Early vs. late transcatheter aortic valve replacement in acute heart failure hospitalizations: A comparative nationwide analysis.

BACKGROUND: Severe aortic stenosis (AS) is the most common valvular disease in the USA. Patients undergoing urgent or emergent transcatheter aortic valve replacement (TAVR) have worse clinical outcomes than those undergoing non-urgent procedures. No studies have examined the impact of procedural TAVR timing on outcomes in AS complicated by acute heart failure (AHF). AIMS: We aimed to evaluate differences in in-hospital mortality and clinical outcomes between early (<48 h) vs. late (≥48 h) TAVR in patients hospitalized with AHF using a real-world US database. METHODS: We queried the National Inpatient Sample database to identify hospitalizations with a diagnosis of AHF, aortic valve disease, and a TAVR procedure (2015-2020). The associations between TAVR timing and clinical outcomes were examined using logistic regression model. RESULTS: A total of 25,290 weighted AHF hospitalizations were identified, of which 6855 patients (27.1 %) underwent early TAVR, and 18,435 (72.9 %) late TAVR. Late TAVR patients had higher in-hospital mortality rate (2.2 % vs. 2.8 %, p < 0.01) on unadjusted analysis but no significant difference following adjustment for demographic, clinical, and hospital characteristics [aOR 1.00 (0.82-1.23)]. Late TAVR was associated with higher odds of cardiac arrest (aOR 1.50, 95 % CI: 1.18-1.90) and use of mechanical circulatory support (aOR 2.05, 95 % CI: 1.68-2.51). Late TAVR was associated with longer hospital stay (11 days vs. 4 days, p < 0.01) and higher costs ($72,851 vs. $53,209, p < 0.01). CONCLUSION: Early TAVR was conducted in approximately 25 % of the AS patients admitted with AHF, showing improved in-hospital outcomes before adjustment, with no significant differences observed after adjustment.

Racial, ethnic and sex disparity in acute heart failure patients with COVID-19: A nationwide analysis.

Background: Patients with acute heart failure (AHF) exacerbation are susceptible to complications in the setting of COVID-19 infection. Data regarding the racial/ethnic and sex disparities in patients with AHF and COVID-19 remains limited. Objective: We aim to evaluate the impact of race, ethnicity, and sex on the in-hospital outcomes of AHF with COVID-19 infection using the data from the National Inpatient Sample (NIS). Methods: We extracted data from the NIS (2020) by using ICD-10-CM to identify all hospitalizations with a diagnosis of AHF and COVID-19 in the year 2020. The associations between sex, race/ethnicity, and outcomes were examined using a multivariable logistic regression model. Results: We identified a total of 158,530 weighted AHF hospitalizations with COVID-19 infection in 2020. The majority were White (63.9 %), 23.3 % were Black race, and 12.8 % were of Hispanic ethnicity, mostly males (n = 84,870 [53.5 %]). After adjustment, the odds of in-hospital mortality were lowest in White females (aOR 0.83, [0.78-0.98]) and highest in Hispanic males (aOR 1.27 [1.13-1.42]) compared with White males. Overall, the odds of cardiac arrest (aOR 1.54 [1.27-1.85]) and AKI (aOR 1.36 [1.26-1.47] were higher, while odds for procedural interventions such as PCI (aOR 0.23 [0.10-0.55]), and placement on a ventilator (aOR 0.85 [0.75-0.97]) were lower among Black males in comparison to White males. Conclusion: Male sex was associated with a higher risk of in-hospital mortality in white and black racial groups, while no such association was noted in the Hispanic group. Hispanic males had the highest odds of death compared with White males.

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.

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.

Outcomes of Device-detected Atrial High-rate Episodes in Patients with No Prior History of Atrial Fibrillation: A Systematic Review and Meta-analysis.

