A PREVENTIVE TOOL FOR PREDICTING BLOODSTREAM INFECTIONS IN CHILDREN WITH BURNS

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.

Fig. 1.

Description of the study design and patient population. (A) The study design shows the timing of blood collection and outcome. (B) Patients who were included in the study and those who were excluded.

Fig. 1.

Description of the study design and patient population. (A) The study design shows the timing of blood collection and outcome. (B) Patients who were included in the study and those who were excluded.

Fig. 2.

Gene expression alterations among those susceptible to BSI. (A) Volcano plot using the initial 29,917 probe sets included in the analyses. Black dots indicate probe sets with a 1.5-fold difference between BSI-cases versus non-cases (86 probe sets), and red squares correspond to the ten probe-set panel selected with LASSO. (B) Network constructed using GeneMania. Black nodes correspond to the gene set selected by LASSO; gray nodes correspond to connections found to be relevant to the selected gene set; green edges indicate evidence of genetic interactions, and purple edges indicate evidence of co-expression.

Fig. 2.

Gene expression alterations among those susceptible to BSI. (A) Volcano plot using the initial 29,917 probe sets included in the analyses. Black dots indicate probe sets with a 1.5-fold difference between BSI-cases versus non-cases (86 probe sets), and red squares correspond to the ten probe-set panel selected with LASSO. (B) Network constructed using GeneMania. Black nodes correspond to the gene set selected by LASSO; gray nodes correspond to connections found to be relevant to the selected gene set; green edges indicate evidence of genetic interactions, and purple edges indicate evidence of co-expression.

Fig. 3.

Comparisons of the performance of the multi-biomarker model, clinical models, and the combined model. (A) ROC curves, with the AUROC [95% CI] of the multi-biomarker panel (black), TBSA only clinical model (blue), TBSA and inhalation injury status clinical model (green), combined multi-biomarker with TBSA (purple), combined multi-biomarker with TBSA and inhalation injury status model (red). (B) AUROC [95% CI] from five-fold CV results for the multi-biomarker panel (black), TBSA only clinical model (blue), TBSA and inhalation injury status clinical model (green), combined multi-biomarker with TBSA (purple), combined multi-biomarker with TBSA and inhalation injury status model (red).

Fig. 3.

Comparisons of the performance of the multi-biomarker model, clinical models, and the combined model. (A) ROC curves, with the AUROC [95% CI] of the multi-biomarker panel (black), TBSA only clinical model (blue), TBSA and inhalation injury status clinical model (green), combined multi-biomarker with TBSA (purple), combined multi-biomarker with TBSA and inhalation injury status model (red). (B) AUROC [95% CI] from five-fold CV results for the multi-biomarker panel (black), TBSA only clinical model (blue), TBSA and inhalation injury status clinical model (green), combined multi-biomarker with TBSA (purple), combined multi-biomarker with TBSA and inhalation injury status model (red).