Predictors of ECG Interpretation Proficiency in Healthcare Professionals.

Accurate ECG interpretation is vital, but variations in skills exist among healthcare professionals. This study aims to identify factors contributing to ECG interpretation proficiency. Survey data and ECG interpretation test scores from participants in the EDUCATE Trial were analyzed to identify predictors of performance for 30 sequential 12-lead ECGs. Nonmodifiable factors (being a physician, clinical experience, patient care impact) and modifiable factors (weekly interpretation volume, training hours, expert supervision frequency) were analyzed. Bivariate and multivariate analyses were used to generate a Comprehensive Model (incorporating all factors) and Actionable Model (incorporating modifiable factors only). Among 1206 participants analyzed, there were 72 (6.0%) primary care physicians, 146 (12.1%) cardiology fellows-in-training, 353 (29.3%) resident physicians, 182 (15.1%) medical students, 84 (7.0%) advanced practice providers, 120 (9.9%) nurses, and 249 (20.7%) allied health professionals. Among them, 571 (47.3%) were physicians and 453 (37.6%) were nonphysicians. The average test score was 56.4% ± 17.2%. Bivariate analysis demonstrated significant associations between test scores and >10 weekly ECG interpretations, being a physician, >5 training hours, patient care impact, and expert supervision but not clinical experience. In the Comprehensive Model, independent associations were found with weekly interpretation volume (9.9 score increase; 95% CI, 7.9-11.8; P < 0.001), being a physician (9.0 score increase; 95% CI, 7.2-10.8; P < 0.001), and training hours (5.7 score increase; 95% CI, 3.7-7.6; P < 0.001). In the Actionable Model, scores were independently associated with weekly interpretation volume (12.0 score increase; 95% CI, 10.0-14.0; P < 0.001) and training hours (4.7 score increase; 95% CI, 2.6-6.7; P < 0.001). The Comprehensive and Actionable Models explained 18.7% and 12.3% of the variance in test scores, respectively. Predictors of ECG interpretation proficiency include nonmodifiable factors like physician status and modifiable factors such as training hours and weekly ECG interpretation volume.

Impact of Computer-Interpreted ECGs on the Accuracy of Healthcare Professionals.

The interpretation of electrocardiograms (ECGs) involves a dynamic interplay between computerized ECG interpretation (CEI) software and human overread. However, the impact of computer ECG interpretation on the performance of healthcare professionals remains largely unexplored. The aim of this study was to evaluate the interpretation proficiency of various medical professional groups, with and without access to the CEI report. Healthcare professionals from diverse disciplines, training levels, and countries sequentially interpreted 60 standard 12-lead ECGs, demonstrating both urgent and nonurgent findings. The interpretation process consisted of 2 phases. In the first phase, participants interpreted 30 ECGs with clinical statements. In the second phase, the same 30 ECGs and clinical statements were randomized and accompanied by a CEI report. Diagnostic performance was evaluated based on interpretation accuracy, time per ECG (in seconds [s]), and self-reported confidence (rated 0 [not confident], 1 [somewhat confident], or 2 [confident]). A total of 892 participants from various medical professional groups participated in the study. This cohort included 44 (4.9%) primary care physicians, 123 (13.8%) cardiology fellows-in-training, 259 (29.0%) resident physicians, 137 (15.4%) medical students, 56 (6.3%) advanced practice providers, 82 (9.2%) nurses, and 191 (21.4%) allied health professionals. The inclusion of the CEI was associated with a significant improvement in interpretation accuracy by 15.1% (95% confidence interval, 14.3-16.0; P < 0.001), decrease in interpretation time by 52 s (-56 to -48; P < 0.001), and increase in confidence by 0.06 (0.03-0.09; P = 0.003). Improvement in interpretation accuracy was seen across all professional subgroups, including primary care physicians by 12.9% (9.4-16.3; P = 0.003), cardiology fellows-in-training by 10.9% (9.1-12.7; P < 0.001), resident physicians by 14.4% (13.0-15.8; P < 0.001), medical students by 19.9% (16.8-23.0; P < 0.001), advanced practice providers by 17.1% (13.3-21.0; P < 0.001), nurses by 16.2% (13.4-18.9; P < 0.001), allied health professionals by 15% (13.4-16.6; P < 0.001), physicians by 13.2% (12.2-14.3; P < 0.001), and nonphysicians by 15.6% (14.3-17.0; P < 0.001).CEI integration improves ECG interpretation accuracy, efficiency, and confidence among healthcare professionals.

