractAbstract Introduction: Electrocardiograms (ECGs) are ordered frequently in the pediatric emergency department (ED). The majority of these ECGs are interpreted initially by pediatric emergency medicine (PEM) physicians, with a cardiologist reinterpreting the ECG after patient disposition has been made. Previous studies have shown that neither emergency medicine nor pediatric residency training programs adequately train residents to read ECGs. We designed a web-based pediatric ECG curriculum aimed specifically at fellow and attending PEM providers. The overall goal of this curriculum is to improve the “first read” accuracy of ECG interpretation and decrease the number of missed diagnoses. Methods: The curriculum consists of seven web-based modules focusing on pediatric ECGs and their interpretation in the ED setting. These modules cover the basics of pediatric ECGs, long QT syndrome & brugada, supraventricular tachycardia and Wolff Parkinson White syndrome, heart block and sick sinus syndrome, ventricular tachycardia, ischemia and infarct, and congenital heart disease. The case-based modules run for 7-12 minutes and are interactive, requiring the user to recognize abnormalities and perform necessary calculations before proceeding. Results: This module was administered to PEM fellows at a large urban pediatric teaching hospital. A pretest consisting of 20 ECGs was administered to all PEM fellows and a self-selected group of PEM attendings. At the completion of the curriculum, a posttest was administered to assess improvement. The average accuracy of identifying the ECG abnormality at pretest was 42.8% (PEM Fellows M = 40.6%, N = 9; PEM Attendings M = 45.7% N = 7). All nine fellows completed the posttest. There was a statistically significant improvement in the PEM fellows' ability to accurately interpret ECG findings using these modules were used (42.0% v 53.1%, p = .047, Wilcoxon Signed-Rank test). On an accompanying satisfaction survey, 100% would recommend the curriculum to a colleague and 76.5% thought the ECG modules could stand alone in teaching ECG interpretation to PEM providers. Discussion: There is a need to improve the ability of PEM physicians to read ECGs, particularly those with clinically significant findings. We designed a web-based pediatric ECG curriculum aimed specifically at PEM providers with the overall goal of improving the “first read” accuracy of ECG interpretation and to decrease the number of missed diagnoses. Based on learner feedback, we determined that the implementation of a combined module-based and didactic pediatric ECG curriculum improved the ability of PEM fellows to accurately interpret ECGs with abnormalities. Educational Objectives By the end of this session, learners will be able to: Demonstrate a systematic approach to reading pediatric ECGs.Describe clinically significant findings on pediatric ECGs.Formulate the initial treatment plan for pediatric patients with clinically significant findings on ECGs. Sign up for the latest publications from MedEdPORTAL Add your email below FILES INCLUDEDReferencesRelatedDetails FILES INCLUDED Included in this publication: MedEdPORTAL Instructors Guide.docx ECG Basics.mp4 LQTS Brugada.swf SVT and WPW.swf HB and SSS.swf VT.swf Ischemia Infarct.swf Congenital Heart Disease.swf To view all publication components, extract (i.e., unzip) them from the downloaded .zip file. Download editor’s noteThis publication may contain technology or a display format that is no longer in use. Copyright & Permissions© 2014 Gopwani et al. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivatives license.KeywordsPediatricsElectrocardiographyPediatric Emergency MedicineElectrocardiogramsECGs Disclosures None to report. Funding/Support None to report. Loading ...

Paramedic Electrocardiogram and Rhythm Identification: A Convenient Training Device

