ArticleJournal of translational medicine2025
Maternal predictors of early-onset sepsis in neonates: a multicenter retrospective cohort study and risk prediction model.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction models for the occurrence and mortality of sepsis-associated lung injury: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Maternal obesity and the risk of neonatal sepsis: a meta-analysis.Frontiers in pediatrics · 2026Pooled it
- Development and validation of a LASSO-logistic regression model for predicting subtherapeutic infliximab trough concentrations in patients with Crohn's disease.International journal of clinical pharmacy · 2026Article
- Prevalence, Maternal Risk Factors, and Neonatal Outcomes of Early-Onset Neonatal Septicemia: A Retrospective Study.Cureus · 2026Article
- Molecular determinants ofFrontiers in cellular and infection microbiology · 2026Article
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Authors and funding
12 authors.
Funding
Abstract
objectivesEarly-onset sepsis (EOS) is a common and serious neonatal infection that contributes significantly to morbidity and mortality. A comprehensive understanding of maternal risk factors and their association with EOS is essential for developing effective preventive strategies.
methodsIn this study, Clinical and laboratory data of the patients were extracted from two hospitals in Chongqing, China (2017-2023). Collinearity analysis was performed to exclude variables with multicollinearity. Restricted cubic spline (RCS) analyses were applied to evaluate potential linear or nonlinear associations between continuous maternal variables and the risk of EOS. A multivariable Poisson regression model was developed to identify independent maternal predictors of EOS and construct a predictive model. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). In addition, a nomogram based on the final Poisson regression model was established to enable individualized risk prediction and support clinical decision-making.
resultsThis multicenter retrospective cohort study included 79,570 mother-infant pairs, the incidence of EOS was 0.56%. Univariate and multivariable Poisson regression identified key risk factors, such as chorioamnionitis, puerperal infection, intrauterine fetal distress, preterm birth, twin pregnancy, and elevated maternal WBC, HGI, M%, and PWR. Conversely, higher maternal globulin and cesarean delivery were associated with reduced EOS risk. A predictive model with good discrimination (AUC = 0.762) was developed and visualized through a nomogram to facilitate individualized risk assessment.
conclusionThis study highlights the importance of maternal factors in early EOS prediction and supports their integration into perinatal risk assessment. Further research is needed to investigate the contribution of maternal psychosocial and nutritional status and validate the model across diverse populations.
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