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.

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.

Stats on the desats: alarm fatigue and the implications for patient safety.

BACKGROUND: Physiological monitoring systems, like Masimo, used during inpatient hospitalisation, offer a non-invasive approach to capture critical vital signs data. These systems trigger alarms when measurements deviate from preset parameters. However, often non-urgent or potentially false alarms contribute to 'alarm fatigue,' a form of sensory overload that can have adverse effects on both patients and healthcare staff. The Joint Commission, in 2021, announced a target to mitigate alarm fatigue-related fatalities through improved alarm management. Yet, no established guidelines are presently available. This study aims to address alarm fatigue at the Mayo Clinic to safeguard patient safety, curb staff burnout and improve the sensitivity of oxygen saturation monitoring to promptly detect emergencies. METHODS: A quality improvement project was conducted to combat minimise the false alarm burden, with data collected 2 months prior to intervention commencement. The project's goal was to decrease the total alarm value by 20% from 55%-85% to 35%-75% within 2 months, leveraging quality improvement methodologies. INTERVENTIONS: February to April 2021, we implemented a two-pronged intervention: (1) instituting a protocol to evaluate patients' continuous monitoring needs and discontinuing it when appropriate, and (2) introducing educational signage for patients and Mayo Clinic staff on monitoring best practices. RESULTS: Baseline averages of red alarms (158.6), manual snoozes (37.8) and self-resolves (120.7); the first postintervention phase showed reductions in red alarms (125.5), manual snoozes (17.8) and self-resolves (107.8). Second postintervention phase recorded 138 red alarms, 13 manual snoozes and 125 self-resolves. Baseline comparison demonstrated an average of 16.92% reduction of alarms among both interventions (p value: 0.25). CONCLUSION: Simple interventions like education and communication techniques proved instrumental in lessening the alarm burden for patients and staff. The findings underscore the practical use and efficacy of these methods in any healthcare setting, thus contributing to mitigating the prevalent issue of alarm fatigue.

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.

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.

Contemporary trends and barriers to oral anticoagulation therapy in Non-valvular atrial fibrillation during DOAC predominant era.

There is a need to reassess contemporary oral anticoagulation (OAC) trends and barriers against guideline directed therapy in the United States. Most previous studies were performed before major guideline changes recommended direct oral anticoagulant (DOAC) use over warfarin or have otherwise lacked patient level data. Data on overuse of OAC in low-risk group is also limited. To address these knowledge gaps, we performed a nationwide analysis to analyze current trends. This is a retrospective cohort study assessing non-valvular AF identified using a large United States de-identified administrative claims database, including commercial and Medicare Advantage enrollees. Prescription fills were assessed within a 90-day follow-up from the patient's index AF encounter between January 1, 2016, and December 31, 2020. Among the 339,197 AF patients, 4.4%, 8.0%, and 87.6% were in the low-, moderate-, and high-risk groups (according to CHA2DS2-VASc score). An over (29.6%) and under (52.2%) utilization of OAC was reported in low- and high-risk AF patients. A considerably high frequency for warfarin use was also noted among high-risk group patients taking OAC (33.1%). The results suggest that anticoagulation use for stroke prevention in the United States is still comparable to the pre-DOAC era studies. About half of newly diagnosed high-risk non-valvular AF patients remain unprotected against stroke risk. Several predictors of OAC and DOAC use were also identified. Our findings may identify a population at risk of complications due to under- or over-treatment and highlight the need for future quality improvement efforts.

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.

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.

Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches.

The medical complexity and high acuity of patients in the cardiac intensive care unit make for a unique patient population with high morbidity and mortality. While there are many tools for predictions of mortality in other settings, there is a lack of robust mortality prediction tools for cardiac intensive care unit patients. The ongoing advances in artificial intelligence and machine learning also pose a potential asset to the advancement of mortality prediction. Artificial intelligence algorithms have been developed for application of electrocardiogram interpretation with promising accuracy and clinical application. Additionally, artificial intelligence algorithms applied to electrocardiogram interpretation have been developed to predict various variables such as structural heart disease, left ventricular systolic dysfunction, and atrial fibrillation. These variables can be used and applied to new mortality prediction models that are dynamic with the changes in the patient's clinical course and may lead to more accurate and reliable mortality prediction. The application of artificial intelligence to mortality prediction will fill the gaps left by current mortality prediction tools.

Artificial Intelligence-Enabled ECG: Physiologic and Pathophysiologic Insights and Implications.

Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI-ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417-3424, 2022.

Artificial intelligence and atrial fibrillation.

BACKGROUND: In the context of atrial fibrillation (AF), traditional clinical practices have thus fallen short in several domains, such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems. AIMS: To discuss the roles of artificial intelligence (AI)-enabled electrocardiogram (ECG) pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. MATERIALS & METHODS: An extensive search and review of the currently available literature on the topics. RESULTS: One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Challenges with regards to the benefits and harms of AF screening remain. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm. DISCUSSION: Knowledge gaps remain regarding the best ways to monitor patients with embolic stroke of undetermined source (ESUS) and identifying those who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. The role of DL models assessing AF burden from long-duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, the role of adequate external validation of the models and clinical trials to study true performance is discussed. CONCLUSION: Algorithms using AI to interpret ECGs in various new ways have been developed. While still, much work needs to be done, these technologies have shown enormous potential in a short span of time. With further advancements and continuous research, these novel ways of interpretation may well become part of everyday clinical workflow.

Automatic wide complex tachycardia differentiation using mathematically synthesized vectorcardiogram signals.

BACKGROUND: Automated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification. METHODS: A derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one "all-inclusive" model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance. RESULTS: The VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X-lead QRS amplitude change, Y-lead QRS amplitude change, and Z-lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95). CONCLUSION: Custom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.

Subclinical Atrial Fibrillation: A Silent Threat with Uncertain Implications.

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.