Bridging ECG learning with emerging technologies: Advancing clinical excellence.

As ECG technology rapidly evolves to improve patient care, accurate ECG interpretation will continue to be foundational for maintaining high clinical standards. Recent studies have exposed significant educational gaps, with many healthcare professionals lacking sufficient training and proficiency. Furthermore, integrating new software and hardware ECG technologies poses challenges about potential knowledge and skill erosion. This underscores the need for clinicians who are adept at integrating clinical expertise with technological proficiency. It also highlights the need for innovative solutions to enhance ECG interpretation among healthcare professionals in this rapidly evolving environment. This work explores the importance of aligning ECG education with technological advancements and proposes how this synergy could advance patient care in the future.

A novel way to prospectively evaluate of AI-enhanced ECG algorithms.

Significant strides will be made in the field of computerized electrocardiology through the development of artificial intelligence (AI)-enhanced ECG (AI-ECG) algorithms. Yet, the scientific discourse has primarily relied upon on retrospective analyses for deriving and externally validating AI-ECG classification algorithms, an approach that fails to fully judge their real-world effectiveness or reveal potential unintended consequences. Prospective trials and analyses of AI-ECG algorithms will be crucial for assessing real-world diagnostic scenarios and understanding their practical utility and degree influence they confer onto clinicians. However, conducting such studies is challenging due to their resource-intensive nature and associated technical and logistical hurdles. To overcome these challenges, we propose an innovative approach to assess AI-ECG algorithms using a virtual testing environment. This strategy can yield critical insights into the practical utility and clinical implications of novel AI-ECG algorithms. Moreover, such an approach can enable an assessment of the influence of AI-ECG algorithms have their users. Herein, we outline a proposed randomized control trial for evaluating the diagnostic efficacy of new AI-ECG algorithm(s) specifically designed to differentiate between wide complex tachycardias into ventricular tachycardia and supraventricular wide complex tachycardia.

Direct Comparison of Methods to Differentiate Wide Complex Tachycardias: Novel Automated Algorithms Versus Manual ECG Interpretation Approaches.

BACKGROUND: Differentiating wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide tachycardia via 12-lead ECG interpretation is a crucial but difficult task. Automated algorithms show promise as alternatives to manual ECG interpretation, but direct comparison of their diagnostic performance has not been undertaken. METHODS: Two electrophysiologists applied 3 manual WCT differentiation approaches (ie, Brugada, Vereckei aVR, and VT score). Simultaneously, computerized data from paired WCT and baseline ECGs were processed by 5 automated WCT differentiation algorithms (WCT Formula, WCT Formula II, VT Prediction Model, Solo Model, and Paired Model). The diagnostic performance of automated algorithms was compared with manual ECG interpretation approaches. RESULTS: A total of 212 WCTs (111 VT and 101 supraventricular wide tachycardia) from 104 patients were analyzed. WCT Formula demonstrated superior accuracy (85.8%) and specificity (87.1%) compared with Brugada (75.2% and 57.4%, respectively) and Vereckei aVR (65.3% and 36.4%, respectively). WCT Formula II achieved higher accuracy (89.6%) and specificity (85.1%) against Brugada and Vereckei aVR. Performance metrics of the WCT Formula (accuracy 85.8%, sensitivity 84.7%, and specificity 87.1%) and WCT Formula II (accuracy 89.8%, sensitivity 89.6%, and specificity 85.1%) were similar to the VT score (accuracy 84.4%, sensitivity 93.8%, and specificity 74.2%). Paired Model was superior to Brugada in accuracy (89.6% versus 75.2%), specificity (97.0% versus 57.4%), and F1 score (0.89 versus 0.80). Paired Model surpassed Vereckei aVR in accuracy (89.6% versus 65.3%), specificity (97.0% versus 75.2%), and F1 score (0.89 versus 0.74). Paired Model demonstrated similar accuracy (89.6% versus 84.4%), inferior sensitivity (79.3% versus 93.8%), but superior specificity (97.0% versus 74.2%) to the VT score. Solo Model and VT Prediction Model accuracy (82.5% and 77.4%, respectively) was superior to the Vereckei aVR (65.3%) but similar to Brugada (75.2%) and the VT score (84.4%). CONCLUSIONS: Automated WCT differentiation algorithms demonstrated favorable diagnostic performance compared with traditional manual ECG interpretation approaches.

