Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm.

Autor:	Dupulthys S; RADar Learning and Innovation Centre, AZ Delta, Deltalaan 1, 8800 Roeselare, Belgium., Dujardin K; Department of Cardiology, AZ Delta, Roeselare, Belgium., Anné W; Department of Cardiology, AZ Delta, Roeselare, Belgium., Pollet P; Department of Cardiology, AZ Delta, Roeselare, Belgium., Vanhaverbeke M; Department of Cardiology, AZ Delta, Roeselare, Belgium., McAuliffe D; Resero Limited, Dublin, Ireland., Lammertyn PJ; RADar Learning and Innovation Centre, AZ Delta, Deltalaan 1, 8800 Roeselare, Belgium., Berteloot L; RADar Learning and Innovation Centre, AZ Delta, Roeselare, Belgium., Mertens N; RADar Learning and Innovation Centre, AZ Delta, Deltalaan 1, 8800 Roeselare, Belgium., De Jaeger P; RADar Learning and Innovation Centre, AZ Delta, Deltalaan 1, 8800 Roeselare, Belgium.; Department of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium.
Jazyk:	angličtina
Zdroj:	Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology [Europace] 2024 Feb 01; Vol. 26 (2).
DOI:	10.1093/europace/euad354
Abstrakt:	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. Competing Interests: Conflict of interest: None declared. (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)
Databáze:	MEDLINE
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