An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk.

Autor: Raghunath S; Geisinger, Danville, PA, USA; Tempus Labs Inc., Chicago, IL, USA., Pfeifer JM; Geisinger, Danville, PA, USA; Tempus Labs Inc., Chicago, IL, USA., Kelsey CR; Geisinger, Danville, PA, USA., Nemani A; Tempus Labs Inc., Chicago, IL, USA., Ruhl JA; Geisinger, Danville, PA, USA., Hartzel DN; Geisinger, Danville, PA, USA., Ulloa Cerna AE; Geisinger, Danville, PA, USA., Jing L; Geisinger, Danville, PA, USA., vanMaanen DP; Geisinger, Danville, PA, USA., Leader JB; Geisinger, Danville, PA, USA; Tempus Labs Inc., Chicago, IL, USA., Schneider G; Geisinger, Danville, PA, USA., Morland TB; Geisinger, Danville, PA, USA., Chen R; Geisinger, Danville, PA, USA; Tempus Labs Inc., Chicago, IL, USA., Zimmerman N; Tempus Labs Inc., Chicago, IL, USA., Fornwalt BK; Geisinger, Danville, PA, USA; Tempus Labs Inc., Chicago, IL, USA., Haggerty CM; Geisinger, Danville, PA, USA; Tempus Labs Inc., Chicago, IL, USA. Electronic address: chaggerty3@gatech.edu.
Jazyk: angličtina
Zdroj: Journal of electrocardiology [J Electrocardiol] 2023 Jan-Feb; Vol. 76, pp. 61-65. Date of Electronic Publication: 2022 Nov 08.
DOI: 10.1016/j.jelectrocard.2022.11.001
Abstrakt: Background: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained to predict AF risk from 12‑lead electrocardiogram (ECG) would be more efficient than criteria based on clinical variables in indicating a population for AF screening to potentially prevent AF-related stroke.
Methods: We retrospectively included all patients with clinical encounters in Geisinger without a prior history of AF. Incidence of AF within 1 year and AF-related strokes within 3 years of the encounter were identified. AF-related stroke was defined as a stroke where AF was diagnosed at the time of stroke or within a year after the stroke. The efficiency of five methods was evaluated for selecting a cohort for AF screening. The methods were selected from four clinical trials (mSToPS, GUARD-AF, SCREEN-AF and STROKESTOP) and the ECG-based ML model. We simulated patient selection for the five methods between the years 2011 and 2014 and evaluated outcomes for 1 year intervals between 2012 and 2015, resulting in a total of twenty 1-year periods. Patients were considered eligible if they met the criteria before the start of the given 1-year period or within that period. The primary outcomes were numbers needed to screen (NNS) for AF and AF-associated stroke.
Results: The clinical trial models indicated large proportions of the population with a prior ECG for AF screening (up to 31%), coinciding with NNS ranging from 14 to 18 for AF and 249-359 for AF-associated stroke. At comparable sensitivity, the ECG ML model indicated a modest number of patients for screening (14%) and had the highest efficiency in NNS for AF (7.3; up to 60% reduction) and AF-associated stroke (223; up to 38% reduction).
Conclusions: An ECG-based ML risk prediction model is more efficient than contemporary AF-screening criteria based on age alone or age and clinical features at indicating a population for AF screening to potentially prevent AF-related strokes.
(Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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