Assessments of Physicians' Electrocardiogram Interpretation Skill: A Systematic Review

Comparison of Electrocardiogram between Dilated Cardiomyopathy and Ischemic Cardiomyopathy Based on Empirical Mode Decomposition and Variational Mode Decomposition

The clinical manifestations of ischemic cardiomyopathy (ICM) bear resemblance to dilated cardiomyopathy (DCM), yet their treatments and prognoses are quite different. Early differentiation between these conditions yields positive outcomes, but the gold standard (coronary angiography) is invasive. The potential use of ECG signals based on variational mode decomposition (VMD) as an alternative remains underexplored. An ECG dataset containing 87 subjects (44 DCM, 43 ICM) is pre-processed for denoising and heartbeat division. Firstly, the ECG signal is processed by empirical mode decomposition (EMD) and VMD. And then, five modes are determined by correlation analysis. Secondly, bispectral analysis is conducted on these modes, extracting corresponding bispectral and nonlinear features. Finally, the features are processed using five machine learning classification models, and a comparative assessment of their classification efficacy is facilitated. The results show that the technique proposed provides a better categorization for DCM and ICM using ECG signals compared to previous approaches, with a highest classification accuracy of 98.30%. Moreover, VMD consistently outperforms EMD under diverse conditions such as different modes, leads, and classifiers. The superiority of VMD on ECG analysis is verified.

Utility of RAND/UCLA appropriateness method in validating multiple-choice questions on ECG

OBJECTIVES

This study aimed to investigate the utility of the RAND/UCLA appropriateness method (RAM) in validating expert consensus-based multiple-choice questions (MCQs) on electrocardiogram (ECG).

METHODS

According to the RAM user's manual, nine panelists comprising various experts who routinely handle ECGs were asked to reach a consensus in three phases: a preparatory phase (round 0), an online test phase (round 1), and a face-to-face expert panel meeting (round 2). In round 0, the objectives and future timeline of the study were elucidated to the nine expert panelists with a summary of relevant literature. In round 1, 100 ECG questions prepared by two skilled cardiologists were answered, and the success rate was calculated by dividing the number of correct answers by 9. Furthermore, the questions were stratified into "Appropriate," "Discussion," or "Inappropriate" according to the median score and interquartile range (IQR) of appropriateness rating by nine panelists. In round 2, the validity of the 100 ECG questions was discussed in an expert panel meeting according to the results of round 1 and finally reassessed as "Appropriate," "Candidate," "Revision," and "Defer."

RESULTS

In round 1 results, the average success rate of the nine experts was 0.89. Using the median score and IQR, 54 questions were classified as " Discussion." In the expert panel meeting in round 2, 23% of the original 100 questions was ultimately deemed inappropriate, although they had been prepared by two skilled cardiologists. Most of the 46 questions categorized as "Appropriate" using the median score and IQR in round 1 were considered "Appropriate" even after round 2 (44/46, 95.7%).

CONCLUSIONS

The use of the median score and IQR allowed for a more objective determination of question validity. The RAM may help select appropriate questions, contributing to the preparation of higher-quality tests.

Protocol for development of an assessment tool for competency of ECG interpretation: expert consensus by the RAND/UCLA appropriateness method and cross-sectional testing using multidimensional item response theory

INTRODUCTION

Although the ECG is an important diagnostic tool in medical practice, the competency of ECG interpretation is considered to be poor. Diagnostic inaccuracy involving the misinterpretation of ECG can lead to inappropriate medical judgements and cause negative clinical outcomes, unnecessary medical testing and even fatalities. Despite the importance of assessing ECG interpretation skills, there is currently no established universal, standardised assessment tool for ECG interpretation. The current study seeks to (1) develop a set of items (ECG questions) for estimating competency of ECG interpretation by medical personnel by consensus among expert panels following a process based on the RAND/UCLA Appropriateness Method (RAM) and (2) analyse item parameters and multidimensional latent factors of the test set to develop an assessment tool.

METHODS AND ANALYSIS

This study will be conducted in two steps: (1) selection of question items for ECG interpretation assessment by expert panels via a consensus process following RAM and (2) cross-sectional, web-based testing using a set of ECG questions. A multidisciplinary panel of experts will evaluate the answers and appropriateness and select 50 questions as the next step. Based on data collected from a predicted sample size of 438 test participants recruited from physicians, nurses, medical and nursing students, and other healthcare professionals, we plan to statistically analyse item parameters and participant performance using multidimensional item response theory. Additionally, we will attempt to detect possible latent factors in the competency of ECG interpretation. A test set of question items for ECG interpretation will be proposed on the basis of the extracted parameters.

ETHICS AND DISSEMINATION

The protocol of this study was approved by the Institutional Review Board of Ehime University Graduate School of Medicine (IRB number: 2209008). We will obtain informed consent from all participants. The findings will be submitted for publication in peer-reviewed journals.