Background: Outcomes of device-detected AF remain unclear in individuals without a prior history of AF. Methods: A meta-analysis was conducted to evaluate outcomes in individuals with no prior history of AF who experienced device-detected AF. Outcomes assessed were clinical AF, thromboembolism and all-cause mortality. A fixed-effects model was used to calculate RRs with 95% CI. Results: Compared to individuals who did not experience device-detected AF, those who did had increased risks of clinical AF (RR 3.33, 95% CI [1.99.5.57]; p<0.0001) and thromboembolic events (RR 2.21; 95% CI [1.72.2.85]; p<0.0001). The risk of all-cause mortality was similar between both groups (RR 1.19; 95% CI [0.95.1.49]; p=0.13). Subgroup analysis revealed an increased risk of thromboembolic events among device-detected AF .24 hours (RR 12.34; 95% CI [2.70.56.36]). Conclusion: While there is an increased risk of clinical AF and thromboembolism in individuals with device-detected AF, mortality was insignificant.

Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development.

AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987-2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0-7 years), peripubertal (8-14 years), and postpubertal (15-18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model's discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.

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.

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.

Feasibility and Safety of Intracardiac Echocardiography Guidance in Mitral Transcatheter Edge-to-Edge Repair: Analysis of the National Inpatient Sample Data From 2015 to 2020.

Data on the use of intracardiac echocardiography (ICE) guidance in mitral transcatheter edge-to-edge repair (mTEER) procedure is limited to case reports and small case series. Our study aims to assess the feasibility, safety, utilization patterns, and clinical outcomes of mTEER procedure with ICE guidance using a nationally representative real-world cohort of patients. This study used the National Inpatient Sample database from quarter 4 of 2015 to 2020. We used a propensity-matched analysis and adjusted odds ratios for in-hospital outcomes/complications. A P value of < 0.05 was considered significant. A total of 38,770 weighted cases of mTEER were identified. Of the included patients 665 patients underwent ICE-guided mTEER while 38,105 had TEE-guided mTEER. There were no differences in the in-hospital mortality between both groups (2.5% vs 3.0%, P = 0.58). Adjusted odds of in-hospital mortality (aOR 0.83, 95%CI [0.42-1.64]) were not significantly different. There were no differences in periprocedural complications including cardiac (aOR 0.85, 95%CI [0.54-1.35]), bleeding (aOR 1.45, 95%CI [0.93-2.33]), respiratory (aOR 0.88, 95%CI [0.61-1.25]), and renal (aOR 0.89, 95%CI [0.66-1.20]) complications between patients undergoing ICE-guided vs TEE-guided mTEER. There was no difference in GI complications between both groups (aOR 1.11, 95%CI [0.46-2.70]). The adjusted length of stay was less among ICE-guided mTEER (median: 1 vs 2, P < 0.01) with lower inflation-adjusted costs of hospitalization ($35,513 vs $47,067, P < 0.01). ICE-guided mTEER is safe when compared with TEE guided mTEER with no significant differences in in-hospital mortality, cardiac, bleeding, respiratory, and renal complications.

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.

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.

Outcomes of Percutaneous Atrial Septal Defect Closure With Mitral Transcatheter Edge-to-Edge Repair and Transseptal Mitral Valve Replacement (2015 to 2020).

Patients who underwent transcatheter edge-to-edge repair (TEER) or transcatheter mitral valve replacement (TMVR) have a transeptal access created by an iatrogenic atrial septal defect (ASD) which leads to significant complications requiring closure. Given limited data, we used the National Inpatient Sample between 2015 and 2020 to evaluate the clinical outcomes of percutaneous closure of ASD (PC-ASD) in TEER/TMVR hospitalizations. A total of 44,065 eligible weighted hospitalizations with either TEER (n = 39,625, 89.9%) or TMVR (n = 4,440, 10.1%) with a higher rate of PC-ASD in the TMVR group (10.7% vs 2.0%, p <0.01). The TEER with PC-ASD group were more likely to experience acute heart failure and right ventricular failure and had longer hospital stays but there was no difference in in-hospital mortality compared with the no PC-ASD group. In the TMVR group, there was no difference in the odds of acute heart failure, right ventricular failure, cardiogenic shock, or acute hypoxic respiratory failure, but the odds of mechanical circulatory support, in-hospital mortality, and length of stay were significantly higher in patients with PC-ASD in the TMVR group. In conclusion, rates of percutaneous closure of ASD after TEER were lower than after TMVR and associated with worse in-hospital mortality in TMVR but not in TEER. Further prospective clinical trials are needed to identify patients who would benefit from the closure of iatrogenic ASD.

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.