Tag: radiofrequency catheter ablation

Medical Economic Consequences, Predictors, and Outcomes of Immediate Atrial Fibrillation Recurrence after Radiofrequency Ablation

Atrial Fibrillation Recurrence

Announcing a new article publication for Cardiovascular Innovations and Applications journal. Immediate recurrence (Im-Recurr), a type of atrial fibrillation (AF) recurrence occurring during the blanking period after radiofrequency catheter ablation (RFCA), has received little attention. Therefore, this study was aimed at exploring the clinical significance of Im-Recurr in patients with AF after RFCA.

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Deep Learning-based Handheld Device-Enabled Symptom-driven Recording: A Pragmatic Approach for the Detection of Post-ablation Atrial Fibrillation Recurrence

Announcing a new article publication for Cardiovascular Innovations and Applications journal.  Symptom-driven electrocardiogram (ECG) recording plays a significant role in the detection of post-ablation atrial fibrillation recurrence (AFR). However, making timely medical contact whenever symptoms occur may not be practical. The authors of this article deployed a deep learning (DL)-based handheld device to facilitate symptom-driven monitoring.

A cohort of patients with paroxysmal atrial fibrillation (AF) was trained to use a DL-based handheld device to record ECG signals whenever symptoms presented after the ablation. Additionally, 24-hour Holter monitoring and 12-lead ECG were scheduled at 3, 6, 9, and 12 months post-ablation. The detection of AFR by the different modalities was explored.

A total of 22 of 67 patients experienced AFR. The handheld device and 24-hour Holter monitor detected 19 and 8 AFR events, respectively, five of which were identified by both modalities. A larger portion of ECG tracings was recorded for patients with than without AFR [362(330) vs. 132(133), P=0.01)], and substantial numbers of AFR events were recorded from 18:00 to 24:00. Compared to Holter, more AFR events were detected by the handheld device in earlier stages (HR=1.6, 95% CI 1.2–2.2, P<0.01).

The DL-based handheld device-enabled symptom-driven recording, compared with the conventional monitoring strategy, improved AFR detection and enabled more timely identification of symptomatic episodes.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2023.0048

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Laite Chen and Chenyang Jiang. Deep Learning-based Handheld Device-Enabled Symptom-driven Recording: A Pragmatic Approach for the Detection of Post-ablation Atrial Fibrillation Recurrence. CVIA. 2023. Vol. 8(1). DOI: 10.15212/CVIA.2023.0048

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