A large-scale CRISPR screen reveals context-specific genetic regulation of retinal ganglion cell regeneration.

Many genes are known to regulate retinal regeneration after widespread tissue damage. Conversely, genes controlling regeneration after limited cell loss, as per degenerative diseases, are undefined. As stem/progenitor cell responses scale to injury levels, understanding how the extent and specificity of cell loss impact regenerative processes is important. Here, transgenic zebrafish enabling selective retinal ganglion cell (RGC) ablation were used to identify genes that regulate RGC regeneration. A single cell multiomics-informed screen of 100 genes identified seven knockouts that inhibited and 11 that promoted RGC regeneration. Surprisingly, 35 out of 36 genes known and/or implicated as being required for regeneration after widespread retinal damage were not required for RGC regeneration. The loss of seven even enhanced regeneration kinetics, including the proneural factors neurog1, olig2 and ascl1a. Mechanistic analyses revealed that ascl1a disruption increased the propensity of progenitor cells to produce RGCs, i.e. increased 'fate bias'. These data demonstrate plasticity in the mechanism through which Müller glia convert to a stem-like state and context specificity in how genes function during regeneration. Increased understanding of how the regeneration of disease-relevant cell types is specifically controlled will support the development of disease-tailored regenerative therapeutics.

EDUCATE: An international, randomized controlled trial for teaching electrocardiography.

INTRODUCTION: Despite the critical role of electrocardiograms (ECGs) in patient care, evident gaps exist in ECG interpretation competency among healthcare professionals across various medical disciplines and training levels. Currently, no practical, evidence-based, and easily accessible ECG learning solution is available for healthcare professionals. The aim of this study was to assess the effectiveness of web-based, learner-directed interventions in improving ECG interpretation skills in a diverse group of healthcare professionals. METHODS: In an international, prospective, randomized controlled trial, 1206 healthcare professionals from various disciplines and training levels were enrolled. They underwent a pre-intervention test featuring 30 12-lead ECGs with common urgent and non-urgent findings. Participants were randomly assigned to four groups: (i) practice ECG interpretation question bank (question bank), (ii) lecture-based learning resource (lectures), (iii) hybrid question- and lecture-based learning resource (hybrid), or (iv) no ECG learning resources (control). After four months, a post-intervention test was administered. The primary outcome was the overall change in ECG interpretation performance, with secondary outcomes including changes in interpretation time, self-reported confidence, and accuracy for specific ECG findings. Both unadjusted and adjusted scores were used for performance assessment. RESULTS: Among 1206 participants, 863 (72 %) completed the trial. Following the intervention, the question bank, lectures, and hybrid intervention groups each exhibited significant improvements, with average unadjusted score increases of 11.4 % (95 % CI, 9.1 to 13.7; P<0.01), 9.8 % (95 % CI, 7.8 to 11.9; P<0.01), and 11.0 % (95 % CI, 9.2 to 12.9; P<0.01), respectively. In contrast, the control group demonstrated a non-significant improvement of 0.8 % (95 % CI, -1.2 to 2.8; P=0.54). While no differences were observed among intervention groups, all outperformed the control group significantly (P<0.01). Intervention groups also excelled in adjusted scores, confidence, and proficiency for specific ECG findings. CONCLUSION: Web-based, self-directed interventions markedly enhanced ECG interpretation skills across a diverse range of healthcare professionals, providing an accessible and evidence-based solution.