Exploring Factors Influencing ECG Interpretation Proficiency of Medical Professionals.

The electrocardiogram (ECG) is a crucial diagnostic tool in medicine with concerns about its interpretation proficiency across various medical disciplines. Our study aimed to explore potential causes of these issues and identify areas requiring improvement. A survey was conducted among medical professionals to understand their experiences with ECG interpretation and education. A total of 2515 participants from diverse medical backgrounds were surveyed. A total of 1989 (79%) participants reported ECG interpretation as part of their practice. However, 45% expressed discomfort with independent interpretation. A significant 73% received less than 5 hours of ECG-specific education, with 45% reporting no education at all. Also, 87% reported limited or no expert supervision. Nearly all medical professionals (2461, 98%) expressed a desire for more ECG education. These findings were consistent across all groups and did not vary between primary care physicians, cardiology FIT, resident physicians, medical students, APPs, nurses, physicians, and nonphysicians. This study reveals substantial deficiencies in ECG interpretation training, supervision, and confidence among medical professionals, despite a strong interest in increased ECG education.

Comparison of automated interval measurements by widely used algorithms in digital electrocardiographs.

BACKGROUND: Automated measurements of electrocardiographic (ECG) intervals by current-generation digital electrocardiographs are critical to computer-based ECG diagnostic statements, to serial comparison of ECGs, and to epidemiological studies of ECG findings in populations. A previous study demonstrated generally small but often significant systematic differences among 4 algorithms widely used for automated ECG in the United States and that measurement differences could be related to the degree of abnormality of the underlying tracing. Since that publication, some algorithms have been adjusted, whereas other large manufacturers of automated ECGs have asked to participate in an extension of this comparison. METHODS: Seven widely used automated algorithms for computer-based interpretation participated in this blinded study of 800 digitized ECGs provided by the Cardiac Safety Research Consortium. All tracings were different from the study of 4 algorithms reported in 2014, and the selected population was heavily weighted toward groups with known effects on the QT interval: included were 200 normal subjects, 200 normal subjects receiving moxifloxacin as part of an active control arm of thorough QT studies, 200 subjects with genetically proved long QT syndrome type 1 (LQT1), and 200 subjects with genetically proved long QT syndrome Type 2 (LQT2). RESULTS: For the entire population of 800 subjects, pairwise differences between algorithms for each mean interval value were clinically small, even where statistically significant, ranging from 0.2 to 3.6milliseconds for the PR interval, 0.1 to 8.1milliseconds for QRS duration, and 0.1 to 9.3milliseconds for QT interval. The mean value of all paired differences among algorithms was higher in the long QT groups than in normals for both QRS duration and QT intervals. Differences in mean QRS duration ranged from 0.2 to 13.3milliseconds in the LQT1 subjects and from 0.2 to 11.0milliseconds in the LQT2 subjects. Differences in measured QT duration (not corrected for heart rate) ranged from 0.2 to 10.5milliseconds in the LQT1 subjects and from 0.9 to 12.8milliseconds in the LQT2 subjects. CONCLUSIONS: Among current-generation computer-based electrocardiographs, clinically small but statistically significant differences exist between ECG interval measurements by individual algorithms. Measurement differences between algorithms for QRS duration and for QT interval are larger in long QT interval subjects than in normal subjects. Comparisons of population study norms should be aware of small systematic differences in interval measurements due to different algorithm methodologies, within-individual interval measurement comparisons should use comparable methods, and further attempts to harmonize interval measurement methodologies are warranted.

The Comparison of Physician to Computer Interpreted Electrocardiograms on ST-elevation Myocardial Infarction Door-to-balloon Times.

