Citations

Background: Recognition of the causes of early mortality after atrial fibrillation (AF) catheter ablation is essential for the improvement of patient safety. This study sought to determine the causes of early mortality (≤90 days) after AF ablation. Methods: We performed a retrospective analysis of AF ablation from January 1, 2013, to December 1, 2021 at the Mayo Clinic (Rochester, Phoenix, and Jacksonville). Causes of death were identified through a comprehensive chart review of the electronic health record from within the Mayo Clinic system and outside records when available. Results: A total of 6723 patients were included in the study. The 90-day all-cause mortality rate was 0.22% (n=15). Among all 90-day deaths, majority of the deaths (73.3%) did not have a direct relationship with the procedure. Sudden death was the most common cause of early death (20%), followed by peri-procedural stroke (13%), respiratory failure (13%), atrioesophageal fistula (13%), infection (7%), heart failure (7%), and traumatic brain injury (7%). The 90-day mortality rate directly due to AF ablation procedural complications was 0.06% (n=4). Conclusions: AF ablation procedure has a 90-day mortality of 0.22%, and the most common cause of early mortality was sudden death. The majority (73.3%) of early mortality was not directly associated with a procedural complication, and the mortality rate due to complications associated with the AF ablation procedure was low at 0.06%. Further studies are required to investigate causes and risk factors associated with sudden death in this patient population.

AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.

Background: A recent subanalysis of the EAST-AFNET 4 (Early Treatment of Atrial Fibrillation for Stroke Prevention Trial) suggests a stronger benefit of early rhythm control (ERC) in patients with atrial fibrillation and a high comorbidity burden when compared to patients with a lower comorbidity burden. Methods: We identified 109 739 patients with newly diagnosed atrial fibrillation in a large United States deidentified administrative claims database (OptumLabs) and 11 625 patients in the population-based UKB (UK Biobank). ERC was defined as atrial fibrillation ablation or antiarrhythmic drug therapy within the first year after atrial fibrillation diagnosis. Patients were classified as (1) ERC and high comorbidity burden (CHA 2 DS 2 -VASc score ≥4); (2) ERC and lower comorbidity burden (CHA 2 DS 2 -VASc score 2–3); (3) no ERC and high comorbidity burden; and (4) no ERC and lower comorbidity burden. Patients without an elevated comorbidity burden (CHA 2 DS 2 -VASc score 0–1) were excluded. Propensity score overlap weighting and cox proportional hazards regression were used to balance patients and compare groups for the primary composite outcome of all-cause mortality, stroke, or hospitalization with the diagnoses heart failure or myocardial infarction as well as for a primary composite safety outcome of death, stroke, and serious adverse events related to ERC. Results: In both cohorts, ERC was associated with a reduced risk for the primary composite outcome in patients with a high comorbidity burden (OptumLabs: hazard ratio, 0.83 [95% CI 0.72–0.95]; P =0.006; UKB: hazard ratio, 0.77 [95% CI, 0.63–0.94]; P =0.009). In patients with a lower comorbidity burden, the difference in outcomes was not significant (OptumLabs: hazard ratio, 0.92 [95% CI, 0.54–1.57]; P =0.767; UKB: hazard ratio, 0.94 [95% CI, 0.83–1.06]; P =0.310). The comorbidity burden interacted with ERC in the UKB (interaction- P =0.027) but not in OptumLabs (interaction- P =0.720). ERC was not associated with an increased risk for the primary safety outcome. Conclusions: ERC is safe and may be more favorable in a population-based sample of patients with high a comorbidity burden (CHA 2 DS 2 -VASc score ≥4).

Atrial fibrillation (AF) is one of the strongest risk factors for ischemic stroke, which is a leading cause of disability and death. Given the aging population, increasing prevalence of AF risk factors, and improved survival in those with cardiovascular disease, the number of individuals affected by AF will continue increasing over time. While multiple proven stroke prevention therapies exist, important questions remain about the optimal approach to stroke prevention at the population and individual patient levels. Our report summarizes the National Heart, Lung, and Blood Institute virtual workshop focused on identifying key research opportunities related to stroke prevention in AF. The workshop reviewed major knowledge gaps and identified targeted research opportunities to advance stroke prevention in AF in the following areas: (1) improving risk stratification tools for stroke and intracranial hemorrhage; (2) addressing challenges with oral anticoagulants; and (3) delineating the optimal roles of percutaneous left atrial appendage occlusion and surgical left atrial appendage closure/excision. This report aims to promote innovative, impactful research that will lead to more personalized, effective use of stroke prevention strategies in people with AF.

