External Validation of the HELT-E&lt;sub&gt;2&lt;/sub&gt;S&lt;sub&gt;2&lt;/sub&gt; Score in Japanese Patients With Nonvalvular Atrial Fibrillation　― A Pooled Analysis of the RAFFINE and SAKURA Registries ―

Yamauchi, Takahiro; Okumura, Yasuo; Nagashima, Koichi; Watanabe, Ryuta; Saito, Yuki; Yokoyama, Kenichi; Matsumoto, Naoya; Miyauchi, Katsumi; Miyazaki, Sakiko; Hayashi, Hidemori; Matsue, Yuya; Nishizaki, Yuji; Nojiri, Shuko; Minamino, Tohru; Daida, Hiroyuki

doi:10.1253/circj.cj-23-0318

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…In this pooled database, the AUC was 0.644 for the CHADS2 score, 0.650 for the CHA2DS2-VASc score, and 0.661, the highest value, for the HELT-E2S2 score. The independent risk factors for ischemic stroke identified in this pooled database 6 were mostly comparable to those in the J-RISK study. Notably, age ≥85 years, persistent or permanent AF, and low BMI (<18.5 kg/m 2 ), which were specifically identified in Japanese patients in the J-RISK study and form the novel aspects of the HELT-E2S2 score, were similarly identified in the pooled database of the RAFFINE and SAKURA registries.…”

Section: Declarationssupporting

confidence: 59%

Choosing Between HELT-E2S2 and CHA2DS2-VASc in Japan　― Moving Beyond CHADS2 Reliance? ―

Suzuki

2023

Circ J

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Section: Declarationssupporting

confidence: 59%

Choosing Between HELT-E2S2 and CHA2DS2-VASc in Japan　― Moving Beyond CHADS2 Reliance? ―

Suzuki

2023

Circ J

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Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography

Suzuki,

Motogi,

Umemoto

et al. 2024

Circ Rep

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Background: We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. Methods and Results: In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR-ECGs obtained within 31 days from AF-ECGs in AF cases and SR cases with follow-up ≥1,095 days. We evaluated the CNN model’s performance for AF detection using 8-lead (I, II, and V1–6), single-lead, and double-lead ECGs through 5-fold cross-validation. The CNN model achieved an area under the curve (AUC) of 0.872 (95% confidence interval (CI): 0.856–0.888) and an odds ratio of 15.24 (95% CI: 12.42–18.72) for AF detection using the eight-lead ECG. Among the single-lead and double-lead ECGs, the double-lead ECG using leads I and V1 yielded an AUC of 0.871 (95% CI: 0.856–0.886) with an odds ratio of 14.34 (95% CI: 11.64–17.67). Conclusions: We assessed the performance of a CNN model for detecting AF using eight-lead, single-lead, and double-lead SR-ECGs. The model’s performance with a double-lead (I, V1) ECG was comparable to that of the 8-lead ECG, suggesting its potential as an alternative for AF screening using SR-ECG.

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External Validation of the HELT-E<sub>2</sub>S<sub>2</sub> Score in Japanese Patients With Nonvalvular Atrial Fibrillation　― A Pooled Analysis of the RAFFINE and SAKURA Registries ―

Cited by 2 publications

References 30 publications

Choosing Between HELT-E<sub>2</sub>S<sub>2</sub> and CHA<sub>2</sub>DS<sub>2</sub>-VASc in Japan　― Moving Beyond CHADS<sub>2</sub> Reliance? ―

Choosing Between HELT-E<sub>2</sub>S<sub>2</sub> and CHA<sub>2</sub>DS<sub>2</sub>-VASc in Japan　― Moving Beyond CHADS<sub>2</sub> Reliance? ―

Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography

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External Validation of the HELT-E<sub>2</sub>S<sub>2</sub> Score in Japanese Patients With Nonvalvular Atrial Fibrillation ― A Pooled Analysis of the RAFFINE and SAKURA Registries ―

Cited by 2 publications

References 30 publications

Choosing Between HELT-E<sub>2</sub>S<sub>2</sub> and CHA<sub>2</sub>DS<sub>2</sub>-VASc in Japan ― Moving Beyond CHADS<sub>2</sub> Reliance? ―

Choosing Between HELT-E<sub>2</sub>S<sub>2</sub> and CHA<sub>2</sub>DS<sub>2</sub>-VASc in Japan ― Moving Beyond CHADS<sub>2</sub> Reliance? ―

Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography

Contact Info

Product

Resources

About

External Validation of the HELT-E<sub>2</sub>S<sub>2</sub> Score in Japanese Patients With Nonvalvular Atrial Fibrillation　― A Pooled Analysis of the RAFFINE and SAKURA Registries ―

Choosing Between HELT-E<sub>2</sub>S<sub>2</sub> and CHA<sub>2</sub>DS<sub>2</sub>-VASc in Japan　― Moving Beyond CHADS<sub>2</sub> Reliance? ―

Choosing Between HELT-E<sub>2</sub>S<sub>2</sub> and CHA<sub>2</sub>DS<sub>2</sub>-VASc in Japan　― Moving Beyond CHADS<sub>2</sub> Reliance? ―