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@ARTICLE{Appel:284986,
      author       = {K. S. Appel and R. Geisler and D. Maier$^*$ and O. Miljukov
                      and S. M. Hopff and J. J. Vehreschild},
      title        = {{A} {S}ystematic {R}eview of {P}redictor {C}omposition,
                      {O}utcomes, {R}isk of {B}ias, and {V}alidation of {COVID}-19
                      {P}rognostic {S}cores.},
      journal      = {Clinical infectious diseases},
      volume       = {78},
      number       = {4},
      issn         = {1058-4838},
      address      = {Oxford},
      publisher    = {Oxford Journals},
      reportid     = {DKFZ-2023-02170},
      pages        = {889-899},
      year         = {2024},
      note         = {2024 Apr 10;78(4):889-899},
      abstract     = {Numerous prognostic scores have been published to support
                      risk stratification for patients with Coronavirus disease
                      2019 (COVID-19).We performed a systematic review to identify
                      the scores for confirmed or clinically assumed COVID-19
                      cases. An in-depth assessment and risk of bias (ROB)
                      analysis (Prediction model Risk Of Bias ASsessment Tool
                      (PROBAST)) was conducted for scores fulfilling predefined
                      criteria ((I) area under the curve (AUC) ≥ 0.75; (II) a
                      separate validation cohort present; (III) training data from
                      a multicenter setting (≥ 2 centers); (IV) point-scale
                      scoring system).Out of 1,522 studies extracted from
                      MEDLINE/Web of Science (20/02/2023), we identified 242
                      scores for COVID-19 outcome prognosis (mortality 109,
                      severity 116, hospitalization 14, long-term sequelae 3).
                      Most scores were developed using retrospective $(75.2\%)$ or
                      single-center $(57.1\%)$ cohorts. Predictor analysis
                      revealed the primary use of laboratory data and
                      sociodemographic information in mortality and severity
                      scores. Forty-nine scores were included in the in-depth
                      analysis. The results indicated heterogeneous quality and
                      predictor selection, with only five scores featuring low
                      ROB. Among those, based on the number and heterogeneity of
                      validation studies, only the 4C Mortality Score can be
                      recommended for clinical application so far.The application
                      and translation of most existing COVID scores appear
                      unreliable. Guided development and predictor selection would
                      have improved the generalizability of the scores and may
                      enhance pandemic preparedness in the future.},
      subtyp        = {Review Article},
      keywords     = {COVID-19 (Other) / Pandemic preparedness (Other) /
                      Prediction models (Other) / Predictors (Other) / Scores
                      (Other)},
      cin          = {FM01},
      ddc          = {610},
      cid          = {I:(DE-He78)FM01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37879096},
      doi          = {10.1093/cid/ciad618},
      url          = {https://inrepo02.dkfz.de/record/284986},
}