A common reason for utilizing local paramedics and the emergency medical services is for the recognition and immediate treatment of chest pain, a complaint that has multiple possible etiologies. While many of those complaining of disease processes responsible for chest pain are benign, some will be life-threatening and will require immediate identification and treatment. The ability of paramedics to not only perform field electrocardiograms (ECGs), but to accurately diagnose various unstable cardiac rhythms has shown significant reduction in time to specific treatments. Increasing the overall accuracy of ECG interpretation by paramedics has the potential to facilitate early and appropriate treatment and decrease patient morbidity and mortality. A convenient training device (flip book) on ambulances and in common areas in the fire station could improve field interpretation of certain cardiac rhythms. This training device consists of illustrated sample ECG tracings and their associated diagnostic criteria. The goal was to enhance the recognition and interpretation of ECGs, and thereby, reduce delays in the initiation of treatment and potential complications associated with misinterpretation.This study was a prospective, observational study using a matched pre-test/post-test design. The study period was from November 2008 to December 2008. A total of 136 paramedics were approached to participate in this study. A pre-test consisting of 15 12-lead ECGs was given to all paramedics who agreed to participate in the study. Once the pre-tests were completed, the flip books were placed in common areas. Approximately one month after the flip books were made available to the paramedics, a post-test was administered.Statistical comparisons were made between the pre- and post-test scores for both the global test and each type of rhythm. Using these data, there were no statistically significant improvements in the global ECG interpretation or on individual rhythm interpretations. A flip book with multiple ECG rhythms and definitions without the benefit of any outside support was not effective in improving paramedic identification of ECG rhythms on a post-test. Suggestions for further research include repeating the study with a larger sample size; utilizing a lecturer to explain how to use the flip book in the most efficient manner; reiterating how to read and interpret ECGs; and answering questions. Comparing test scores of paramedic students, and newly certified paramedics as opposed to veteran paramedics also may indicate that the flip books are more suited for one group over another.

Diagnosis of multiple infarcts from complex electrocardiograms during normal rhythm, left bundle-branch block, and ventricular pacing

IntroductionBecause of better treatment of chronic coronary artery disease, patients with multiple ischemic insults are living longer and requiring long-term management. Electrocardiograms (ECGs) in patients with multiple myocardial infarctions (MMIs) are a challenge to the clinician or cardiologist. This is because second myocardial infarctions (MIs) often modify or obscure the ECG effects of the first MI and vice versa. This can result in complex tracings, which are difficult to interpret and predict the locations of damaged regions. It is important to identify MMI because, additively, they can result in poor ventricular function. MethodsNew criteria for the diagnosis of MMI were derived from studies in which ECGs and body surface potential distribution maps were correlated with epicardial and intramural depolarization in animal studies of MI and in patients at the time of coronary artery bypass graft and with the pathologic anatomy in a few patients. The initial criteria were fine tuned by correlations between ECGs and independent data obtained from catheterization and other imaging modalities (ultrasound and radionuclide single photon emission computed tomography). ResultsOften 2 or more MIs are concealed according to classical Q- and R-wave criteria. Myocardial infarctions located in opposing walls (180°), that is, inferior-superior, anterior-posterior, are the most difficult to identify. Two MIs that are not opposing (90°) are usually readily detectable. The pathologic Q waves produced by each separate MI, when opposing, are replaced by multiple QRS deflections or baseline crossings, which are referred to as M and W complexes. These waveforms replace Q waves and are equally weighted in a scoring system to improve diagnostic accuracy of ECG interpretation. These new criteria can also be used to predict MIs in left bundle-branch block, intraventricular conduction delay, and ventricular pacing. ConclusionMultiple myocardial infarctions modify the ECG because they eliminate Q waves and result in confusing, splintered QRS waveforms, referred to as M and W complexes. New criteria are presented to diagnose the presence and locations of these MMIs, which predict reduced left ventricular function, in normal sinus rhythm, left bundle-branch block, and ventricular pacing.

Improving electrocardiogram interpretation in the clinical setting

The 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter.An interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model ‘Interactive Progressive based Interpretation’ (IPI) as the user cannot ‘progress’ unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort).For the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45%; SD = 18.1%; CI = 42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85% (SD = 42.4%; CI = 49.12, 68.58), which indicates a positive mean difference of 13.4%. (CI = 4.45, 22.35) An N − 1 Chi-square test of independence indicated a 92% chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p = 0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort.We have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice.

