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@ARTICLE{Sauerbier:291075,
      author       = {F. Sauerbier and J. Haerting and D. Sedding and R.
                      Mikolajczyk and K. Werdan and S. Nuding and K. H.
                      Greiser$^*$ and C. A. Swenne and J. A. Kors and A. Kluttig},
      title        = {{I}mpact of {QRS} misclassifications on
                      heart-rate-variability parameters (results from the {CARLA}
                      cohort study).},
      journal      = {PLOS ONE},
      volume       = {19},
      number       = {6},
      issn         = {1932-6203},
      address      = {San Francisco, California, US},
      publisher    = {PLOS},
      reportid     = {DKFZ-2024-01295},
      pages        = {e0304893 -},
      year         = {2024},
      abstract     = {Heart rate variability (HRV), an important marker of
                      autonomic nervous system activity, is usually determined
                      from electrocardiogram (ECG) recordings corrected for
                      extrasystoles and artifacts. Especially in large
                      population-based studies, computer-based algorithms are used
                      to determine RR intervals. The Modular ECG Analysis System
                      MEANS is a widely used tool, especially in large studies.
                      The aim of this study was therefore to evaluate MEANS for
                      its ability to detect non-sinus ECG beats and artifacts and
                      to compare HRV parameters in relation to ECG processing.
                      Additionally, we analyzed how ECG processing affects the
                      statistical association of HRV with cardiovascular disease
                      (CVD) risk factors.20-min ECGs from 1,674 subjects of the
                      population-based CARLA study were available for HRV
                      analysis. All ECGs were processed with the ECG computer
                      program MEANS. A reference standard was established by
                      experienced clinicians who visually inspected the
                      MEANS-processed ECGs and reclassified beats if necessary.
                      HRV parameters were calculated for 5-minute segments
                      selected from the original 20-minute ECG. The effects of
                      misclassified typified normal beats on i) HRV calculation
                      and ii) the associations of CVD risk factors (sex, age,
                      diabetes, myocardial infarction) with HRV were modeled using
                      linear regression.Compared to the reference standard, MEANS
                      correctly classified $99\%$ of all beats. The averaged
                      sensitivity of MEANS across all ECGs to detect non-sinus
                      beats was $76\%$ $[95\%$ CI: 74.1;78.5], but for
                      supraventricular extrasystoles detection sensitivity dropped
                      to $38\%$ $[95\%$ CI: 36.8;38.5]. Time-domain parameters
                      were less affected by false sinus beats than frequency
                      parameters. Compared to the reference standard, MEANS
                      resulted in a higher SDNN on average (mean absolute
                      difference 1.4ms $[95\%$ CI: 1.0;1.7], relative $4.9\%).$
                      Other HRV parameters were also overestimated as well
                      (between 6.5 and $29\%).$ The effect estimates for the
                      association of CVD risk factors with HRV did not differ
                      between the editing methods.We have shown that the use of
                      the automated MEANS algorithm may lead to an overestimation
                      of HRV due to the misclassification of non-sinus beats,
                      especially in frequency domain parameters. However, in
                      population-based studies, this has no effect on the observed
                      associations of HRV with risk factors, and therefore an
                      automated ECG analyzing algorithm as MEANS can be
                      recommended here for the determination of HRV parameters.},
      keywords     = {Humans / Heart Rate: physiology / Electrocardiography:
                      methods / Female / Male / Middle Aged / Aged / Cohort
                      Studies / Algorithms / Cardiovascular Diseases: diagnosis /
                      Cardiovascular Diseases: physiopathology / Risk Factors},
      cin          = {C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38885223},
      doi          = {10.1371/journal.pone.0304893},
      url          = {https://inrepo02.dkfz.de/record/291075},
}