Computer-Interpreted Electrocardiograms: Impact on Cardiology Practice

Abstract In the realm of modern cardiology, the integration of computer-interpreted electrocardiograms (CI-ECGs) has marked the beginning of a new era of diagnostic precision and efficiency. Contemporary electrocardiogram (ECG) integration systems, applying algorithms and artificial intelligence, have modernized the interpretation of heart rhythms and cardiac morphology. Due to their ability to rapidly analyze and interpret ECG recordings CI-ECGs have already profoundly impacted clinical practice. This review explores the evolution of computer interpreted ECG technology, evaluates the pros and cons of current automatic reporting systems, analyzes the growing role of artificial intelligence on ECG interpretation technologies, and discusses emerging applications that may have transformative effects on patient outcomes. Emphasis is placed on the role of ECGs in the automatic diagnosis of occlusion myocardial infarctions (OMI). AI models enhance accuracy and efficiency in ECG interpretation, offering insights into cardiac function and aiding timely detection of concerning patterns for accurate clinical diagnoses. The shift to AI-driven diagnostics has emphasized the importance of data in the realm of cardiology by improving patient care. The integration of novel AI models in ECG analysis has created a promising future for ECG diagnostics through a synergistic fusion of feature-based machine learning models, deep learning approaches, and clinical acumen. Overall, CI-ECGs have transformed cardiology practice, offering rapid, accurate, and standardized analyses. These systems reduce interpretation time significantly, allowing for quick identification of abnormalities. However, sole reliance on automated interpretations may overlook nuanced findings, risking diagnostic errors. Therefore, a balanced approach in integrating automated analysis with clinical judgment is necessary.

Molecular regulation of retinal regeneration is context specific.

Many genes are known to regulate retinal regeneration following widespread tissue damage. Conversely, genes controlling regeneration following limited retinal cell loss, akin to disease conditions, are undefined. Combining a novel retinal ganglion cell (RGC) ablation-based glaucoma model, single cell omics, and rapid CRISPR/Cas9-based knockout methods to screen 100 genes, we identified 18 effectors of RGC regeneration kinetics. Surprisingly, 32 of 33 previously known/implicated regulators of retinal tissue regeneration were not required for RGC replacement; 7 knockouts accelerated regeneration, including sox2, olig2, and ascl1a . Mechanistic analyses revealed loss of ascl1a increased "fate bias", the propensity of progenitors to produce RGCs. These data demonstrate plasticity and context-specificity in how genes function to control regeneration, insights that could help to advance disease-tailored therapeutics for replacing lost retinal cells. One sentence summary: We discovered eighteen genes that regulate the regeneration of retinal ganglion cells in zebrafish.

Mayo Clinic VT calculator: A practical tool for accurate wide complex tachycardia differentiation.

The discrimination of ventricular tachycardia (VT) versus supraventricular wide complex tachycardia (SWCT) via 12-lead electrocardiogram (ECG) is crucial for achieving appropriate, high-quality, and cost-effective care in patients presenting with wide QRS complex tachycardia (WCT). Decades of rigorous research have brought forth an expanding arsenal of applicable manual algorithm methods for differentiating WCTs. However, these algorithms are limited by their heavy reliance on the ECG interpreter for their proper execution. Herein, we introduce the Mayo Clinic ventricular tachycardia calculator (MC-VTcalc) as a novel generalizable, accurate, and easy-to-use means to estimate VT probability independent of ECG interpreter competency. The MC-VTcalc, through the use of web-based and mobile device platforms, only requires the entry of computerized measurements (i.e., QRS duration, QRS axis, and T-wave axis) that are routinely displayed on standard 12-lead ECG recordings.

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.

ECG Interpretation Proficiency of Healthcare Professionals.

