Titel: | Prediction of atrial fibrillation recurrence following one-year catheter ablation using atrial mechanical dispersion assessed by speckle tracking echocardiography | Sprache: | Englisch | Autor*in: | Xin, Kaiyue | Schlagwörter: | Atrial fibrillation; Atrial mechanical dispersion; Speckle tracking echocardiography | Erscheinungsdatum: | 2024 | Tag der mündlichen Prüfung: | 2024-10-15 | Zusammenfassung: | Atrial fibrillation (AF) is the most common arrhythmia, impacting life quality and healthcare costs. Catheter ablation has become the cornerstone for rhythm control but is associated with a high recurrence rate, emphasizing the need for early diagnosis to optimize treatment. AF progresses through atrial remodeling, affecting structure, function, and electrical properties. Although left atrial size remains a routine measure, AF recurrence can occur in patients without left atrial enlargement, suggesting that functional changes may precede structural alterations. Intra-atrial dyssynchrony, a predictor of AF recurrence, is represented by atrial mechanical dispersion, defined as the standard deviation of the time to peak positive strain (SD-TPS). This study retrospectively enrolled 132 AF patients undergoing their first catheter ablation between December 2017 and January 2019. All analyses were censored after a follow-up time of 1 year. Key findings included: (1) a 22.7% recurrence rate post-ablation; (2) no significant difference in left atrial volume index (LAVI) among patients with and without AF recurrence; (3) an association between atrial mechanical dispersion and AF recurrence risk; (4) an SD-TPS cut-off of 38.6 msec that may help identify high-risk patients; and (5) multivariable analysis highlighted age as the most critical risk factor, with SD-TPS being the most relevant imaging variable. These results suggest that SD-TPS can detect early atrial dysfunction, even in patients with normal LAVI, and may help predict AF recurrence. Incorporating atrial mechanical dispersion into comprehensive assessments could improve risk stratification. |
URL: | https://ediss.sub.uni-hamburg.de/handle/ediss/11268 | URN: | urn:nbn:de:gbv:18-ediss-122776 | Dokumenttyp: | Dissertation | Betreuer*in: | Metzner, Andreas |
Enthalten in den Sammlungen: | Elektronische Dissertationen und Habilitationen |
Dateien zu dieser Ressource:
Datei | Prüfsumme | Größe | Format | |
---|---|---|---|---|
Dissertation_Kaiyue Xin.pdf | b1177a8ca2a19eea842160014ec72947 | 1.82 MB | Adobe PDF | Öffnen/Anzeigen |
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