% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Schwerdtfeger:300807,
      author       = {T. Schwerdtfeger and L. Brualla$^*$},
      title        = {{A} {M}onte {C}arlo method for the quantitative analysis of
                      triage algorithms in mass casualty events.},
      journal      = {Physics in medicine and biology},
      volume       = {70},
      number       = {10},
      issn         = {0031-9155},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {DKFZ-2025-00927},
      pages        = {105003},
      year         = {2025},
      abstract     = {Objective.In mass casualty scenarios, efficient triage
                      algorithms are used to prioritize medical care when
                      resources are outnumbered by victims. This research proposes
                      a computational approach to quantitatively analyze and
                      optimize triage algorithms by developing a Monte Carlo code
                      which is subsequently validated against the few quantitative
                      data.Approach. The developed Monte Carlo code is used to
                      simulate several mass casualty events, namely car accidents,
                      burns, shootings, sinking ships and a human stampede. Four
                      triage algorithms- modified simple triage and rapid
                      treatment, primäres Ranking zur initialen Orientierung im
                      Rettungsdienst, CareFlight, and field triage score (FTS)-are
                      evaluated using metrics like mortality, overtriage,
                      undertriage, sensitivity, and specificity.Main
                      results.Results indicate that, on average, the analyzed
                      algorithms achieve about $35\%$ accuracy in classifying
                      critical casualties when compared to a perfect algorithm,
                      with FTS being the less accurate. However, when all
                      casualties are considered, algorithm performance improves to
                      around $63\%$ of a perfect algorithm, except for FTS. The
                      study identifies an increased probability of false positives
                      for red categorization due to comorbidities and a higher
                      tendency for false negatives in casualties with burns or
                      internal trunk injuries.Significance.Despite variations in
                      vital sign measurements, triage classification results do
                      not depend on the measurement uncertainties of the
                      paramedics. The ethically challenging decision, of
                      withholding medical care from low-survival probability
                      victims, leads to a $63\%$ reduction in mortality among
                      critical casualties. This research establishes a
                      quantitative method for triage algorithm studies,
                      highlighting their robustness to measurement uncertainties.},
      keywords     = {Triage: methods / Mass Casualty Incidents / Monte Carlo
                      Method / Algorithms / Humans / Monte Carlo (Other) /
                      disaster (Other) / mass casualty event (Other) / prehospital
                      (Other) / simulation (Other) / triage (Other)},
      cin          = {ED01},
      ddc          = {530},
      cid          = {I:(DE-He78)ED01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40216003},
      doi          = {10.1088/1361-6560/adcbfc},
      url          = {https://inrepo02.dkfz.de/record/300807},
}