Selecting an antiarrhythmic agent for atrial fibrillation should be a patient-specific, data-driven decision

1998 ◽  
Vol 82 (7) ◽  
pp. 72N-81N ◽  
Author(s):  
James A Reiffel
Keyword(s):  
Atrial Fibrillation ◽  
Antiarrhythmic Agent ◽  
Data Driven ◽  
Patient Specific ◽  
Specific Data

Evaluation of a computerized cardiovascular simulation system using patient-specific data

2013 ◽  
Vol 61 (S 01) ◽  
Author(s):  
M Kaur ◽  
N Sprunk ◽  
U Schreiber ◽  
R Lange ◽  
J Weipert ◽  
...  
Keyword(s):  
Simulation System ◽  
Patient Specific ◽  
Specific Data ◽  
Cardiovascular Simulation

Abstract 14839: High-resolution Spatiotemporal Changes in Dominant Frequency and Structural Organization During Persistent Atrial Fibrillation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Terrence Pong ◽  
Joy Aparicio Valenzuela ◽  
Kevin J Cyr ◽  
Cody Carlton ◽  
Sasank Sakhamuri ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
High Resolution ◽  
Structural Organization ◽  
Porcine Model ◽  
Dominant Frequency ◽  
Patient Specific ◽  
Structural Remodeling ◽  
Atrial Activity ◽  
Spatiotemporal Changes ◽  
Mapping Arrays

Introduction: Spatiotemporal differences in atrial activity are thought to contribute to the maintenance of atrial fibrillation (AF). While recent evidence has identified changes in dominant frequency (DF) during the transition from paroxysmal to persistent AF, little is known about the frequency characteristics of the epicardium during this transition. The purpose of this study was to perform high-resolution mapping of the atrial epicardium and to characterize changes in frequency activity and structural organization during the transition from paroxysmal to persistent AF. Hypothesis: In a porcine model of persistent AF, we tested the hypothesis that the epicardium undergoes spatiotemporal changes in atrial activity and structural organization during persistent AF. Methods: Paroxysmal and persistent AF was induced in adult Yorkshire swine by atrial tachypacing. Atrial morphology was segmented from magnetic resonance imaging and high-resolution patient-specific flexible mapping arrays were 3D printed to match the epicardial contours of the atria. Epicardial activation and DF mapping was performed in four paroxysmal and four persistent AF animals using personalized mapping arrays. Histological analysis was performed to determine structural differences between paroxysmal and persistent AF. Results: The left atrial epicardium was associated with a significant increase in DF between paroxysmal and persistent AF (6.5 ± 0.2 vs. 7.4 ± 0.5 Hz, P = 0.03). High-resolution spatiotemporal mapping identified organized clusters of DF during paroxysmal AF which were lost during persistent AF. The development of persistent AF led to structural remodeling with increased atrial epicardial fibrosis. The organization index (OI) significantly decreased during persistent AF in both the left atria (0.3 ± 0.03 vs. 0.2 ± 0.03, P = 0.01) and right atria (0.33 ± 0.04 vs. 0.23 ± 0.02, P = 0.02). Conclusions: In the porcine model of persistent AF, the epicardium undergoes structural remodeling with increased epicardial fibrosis, reflected by changes in atrial organization index and dominant frequency.

On-device Prior Knowledge Incorporated Learning for Personalized Atrial Fibrillation Detection

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Zhenge Jia ◽  
Yiyu Shi ◽  
Samir Saba ◽  
Jingtong Hu
Keyword(s):  
Atrial Fibrillation ◽  
Prior Knowledge ◽  
Domain Knowledge ◽  
Fine Tuning ◽  
Cardiac Monitoring ◽  
Patient Specific ◽  
Detection Accuracy ◽  
Specific Patient ◽  
Deep Model ◽  
Tuning Strategy

