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Introduction: The American College of Cardiology developed the National Cardiovascular Data Registry (NCDR) AFib Ablation Registry to assess prevalence, demographics, procedural management and outcomes of patients undergoing atrial fibrillation (AFib) ablation procedures. The objective of this registry’s first presentation is to summarize initial AFib Ablation Registry data regarding patient characteristics, procedural characteristics, and in-hospital adverse events and outcomes in those undergoing AFib ablation enrolled in the registry. Methods: Data were tabulated and summarized with simple proportions for categorical data, and mean ± standard deviation for continuous data. Results: A total of 31,345 patients who underwent AFib ablation were included in the registry (mean age 65.0 ± 10.4, 65.2% male, 57.6% paroxysmal AFib), with ablations performed at 120 hospitals, by 501 physicians. The mean CHA2DS2-VASc score of was 2.6 ± 1.6, and mean HAS-BLED score was 1.1 ± 0.9. Successful pulmonary vein isolation was able to be achieved in all veins in 94.5% of patients. The prevalence of any complication during procedural admission was 3.1%; the most common were hematoma (0.5%) and pericardial effusion requiring intervention (0.42%). The mean length of stay during the index admission (days) was 1.4 ± 7.6, and in-hospital death prevalence was 0.04% [FIGURE] . Conclusions: In conclusion, the NCDR AFib Ablation Registry is a large, national, multi-center, prospective cohort study to describe events outside the clinical trial or research setting, demonstrating successful pulmonary vein isolation in the majority of patients and a low prevalence of adverse events. Future studies from the registry will be able to help support and develop evidence-based guidelines for AFib ablation treatment and optimize patient outcomes.

Background: Clinical factors including previous history of AF, heart failure, hypertension, valvular heart disease, increased age and male gender increase the risk of AF in CLL patients (Shanafelt, Leukemia and Lymphoma 2017). Treatment with Bruton tyrosine kinase inhibitors (BTKi) such as ibrutinib has also been associated with an increased risk of AF in CLL. We evaluated the role of artificial intelligence electrocardiography (AI-ECG) in predicting ibrutinib-induced AF (and, for reference, AF unrelated to ibrutinib) in patients with CLL. Methods: We identified two cohorts of CLL patients using the Mayo Clinic CLL Database. Cohort 1 included patients evaluated within 12 months of CLL diagnosis who did not ever receive ibrutinib. Cohort 2 included patients who were treated with ibrutinib. The electrocardiographic signature of AF in sinus rhythm was detected by an AI-ECG algorithm previously developed using a convolutional neural network (Attia, Lancet 2019). The baseline AI-ECG AF score (positive defined as >0.10 on a scale of 0-1 which offers best balance between sensitivity and specificity per Attia et al.) was computed based on ECGs obtained within 10 years prior to CLL diagnosis (Cohort 1) or 10 years prior to initiation of ibrutinib therapy (Cohort 2). Patients with AF at baseline, missing data, or with ECGs previously used to train the AI algorithm were excluded. Reverse Kaplan Meier diagrams were plotted for both cohorts grouped by AI-ECG positivity. Cox proportional hazards were fitted to assess the predictive ability of AI-ECG in both cohorts. Results: After screening 2,739 patients and applying exclusion criteria (126 patients had baseline AF) a total of 1,149 patients with median 4 (interquartile range [IQR] 2-9) baseline ECGs were included in the analysis (Figure 1A). Cohort 1 included 951 patients with a median follow up of 3.0 (IQR 0.6-7.0) years and positive baseline AI-ECG in 546 (57%) patients. Cohort 2 included 198 patients with a median follow up of 1.6 (IQR 0.7-3.2) years and positive baseline AI-ECG in 91 (46%) patients. In Cohort 1, the median age was 67 years (IQR 58-72), 681 (72%) of patients were men, 68% had low/intermediate risk CLL-International Prognostic Index (IPI), and 32% had high/very high-risk CLL-IPI. In Cohort 2, the median age was 69 years (IQR 62-75), 139 (70%) of patients were men, 13% had low/intermediate risk CLL-IPI, and 87% had high/very high-risk CLL-IPI. AF occurred during follow up in 164 patients (17%) in Cohort 1 and 46 patients (23%) in Cohort 2. In both Cohorts 1 and 2, a positive baseline AI-ECG significantly increased the incidence of AF during follow up (log rank <0.001) (Figure 1B and C). Hazard ratios (for positive vs. negative AI-ECG) were 33.9 (95% confidence interval [CI] 15.0-76.6) for Cohort 1 and 14.8 (95% CI 5.3-41.3) for Cohort 2. Conclusion: The addition of AI to a standard 12-lead ECG obtained during normal sinus rhythm - an inexpensive and ubiquitous test - predicts the occurrence of future AF in patients with CLL. This holds true irrespective of BTKi -based therapy and has important implications for the management of CLL patients. Disclosures Ding: Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; DTRM: Research Funding; Astra Zeneca: Research Funding; Abbvie: Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees. Kenderian:BMS: Research Funding; Gilead: Research Funding; Novartis: Patents & Royalties, Research Funding; Mettaforge: Patents & Royalties; Juno: Research Funding; MorphoSys: Research Funding; Lentigen: Research Funding; Sunesis: Research Funding; Tolero: Research Funding; Kite: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Torque: Consultancy. Wang:Novartis: Research Funding; Innocare: Research Funding; Incyte: Research Funding. Kay:Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Abbvie: Research Funding; MEI Pharma: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kapoor:Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Celgene: Honoraria; GlaxoSmithKline: Research Funding. Parikh:MorphoSys: Research Funding; Janssen: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Merck: Research Funding; AbbVie: Honoraria, Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; TG Therapeutics: Research Funding; Pharmacyclics: Honoraria, Research Funding.

