ArticleJournal of inflammation research2025
Construction and Validation of a Nomogram Model for Predicting the Risk of Neonatal Sepsis Complicated by Purulent Meningitis.
Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Construction and validation of neonatal sepsis diagnostic nomograms: a single-site retrospective study.Frontiers in pediatrics · 2026Article
- Establishment of a risk prediction model for hydrocephalus complicated by neonatal bacterial meningitis.BMC infectious diseases · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Neonatal purulent meningitis (NPM) is a severe infection with high morbidity and mortality. NPM is a common complication in cases of neonatal sepsis (NS). This study aims to develop and validate a risk prediction model for NS complicated by NPM. Methods: A retrospective study of 535 neonates diagnosed with sepsis at the Affiliated Children's Hospital of Zhengzhou University between January 2016 and October 2024 was conducted. The primary outcome was the presence of NPM. Multivariate logistic regression was used to identify predictive factors, and a nomogram model was created using R software. Results: Multivariate analysis identified fever, seizures, tachycardia, and decreased levels of alkaline phosphatase (ALP) and total bilirubin (TBIL) as independent risk factors for NS complicated by NPM (P < 0.05). The area under the receiver operating characteristic curve (ROC) for the training set was 0.765 (95% CI: 0.711-0.819), and 0.713 (95% CI: 0.625-0.800) for the validation set. The Hosmer-Lemeshow test confirmed good model fit (χ² = 8.963, P = 0.345). Calibration and decision curve analysis showed high predictive performance and clinical applicability. Conclusion: The nomogram developed in this study demonstrates promising predictive ability and clinical value for NS complicated by NPM.
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