Pragmatic screening for heart failure in the general population using an electrocardiogram-based neural network.

Autor: Surendra K; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany., Nürnberg S; Institute of Applied Medical Informatics, University Hospital Hamburg-Eppendorf, Hamburg, Germany., Bremer JP; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.; Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany., Knorr MS; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.; Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany., Ückert F; Institute of Applied Medical Informatics, University Hospital Hamburg-Eppendorf, Hamburg, Germany., Wenzel JP; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany., Bei der Kellen R; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany., Westermann D; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.; German Center for Cardiovascular Research (DZHK), Partner site Hamburg/Kiel/Lübeck, Hamburg, Germany., Schnabel RB; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.; German Center for Cardiovascular Research (DZHK), Partner site Hamburg/Kiel/Lübeck, Hamburg, Germany., Twerenbold R; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.; German Center for Cardiovascular Research (DZHK), Partner site Hamburg/Kiel/Lübeck, Hamburg, Germany., Magnussen C; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.; German Center for Cardiovascular Research (DZHK), Partner site Hamburg/Kiel/Lübeck, Hamburg, Germany., Kirchhof P; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.; German Center for Cardiovascular Research (DZHK), Partner site Hamburg/Kiel/Lübeck, Hamburg, Germany., Blankenberg S; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.; German Center for Cardiovascular Research (DZHK), Partner site Hamburg/Kiel/Lübeck, Hamburg, Germany., Neumann J; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.; German Center for Cardiovascular Research (DZHK), Partner site Hamburg/Kiel/Lübeck, Hamburg, Germany., Schrage B; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.; German Center for Cardiovascular Research (DZHK), Partner site Hamburg/Kiel/Lübeck, Hamburg, Germany.
Jazyk: angličtina
Zdroj: ESC heart failure [ESC Heart Fail] 2023 Apr; Vol. 10 (2), pp. 975-984. Date of Electronic Publication: 2022 Dec 08.
DOI: 10.1002/ehf2.14263
Abstrakt: Aims: We aim to develop a pragmatic screening tool for heart failure at the general population level.
Methods and Results: This study was conducted within the Hamburg-City-Health-Study, an ongoing, prospective, observational study enrolling randomly selected inhabitants of the city of Hamburg aged 45-75 years. Heart failure was diagnosed per current guidelines. Using only digital electrocardiograms (ECGs), a convolutional neural network (CNN) was built to discriminate participants with and without heart failure. As comparisons, known risk variables for heart failure were fitted into a logistic regression model and a random forest classifier. Of the 5299 individuals included into this study, 318 individuals (6.0%) had heart failure. Using only the digital ECGs instead of several risk variables as an input, the CNN provided a comparable predictive accuracy for heart failure versus the logistic regression model and the random forest classifier [area under the curve (AUC) of 0.75, a sensitivity of 0.67 and a specificity of 0.69 for the CNN; AUC 0.77, a sensitivity of 0.63 and a specificity of 0.76 for the logistic regression; AUC 0.79, a sensitivity of 0.67 and a specificity of 0.72 for the random forest classifier].
Conclusions: Using a CNN build on digital ECGs only and requiring no additional input, we derived a screening tool for heart failure in the general population. This could be perfectly embedded into clinical routine of general practitioners, as it builds on an already established diagnostic tool and does not require additional, time-consuming input. This could help to alleviate the underdiagnosis of heart failure.
(© 2022 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)
Databáze: MEDLINE
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