A PREVENTIVE TOOL FOR PREDICTING BLOODSTREAM INFECTIONS IN CHILDREN WITH BURNS
Amy Tsurumi, Patrick J Flaherty1, Yok-Ai Que2
1Department of Mathematics and Statistics, University of Massachusetts at Amherst, Amherst, Massachusetts.
Insights
A new 10-biomarker panel accurately predicts bloodstream infections (BSIs) in pediatric burn patients before symptoms appear. This personalized medicine approach aids early intervention, improving outcomes and antibiotic stewardship.
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.
Abstract:
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.
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