ESC Heart Failure (Apr 2023)

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

  • Kishore Surendra,
  • Sylvia Nürnberg,
  • Jan P. Bremer,
  • Marius S. Knorr,
  • Frank Ückert,
  • Jan Per Wenzel,
  • Ramona Bei der Kellen,
  • Dirk Westermann,
  • Renate B. Schnabel,
  • Raphael Twerenbold,
  • Christina Magnussen,
  • Paulus Kirchhof,
  • Stefan Blankenberg,
  • Johannes Neumann,
  • Benedikt Schrage

DOI
https://doi.org/10.1002/ehf2.14263
Journal volume & issue
Vol. 10, no. 2
pp. 975 – 984

Abstract

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Abstract 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.

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