A PREVENTIVE TOOL FOR PREDICTING BLOODSTREAM INFECTIONS IN CHILDREN WITH BURNS. Issue 3 (4th March 2023)

• Record Type: Journal Article
• Authors: Tsurumi, Amy; Flaherty, Patrick J.; Que, Yok-Ai; Ryan, Colleen M.; Banerjee, Ankita; Chakraborty, Arijit; Almpani, Marianna; Shankar, Malavika; Goverman, Jeremy; Schulz, John T.; Sheridan, Robert L.; Friedstat, Jonathan; Hickey, Sean A.; Tompkins, Ronald G.; Rahme, Laurence G.
• Is Part Of: Shock. Volume 59:Issue 3(2023)
• Page Start: 393
• Page End: 399
• Publication Date: 2023-03-04
• Subjects: Injury -- machine learning -- microbes -- prediction -- transcriptomics -- trauma -- AUC—area under the curve -- BSI—bloodstream infection -- ICU—intensive care unit -- LASSO—least absolute shrinkage and selection operator -- LOS—length of hospital stay -- NOS—not otherwise specified -- NPV—negative predictive value -- OR—odds ratio -- PPV—positive predictive value -- ROC—receiver operating characteristic -- TBSA—total body surface area; Shock -- Periodicals; Shock -- Periodicals; Choc (Pathologie) -- Périodiques; Shock; Periodicals; 616.0475
• DOI: 10.1097/SHK.0000000000002075
• Languages: English
• ISSNs: 1073-2322

Abstract

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.