Temporal Trends in the Incidence, Prevalence, and Survival of Patients With Atrial Fibrillation From 2004 to 2016

Williams, Brent A.; Honushefsky, Ashley; Berger, Peter B.

doi:10.1016/j.amjcard.2017.08.014

Cited by 57 publications

(44 citation statements)

References 28 publications

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“…Edoxaban was non-inferior to warfarin in the ENGAGE AF-TIMI trial with respect to the prevention of stroke or systemic embolism and was associated with significantly lower rates of bleeding and death from cardiovascular causes [ 3 ]. While the yearly risk of major bleeding was higher with warfarin (3.43%), the bleeding risk with a high dose of 60 mg/day of edoxaban (2.75%) was not insignificant [ 1 ].…”

Section: Discussionmentioning

confidence: 99%

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In-Vitro Sorbent-Mediated Removal of Edoxaban from Human Plasma and Albumin Solution

et al. 2020

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Background and Objective Based on previous experience of sorbent-mediated ticagrelor, dabigatran, and radiocontrast agent removal, we set out in this study to test the effect of two sorbents on the removal of edoxaban, a factor Xa antagonist direct oral anticoagulant. Methods We circulated 100 mL of edoxaban solution during six first-pass cycles through 40-mL sorbent columns (containing either CytoSorb in three passes or Porapak Q 50–80 mesh in the remaining three passes) during experiments using human plasma and 4% bovine serum albumin solution as drug vehicles. Drug concentration was measured by liquid chromatography-tandem mass spectrometry. Results Edoxaban concentration in two experiments performed with human plasma dropped from 276.8 to 2.7 ng/mL and undetectable concentrations, respectively, with CytoSorb or Porapak Q 50–80 mesh ( p = 0.0031). The average edoxaban concentration decreased from 407 ng/mL ± 216 ng/mL to 3.3 ng/mL ± 7 ng/mL ( p = 0.017), for a removal rate of 99% across all six samples of human plasma (two samples) and bovine serum albumin solution (four samples). In four out of the six adsorbed samples, the drug concentrations were undetectable. Conclusion Sorbent-mediated technology may represent a viable pathway for edoxaban removal from human plasma or albumin solution. Electronic supplementary material The online version of this article (10.1007/s40268-020-00308-1) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

“…The American Heart Association estimates that at least 6.1 million Americans are currently diagnosed on a yearly basis with atrial fibrillation [ 1 ]. A novel class of anticoagulants currently widely used in patients with atrial fibrillation are factor Xa antagonists, such as edoxaban.…”

Section: Introductionmentioning

confidence: 99%

In-Vitro Sorbent-Mediated Removal of Edoxaban from Human Plasma and Albumin Solution

et al. 2020

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“…16,17 Similar methodology has been used in prior studies using large linked data bases with identification of newonset atrial fibrillation and exclusion of comorbidities using ICD-9 codes without medical record review or with medical record review of a small sample. [18][19][20][21] Even though the accuracy of using the ICD-9 code for the identification of atrial fibrillation cases has been evaluated previously and used in other large linked database studies, [22][23][24][25][26] the lack of medical record and/or electrocardiogram review to confirm cases might have resulted in the inclusion of individuals who did not have atrial fibrillation in the case group. We performed a secondary analysis to assess whether this might be a potential limitation of our study.…”

Section: Discussionmentioning

confidence: 99%

Vaccination and risk of lone atrial fibrillation in the active component United States military