Background: Patients with atrial fibrillation and severely decreased kidney function were excluded from the pivotal non–vitamin K antagonist oral anticoagulants (NOAC) trials, thereby raising questions about comparative safety and effectiveness in patients with reduced kidney function. The study aimed to compare oral anticoagulants across the range of kidney function in patients with atrial fibrillation. Methods and Results: Using a US administrative claims database with linked laboratory data, 34 569 new users of oral anticoagulants with atrial fibrillation and estimated glomerular filtration rate ≥15 mL/(min·1.73 m 2 ) were identified between October 1, 2010 to November 29, 2017. The proportion of patients using NOACs declined with decreasing kidney function—73.5%, 69.6%, 65.4%, 59.5%, and 45.0% of the patients were prescribed a NOAC in estimated glomerular filtration rate ≥90, 60 to 90, 45 to 60, 30 to 45, 15 to 30 mL/min per 1.73 m 2 groups, respectively. Stabilized inverse probability of treatment weighting was used to balance 4 treatment groups (apixaban, dabigatran, rivaroxaban, and warfarin) on 66 baseline characteristics. In comparison to warfarin, apixaban was associated with a lower risk of stroke (hazard ratio [HR], 0.57 [0.43–0.75]; P <0.001), major bleeding (HR, 0.51 [0.44–0.61]; P <0.001), and mortality (HR, 0.68 [0.56–0.83]; P <0.001); dabigatran was associated with a similar risk of stroke but a lower risk of major bleeding (HR, 0.57 [0.43–0.75]; P <0.001) and mortality (HR, 0.68 [0.48-0.98]; P =0.04); rivaroxaban was associated with a lower risk of stroke (HR, 0.69 [0.51–0.94]; P =0.02), major bleeding (HR, 0.84 [0.72–0.99]; P =0.04), and mortality (HR, 0.73 [0.58–0.91]; P =0.006). There was no significant interaction between treatment and estimated glomerular filtration rate categories for any outcome. When comparing one NOAC to another NOAC, there was no significant difference in mortality, but some differences existed for stroke or major bleeding. No relationship between treatments and falsification end points was found, suggesting no evidence for substantial residual confounding. Conclusions: Relative to warfarin, NOACs are used less frequently as kidney function declines. However, NOACs appears to have similar or better comparative effectiveness and safety across the range of kidney function.

