Predictive biomarker of mortality in children with infectious diseases: a nationwide data analysis

Shinya Miura1,2, Tomohiro Katsuta1, Yukitsugu Nakamura1

  • 1Department of Pediatrics, St. Marianna University School of Medicine, Kawasaki, Japan.

PubMed

Insights

Common infection biomarkers like C-reactive protein and white blood cell counts are unreliable for predicting mortality in children. More effective biomarkers for pediatric infectious diseases include pH, prothrombin time-international normalized ratio, and procalcitonin.

Area of Science:

  • Pediatric Infectious Diseases
  • Clinical Biomarkers
  • Critical Care Medicine

Background:

  • Biomarkers are vital for identifying high-risk children with infections.
  • Few studies have assessed the predictive value of biomarkers for mortality in pediatric infectious diseases.

Purpose of the Study:

  • To evaluate the predictive capabilities of various biomarkers for mortality in children diagnosed with infectious diseases.

Main Methods:

  • Analysis of an inpatient database from over 200 acute-care hospitals in Japan (2012-2021).
  • Inclusion of children who underwent blood culture and received antimicrobial treatment.
  • Assessment of biomarker discriminative capabilities using area under receiver operating characteristic curves (AUCs).

Main Results:

  • Out of 11,365 children with presumed infection, 100 (0.9%) died.
  • C-reactive protein (AUC: 0.44) and white blood cell count (AUC: 0.45) showed limited predictive capability.
  • pH (AUC: 0.77), prothrombin time-international normalized ratio (AUC: 0.77), and procalcitonin (AUC: 0.76) demonstrated strong discriminatory capabilities for mortality.

Conclusions:

  • C-reactive protein and white blood cell counts may not be reliable indicators for predicting mortality in pediatric infections.
  • pH, prothrombin time-international normalized ratio, and procalcitonin show promise as predictive biomarkers.
  • Further research is warranted to explore these promising biomarkers in pediatric infectious disease mortality prediction.