Login / Signup

Investigating the Association between Serum and Hematological Biomarkers and Neonatal Sepsis in Newborns with Premature Rupture of Membranes: A Retrospective Study.

Maura-Adelina HincuGabriela Ildikó ZondaPetronela VicoveanuValeriu HaraborAnamaria HaraborAlexandru CărăuleanuAlina-Sînziana Melinte-PopescuMarian Melinte-PopescuElena MihalceanuMariana Stuparu CretuIngrid Andrada VasilacheDragos NemescuLuminita Păduraru
Published in: Children (Basel, Switzerland) (2024)
(1) Background: Neonatal early-onset sepsis (EOS) is associated with important mortality and morbidity. The aims of this study were to evaluate the association between serum and hematological biomarkers with early onset neonatal sepsis in a cohort of patients with prolonged rupture of membranes (PROM) and to calculate their diagnostic accuracy. (2) Methods: A retrospective cohort study was conducted on 1355 newborns with PROM admitted between January 2017 and March 2020, who were divided into two groups: group A, with PROM ≥ 18 h, and group B, with ROM < 18 h. Both groups were further split into subgroups: proven sepsis, presumed sepsis, and no sepsis. Descriptive statistics, analysis of variance (ANOVA) and a Random Effects Generalized Least Squares (GLS) regression were used to evaluate the data. (3) Results: The statistically significant predictors of neonatal sepsis were the high white blood cell count from the first ( p = 0.005) and third day ( p = 0.028), and high C-reactive protein (CRP) values from the first day ( p = 0.004). Procalcitonin (area under the curve-AUC = 0.78) and CRP (AUC = 0.76) measured on the first day had the best predictive performance for early-onset neonatal sepsis. (4) Conclusions: Our results outline the feasibility of using procalcitonin and CRP measured on the first day taken individually in order to increase the detection rate of early-onset neonatal sepsis, in the absence of positive blood culture.
Keyphrases
  • early onset
  • septic shock
  • acute kidney injury
  • late onset
  • intensive care unit
  • pregnant women
  • stem cells
  • cardiovascular disease
  • single cell
  • machine learning
  • electronic health record
  • type diabetes
  • low birth weight