Key result
Phase singularity distribution exhibits complete global spatial randomness despite local hotspots.
Why the study?
The spatial concentration and distribution of phase singularities in human AF had not been systematically investigated using quantitative statistical approaches.
Observational (n=18)
Yes
Answers are grounded in this paper and cite their sources.
Geospatial statistical approaches can detect spatial concentrations and clustering of phase singularities in human persistent AF, offering a potential new method for mapping and ablating PS clusters.
Background Atrial fibrillation (AF) is characterized by the repetitive regeneration of unstable rotational events, the pivot of which are known as phase singularities (PSs). The spatial concentration and distribution of PSs have not been systematically investigated using quantitative statistical approaches. Objectives We utilized a geospatial statistical approach to determine the presence of local spatial concentration and global clustering of PSs in biatrial human AF recordings. Methods 64‐electrode conventional basket (~5 min, n = 18 patients, persistent AF) recordings were studied. Phase maps were produced using a Hilbert‐transform based approach. PSs were characterized spatially using the following approaches: (i) local “hotspots” of high phase singularity (PS) concentration using Getis‐Ord Gi* ( Z ≥ 1.96, P ≤ .05) and (ii) global spatial clustering using Moran's I (inverse distance matrix). Results Episodes of AF were analyzed from basket catheter recordings (H: 41 epochs, 120 000 s, n = 18 patients). The Getis‐Ord Gi* statistic showed local PS hotspots in 12/41 basket recordings. As a metric of spatial clustering, Moran's I showed an overall mean of 0.033 (95% CI: 0.0003‐0.065), consistent with the notion of complete spatial randomness. Conclusion Using a systematic, quantitative geospatial statistical approach, evidence for the existence of spatial concentrations (“hotspots”) of PSs were detectable in human AF, along with evidence of spatial clustering. Geospatial statistical approaches offer a new approach to map and ablate PS clusters using substrate‐based approaches.
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Schopp et al. (2021) conducted an observational in Persistent Atrial Fibrillation (n=18). Geospatial statistical analysis (Getis-Ord Gi and Moran's I) was evaluated on Global spatial clustering of phase singularities (Moran's I) (95% CI 0.0003-0.065). Geospatial statistical analysis identified local phase singularity hotspots in 12 of 41 recordings, while global distribution showed complete spatial randomness with a mean Moran's I of 0.033.
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