Atrial Fibrillation (AF), one of the most prevalent arrhythmias, is an irregular heart-rate rhythm causing serious health problems such as stroke and heart failure. Deep learning based methods have been exploited to provide an end-to-end AF detection by automatically extracting features from Electrocardiogram (ECG) signal and achieve state-of-the-art results. However, the pre-trained models cannot adapt to each patient’s rhythm due to the high variability of rhythm characteristics among different patients. Furthermore, the deep models are prone to overfitting when fine-tuned on the limited ECG of the specific patient for personalization. In this work, we propose a prior knowledge incorporated learning method to effectively personalize the model for patient-specific AF detection and alleviate the overfitting problems. To be more specific, a prior-incorporated portion importance mechanism is proposed to enforce the network to learn to focus on the targeted portion of the ECG, following the cardiologists’ domain knowledge in recognizing AF. A prior-incorporated regularization mechanism is further devised to alleviate model overfitting during personalization by regularizing the fine-tuning process with feature priors on typical AF rhythms of the general population. The proposed personalization method embeds the well-defined prior knowledge in diagnosing AF rhythm into the personalization procedure, which improves the personalized deep model and eliminates the workload of manually adjusting parameters in conventional AF detection method. The prior knowledge incorporated personalization is feasibly and semi-automatically conducted on the edge, device of the cardiac monitoring system. We report an average AF detection accuracy of 95.3% of three deep models over patients, surpassing the pre-trained model by a large margin of 11.5% and the fine-tuning strategy by 8.6%.

Effective suppression of atrial fibrillation by the antihistaminic agent antazoline: First experimental insights into a novel antiarrhythmic agent

2017 ◽  
Vol 35 (2) ◽  
pp. e12244 ◽  
Author(s):  
Gerrit Frommeyer ◽  
Magdalena Sterneberg ◽  
Dirk G. Dechering ◽  
Sven Kaese ◽  
Nils Bögeholz ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Antiarrhythmic Agent ◽  
Effective Suppression ◽  
Antihistaminic Agent

Dronedarone: a novel antiarrhythmic agent for the treatment of atrial fibrillation

2010 ◽  
Vol 25 (1) ◽  
pp. 53-58 ◽  
Author(s):  
Gabor Z Duray ◽  
Joachim R Ehrlich ◽  
Stefan H Hohnloser
Keyword(s):  
Atrial Fibrillation ◽  
Antiarrhythmic Agent

scholarly journals Computational Analysis of Mapping Catheter Geometry and Contact Quality Effects on Rotor Detection in Atrial Fibrillation

2021 ◽  
Vol 12 ◽  
Author(s):  
Chiara Bartolucci ◽  
Claudio Fabbri ◽  
Corrado Tomasi ◽  
Paolo Sabbatani ◽  
Stefano Severi ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Structural Parameters ◽  
Spiral Wave ◽  
Spatial Location ◽  
Electrode Spacing ◽  
Patient Specific ◽  
Electrode Distance ◽  
Detection Failure ◽  
Distance Position ◽  
Mapping Catheter

Atrial fibrillation (AF) is the most common cardiac arrhythmia and catheter mapping has been proved to be an effective approach for detecting AF drivers to be targeted by ablation. Among drivers, the so-called rotors have gained the most attention: their identification and spatial location could help to understand which patient-specific mechanisms are acting, and thus to guide the ablation execution. Since rotor detection by multi-electrode catheters may be influenced by several structural parameters including inter-electrode spacing, catheter coverage, and endocardium-catheter distance, in this study we proposed a tool for testing the ability of different catheter shapes to detect rotors in different conditions. An approach based on the solution of the monodomain equations coupled with a modified Courtemanche ionic atrial model, that considers an electrical remodeling, was applied to simulate spiral wave dynamics on a 2D model for 7.75 s. The developed framework allowed the acquisition of unipolar signals at 2 KHz. Two high-density multipolar catheters were simulated (Advisor™ HD Grid and PentaRay®) and placed in a 2D region in which the simulated spiral wave persists longer. The configuration of the catheters was then modified by changing the number of electrodes, inter-electrodes distance, position, and atrial-wall distance for assessing how they would affect the rotor detection. In contact with the wall and at 1 mm distance from it, all the configurations detected the rotor correctly, irrespective of geometry, coverage, and inter-electrode distance. In the HDGrid-like geometry, the increase of the inter-electrode distance from 3 to 6 mm caused rotor detection failure at 2 mm distance from the LA wall. In the PentaRay-like configuration, regardless of inter-electrode distance, rotor detection failed at 3 mm endocardium-catheter distance. The asymmetry of this catheter resulted in rotation-dependent rotor detection. To conclude, the computational framework we developed is based on realistic catheter shapes designed with parameter configurations which resemble clinical settings. Results showed it is well suited to investigate how mapping catheter geometry and location affect AF driver detection, therefore it is a reliable tool to design and test new mapping catheters.

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