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Subtle variation in sepsis-III definitions markedly influences predictive performance within and across methods.
Cohen, Samuel N; Foster, James; Foster, Peter; Lou, Hang; Lyons, Terry; Morley, Sam; Morrill, James; Ni, Hao; Palmer, Edward; Wang, Bo; Wu, Yue; Yang, Lingyi; Yang, Weixin.
Afiliación
  • Cohen SN; Mathematical Institute, University of Oxford, Oxford, UK.
  • Foster J; The Alan Turing Institute, London, UK.
  • Foster P; Department of Mathematical Sciences, University of Bath, Bath, UK.
  • Lou H; The Alan Turing Institute, London, UK.
  • Lyons T; Department of Mathematics, University College London, Room 603, 25 Gordon St, London, WC1H 0AY, UK.
  • Morley S; Mathematical Institute, University of Oxford, Oxford, UK.
  • Morrill J; Mathematical Institute, University of Oxford, Oxford, UK.
  • Ni H; Mathematical Institute, University of Oxford, Oxford, UK.
  • Palmer E; Department of Mathematics, University College London, Room 603, 25 Gordon St, London, WC1H 0AY, UK. h.ni@ucl.ac.uk.
  • Wang B; Bloomsbury Institute of Intensive Care Medicine, University College London, London, UK.
  • Wu Y; The Alan Turing Institute, London, UK.
  • Yang L; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
  • Yang W; Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK.
Sci Rep ; 14(1): 1920, 2024 01 22.
Article en En | MEDLINE | ID: mdl-38253623
ABSTRACT
Early detection of sepsis is key to ensure timely clinical intervention. Since very few end-to-end pipelines are publicly available, fair comparisons between methodologies are difficult if not impossible. Progress is further limited by discrepancies in the reconstruction of sepsis onset time. This retrospective cohort study highlights the variation in performance of predictive models under three subtly different interpretations of sepsis onset from the sepsis-III definition and compares this against inter-model differences. The models are chosen to cover tree-based, deep learning, and survival analysis methods. Using the MIMIC-III database, between 867 and 2178 intensive care unit admissions with sepsis were identified, depending on the onset definition. We show that model performance can be more sensitive to differences in the definition of sepsis onset than to the model itself. Given a fixed sepsis definition, the best performing method had a gain of 1-5% in the area under the receiver operating characteristic (AUROC). However, the choice of onset time can cause a greater effect, with variation of 0-6% in AUROC. We illustrate that misleading conclusions can be drawn if models are compared without consideration of the sepsis definition used which emphasizes the need for a standardized definition for sepsis onset.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sepsis Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Reino Unido
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sepsis Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Reino Unido