Abstract Aims An artificial intelligence-enabled electrocardiogram (AI-ECG) is a promising tool to detect patients with aortic stenosis (AS) before developing symptoms. However, functional, structural, or haemodynamic components reflected in AI-ECG responsible for its detection are unknown. Methods and results The AI-ECG model that was developed at Mayo Clinic using a convolutional neural network to identify patients with moderate–severe AS was applied. In patients used as the testing group, the correlation between the AI-ECG probability of AS and echocardiographic parameters was investigated. This study included 102 926 patients (63.0 ± 16.3 years, 52% male), and 28 464 (27.7%) were identified as AS positive by AI-ECG. Older age, atrial fibrillation, hypertension, diabetes, coronary artery disease, and heart failure were more common in the positive AI-ECG group than in the negative group (P < 0.001). The AI-ECG was correlated with aortic valve area (ρ = −0.48, R2 = 0.20), peak velocity (ρ = 0.22, R2 = 0.08), and mean pressure gradient (ρ = 0.35, R2 = 0.08). The AI-ECG also correlated with left ventricular (LV) mass index (ρ = 0.36, R2 = 0.13), E/e′ (ρ = 0.36, R2 = 0.12), and left atrium volume index (ρ = 0.42, R2 = 0.12). Neither LV ejection fraction nor stroke volume index had a significant correlation with the AI-ECG. Age correlated with the AI-ECG (ρ = 0.46, R2 = 0.22) and its correlation with echocardiography parameters was similar to that of the AI-ECG. Conclusion A combination of AS severity, diastolic dysfunction, and LV hypertrophy is reflected in the AI-ECG to detect AS. There seems to be a gradation of the cardiac anatomical/functional features in the model and its identification process of AS is multifactorial.

Abstract Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

Abstract The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient outcomes and covariates from electronic health record data. Several data quality assurance (QA) analyses were implemented on the OMOP CDM to validate the data extraction, transformation, and load (ETL) process. OMOP CDM-based data of relevant patient encounters were successfully established to support studies for FDA regulatory submissions. QA analyses verified that the data transformation was robust between data sources and OMOP CDM. Our efforts provided useful insights in real-world data integration using OMOP CDM for medical device evaluation coordinated across multiple healthcare systems.

Despite the global COVID-19 pandemic, during the past 2 years, there have been numerous advances in our understanding of arrhythmia mechanisms and diagnosis and in new therapies. We increased our understanding of risk factors and mechanisms of atrial arrhythmias, the prediction of atrial arrhythmias, response to treatment, and outcomes using machine learning and artificial intelligence. There have been new technologies and techniques for atrial fibrillation ablation, including pulsed field ablation. There have been new randomized trials in atrial fibrillation ablation, giving insight about rhythm control, and long-term outcomes. There have been advances in our understanding of treatment of inherited disorders such as catecholaminergic polymorphic ventricular tachycardia. We have gained new insights into the recurrence of ventricular arrhythmias in the setting of various conditions such as myocarditis and inherited cardiomyopathic disorders. Novel computational approaches may help predict occurrence of ventricular arrhythmias and localize arrhythmias to guide ablation. There are further advances in our understanding of noninvasive radiotherapy. We have increased our understanding of the role of His bundle pacing and left bundle branch area pacing to maintain synchronous ventricular activation. There have also been significant advances in the defibrillators, cardiac resynchronization therapy, remote monitoring, and infection prevention. There have been advances in our understanding of the pathways and mechanisms involved in atrial and ventricular arrhythmogenesis.

AbstractBackgroundAccurate 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.ObjectiveDevelop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG.MethodsWe 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).ResultsOf 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.ConclusionAccurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.

Abstract Background Accurate automated wide QRS complex tachycardia (WCT) discrimination between ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using novel calculations derived from computerized electrocardiogram (ECG) data from paired WCT and baseline ECGs. Purpose Our aim was to develop and trial novel WCT discrimination approaches for WCT patients with and without a corresponding baseline ECG. Central to this analysis was the creation and use of a novel parameter (i.e., percent monophasic time-voltage area [PMonoTVA] [%]) that may be derived from computerized ECG measurements present on the WCT ECG alone. Methods In a two-part study, we derived and tested WCT differentiation models comprised of novel and previously established parameters formulated from computerized data of paired WCT and baseline ECGs. In Part 1, novel and established parameters generated from WCT and baseline ECG data were used to derive, validate, and compare five different binary classification models: (i) logistic regression [LR], (ii) artificial neural network [ANN], (iii) Random Forests [RF], (iv) support vector machine [SVM], and (v) ensemble learning (EL). In Part 2, two unique LR models were derived, validated, and compared using parameters generated from computerized data of the (i) WCT ECG alone (i.e., Solo Model) and (ii) paired WCT and baseline ECGs (i.e., Paired Model). Results In Part 1, among 103 patients with VT or SWCT diagnoses established from corroborating electrophysiology studies or intra-cardiac device recordings, favorable diagnostic performance was achieved by all modeling technique subtypes: LR (area under the receiver operating characteristic curve [AUC] 0.95), ANN (AUC 0.91), RF (AUC 0.97), SVM (AUC 0.98), and EL (AUC 0.97). In Part 2, among 235 patients with a VT or SWCT diagnosis established with (Gold Standard cohort) or without (Non-Gold Standard cohort) a corroborating electrophysiology procedure or intra-cardiac device recording, favorable diagnostic performance was achieved by the Solo Model (AUC 0.86) and Paired Model (AUC 0.95) (Table). Conclusion Accurate WCT discrimination may be accomplished using novel parameters derived from computerized data of the WCT ECG alone and paired WCT and baseline ECGs. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health