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Title: | Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm | Authors: | Dupulthys, Stijn Dujardin, Karl Anne, Wim Pollet , Peter Vanhaverbeke, Maarten McAuliffe, David Lammertyn, Pieter-Jan Berteloot, Louise Mertens, Nathalie DE JAEGER, Peter |
Issue Date: | 2024 | Publisher: | OXFORD UNIV PRESS | Source: | EUROPACE, 26 (2) (Art N° euad354) | Abstract: | Aims Guidelines recommend opportunistic screening for atrial fibrillation (AF), using a 30 s single-lead electrocardiogram (ECG) recorded by a wearable device. Since many patients have paroxysmal AF, identification of patients at high risk presenting with sinus rhythm (SR) may increase the yield of subsequent long-term cardiac monitoring. The aim is to evaluate an AI-algorithm trained on 10 s single-lead ECG with or without risk factors to predict AF.Methods and results This retrospective study used 13 479 ECGs from AF patients in SR around the time of diagnosis and 53 916 age- and sex-matched control ECGs, augmented with 17 risk factors extracted from electronic health records. AI models were trained and compared using 1- or 12-lead ECGs, with or without risk factors. Model bias was evaluated by age- and sex-stratification of results. Random forest models identified the most relevant risk factors. The single-lead model achieved an area under the curve of 0.74, which increased to 0.76 by adding six risk factors (95% confidence interval: 0.74-0.79). This model matched the performance of a 12-lead model. Results are stable for both sexes, over ages ranging from 40 to 90 years. Out of 17 clinical variables, 6 were sufficient for optimal accuracy of the model: hypertension, heart failure, valvular disease, history of myocardial infarction, age, and sex.Conclusion An AI model using a single-lead SR ECG and six risk factors can identify patients with concurrent AF with similar accuracy as a 12-lead ECG-AI model. An age- and sex-matched data set leads to an unbiased model with consistent predictions across age groups. Graphical Abstract AUC, area under the receiver operating characteristic curve; EHR, electronic health records | Notes: | De Jaeger, P (corresponding author), AZ Delta, RADar Learning & Innovat Ctr, Deltalaan 1, B-8800 Roeselare, Belgium.; De Jaeger, P (corresponding author), Univ Hasselt, Dept Med & Life Sci, Martelarenlaan 42, B-3500 Hasselt, Belgium. peter.dejaeger@azdelta.be |
Keywords: | Atrial fibrillation;Single-lead ECG;Sinus rhythm;Artificial intelligence;Screening | Document URI: | http://hdl.handle.net/1942/42754 | ISSN: | 1099-5129 | e-ISSN: | 1532-2092 | DOI: | 10.1093/europace/euad354 | ISI #: | 001163513100011 | Rights: | The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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