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OpenSep: a generalizable open source pipeline for SOFA score calculation and Sepsis-3 classification.
Hofford, Mackenzie R; Yu, Sean C; Johnson, Alistair E W; Lai, Albert M; Payne, Philip R O; Michelson, Andrew P.
Afiliação
  • Hofford MR; Department of Medicine, Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
  • Yu SC; Division of General Medicine, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
  • Johnson AEW; Department of Medicine, Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
  • Lai AM; Department of Biomedical Engineering, School of Engineering, Washington University School in St. Louis, St. Louis, Missouri, USA.
  • Payne PRO; Program in Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.
  • Michelson AP; Department of Medicine, Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
JAMIA Open ; 5(4): ooac105, 2022 Dec.
Article em En | MEDLINE | ID: mdl-36570030
EHR-based sepsis research often uses heterogeneous definitions of sepsis leading to poor generalizability and difficulty in comparing studies to each other. We have developed OpenSep, an open-source pipeline for sepsis phenotyping according to the Sepsis-3 definition, as well as determination of time of sepsis onset and SOFA scores. The Minimal Sepsis Data Model was developed alongside the pipeline to enable the execution of the pipeline to diverse sources of electronic health record data. The pipeline's accuracy was validated by applying it to the MIMIC-IV version 1.0 data and comparing sepsis onset and SOFA scores to those produced by the pipeline developed by the curators of MIMIC. We demonstrated high reliability between both the sepsis onsets and SOFA scores, however the use of the Minimal Sepsis Data model developed for this work allows our pipeline to be applied to more broadly to data sources beyond MIMIC.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: JAMIA Open Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: JAMIA Open Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos