Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/41458
Title: | Detecting Paroxysmal Atrial Fibrillation From an Electrocardiogram in Sinus Rhythm External Validation of the AI Approach | Authors: | GRUWEZ, Henri BARTHELS, Myrte Haemers , Peter VERBRUGGE, Frederik DHONT, Sebastiaan MEEKERS, Evelyne WOUTERS, Femke NUYENS, Dieter PISON, Laurent VANDERVOORT, Pieter PIERLET, Noella |
Issue Date: | 2023 | Publisher: | ELSEVIER | Source: | JACC-Clinical Electrophysiology, 9 (8) , p. 1771 -1782 | Abstract: | BACKGROUND Atrial fibrillation (AF) may occur asymptomatically and can be diagnosed only with electrocardiography (ECG) while the arrhythmia is present.OBJECTIVES The aim of this study was to independently validate the approach of using artificial intelligence (AI) to identify underlying paroxysmal AF from a 12-lead ECG in sinus rhythm (SR).METHODS An AI algorithm was trained to identify patients with underlying paroxysmal AF, using electrocardiographic data from all in-and outpatients from a single center with at least 1 ECG in SR. For patients without AF, all ECGs in SR were included. For patients with AF, all ECGs in SR starting 31 days before the first AF event were included. The patients were randomly allocated to training, internal validation, and testing datasets in a 7:1:2 ratio. In a secondary analysis, the AF prevalence of the testing group was modified. Additionally, the performance of the algorithm was validated at an external hospital.RESULTS The dataset consisted of 494,042 ECGs in SR from 142,310 patients. Testing the model on the first ECG of each patient (AF prevalence 9.0%) resulted in accuracy of 78.1% (95% CI: 77.6%-78.5%), area under the receiver operating characteristic curve of 0.87 (95% CI: 0.86-0.87), and area under the precision recall curve (AUPRC) of 0.48 (95% CI: 0.46-0.50). In a low-risk group (AF prevalence 3%), the AUPRC decreased to 0.21 (95% CI: 0.18-0.24). In a high-risk group (AF prevalence 30%), the AUPRC increased to 0.76 (95% CI: 0.75-0.78). This performance was robust when validated in an external hospital.CONCLUSIONS The approach of using an AI-enabled electrocardiographic algorithm for the identification of patients with underlying paroxysmal AF from ECGs in SR was independently validated. (J Am Coll Cardiol EP 2023;9:1771-1782)& COPY; 2023 by the American College of Cardiology Foundation. | Notes: | Pierlet, N (corresponding author), Ziekenhuis Oost-Limburg, Schiepse Bos 6, B-3600 Genk, Belgium. noella.pierlet@zol.be |
Keywords: | artificial intelligence;atrial fibrillation;deep neural network;digital health;electrocardiogram | Document URI: | http://hdl.handle.net/1942/41458 | ISSN: | 2405-500X | e-ISSN: | 2405-5018 | DOI: | 10.1016/j.jacep.2023.04.008 | ISI #: | 001068279100001 | Rights: | 2023 BY THE AMERICAN COLLEGE OF CARDIOLOGY FOUNDATION PUBLISHED BY ELSEVIER | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Detecting Paroxysmal Atrial Fibrillation.pdf | Peer-reviewed author version | 588.54 kB | Adobe PDF | View/Open |
Detecting Paroxysmal Atrial Fibrillation From an Electrocardiogram in Sinus Rhythm.pdf Restricted Access | Published version | 1.84 MB | Adobe PDF | View/Open Request a copy |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.