ArticleTranslational pediatrics2022
Establishment of a predictive model for purulent meningitis in preterm infants.
Article in Translational pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Pooled it
- A nomogram to predict probable meningitis in very preterm and/or very low birth weight infants with early-onset sepsis: a multicenter retrospective study.BMC pregnancy and childbirth · 2026Article
- Development and internal validation of a prognostic nomogram for poor outcomes in neonatal bacterial meningitis.Translational pediatrics · 2026Article
- Preoperative risk stratification for pathological upgrading in colorectal polyps using explainable machine learning: implications for screening optimization and resource allocation.Frontiers in public health · 2026Article
- LY294002 and LiCl Mitigate Neonatal ExPEC Meningitis Through Akt/GSK3β Signaling Modulation.Mediators of inflammation · 2026Article
- A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Predicting purulent meningitis in very preterm infants: a novel clinical model.BMC pediatrics · 2025Article
- Predicting mortality in intensive care unit patients with CAUTI using an interpretable machine learning model: a retrospective cohort study from MIMIC-IV database.Frontiers in medicine · 2025Article
- Construction and Validation of a Nomogram Model for Predicting the Risk of Neonatal Sepsis Complicated by Purulent Meningitis.Journal of inflammation research · 2025Article
- The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review.Health science reports · 2024Review
- Exploring the diagnostic value of CLR and CPR in differentiating Kawasaki disease from other infectious diseases based on clinical predictive modeling.Frontiers in pediatrics · 2024Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Purulent meningitis (PM) is an important cause of mortality and morbidity in the newborn population throughout the world. The subtle of specific clinical signs and low success rates of lumbar puncture make diagnosis of PM more difficult in preterm than in older children. The objective of this study was to establish a predict model for preterm PM in hopes of helping clinicians develop new diagnostic and treatment strategies. Methods: Premature infants who were admitted to The First Affiliated Hospital of Zhengzhou University from September 2017 to March 2020 were enrolled in this study. All the patients underwent lumbar puncture. We collected data encompassing maternal diseases and neonatal clinical features. Cerebrospinal fluid (CSF) culture is the gold standard for diagnosing meningitis. The PM was diagnosed according to the diagnostic criteria. All statistical analyses were performed using R 3.63 (https://www.r-project.org/). Logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to establish a risk prediction model of PM. The Brier score, calibration slope, and concordance (C)-index were used to verify the accuracy of prediction model. Results: A total of 168 preterm infants were enrolled in this study, 80 boys and 88 girls, the gestational age (GA) was 26.43-36.86 weeks (32.45±2.79 weeks), the birth weight (BW) was 700-3,400 g (1,814.05±568.84 g). There were 77 preterm infants with PM while 91 without. We identified seven variables as independent risk factors for PM in preterm infants by LASSO analysis [the optimal λ was 0.080960, and log(λ) = -2.5138], including procalcitonin (PCT) on the 1st day after birth, prenatal glucocorticoid use, albumin, the 1-minute Apgar score, the use of non-invasive biphasic positive airway pressure, hemoglobin, and sex. These were used to construct a risk prediction nomogram and verified its accuracy. The Brier score was 0.17, the calibration slope was 0.966, and the concordance index was 0.82018. Conclusions: Our prediction model could predict the risk of PM in preterm infants. Using this prediction model, it may be able to provide reference to determine whether lumbar puncture is performed and whether antibiotics are applied as soon as possible.
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