Personalized imaging and modeling strategies for arrhythmia prevention and therapy

Trayanova, Natalia A.; Boyle, Patrick; Nikolov, Plamen

doi:10.1016/j.cobme.2017.11.007

Cited by 23 publications

(16 citation statements)

References 56 publications

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“…Although the entire chamber AF mapping technique, such as focal impulse and rotor mapping (FIRM) 33 and panoramic mapping 34 , has been developed, there is a limit to the spatial resolution of the AF map. In recent years, high-performance modeling reflecting the MRI late gadolinium-enhanced fibrosis, fiber orientation, and atrial thickness has been developed and has been clinically applied ( Supplementary Table S1) [39][40][41][42] . Using this sophisticated simulation modeling, high-density entire chamber mapping of AF, reflecting personalized anatomy, fiber orientation, and fibrosis is possible.…”

Section: Discussionmentioning

confidence: 99%

In situ procedure for high-efficiency computational modeling of atrial fibrillation reflecting personal anatomy, fiber orientation, fibrosis, and electrophysiology

Lim

Kim

Hwang

et al. 2020

Sci Rep

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We previously reported the feasibility and efficacy of a simulation-guided clinical catheter ablation of atrial fibrillation (AF) in an in-silico AF model. We developed a highly efficient realistic AF model reflecting the patient endocardial voltage and local conduction and tested its clinical feasibility. We acquired > 500 endocardial bipolar electrograms during right atrial pacing at the beginning of the AF ablation procedures. Based on the clinical bipolar electrograms, we generated simulated voltage maps by applying fibrosis and local activation maps adjusted for the fiber orientation. The software's accuracy (CUVIA2.5) was retrospectively tested in 17 patients and feasibility prospectively in 10 during clinical AF ablation. Results: We found excellent correlations between the clinical and simulated voltage maps (R = 0.933, p < 0.001) and clinical and virtual local conduction (R = 0.958, p < 0.001). The proportion of virtual local fibrosis was 15.4, 22.2, and 36.9% in the paroxysmal AF, persistent AF, and post-pulmonary vein isolation (PVI) states, respectively. The reconstructed virtual bipolar electrogram exhibited a relatively good similarities of morphology to the local clinical bipolar electrogram (R = 0.60 ± 0.08, p < 0.001). Feasibility testing revealed an in situ procedural computing time from the clinical data acquisition to wave-dynamics analyses of 48.2 ± 4.9 min. All virtual analyses were successfully achieved during clinical PVI procedures. We developed a highly efficient, realistic, in situ procedural simulation model reflective of individual anatomy, fiber orientation, fibrosis, and electrophysiology that can be applied during AF ablation. Catheter ablation (CA) is an effective approach for rhythm control management of atrial fibrillation (AF) 1,2. However, the recurrence rate after AF ablation procedures is still substantial 3. Although pulmonary vein isolation (PVI) is a well-established target of AF ablation, extra-pulmonary vein (PV) foci or drivers maintain AF in some patients, and extra-PV foci are more commonly found in AF patients with significant left atrial (LA) remodeling 4,5. The Substrate and Trigger Ablation for Reduction of Atrial Fibrillation trial part 2 (STAR AF2) demonstrated that an empirical extra-PV ablation did not improve the rhythm outcome compared with a circumferential PVI alone in patients with persistent AF (PeAF) 6. However, the one-year recurrence rate was higher than 40% regardless of any additional extra-PV ablation after the PVI, and the outcome of the invasive interventional catheter procedure was not adequate. Therefore, an innovative mapping technology to identify the core target of AF is needed in AF catheter ablation (AFCA). Simulation is a very useful computer-aided method for identifying appropriate intervention targets. We recently reported the feasibility of a simulation-guided PeAF ablation by applying a personalized heart computed tomography (CT) image-integrated AF simulation 7,8. To further this method, we developed a more realistic AF simulation r...