Surfing the clinical trials of ECG teaching to medical students: A systematic review

Interpreting an electrocardiogram (ECG) is crucial for every physician. The physician's competency in ECG interpretation needs to be improved at any stage of medical education. The aim of the present study was to review the currently published clinical trials of ECG teaching to medical students and provide suggestions for future works. On May 1, 2022, PubMed, Scopus, Web of Science, Google Scholar, and ERIC were searched to retrieve relevant articles on clinical trials of ECG teaching to medical students. The quality of the included studies was assessed utilizing the Buckley et al. criteria. The screening, data extraction, and quality appraisal processes were duplicated independently. In case of disagreements, consultation with a third author was put forth. In total, 861 citations were found in the databases. After screening abstracts and full texts, 23 studies were deemed eligible. The majority of the studies were of good quality. Peer teaching (7 studies), self-directed learning (6 studies), web-based learning (10 studies), and various assessment modalities (3 studies) comprised the key themes of the studies. Various methods of ECG teaching were encountered in the reviewed studies. Future studies in ECG training should focus on novel and creative teaching methods, the extent to which self-directed learning can be effective, the utility of peer teaching, and the implications of computer-assisted ECG interpretation (e.g., artificial intelligence) for medical students. Long-term knowledge retention assessment studies based on different approaches integrated with clinical outcomes could be beneficial in determining the most efficient modalities.

Modeling Diagnostic Expertise in Cases of Irreducible Uncertainty: The Decision-Aligned Response Model

PURPOSE

Assessing expertise using psychometric models usually yields a measure of ability that is difficult to generalize to the complexity of diagnoses in clinical practice. However, using an item response modeling framework, it is possible to create a decision-aligned response model that captures a clinician's decision-making behavior on a continuous scale that fully represents competing diagnostic possibilities. In this proof-of-concept study, the authors demonstrate the necessary statistical conceptualization of this model using a specific electrocardiogram (ECG) example.

METHOD

The authors collected a range of ECGs with elevated ST segments due to either ST-elevation myocardial infarction (STEMI) or pericarditis. Based on pilot data, 20 ECGs were chosen to represent a continuum from "definitely STEMI" to "definitely pericarditis," including intermediate cases in which the diagnosis was intentionally unclear. Emergency medicine and cardiology physicians rated these ECGs on a 5-point scale ("definitely STEMI" to "definitely pericarditis"). The authors analyzed these ratings using a graded response model showing the degree to which each participant could separate the ECGs along the diagnostic continuum. The authors compared these metrics with the discharge diagnoses noted on chart review.

RESULTS

Thirty-seven participants rated the ECGs. As desired, the ECGs represented a range of phenotypes, including cases where participants were uncertain in their diagnosis. The response model showed that participants varied both in their propensity to diagnose one condition over another and in where they placed the thresholds between the 5 diagnostic categories. The most capable participants were able to meaningfully use all categories, with precise thresholds between categories.

CONCLUSIONS

The authors present a decision-aligned response model that demonstrates the confusability of a particular ECG and the skill with which a clinician can distinguish 2 diagnoses along a continuum of confusability. These results have broad implications for testing and for learning to manage uncertainty in diagnosis.

Physician Training for Electrocardiogram Interpretation: A Systematic Review and Meta-Analysis

PURPOSE

Using electrocardiogram (ECG) interpretation as an example of a widely taught diagnostic skill, the authors conducted a systematic review and meta-analysis to demonstrate how research evidence on instruction in diagnosis can be synthesized to facilitate improvement of educational activities (instructional modalities, instructional methods, and interpretation approaches), guide the content and specificity of such activities, and provide direction for research.

METHOD

The authors searched PubMed/MEDLINE, Embase, Cochrane CENTRAL, PsycInfo, CINAHL, ERIC, and Web of Science databases through February 21, 2020, for empirical investigations of ECG interpretation training enrolling medical students, residents, or practicing physicians. They appraised study quality with the Medical Education Research Study Quality Instrument and pooled standardized mean differences (SMDs) using random effects meta-analysis.

RESULTS

Of 1,002 articles identified, 59 were included (enrolling 17,251 participants). Among 10 studies comparing instructional modalities, 8 compared computer-assisted and face-to-face instruction, with pooled SMD 0.23 (95% CI, 0.09, 0.36) indicating a small, statistically significant difference favoring computer-assisted instruction. Among 19 studies comparing instructional methods, 5 evaluated individual versus group training (pooled SMD -0.35 favoring group study [95% CI, -0.06, -0.63]), 4 evaluated peer-led versus faculty-led instruction (pooled SMD 0.38 favoring peer instruction [95% CI, 0.01, 0.74]), and 4 evaluated contrasting ECG features (e.g., QRS width) from 2 or more diagnostic categories versus routine examination of features within a single ECG or diagnosis (pooled SMD 0.23 not significantly favoring contrasting features [95% CI, -0.30, 0.76]). Eight studies compared ECG interpretation approaches, with pooled SMD 0.92 (95% CI, 0.48, 1.37) indicating a large, statistically significant effect favoring more systematic interpretation approaches.

CONCLUSIONS

Some instructional interventions appear to improve learning in ECG interpretation; however, many evidence-based instructional strategies are insufficiently investigated. The findings may have implications for future research and design of training to improve skills in ECG interpretation and other types of visual diagnosis.