Phenotypic Clustering of Atrial Fibrillation Patients Using Latent Class Analysis and Its Potential Utility for Ablation Strategy: Insights from the DIRECT-Extend and EARNEST-PVI Studies

Clicks: 15
ID: 327932
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #48 of 80 articles by views in European Heart Journal Open

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Aims Atrial fibrillation (AF) is a heterogeneous syndrome. We aimed to assess whether latent class analysis (LCA) can identify phenotypes that stratify prognosis and predict differential responses to catheter ablation strategies. Methods We first developed an LCA-based phenotyping model using the DIRECT-Extend registry (N = 7,512), a pooled AF cohort of patients receiving anticoagulation therapy. Patients with echocardiographic data (N = 2,741) were classified into latent clusters based on 16 clinical, laboratory, and echocardiographic variables. This phenotypic classification was then applied to the randomized EARNEST-PVI trial to evaluate whether the effectiveness of ablation strategies—pulmonary vein isolation (PVI) alone versus PVI plus extensive ablation (PVI-plus)—varied across phenotypes. Results In the DIRECT-Extend registry, three phenotypes were identified: Phenotype 1 (N = 1,565; “Low comorbidity”), Phenotype 2 (N = 981; “Cardiomyopathy & CKD”), and Phenotype 3 (N = 195; “Inflammatory-enriched AF”). Over a median 600 days, the primary endpoint -a composite of all-cause death, acute myocardial infarction, any stroke and major bleeding differed across phenotypes (log-rank p < 0.001). In EARNEST-PVI, patients were stratified into Phenotype 1 (n = 328), Phenotype 2 (n = 139), and Phenotype 3 (n = 30) according to the classification model. AF recurrence was significantly lower in the PVI-plus group compared to the PVI-alone group in phenotype 1 (log-rank p = 0.013), whereas there was no significant difference in Phenotypes 2 and 3. Conclusions LCA identified distinct AF phenotypes associated with prognoses. These phenotypes may also provide insights into heterogeneity in response to ablation strategies. This phenotyping approach may support risk stratification and individualized AF management.
Reference Key
openalex_W7211972740 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shun Sasaki, Yuki Matsuoka, Akihiro Sunaga, Daisuke Sakamoto, Hideaki Hasegawa, Tomoharu Dohi, Hirota Kida, Tetsuhisa Kitamura, Katsuki Okada, Daisaku Nakatani, Hidetaka Kioka, Nobuaki Tanaka, Masaharu Masuda, T Watanabe, Yasuo Okumura, Yoshiharu Higuchi, Koichi Inoue, Shungo Hikoso, Yohei Sotomi, Yasushi Sakata
Are you one of these authors? Claim your profile
Journal European Heart Journal Open
Year 2026
DOI
10.1093/ehjopen/oeag135
URL
https://doi.org/10.1093/ehjopen/oeag135
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.