| Title: |
Artificial intelligence-predicted poor responders to catheter ablation for atrial fibrillation |
| Authors: |
Park, JW; Kwon, OS; Shim, JM; Yu, HT; Kim, TH; Uhm, JS; Kim, JY; Choi, JI; Joung, BY; Lee, MH; Kim, YH; Pak, HN |
| Source: |
EP Europace ; volume 23, issue Supplement_3 ; ISSN 1099-5129 1532-2092 |
| Publisher Information: |
Oxford University Press (OUP) |
| Publication Year: |
2021 |
| Description: |
Funding Acknowledgements Type of funding sources: None. Introduction Although atrial fibrillation (AF) catheter ablation is effective for rhythm control, in some patients it is hard to maintain sinus rhythm in spite of repeated AF catheter ablation (AFCA) procedures and anti-arrhythmic drugs (AADs). We explored the pre-procedural predictors for poor responders to AFCA and tested whether artificial intelligence (AI) assists the prediction of poor responders in the independent cohort by determining the invasive parameters. Methods Among 1,214 patients who underwent AFCA and regular rhythm follow-up for 56.2 ± 33.8 months (59 ± 11 years, 73.5% male, 68.6% paroxysmal AF), we differentiated 92 poor responders defined as those with sustained AF despite repeat AFCAs, AADs, or electrical cardioversion. Using the Youden index, we identified advanced LA remodeling with lower LA voltage under 1.109mV. AI model, which was derived from development cohort using medical record, was applied to predict LA voltage |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1093/europace/euab116.228 |
| Availability: |
https://doi.org/10.1093/europace/euab116.228; http://academic.oup.com/europace/article-pdf/23/Supplement_3/euab116.228/38130900/euab116.228.pdf |
| Rights: |
https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model |
| Accession Number: |
edsbas.F14F330 |
| Database: |
BASE |