Accuracy of electrocardiogram interpretation improves with emergency medicine training

To assess whether electrocardiogram (ECG) interpretation accuracy improves with advancing years of emergency medicine training. A prospective cross-sectional double-blinded study of emergency medicine trainees attending teaching sessions in ACEM accredited Victorian hospitals. Subjects completed a survey about level of training, rotations completed and ECG training. They were then asked for the 'main diagnosis' on 10 clinically significant ECG. Those in their fourth year of advanced training onwards, or in active preparation for fellowship examination (senior trainees) were compared with trainees in earlier years (other trainees). There were 122 trainees surveyed in total. In the present study, 48/122 were senior trainees and 74/122 were other trainees. The overall accuracy of ECG interpretation was 67.5% (95% confidence interval [CI] 63.2-71.8%) for the senior trainees and 49.6% (95% CI 45.2-53.9%) for the others. Results for some of the individual ECG were: left bundle branch block: 81.3% (95% CI 69.9-92.6%) seniors and 58.1% (95% CI 46.6-69.7%) others; ventricular tachycardia: 43.8% (95% CI 29.3-58.2%) seniors and 37.8% (95% CI 26.5-49.2%) others; and ventricular fibrillation: 70.8% (95% CI 57.6-84.1%) seniors and 63.5% (95% CI 52.2-74.9%) others. There is an improvement in ECG interpretation accuracy with advancing years of emergency medicine training in Victoria. There exists, however, a low level of accuracy for some critical ECG diagnoses. There is a call by trainees for more formalized and regular ECG education to begin earlier in their training.

Accuracy of electrocardiogram interpretation by cardiologists in the setting of incorrect computer analysis

Background Overreading of 12 lead electrocardiograms (ECGs) is required to circumvent errors of computerized ECG interpretation. The accuracy of the overreading physician's interpretation of ECGs that were incorrectly read as atrial fibrillation by a computer algorithm has not been systematically examined. Methods A total of 2298 ECGs with the computerized interpretation of atrial fibrillation from 1085 patients were analyzed by 2 electrophysiologists, who identified 442 ECGs (19%) from 382 patients (35%) that were incorrectly interpreted as atrial fibrillation. Charts were reviewed to determine the interpretation of the ECG by the ordering physician (primary reader) and the overreading cardiologist. Results Cardiologists as primary readers more often corrected the misinterpreted ECGs as compared with internists, emergency physicians, or other specialists (94% vs 71%, P < .001). Surprisingly, interpretations by cardiologists as primary readers were more accurate than the interpretation provided by overreading cardiologists (94% vs 72%, P < .001). Conclusion Knowledge of an individual patient on whom an ECG is ordered may result in a more critical rhythm assessment and might account for the higher accuracy of rhythm interpretation by the cardiologist as compared with the interpretation by the overreading cardiologist who is lacking relevant clinical information.

Can Pediatric Residents Interpret Electrocardiograms?

The purpose of this study was to assess the electrocardiogram (ECG) interpretation skills of pediatric residents in a controlled environment and determine if the level of residency training (intern vs senior) improves accuracy. A list of ECG diagnoses was provided to four pediatric residency educators with instructions to categorize each diagnosis as follows: I, all residents; II, the majority of residents, including all senior residents; III, less than the majority of residents; and IV, few residents should be able to interpret correctly. Only those categories that the entire panel believed all residents (category I) or all senior residents (category II) should be able to interpret correctly were included. The test included 17 ECGs: 14 category I and 3 category II. A total of 132 residents participated: 78 interns and 54 seniors. Both groups scored below expected levels. Mean correct score among seniors was 10.9 out of the expected 17 (p < 0.001). Mean correct score for interns was 7.7 out of the expected 14 (p < 0.00l). No difference in ECG interpretation accuracy was found between residency programs. In general, pediatric residents' ECG interpretation skills are less accurate than expected. Although there is a trend toward improvement during training, senior residents fell short of the expectations of the panel. We speculate that focused education in this area will improve resident ECG interpretation and benefit patient care by (1) facilitating referral and treatment of patients with cardiovascular disease and (2) decreasing referrals for erroneous interpretations.