ECG interpretation is essential in modern medicine, yet achieving and maintaining competency can be challenging for healthcare professionals. Quantifying proficiency gaps can inform educational interventions for addressing these challenges. Medical professionals from diverse disciplines and training levels interpreted 30 12-lead ECGs with common urgent and nonurgent findings. Average accuracy (percentage of correctly identified findings), interpretation time per ECG, and self-reported confidence (rated on a scale of 0 [not confident], 1 [somewhat confident], or 2 [confident]) were evaluated. Among the 1206 participants, there were 72 (6%) primary care physicians (PCPs), 146 (12%) cardiology fellows-in-training (FITs), 353 (29%) resident physicians, 182 (15%) medical students, 84 (7%) advanced practice providers (APPs), 120 (10%) nurses, and 249 (21%) allied health professionals (AHPs). Overall, participants achieved an average overall accuracy of 56.4% ± 17.2%, interpretation time of 142 ± 67 seconds, and confidence of 0.83 ± 0.53. Cardiology FITs demonstrated superior performance across all metrics. PCPs had a higher accuracy compared to nurses and APPs (58.1% vs 46.8% and 50.6%; P < 0.01), but a lower accuracy than resident physicians (58.1% vs 59.7%; P < 0.01). AHPs outperformed nurses and APPs in every metric and showed comparable performance to resident physicians and PCPs. Our findings highlight significant gaps in the ECG interpretation proficiency among healthcare professionals.

Education curriculum assessment for teaching electrocardiography: Rationale and design for the prospective, international, randomized controlled EDUCATE trial.

BACKGROUND: Electrocardiogram (ECG) interpretation training is a fundamental component of medical education across disciplines. However, the skill of interpreting ECGs is not universal among medical graduates, and numerous barriers and challenges exist in medical training and clinical practice. An evidence-based and widely accessible learning solution is needed. DESIGN: The EDUcation Curriculum Assessment for Teaching Electrocardiography (EDUCATE) Trial is a prospective, international, investigator-initiated, open-label, randomized controlled trial designed to determine the efficacy of self-directed and active-learning approaches of a web-based educational platform for improving ECG interpretation proficiency. Target enrollment is 1000 medical professionals from a variety of medical disciplines and training levels. Participants will complete a pre-intervention baseline survey and an ECG interpretation proficiency test. After completion, participants will be randomized into one of four groups in a 1:1:1:1 fashion: (i) an online, question-based learning resource, (ii) an online, lecture-based learning resource, (iii) an online, hybrid question- and lecture-based learning resource, or (iv) a control group with no ECG learning resources. The primary endpoint will be the change in overall ECG interpretation performance according to pre- and post-intervention tests, and it will be measured within and compared between medical professional groups. Secondary endpoints will include changes in ECG interpretation time, self-reported confidence, and interpretation accuracy for specific ECG findings. CONCLUSIONS: The EDUCATE Trial is a pioneering initiative aiming to establish a practical, widely available, evidence-based solution to enhance ECG interpretation proficiency among medical professionals. Through its innovative study design, it tackles the currently unaddressed challenges of ECG interpretation education in the modern era. The trial seeks to pinpoint performance gaps across medical professions, compare the effectiveness of different web-based ECG content delivery methods, and create initial evidence for competency-based standards. If successful, the EDUCATE Trial will represent a significant stride towards data-driven solutions for improving ECG interpretation skills in the medical community.

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.

The emergence and destiny of automated methods to differentiate wide QRS complex tachycardias.

Accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using non-invasive methods such as 12­lead electrocardiogram (ECG) interpretation is crucial in clinical practice. Recent studies have demonstrated the potential for automated approaches utilizing computerized ECG interpretation software to achieve accurate WCT differentiation. In this review, we provide a comprehensive analysis of contemporary automated methods for VT and SWCT differentiation. Our objectives include: (i) presenting a general overview of the emergence of automated WCT differentiation methods, (ii) examining the role of machine learning techniques in automated WCT differentiation, (iii) reviewing the electrophysiology concepts leveraged existing automated algorithms, (iv) discussing recently developed automated WCT differentiation solutions, and (v) considering future directions that will enable the successful integration of automated methods into computerized ECG interpretation platforms.

Variation in the Use of Targeted Temperature Management for Cardiac Arrest.

