Ventricular Restitution Predicts Paroxysmal Atrial Fibrillation in Horses

Therapeutic advances in atrial fibrillation based on animal models

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia among humans, with its incidence increasing significantly with age. Despite the high frequency of AF in clinical practice, its etiology and management remain elusive. To develop effective treatment strategies, it is imperative to comprehend the underlying mechanisms of AF; therefore, the establishment of animal models of AF is vital to explore its pathogenesis. While spontaneous AF is rare in most animal species, several large animal models, particularly those of pigs, dogs, and horses, have proven as invaluable in recent years in advancing our knowledge of AF pathogenesis and developing novel therapeutic options. This review aims to provide a comprehensive discussion of various animal models of AF, with an emphasis on the unique features of each model and its utility in AF research and treatment. The data summarized in this review provide valuable insights into the mechanisms of AF and can be used to evaluate the efficacy and safety of novel therapeutic interventions.

CVAR-Seg: An Automated Signal Segmentation Pipeline for Conduction Velocity and Amplitude Restitution

BACKGROUND

Rate-varying S1S2 stimulation protocols can be used for restitution studies to characterize atrial substrate, ionic remodeling, and atrial fibrillation risk. Clinical restitution studies with numerous patients create large amounts of these data. Thus, an automated pipeline to evaluate clinically acquired S1S2 stimulation protocol data necessitates consistent, robust, reproducible, and precise evaluation of local activation times, electrogram amplitude, and conduction velocity. Here, we present the CVAR-Seg pipeline, developed focusing on three challenges: (i) No previous knowledge of the stimulation parameters is available, thus, arbitrary protocols are supported. (ii) The pipeline remains robust under different noise conditions. (iii) The pipeline supports segmentation of atrial activities in close temporal proximity to the stimulation artifact, which is challenging due to larger amplitude and slope of the stimulus compared to the atrial activity.

METHODS AND RESULTS

The S1 basic cycle length was estimated by time interval detection. Stimulation time windows were segmented by detecting synchronous peaks in different channels surpassing an amplitude threshold and identifying time intervals between detected stimuli. Elimination of the stimulation artifact by a matched filter allowed detection of local activation times in temporal proximity. A non-linear signal energy operator was used to segment periods of atrial activity. Geodesic and Euclidean inter electrode distances allowed approximation of conduction velocity. The automatic segmentation performance of the CVAR-Seg pipeline was evaluated on 37 synthetic datasets with decreasing signal-to-noise ratios. Noise was modeled by reconstructing the frequency spectrum of clinical noise. The pipeline retained a median local activation time error below a single sample (1 ms) for signal-to-noise ratios as low as 0 dB representing a high clinical noise level. As a proof of concept, the pipeline was tested on a CARTO case of a paroxysmal atrial fibrillation patient and yielded plausible restitution curves for conduction speed and amplitude.

CONCLUSION

The proposed openly available CVAR-Seg pipeline promises fast, fully automated, robust, and accurate evaluations of atrial signals even with low signal-to-noise ratios. This is achieved by solving the proximity problem of stimulation and atrial activity to enable standardized evaluation without introducing human bias for large data sets.