AbstractAims The Food and Drug Administration (FDA) approval of the Watchman device [percutaneous left atrial appendage occlusion (LAAO)] has resulted in the widespread use of this procedure in many centres across the USA. We sought to estimate the nationwide utilization and frequency of adverse outcomes associated with Watchman device implantation. The objective of this study was to evaluate the Watchman device implantation peri-procedural complications and comparison of the results with the previous studies.Methods and results The National Inpatient Sample (NIS) was queried for all hospitalizations with a primary diagnosis of atrial fibrillation or atrial flutter during the year 2016 with percutaneous LAAO during the same admission (ICD-10 code—02L73DK). The frequency of peri-procedural complications, including mortality, procedure-related stroke, major bleeding requiring blood transfusion, and pericardial effusion, was assessed. We compared the complication rates with the published randomized controlled trials and the European Watchman registry. An estimated 5175 LAAO procedures were performed in 2016. The majority of procedures was performed in males (59.1%), age ≥75 years (58.7%), and White (83.1%). The overall complication rate was 1.9%. The in-hospital mortality was 0.29%. Pericardial effusion requiring pericardiocentesis was the most frequent complication (0.68%). Bleeding requiring transfusion was noted in 0.1% of device implants. The rates of post-procedure stroke and systemic embolism were 0% and 0.29%, respectively.Conclusion Percutaneous LAAO with the Watchman device in the USA is associated with low in-hospital complications and a similar safety profile to a recently published EWOLUTION cohort. The complication rates were lower than those reported in the major randomized clinical trials (RCTs). We report the frequency of peri-procedural complications of the LAAO using the Watchman device from the NIS database. We also compare the frequency of peri-procedural complications with the previously published RCTs and EWOLUTION cohort. Our findings are in concordance with findings from EWOLUTION cohort and compare favourably with RCTs.

Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83–0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.

Background: The improved life expectancy observed in patients living with Human Immunodeficiency Virus (HIV) infection has made age-related cardiovascular complications, including arrhythmias, a growing health concern. We describe the temporal trends in frequency of various arrhythmias and assess impact of arrhythmias on hospitalized HIV patients using the Nationwide Inpatient Sample (NIS) Methods and Results: Data on HIV-related hospitalizations from 2005 to 2014 were obtained from the NIS using International Classification of Diseases, 9th Revision (ICD-9) codes. Data was further subclassified into hospitalizations with associated arrhythmias and those without arrhythmia. Baseline demographics and comorbidities were determined. Outcomes including in-hospital mortality, cost of care, and length of stay were extracted. SAS 9.4 (SAS Institute Inc., Cary, North Carolina) was utilized for analysis. A multivariable analysis was performed to identify predictors of arrhythmias among hospitalized HIV patients. Among 2,370,751 HIV-related hospitalizations identified, the overall frequency of any arrhythmia was 3.01%. Atrial fibrillation (AF) was the most frequent arrhythmia (2110 per 100,000). The overall frequency of arrhythmias has increased over time by 108%, primarily due to a 132% increase in AF. Arrhythmias are more frequent among older males, lowest income quartile and non-elective admissions. Patients with arrhythmias had a higher in-hospital mortality rate (9.6%). In-hospital mortality among patients with arrhythmias has decreased over time by 43.8%. The cost of care and length of stay associated with arrhythmia-related hospitalizations were mostly unchanged. Conclusions: Arrhythmias are associated with significant morbidity and mortality in hospitalized HIV patients. AF is the most frequent arrhythmia in hospitalized HIV patients.

SummaryBackgroundGastrointestinal bleeding (GIB) frequently occurs following percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS) with the prescription of P2Y12 inhibiting antiplatelet agents. Compared with clopidogrel, the newer P2Y12 inhibitors lower major adverse cardiac events with similar or possibly higher major bleeding events. The comparative GIB rates of these medications remain poorly understood.AimTo compare GIB rates associated with clopidogrel, prasugrel and ticagrelor using national medical and pharmacy claims data from privately insured and Medicare Advantage enrollees .MethodsPropensity score and inverse probability treatment weighting were used to balance baseline characteristics among treatment groups. The 1‐year GIB risk was calculated using weighted Cox proportional hazard models and expressed as hazard ratios (HR) with 95% confidence intervals (CI) and number needed to harm (NNH).ResultsWe identified 37 019 patients with ACS (non‐ST elevation ACS [NSTE‐ACS] and ST‐elevation myocardial infarction [STEMI]) within 14 days of a PCI (mean age 63 years and 70% male). Clopidogrel prescription was most common (69%) with prasugrel (16%) and ticagrelor (14%) prescribed less frequently. When compared with clopidogrel, ticagrelor was associated with a 34% risk reduction (HR 0.66; 95% CI: 0.54–0.81) in GIB overall and with NSTE‐ACS, and a 37% GIB risk reduction (HR 0.63; 95% CI: 0.42–0.93) in STEMI patients. When compared with clopidogrel, prasugrel was associated with a 21% risk reduction (HR 0.79; 95% CI: 0.64–0.97) overall, a 36% GIB risk reduction (HR 0.64; 95% CI: 0.49–0.85) in STEMI patients but no reduction of GIB risk in NSTE‐ACS patients.ConclusionsIn the first year following PCI, ticagrelor or prasugrel are associated with fewer GIB events than clopidogrel.

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.