Pragmatic screening for heart failure in the general population using an electrocardiogram-based neural network
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Pragmatic screening for heart failure in the general population using an electrocardiogram-based neural network. / Surendra, Kishore; Nürnberg, Sylvia; Bremer, Jan P; Knorr, Marius S; Ückert, Frank; Wenzel, Jan Per; Bei der Kellen, Ramona; Westermann, Dirk; Schnabel, Renate B; Twerenbold, Raphael; Magnussen, Christina; Kirchhof, Paulus; Blankenberg, Stefan; Neumann, Johannes; Schrage, Benedikt.
in: ESC HEART FAIL, Jahrgang 10, Nr. 2, 04.2023, S. 975-984.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Pragmatic screening for heart failure in the general population using an electrocardiogram-based neural network
AU - Surendra, Kishore
AU - Nürnberg, Sylvia
AU - Bremer, Jan P
AU - Knorr, Marius S
AU - Ückert, Frank
AU - Wenzel, Jan Per
AU - Bei der Kellen, Ramona
AU - Westermann, Dirk
AU - Schnabel, Renate B
AU - Twerenbold, Raphael
AU - Magnussen, Christina
AU - Kirchhof, Paulus
AU - Blankenberg, Stefan
AU - Neumann, Johannes
AU - Schrage, Benedikt
N1 - © 2022 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.
PY - 2023/4
Y1 - 2023/4
N2 - 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.
AB - 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.
U2 - 10.1002/ehf2.14263
DO - 10.1002/ehf2.14263
M3 - SCORING: Journal article
C2 - 36482800
VL - 10
SP - 975
EP - 984
JO - ESC HEART FAIL
JF - ESC HEART FAIL
SN - 2055-5822
IS - 2
ER -
