Prediction of multiple infections after severe burn trauma: a prospective cohort study.

Détails

Ressource 1Télécharger: BIB_05B445EAC18B.P001.pdf (1910.08 [Ko])
Etat: Serval
Version: de l'auteur
ID Serval
serval:BIB_05B445EAC18B
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Prediction of multiple infections after severe burn trauma: a prospective cohort study.
Périodique
Annals of Surgery
Auteur(s)
Yan S., Tsurumi A., Que Y.A., Ryan C.M., Bandyopadhaya A., Morgan A.A., Flaherty P.J., Tompkins R.G., Rahme L.G.
ISSN
1528-1140 (Electronic)
ISSN-L
0003-4932
Statut éditorial
Publié
Date de publication
2015
Peer-reviewed
Oui
Volume
261
Numéro
4
Pages
781-792
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.Publication Status: ppublish
Résumé
OBJECTIVE: To develop predictive models for early triage of burn patients based on hypersusceptibility to repeated infections.
BACKGROUND: Infection remains a major cause of mortality and morbidity after severe trauma, demanding new strategies to combat infections. Models for infection prediction are lacking.
METHODS: Secondary analysis of 459 burn patients (≥16 years old) with 20% or more total body surface area burns recruited from 6 US burn centers. We compared blood transcriptomes with a 180-hour cutoff on the injury-to-transcriptome interval of 47 patients (≤1 infection episode) to those of 66 hypersusceptible patients [multiple (≥2) infection episodes (MIE)]. We used LASSO regression to select biomarkers and multivariate logistic regression to built models, accuracy of which were assessed by area under receiver operating characteristic curve (AUROC) and cross-validation.
RESULTS: Three predictive models were developed using covariates of (1) clinical characteristics; (2) expression profiles of 14 genomic probes; (3) combining (1) and (2). The genomic and clinical models were highly predictive of MIE status [AUROCGenomic = 0.946 (95% CI: 0.906-0.986); AUROCClinical = 0.864 (CI: 0.794-0.933); AUROCGenomic/AUROCClinical P = 0.044]. Combined model has an increased AUROCCombined of 0.967 (CI: 0.940-0.993) compared with the individual models (AUROCCombined/AUROCClinical P = 0.0069). Hypersusceptible patients show early alterations in immune-related signaling pathways, epigenetic modulation, and chromatin remodeling.
CONCLUSIONS: Early triage of burn patients more susceptible to infections can be made using clinical characteristics and/or genomic signatures. Genomic signature suggests new insights into the pathophysiology of hypersusceptibility to infection may lead to novel potential therapeutic or prophylactic targets.
Mots-clé
burn, genomics, infection, predictive models, prognosis, sepsis, trauma
Pubmed
Web of science
Création de la notice
28/10/2014 10:12
Dernière modification de la notice
03/03/2018 13:24
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