Targeted temperature management (TTM) is recommended for patients who do not respond after return of spontaneous circulation after cardiac arrest. However, the degree to which patients with cardiac arrest have access to this therapy on a national level is not known. Understanding hospital- and patient-level factors associated with receipt of TTM could inform interventions to improve access to this treatment among appropriate patients. Therefore, we performed a retrospective analysis using National Inpatient Sample data from 2016 to 2019. We used International Classification of Diseases, Tenth Edition diagnosis and procedure codes to identify adult patients with in-hospital and out-of-hospital cardiac arrest and receipt of TTM. We evaluated patient and hospital factors associated with receiving TTM. We identified 478,419 patients with cardiac arrest. Of those, 4,088 (0.85%) received TTM. Hospital use of TTM was driven by large, nonprofit, urban, teaching hospitals, with less use at other hospital types. There was significant regional variation in TTM capabilities, with the proportion of hospitals providing TTM ranging from >21% in the Mid-Atlantic region to <11% in the East and West South Central and Mountain regions. At the patient level, age >74 years (odds ratio [OR] 0.54, p <0.001), female gender (OR 0.89, p >0.001), and Hispanic ethnicity (OR 0.74, p <0.001) were all associated with decreased odds of receiving TTM. Patients with Medicare (OR 0.75, p <0.001) and Medicaid (OR 0.89, p = 0.027) were less likely than patients with private insurance to receive TTM. Part of these differences was driven by inequitable access to TTM-capable hospitals. In conclusion, TTM is rarely used after cardiac arrest. Hospital use of TTM is predominately limited to a subset of academic hospitals with substantial regional variation. Older age, female gender, Hispanic ethnicity, and Medicare or Medicaid insurance are all associated with a decreased likelihood of receiving TTM.

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.

Multidisciplinary Critical Care Management of Electrical Storm: JACC State-of-the-Art Review.

Electrical storm (ES) reflects life-threatening cardiac electrical instability with 3 or more ventricular arrhythmia episodes within 24 hours. Identification of underlying arrhythmogenic cardiac substrate and reversible triggers is essential, as is interrogation and programming of an implantable cardioverter-defibrillator, if present. Medical management includes antiarrhythmic drugs, beta-adrenergic blockade, sedation, and hemodynamic support. The initial intensity of these interventions should be matched to the severity of ES using a stepped-care algorithm involving escalating treatments for higher-risk presentations or recurrent ventricular arrhythmias. Many patients with ES are considered for catheter ablation, which may require the use of temporary mechanical circulatory support. Outcomes after ES are poor, including frequent ES recurrences and deaths caused by progressive heart failure and other cardiac causes. A multidisciplinary collaborative approach to the management of ES is crucial, and evaluation for heart transplantation or palliative care is often appropriate, even for patients who survive the initial episode.

Implementation of high-sensitivity troponin with a rapid diagnostic algorithm reduces emergency department length of stay for discharged patients.

INTRODUCTION: High sensitivity troponin (hs-cTn) and diagnostic algorithms are used to rapidly triage patients with symptoms of acute myocardial infarction in emergency departments (ED). However, few studies have evaluated the impact of simultaneously implementing hs-cTn and a rapid rule-out algorithm on length of stay (LOS). METHODS: We assessed the impact of transitioning from contemporary cTnI to hs-cTnI in 59,232 ED encounters over three years. hs-cTnI was implemented with an orderable series that included baseline, two-, four-, and six-hour specimens collected at provider discretion and operationalized with an algorithm to calculate the change in hs-cTnI from baseline and provide interpretations of "insignificant", "significant," or "equivocal." Patient demographics, results, chief complaint, disposition, and ED LOS were captured from the electronic medical record. RESULTS: cTnI was ordered for 31,875 encounters prior to hs-cTnI implementation and 27,357 after. The proportion of cTnI results above the 99th percentile upper reference limit decreased from 35.0% to 27.0% for men and increased from 27.8% to 34.8% for women. Among discharged patients, the median LOS decreased by 0.6 h (0.5-0.7). LOS among discharged patients with a chief complaint of chest pain decreased by 1.0 h (0.8-1.1) and further decreased by 1.2 h (1.0-1.3) if the initial hs-cTnI was below the limit of quantitation. The rate of acute coronary syndrome upon re-presentation within 30 days did not change post-implementation (0.10% versus 0.07%). CONCLUSION: Implementation of an hs-cTnI assay with a rapid rule-out algorithm decreased ED LOS among discharged patients, particularly among those with a chief complaint of chest pain.

