Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis.

Autor: Taneja I; Prenosis Inc., Chicago, Illinois, USA., Damhorst GL; Prenosis Inc., Chicago, Illinois, USA.; Department of Medicine, Emory University, Atlanta, Georgia, USA., Lopez-Espina C; Prenosis Inc., Chicago, Illinois, USA., Zhao SD; Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA., Zhu R; Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA., Khan S; Prenosis Inc., Chicago, Illinois, USA., White K; Biomedical Research Center, Carle Foundation Hospital, Urbana, Illinois, USA., Kumar J; Biomedical Research Center, Carle Foundation Hospital, Urbana, Illinois, USA., Vincent A; OSF Saint Francis Medical Center, Peoria, Illinois, USA., Yeh L; OSF Saint Francis Medical Center, Peoria, Illinois, USA., Majdizadeh S; Biomedical Research Center, Carle Foundation Hospital, Urbana, Illinois, USA., Weir W; Biomedical Research Center, Carle Foundation Hospital, Urbana, Illinois, USA., Isbell S; Department of Pathology, Saint Louis University School of Medicine, St. Louis, Missouri, USA., Skinner J; Biomedical Research Center, Carle Foundation Hospital, Urbana, Illinois, USA., Devanand M; Biomedical Research Center, Carle Foundation Hospital, Urbana, Illinois, USA., Azharuddin S; Biomedical Research Center, Carle Foundation Hospital, Urbana, Illinois, USA., Meenakshisundaram R; Biomedical Research Center, Carle Foundation Hospital, Urbana, Illinois, USA., Upadhyay R; Biomedical Research Center, Carle Foundation Hospital, Urbana, Illinois, USA., Syed A; OSF Saint Francis Medical Center, Peoria, Illinois, USA., Bauman T; OSF Saint Francis Medical Center, Peoria, Illinois, USA., Devito J; OSF Saint Francis Medical Center, Peoria, Illinois, USA., Heinzmann C; OSF Saint Francis Medical Center, Peoria, Illinois, USA., Podolej G; OSF Saint Francis Medical Center, Peoria, Illinois, USA., Shen L; Prenosis Inc., Chicago, Illinois, USA., Timilsina SS; Prenosis Inc., Chicago, Illinois, USA., Quinlan L; Prenosis Inc., Chicago, Illinois, USA., Manafirasi S; Prenosis Inc., Chicago, Illinois, USA., Valera E; Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA., Reddy B Jr; Prenosis Inc., Chicago, Illinois, USA.; Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA., Bashir R; Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.
Jazyk: angličtina
Zdroj: Clinical and translational science [Clin Transl Sci] 2021 Jul; Vol. 14 (4), pp. 1578-1589. Date of Electronic Publication: 2021 May 02.
DOI: 10.1111/cts.13030
Abstrakt: Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine-learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine-learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30-day mortality, and 3-day inpatient re-admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium, and high-risk groups showed significant differences in LOS (p < 0.0001), 30-day mortality (p < 0.0001), and 30-day inpatient readmission (p < 0.0001). In conclusion, a machine-learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.
(© 2021 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.)
Databáze: MEDLINE
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