McNeil

Duderstadt

Sabatier

et al. 2019

Human Vaccines & Immunotherapeutics

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Purpose: To evaluate the hypothesis that receipt of anthrax vaccine adsorbed (AVA) increases the risk of atrial fibrillation in the absence of identifiable underlying risk factors or structural heart disease (lone atrial fibrillation). Methods: We conducted a retrospective population-based cohort study among U.S. military personnel who were on active duty during the period from January 1, 1998 through December 31, 2006. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes were used to identify individuals diagnosed with atrial fibrillation in the Defense Medical Surveillance System, and electronic records were screened to include only individuals without evidence of predisposing medical conditions. We used multivariable Poisson regression to estimate the risk of lone atrial fibrillation after exposure to AVA. We also evaluated possible associations with influenza and smallpox vaccines. Results: Our study population consisted of 2,957,091individuals followed for 11,329,746 person-years of service. Of these, 2,435 met our case definition for lone atrial fibrillation, contributing approximately 8,383 person-years of service. 1,062,176 (36%) individuals received at least one dose of AVA; the median person time observed post-exposure was 3.6 years. We found no elevated risk of diagnosed lone atrial fibrillation associated with AVA (adjusted risk ratio = 0.99; 95% confidence interval = 0.90, 1.09; p = 0.84). No elevated risk was observed for lone atrial fibrillation associated with influenza or smallpox vaccines given during military service. Conclusions: We did not find an increased risk of lone atrial fibrillation after AVA, influenza or smallpox vaccine. These findings may be helpful in planning future vaccine safety research.

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“…Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice [1]. The prevalence and incidence of AF have risen over the years with the aging population [2].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

Kwon¹,

Hong²,

Choi³

et al. 2019

JMIR Mhealth Uhealth

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Background Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-based device with good accuracy. However, patients with paroxysmal AF frequently exhibit premature atrial complexes (PACs), which result in poor unmanned AF detection, mainly because of rule-based or handcrafted machine learning techniques that are limited in terms of diagnostic accuracy and reliability. Objective This study aimed to develop deep learning (DL) classifiers using PPG data to detect AF from the sinus rhythm (SR) in the presence of PACs after successful cardioversion. Methods We examined 75 patients with AF who underwent successful elective direct-current cardioversion (DCC). Electrocardiogram and pulse oximetry data over a 15-min period were obtained before and after DCC and labeled as AF or SR. A 1-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) were chosen as the 2 DL architectures. The PAC indicator estimated the burden of PACs on the PPG dataset. We defined a metric called the confidence level (CL) of AF or SR diagnosis and compared the CLs of true and false diagnoses. We also compared the diagnostic performance of 1D-CNN and RNN with previously developed AF detectors (support vector machine with root-mean-square of successive difference of RR intervals and Shannon entropy, autocorrelation, and ensemble by combining 2 previous methods) using 10 5-fold cross-validation processes. Results Among the 14,298 training samples containing PPG data, 7157 samples were obtained during the post-DCC period. The PAC indicator estimated 29.79% (2132/7157) of post-DCC samples had PACs. The diagnostic accuracy of AF versus SR was 99.32% (70,925/71,410) versus 95.85% (68,602/71,570) in 1D-CNN and 98.27% (70,176/71,410) versus 96.04% (68,736/71,570) in RNN methods. The area under receiver operating characteristic curves of the 2 DL classifiers was 0.998 (95% CI 0.995-1.000) for 1D-CNN and 0.996 (95% CI 0.993-0.998) for RNN, which were significantly higher than other AF detectors ( P <.001). If we assumed that the dataset could emulate a sufficient number of patients in training, both DL classifiers improved their diagnostic performances even further especially for the samples with a high burden of PACs. The average CLs for true versus false classification were 98.56% versus 78.75% for 1D-CNN and 98.37% versus 82.57% for RNN ( P <.001 for all cases). Conclusions New DL classifiers could detect AF using PPG monitoring signals with high diagnostic accuracy even with frequent PACs and could outperform previously developed AF detectors. Although diagnostic performance decreased as the burden of PACs increased, performance improved when samples from more patients were trained. Moreover, the reliability of th...

show abstract

Temporal Trends in the Incidence, Prevalence, and Survival of Patients With Atrial Fibrillation From 2004 to 2016

Cited by 57 publications

References 28 publications

In-Vitro Sorbent-Mediated Removal of Edoxaban from Human Plasma and Albumin Solution

In-Vitro Sorbent-Mediated Removal of Edoxaban from Human Plasma and Albumin Solution

Vaccination and risk of lone atrial fibrillation in the active component United States military

Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

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