Comparison of two artificial intelligence-augmented ECG approaches: Machine learning and deep learning.

BACKGROUND: Artificial intelligence-augmented ECG (AI-ECG) refers to the application of novel AI solutions for complex ECG interpretation tasks. A broad variety of AI-ECG approaches exist, each having differing advantages and limitations relating to their creation and application. PURPOSE: To provide illustrative comparison of two general AI-ECG modeling approaches: machine learning (ML) and deep learning (DL). METHOD COMPARISON: Two AI-ECG algorithms were developed to carry out two separate tasks using ML and DL, respectively. ML modeling techniques were used to create algorithms designed for automatic wide QRS complex tachycardia differentiation into ventricular tachycardia and supraventricular tachycardia. A DL algorithm was formulated for the task of comprehensive 12­lead ECG interpretation. First, we describe the ML models for WCT differentiation, which rely upon expert domain knowledge to identify and formulate ECG features (e.g., percent monophasic time-voltage area [PMonoTVA]) that enable strong diagnostic performance. Second, we describe the DL method for comprehensive 12­lead ECG interpretation, which relies upon the independent recognition and analysis of a virtually incalculable number of ECG features from a vast collection of standard 12­lead ECGs. CONCLUSION: We have showcased two different AI-ECG methods, namely ML and DL respectively. In doing so, we highlighted the strengths and weaknesses of each approach. It is essential for investigators to understand these differences when attempting to create and apply novel AI-ECG solutions.

Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias.

BACKGROUND: Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs. OBJECTIVE: Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG. METHODS: We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model). RESULTS: Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording. CONCLUSION: Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.

Wide complex tachycardia discrimination tool improves physicians' diagnostic accuracy.

BACKGROUND: Timely and accurate discrimination of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular WCT (SWCT) is critically important. Previously we developed and validated an automated VT Prediction Model that provides a VT probability estimate using the paired WCT and baseline 12-lead ECGs. Whether this model improves physicians' diagnostic accuracy has not been evaluated. OBJECTIVE: We sought to determine whether the VT Prediction Model improves physicians' WCT differentiation accuracy. METHODS: Over four consecutive days, nine physicians independently interpreted fifty WCT ECGs (25 VTs and 25 SWCTs confirmed by electrophysiological study) as either VT or SWCT. Day 1 used the WCT ECG only, Day 2 used the WCT and baseline ECG, Day 3 used the WCT ECG and the VT Prediction Model's estimation of VT probability, and Day 4 used the WCT ECG, baseline ECG, and the VT Prediction Model's estimation of VT probability. RESULTS: Inclusion of the VT Prediction Model data increased diagnostic accuracy versus the WCT ECG alone (Day 3: 84.2% vs. Day 1: 68.7%, p 0.009) and WCT and baseline ECGs together (Day 3: 84.2% vs. Day 2: 76.4%, p 0.003). There was no further improvement of accuracy with addition of the baseline ECG comparison to the VT Prediction Model (Day 3: 84.2% vs. Day 4: 84.0%, p 0.928). Overall sensitivity (Day 3: 78.2% vs. Day 1: 67.6%, p 0.005) and specificity (Day 3: 90.2% vs. Day 1: 69.8%, p 0.016) for VT were superior after the addition of the VT Prediction Model. CONCLUSION: The VT Prediction Model improves physician ECG diagnostic accuracy for discriminating WCTs.