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Section: Discussionmentioning

confidence: 99%

In situ procedure for high-efficiency computational modeling of atrial fibrillation reflecting personal anatomy, fiber orientation, fibrosis, and electrophysiology

Lim

Kim

Hwang

et al. 2020

Sci Rep

View full text Add to dashboard Cite

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“…Monitoring and timely detecting dangerous arrhythmias in a patient will help preventing the threat of stroke or sudden death from heart failure [1]. In a lot of works [2,3], studies have been carried out aimed at identifying life-threatening arrhythmias. This process is a long and tedious procedure for both the patient and the cardiologist.…”

Section: Introductionmentioning

confidence: 99%

Methods of extracting electrocardiograms from electronic signals and images in the Python environment

Zholmagambetova¹,

Mazakov²,

Jomartova³

et al. 2020

Diagnostyka

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High-quality signal processing of an electrocardiogram (ECG) is an urgent problem in present day diagnostics for revealing dangerous signs of cardiovascular diseases and arrhythmias in patients. The used methods and programs of signal analysis and classification work with the arrays of points for mathematical modeling that must be extracted from an image or recording of an electrocardiogram. The aim of this work is developing a method of extracting images of ECG signals into a one-dimensional array. An algorithm is proposed based on sequential color processing operations and improving the image quality, masking and building a one-dimensional array of points using Python tools and libraries with open access. The results of testing samples from the ECG database and comparing images before and after processing show that the signal extraction accuracy is approximately 95 %. In addition, the presented application design is simple and easy to use. The proposed program for analyzing and processing the ECG data has a great potential in the future for the development of more complex software applications for automatic analyzing the data and determining arrhythmias or other pathologies.

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“…Recently, computational modeling has been making remarkable progress, and its clinical usefulness is gradually increasing. Computational AF modeling reflecting a sophisticated histology such as fibrosis and a personalized fiber orientation using magnetic resonance imaging (MRI)late gadolinium enhancement has been developed and is now applicable to clinical data (Jacquemet, 2015;Trayanova et al, 2018). Because the wave-dynamics mechanism of AF is highly affected by the atrial anatomy and surface curvature (Song et al, 2018), we hypothesized that a customized extra-PV ablation according to the anatomy of the atrium would reduce the recurrence rate after PeAF or longstanding PeAF ablation.…”

Section: Introductionmentioning

confidence: 99%

Clinical Usefulness of Computational Modeling-Guided Persistent Atrial Fibrillation Ablation: Updated Outcome of Multicenter Randomized Study

et al. 2019

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Objective: Catheter ablation of persistent atrial fibrillation (AF) is still challenging, no optimal extra-pulmonary vein lesion set is known. We previously reported the clinical feasibility of computational modeling-guided AF catheter ablation. Methods: We randomly assigned 118 patients with persistent AF (77.8% men, age 60.8 ± 9.9 years) to the computational modeling-guided ablation group (53 patients) and the empirical ablation group (55 patients) based on the operators' experience. For virtual ablation, four virtual linear and one electrogram-guided lesion sets were tested on patient heart computed tomogram-based models, and the lesion set with the fastest termination time was reported to the operator in the modeling-guided ablation group. The primary outcome was freedom from atrial tachyarrhythmias lasting longer than 30 s after a single procedure. Results: During 31.5 ± 9.4 months, virtual ablation procedures were available in 95.2% of the patients (108/118). Clinical recurrence rate was significantly lower after a modeling-guided ablation than after an empirical ablation (20.8 vs. 40.0%, log-rank p = 0.042). Modeling-guided ablation was independently associated with a better longterm rhythm outcome of persistent AF ablation (HR = 0.29 [0.12-0.69], p = 0.005). The rhythm outcome of the modeling-guided ablation showed better trends in males, nonobese patients with a less remodeled atrium (left atrial dimension < 50 mm), ejection fraction ≥ 50%, and those without hypertension or diabetes (p < 0.01). There were no significant differences between the groups for the total procedure time (p = 0.403), ablation time (p = 0.510), and major complication rate (p = 0.900).

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Personalized imaging and modeling strategies for arrhythmia prevention and therapy

Cited by 23 publications

References 56 publications

In situ procedure for high-efficiency computational modeling of atrial fibrillation reflecting personal anatomy, fiber orientation, fibrosis, and electrophysiology

In situ procedure for high-efficiency computational modeling of atrial fibrillation reflecting personal anatomy, fiber orientation, fibrosis, and electrophysiology

Methods of extracting electrocardiograms from electronic signals and images in the Python environment

Clinical Usefulness of Computational Modeling-Guided Persistent Atrial Fibrillation Ablation: Updated Outcome of Multicenter Randomized Study

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