A PREVENTIVE TOOL FOR PREDICTING BLOODSTREAM INFECTIONS IN CHILDREN WITH BURNS.

Area of Science:

• Genomics and Bioinformatics
• Pediatric Critical Care Medicine
• Infectious Disease Epidemiology

Background:

• Bloodstream infections (BSIs) pose a significant challenge in pediatric burn care recovery.
• Accurate prediction of BSIs is crucial for effective prevention and improved patient outcomes.
• Personalized medicine approaches are needed to anticipate infectious complications.

Purpose of the Study:

• To develop and validate a multibiomarker panel for predicting BSIs in pediatric burn patients.
• To assess the predictive accuracy of the biomarker panel compared to clinical factors like total burn surface area (TBSA) and inhalation injury.

Main Methods:

• Analysis of blood transcriptome data from 82 pediatric patients with severe burns (TBSA ≥20%).
• Application of the least absolute shrinkage and selection operator (LASSO) machine-learning algorithm.
• Selection of a 10-probe set biomarker panel predictive of BSI development at least 3 days prior to clinical observation.

Main Results:

• A 10-biomarker panel, including genes like ARG2, CPT1A, FYB, ITCH, MACF1, and SSH2, was identified.
• The multibiomarker panel achieved high prediction accuracy for BSIs (AUC = 0.938).
• Combining the panel with TBSA and inhalation injury status further enhanced prediction accuracy (AUC = 0.978).

Conclusions:

• The developed multibiomarker panel accurately predicts BSIs in pediatric burn patients before onset.
• Early risk profiling enables timely preventive measures, supporting antibiotic stewardship and reducing healthcare burden.
• Identified biomarkers may inform the development of novel strategies for infection prevention.