OBJECTIVE: The purpose of the project was to study the impact that immediate physician electrocardiogram (ECG) interpretation would have on door-to-balloon times in ST-elevation myocardial infarction (STEMI) as compared with computer-interpreted ECGs. METHODS: This was a retrospective cohort study of 340 consecutive patients from September 2003 to December 2009 with STEMI who underwent emergent cardiac catheterization and percutaneous coronary intervention. Patients were stratified into 2 groups based on the computer-interpreted ECG interpretation: those with acute myocardial infarction identified by the computer interpretation and those not identified as acute myocardial infarction. Patients (n = 173) from September 2003 to June 2006 had their initial ECG reviewed by the triage nurse, while patients from July 2006 to December 2009 (n = 167) had their ECG reviewed by the emergency department physician within 10 minutes. Times for catheterization laboratory activation and percutaneous coronary intervention were recorded in all patients. RESULTS: Of the 340 patients with confirmed STEMI, 102 (30%) patients were not identified by computer interpretation. Comparing the prior protocol of computer ECG to physician interpretation, the latter resulted in significant improvements in median catheterization laboratory activation time {19 minutes [interquartile range (IQR): 10-37] vs. 16 minutes [IQR: 8-29]; P < 0.029} and in median door-to-balloon time [113 minutes (IQR: 86-143) vs. 85 minutes (IQR: 62-106); P < 0.001]. CONCLUSION: The computer-interpreted ECG failed to identify a significant number of patients with STEMI. The immediate review of ECGs by an emergency physician led to faster activation of the catheterization laboratory, and door-to-balloon times in patients with STEMI.

In memoriam: A tribute to the work and lives of Ron Selvester and Rory Childers.

At the April, 2015 International Society for Computerized Electrocardiology (ISCE) Annual Conference in San Jose, CA, a special session entitled Remembering Ron & Rory was held to pay tribute to the extraordinary work and lives of two experts in electrocardiology. The session was well attended by conference attendees, Childers' family members and friends, and additional colleagues who traveled to San Jose solely to participate in this session. The purpose of the present paper is to document the spirit of this special session as faithfully as possible using the words of the session speakers.

Comparison of automated measurements of electrocardiographic intervals and durations by computer-based algorithms of digital electrocardiographs.

BACKGROUND AND PURPOSE: Automated measurements of electrocardiographic (ECG) intervals are widely used by clinicians for individual patient diagnosis and by investigators in population studies. We examined whether clinically significant systematic differences exist in ECG intervals measured by current generation digital electrocardiographs from different manufacturers and whether differences, if present, are dependent on the degree of abnormality of the selected ECGs. METHODS: Measurements of RR interval, PR interval, QRS duration, and QT interval were made blindly by 4 major manufacturers of digital electrocardiographs used in the United States from 600 XML files of ECG tracings stored in the US FDA ECG warehouse and released for the purpose of this study by the Cardiac Safety Research Consortium. Included were 3 groups based on expected QT interval and degree of repolarization abnormality, comprising 200 ECGs each from (1) placebo or baseline study period in normal subjects during thorough QT studies, (2) peak moxifloxacin effect in otherwise normal subjects during thorough QT studies, and (3) patients with genotyped variants of congenital long QT syndrome (LQTS). RESULTS: Differences of means between manufacturers were generally small in the normal and moxifloxacin subjects, but in the LQTS patients, differences of means ranged from 2.0 to 14.0 ms for QRS duration and from 0.8 to 18.1 ms for the QT interval. Mean absolute differences between algorithms were similar for QRS duration and QT intervals in the normal and in the moxifloxacin subjects (mean ≤6 ms) but were significantly larger in patients with LQTS. CONCLUSIONS: Small but statistically significant group differences in mean interval and duration measurements and means of individual absolute differences exist among automated algorithms of widely used, current generation digital electrocardiographs. Measurement differences, including QRS duration and the QT interval, are greatest for the most abnormal ECGs.

The detection of T-wave variation linked to arrhythmic risk: an industry perspective.

