| Title: |
Predicting post-operative atrial fibrillation after cardiac surgery using an artificial intelligence-enabled electrocardiogram algorithm |
| Authors: |
Gruwez, H; Vandenberghe, E; Barthels, M; Vermunicht, P; Ezzat, D; Lamberigts, M; Rodrigus, I; Van Kerrebroeck, C; Pierlet, N; Heidbuchel, H; Pison, L; Rega, F; Vandervoort, P; Haemers, P |
| Source: |
Europace ; volume 26, issue Supplement_1 ; ISSN 1099-5129 1532-2092 |
| Publisher Information: |
Oxford University Press (OUP) |
| Publication Year: |
2024 |
| Description: |
Introduction Postoperative atrial fibrillation (POAF) is common after cardiac surgery and is associated with adverse outcomes. Systematic monitoring of POAF is cumbersome, specifically beyond discharge. Therefore, risk stratification may aid to identify patients at high risk of POAF and guide monitoring strategies alongside preventive measures. However, the performance of bedside risk stratification models reliant on clinical risk factors remained underwhelming, necessitating the exploration of more sophisticated models that maintain clinical applicability. Hence, artificial intelligence algorithms (AI) have been suggested to reinforce or replace clinical risk scores. As such, a deep neural network (DNN) algorithm was developed to identify patients with AF based on a 12-lead electrocardiogram (ECG) in sinus rhythm. Whether this algorithm can identify patients at high risk of POAF remains unknown. Purpose To evaluate the usability of an AI-enabled ECG algorithm, that was trained to predict AF in non-surgical conditions, for the prediction of POAF. Methods This study retrospectively analyzed data from the SURGICAL-AF trial that monitored patients after cardiac surgery. The inclusion criteria for this subanalysis comprised: (1) patients without a history of AF prior to cardiac surgery; (2) availability of the raw data of a pre-operative 12-lead ECG in sinus rhythm; and (3) patients with POAF (during hospitalization or up to 91 days after discharge) or patients having completed PPG-based rhythm monitoring per protocol. The AF-risk score was calculated by the DNN described elsewere.1 Results In total, 127 patients (mean [SD] age, 63.4 [8.4] years; 30 women [23.6%];, median [interquartile range] CHA2DS2-VASc score, 2 [1-3]) complied with the inclusion criteria, out of the 450 patients randomized in the SURGICAL-AF trial. Testing the DNN on the last ECG before cardiac surgery resulted in an area under the receiver operating curve (AUC) of 0.66 (95% CI, 0.56 - 0.77) and an area under the precision-recall curve ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1093/europace/euae102.571 |
| Availability: |
https://doi.org/10.1093/europace/euae102.571; https://academic.oup.com/europace/article-pdf/26/Supplement_1/euae102.571/57876013/euae102.571.pdf |
| Rights: |
https://creativecommons.org/licenses/by-nc/4.0/ |
| Accession Number: |
edsbas.3AE9D528 |
| Database: |
BASE |