A PREVENTIVE TOOL FOR PREDICTING BLOODSTREAM INFECTIONS IN CHILDREN WITH BURNS.
Journal
Shock (Augusta, Ga.)
ISSN: 1540-0514
Titre abrégé: Shock
Pays: United States
ID NLM: 9421564
Informations de publication
Date de publication:
01 03 2023
01 03 2023
Historique:
pmc-release:
01
03
2024
pubmed:
5
1
2023
medline:
8
3
2023
entrez:
4
1
2023
Statut:
ppublish
Résumé
Introduction: Despite significant advances in pediatric burn care, bloodstream infections (BSIs) remain a compelling challenge during recovery. A personalized medicine approach for accurate prediction of BSIs before they occur would contribute to prevention efforts and improve patient outcomes. Methods: We analyzed the blood transcriptome of severely burned (total burn surface area [TBSA] ≥20%) patients in the multicenter Inflammation and Host Response to Injury ("Glue Grant") cohort. Our study included 82 pediatric (aged <16 years) patients, with blood samples at least 3 days before the observed BSI episode. We applied the least absolute shrinkage and selection operator (LASSO) machine-learning algorithm to select a panel of biomarkers predictive of BSI outcome. Results: We developed a panel of 10 probe sets corresponding to six annotated genes ( ARG2 [ arginase 2 ], CPT1A [ carnitine palmitoyltransferase 1A ], FYB [ FYN binding protein ], ITCH [ itchy E3 ubiquitin protein ligase ], MACF1 [ microtubule actin crosslinking factor 1 ], and SSH2 [ slingshot protein phosphatase 2 ]), two uncharacterized ( LOC101928635 , LOC101929599 ), and two unannotated regions. Our multibiomarker panel model yielded highly accurate prediction (area under the receiver operating characteristic curve, 0.938; 95% confidence interval [CI], 0.881-0.981) compared with models with TBSA (0.708; 95% CI, 0.588-0.824) or TBSA and inhalation injury status (0.792; 95% CI, 0.676-0.892). A model combining the multibiomarker panel with TBSA and inhalation injury status further improved prediction (0.978; 95% CI, 0.941-1.000). Conclusions: The multibiomarker panel model yielded a highly accurate prediction of BSIs before their onset. Knowing patients' risk profile early will guide clinicians to take rapid preventive measures for limiting infections, promote antibiotic stewardship that may aid in alleviating the current antibiotic resistance crisis, shorten hospital length of stay and burden on health care resources, reduce health care costs, and significantly improve patients' outcomes. In addition, the biomarkers' identity and molecular functions may contribute to developing novel preventive interventions.
Identifiants
pubmed: 36597771
doi: 10.1097/SHK.0000000000002075
pii: 00024382-202303000-00010
pmc: PMC9991965
mid: NIHMS1858399
doi:
Types de publication
Multicenter Study
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
393-399Subventions
Organisme : NIAID NIH HHS
ID : R56 AI155505
Pays : United States
Organisme : NIAID NIH HHS
ID : R03 AI151499
Pays : United States
Organisme : NIGMS NIH HHS
ID : U54 GM062119
Pays : United States
Informations de copyright
Copyright © 2023 by the Shock Society.
Déclaration de conflit d'intérêts
L.G.R. has a financial interest in Spero Therapeutics, a company developing therapies to treat bacterial infections. L.G.R.'s financial interests are reviewed and managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. The other authors report no conflicts of interest.
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