Emma Svennberg1, Martin Stridh2, Johan Engdahl3, Faris Al-Khalili1, Leif Friberg1, Viveka Frykman1, Mårten Rosenqvist1. 1. Department of Clinical Sciences, Cardiology Unit, Karolinska Institutet, Danderyd University Hospital, Stockholm SE-182 88, Sweden. 2. Department of Biomedical Engineering, Lund University, Lund and Cardiolund Research AB, Lund, Sweden. 3. Department of Medicine, Halland Hospital, Halmstad, Sweden.
Abstract
AIMS: Screening for atrial fibrillation (AF) using intermittent electrocardiogram (ECG) recordings can identify individuals at risk of AF-related morbidity in particular stroke. We aimed to validate the performance of an AF screening algorithm compared with manual ECG analysis by specially trained nurses and physicians (gold standard) in 30 s intermittent one-lead ECG recordings. METHODS AND RESULTS: The STROKESTOP study is a mass-screening study for AF using intermittent ECG recordings. All individuals in the study without known AF registered a 30-s ECG recording in Lead I two times daily for 2 weeks, and all ECGs were manually interpreted. A computerized algorithm was used to analyse 80 149 ECG recordings in 3209 individuals. The computerized algorithm annotated 87.1% (n = 69 789) of the recordings as sinus rhythm/minor rhythm disturbances. The manual interpretation (gold standard) was that 69 758 ECGs were normal, making the negative predictive value of the algorithm 99.9%. The number of ECGs requiring manual interpretation in order to find one pathological ECG was reduced from 288 to 35. Atrial fibrillation was diagnosed in 84 patients by manual interpretation, in all of whom the algorithm indicated pathology. On an ECG level, 278 ECGs were manually interpreted as AF, and of these the algorithm annotated 272 ECGs as pathological (sensitivity 97.8%). CONCLUSION: Automatic ECG screening using a computerized algorithm safely identifies normal ECGs in Lead I and reduces the need for manual evaluation of individual ECGs with >85% with 100% sensitivity on an individual basis. Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: Screening for atrial fibrillation (AF) using intermittent electrocardiogram (ECG) recordings can identify individuals at risk of AF-related morbidity in particular stroke. We aimed to validate the performance of an AF screening algorithm compared with manual ECG analysis by specially trained nurses and physicians (gold standard) in 30 s intermittent one-lead ECG recordings. METHODS AND RESULTS: The STROKESTOP study is a mass-screening study for AF using intermittent ECG recordings. All individuals in the study without known AF registered a 30-s ECG recording in Lead I two times daily for 2 weeks, and all ECGs were manually interpreted. A computerized algorithm was used to analyse 80 149 ECG recordings in 3209 individuals. The computerized algorithm annotated 87.1% (n = 69 789) of the recordings as sinus rhythm/minor rhythm disturbances. The manual interpretation (gold standard) was that 69 758 ECGs were normal, making the negative predictive value of the algorithm 99.9%. The number of ECGs requiring manual interpretation in order to find one pathological ECG was reduced from 288 to 35. Atrial fibrillation was diagnosed in 84 patients by manual interpretation, in all of whom the algorithm indicated pathology. On an ECG level, 278 ECGs were manually interpreted as AF, and of these the algorithm annotated 272 ECGs as pathological (sensitivity 97.8%). CONCLUSION: Automatic ECG screening using a computerized algorithm safely identifies normal ECGs in Lead I and reduces the need for manual evaluation of individual ECGs with >85% with 100% sensitivity on an individual basis. Published on behalf of the European Society of Cardiology. All rights reserved.
Authors: Niraj Varma; Iwona Cygankiewicz; Mintu P Turakhia; Hein Heidbuchel; Yu-Feng Hu; Lin Yee Chen; Jean-Philippe Couderc; Edmond M Cronin; Jerry D Estep; Lars Grieten; Deirdre A Lane; Reena Mehra; Alex Page; Rod Passman; Jonathan P Piccini; Ewa Piotrowicz; Ryszard Piotrowicz; Pyotr G Platonov; Antonio Luiz Ribeiro; Robert E Rich; Andrea M Russo; David Slotwiner; Jonathan S Steinberg; Emma Svennberg Journal: Circ Arrhythm Electrophysiol Date: 2021-02-12
Authors: Niraj Varma; Iwona Cygankiewicz; Mintu P Turakhia; Hein Heidbuchel; Yufeng Hu; Lin Yee Chen; Jean-Philippe Couderc; Edmond M Cronin; Jerry D Estep; Lars Grieten; Deirdre A Lane; Reena Mehra; Alex Page; Rod Passman; Jonathan P Piccini; Ewa Piotrowicz; Ryszard Piotrowicz; Pyotr G Platonov; Antonio Luiz Ribeiro; Robert E Rich; Andrea M Russo; David Slotwiner; Jonathan S Steinberg; Emma Svennberg Journal: Cardiovasc Digit Health J Date: 2021-01-29
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