Citations

Background: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology–AF) in a population-based sample. Methods: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. Results: A total of 1936 participants with median age 75.8 (interquartile range, 70.4–81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2–14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51–2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58–2.28]) independently predicted future AF without significant interaction ( P =0.54). C statistics were 0.69 (95% CI, 0.66–0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66–0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69–0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.

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.

AbstractTranscatheter mitral valve replacement (TMVR) with off‐label use of aortic balloon‐expandable transcatheter heart valves has emerged as a therapeutic option in high‐surgical risk patients with severe mitral annular calcification. Left ventricular outflow tract (LVOT) obstruction post valve deployment is a major limitation. We describe a case of septal reduction therapy using radiofrequency ablation (RFA) when established methods of septal reduction were either ineffective or not feasible. RFA permitted successful enlargement of the LVOT area and subsequent TMVR.

Abstract Aims Catheter ablation is an effective treatment for post-infarction ventricular tachycardia (VT). However, some patients may experience a worsened arrhythmia phenotype after ablation. We aimed to determine the prevalence and prognostic impact of arrhythmia exacerbation (AE) after post-infarction VT ablation. Methods and results A total of 1187 consecutive patients (93% men, median age 68 years, median ejection fraction 30%) who underwent post-infarction VT ablation at six centres were included. Arrhythmia exacerbation was defined as post-ablation VT storm or incessant VT in patients without prior similar events. During follow-up (median 717 days), 426 (36%) patients experienced VT recurrence. Events qualifying as AE occurred in 67 patients (6%). Median times to VT recurrence with and without AE were 238 [interquartile range (IQR) 35–640] days and 135 (IQR 22–521) days, respectively (P = 0.25). Almost half of the patients (46%) who experienced AE experienced it within 6 months of the index procedure. Patients with AE had had longer ablation times during the ablation procedures compared to the rest of the patients (median 42 vs. 34 min, P = 0.02). Among patients with VT recurrence, the risk of death or heart transplantation was significantly higher in patients with than without AE (hazard ratio 1.99, 95% CI 1.28–3.10; P = 0.002) after adjusting for age, gender, ejection fraction, cardiac resynchronization therapy, post-ablation non-inducibility, and post-ablation amiodarone use. Conclusion Arrhythmia exacerbation after ablation of infarct-related VT is infrequent but is independently associated with an adverse long-term outcome among patients who experience a VT recurrence. The mechanisms and mitigation strategies of AE after catheter ablation require further investigation.

Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, is undergoing a knowledge and practice transformation in the increasingly complex healthcare environment. Among other advances, deep-learning machine learning methods, including convolutional neural networks, have enabled the development of AF screening pathways using the ubiquitous 12-lead ECG to detect asymptomatic paroxysmal AF in at-risk populations (such as those with cryptogenic stroke), the refinement of AF and stroke prediction schemes through comprehensive digital phenotyping using structured and unstructured data abstraction from the electronic health record or wearable monitoring technologies, and the optimization of treatment strategies, ranging from stroke prophylaxis to monitoring of antiarrhythmic drug (AAD) therapy. Although the clinical and population-wide impact of these tools continues to be elucidated, such transformative progress does not come without challenges, such as the concerns about adopting black box technologies, assessing input data quality for training such models, and the risk of perpetuating rather than alleviating health disparities. This review critically appraises the advances of machine learning related to the care of AF thus far, their potential future directions, and its potential limitations and challenges.

Abstract This is a protocol for a co‐registered Cochrane and Campbell Review (Methodology). The objectives are as follows: To identify, describe and assess methods for: when to replicate a systematic review; how to replicate a systematic review.