Although the scientific literature contains ample descriptions of peculiar patterns of repolarization linked to arrhythmic risk, the objective quantification and classification of these patterns continues to be a challenge that impacts their widespread adoption in clinical practice. To advance the science, computerized algorithms spawned in the academic environment have been essential in order to find, extract and measure these patterns. However, outside the strict control of a core lab, these algorithms are exposed to poor quality signals and need to be effective in the presence of different forms of noise that can either obscure or mimic the T-wave variation (TWV) of interest. To provide a practical solution that can be verified and validated for the market, important tradeoffs need to be made that are based on an intimate understanding of the end-user as well as the key characteristics of either the signal or the noise that can be used by the signal processing engineer to best differentiate them. To illustrate this, two contemporary medical devices used for quantifying T-wave variation are presented, including the modified moving average (MMA) for the detection of T-wave Alternans (TWA) and the quantification of T-wave shape as inputs to the Morphology Combination Score (MCS) for the trending of drug-induced repolarization abnormalities.

Second statement of the working group on electrocardiographic diagnosis of left ventricular hypertrophy.

The Working Group on Electrocardiographic Diagnosis of Left Ventricular Hypertrophy, appointed by the Editor of the Journal of Electrocardiology, presents the alternative conceptual model for the ECG diagnosis of left ventricular hypertrophy (LVH). It is stressed that ECG is a record of electrical events, not of mechanical events and/ or anatomical characteristics. Considering the electrical characteristics of pathologically changed myocardium should lead to better understanding and improved clinical usefulness of the ECH in the clinical diagnosis of LVH.

Computerized STEMI recognition: an example of the art and science of building ECG algorithms.

With the advent of thrombolytics, guidelines for ST-elevated myocardial infarction (STEMI) recognition were presented in terms of an ST segment exceeding a particular level (1 or 2 mm) in 2 contiguous leads. However, more than half of prehospital electrocardiograms that exceed these ST criteria are from patients not having an acute myocardial infarction. In contrast, expert physicians (EXMD) maintain a high specificity (>95%) for the recognition of STEMI. Likewise, in terms of increasing sensitivity, it has been found that the EXMD will classify STEMI at lower levels than specified in the guideline. Thus, the EXMD uses additional electrocardiogram features to identify patients for appropriate intervention. Given that STEMI can be defined in terms of a pattern that is recognized by the EXMD as well as a clinical classification that can be evaluated in terms of clinical outcomes, the development and validation of a computer algorithm for STEMI need to include both the art of understanding how the human is detecting STEMI as well as the science required to develop quantified criteria based on clinical outcomes. Evidence is presented that demonstrates that reciprocal depression is a strong indicator of STEMI versus other causes of ST elevation.

Added value of new acute coronary syndrome computer algorithm for interpretation of prehospital electrocardiograms.

A new computerized acute coronary syndrome (ACS) computer algorithm has been developed with the aim of improving the electrocardiographic detection of acute myocardial ischemia and infarction in the emergency department (ED). The purpose of this study was to determine the added value of the new ACS algorithm in assisting ED physicians to obtain a more accurate diagnosis in patients with ACS. The new algorithm combines a rule-based decision tree, which uses well-known clinical criteria and a data-centered neural network model for more robust pattern recognition. Input parameters of the neural network model consist of morphology features of derived Frank X, Y, Z waveforms and the patient's gender and age. The neural network model was trained with electrocardiograms obtained from documented acute myocardial infarction patients at the Mayo Clinic who were a part of a research ACS database, which includes electrocardiograms (ECGs) of more than 5,000 individuals at hospital admission (1st ECG in the ED). The test set portion of the study was conducted in 2 steps: 1) One emergency physician and 1 cardiologist classified 1,902 clinically correlated out-of-hospital ECGs without seeing the interpretation statement from the algorithm into 1 of the following categories: 1) acute myocardial infarction, acute ischemia, or nonischemic; 2) After 9 months, the same 2 physicians classified the same group of ECGs but with the interpretation statement of the algorithm printed on the tracing. The results demonstrated that with the assistance of the new algorithm, the emergency physician and cardiologist improved their sensitivity of interpreting acute myocardial infarction by 50% and 26%, respectively, without a loss of specificity. The new algorithm also improved the emergency physician's acute ischemia interpretation sensitivity by 53% and still maintained a reasonable specificity (91%). The new ACS algorithm provides added value for improving acute ischemia and infarction detection in the ED.