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@ARTICLE{Gramespacher:1028208,
      author       = {Gramespacher, Hannes and Schmieschek, Maximilian H. T. and
                      Warnke, Clemens and Adler, Christoph and Bittner, Stefan and
                      Dronse, Julian and Richter, Nils and Zaeske, Charlotte and
                      Gietzen, Carsten and Schlamann, Marc and Baldus, Stephan and
                      Fink, Gereon Rudolf and Onur, Oezguer A.},
      title        = {{A}nalysis of {C}erebral {CT} {B}ased on {S}upervised
                      {M}achine {L}earning as a {P}redictor of {O}utcome {A}fter
                      {O}ut-of-{H}ospital {C}ardiac {A}rrest},
      journal      = {Neurology},
      volume       = {103},
      number       = {1},
      issn         = {0028-3878},
      address      = {[Erscheinungsort nicht ermittelbar]},
      publisher    = {Ovid},
      reportid     = {FZJ-2024-04403},
      pages        = {e209583},
      year         = {2024},
      note         = {H. Gramespacher was supported by the Cologne Clinician
                      Scientist Program (CCSP)/Faculty of Medicine/University of
                      Cologne. Funded by the German Research Foundation (DFG, FI
                      773/15-1).},
      abstract     = {AbstractBackground and ObjectivesIn light of limited
                      intensive care capacities and a lack of accurate prognostic
                      tools to advise caregivers and family members responsibly,
                      this study aims to determine whether automated cerebral CT
                      (CCT) analysis allows prognostication after out-of-hospital
                      cardiac arrest.MethodsIn this monocentric, retrospective
                      cohort study, a supervised machine learning classifier based
                      on an elastic net regularized logistic regression model for
                      gray matter alterations on nonenhanced CCT obtained after
                      cardiac arrest was trained using 10-fold cross-validation
                      and tested on a hold-out sample (random split $75\%/25\%)$
                      for outcome prediction. Following the literature, a
                      favorable outcome was defined as a cerebral performance
                      category of 1–2 and a poor outcome of 3–5. The
                      diagnostic accuracy was compared with established and
                      guideline-recommended prognostic measures within the sample,
                      that is, gray matter-white matter ratio (GWR),
                      neuron-specific enolase (NSE), and neurofilament light chain
                      (NfL) in serum.ResultsOf 279 adult patients, 132 who
                      underwent CCT within 14 days of cardiac arrest with good
                      imaging quality were identified. Our approach discriminated
                      between favorable and poor outcomes with an area under the
                      curve (AUC) of 0.73 $(95\%$ CI 0.59–0.82). Thus, the
                      prognostic power outperformed the GWR (AUC 0.66, $95\%$ CI
                      0.56–0.76). The biomarkers NfL, measured at days 1 and 2,
                      and NSE, measured at day 2, exceeded the reliability of the
                      imaging markers derived from CT (AUC NfL day 1: 0.87, $95\%$
                      CI 0.75–0.99; AUC NfL day 2: 0.90, $95\%$ CI 0.79–1.00;
                      AUC NSE day: 2 0.78, $95\%$ CI 0.62–0.94).DiscussionOur
                      data show that machine learning–assisted gray matter
                      analysis of CCT images offers prognostic information after
                      out-of-hospital cardiac arrest. Thus, CCT gray matter
                      analysis could become a reliable and time-independent
                      addition to the standard workup with serum biomarkers
                      sampled at predefined time points. Prospective studies are
                      warranted to replicate these findings.},
      cin          = {INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38857458},
      UT           = {WOS:001329378500033},
      doi          = {10.1212/WNL.0000000000209583},
      url          = {https://juser.fz-juelich.de/record/1028208},
}