ArticleAnnals of clinical and translational neurology2021
A sequential guide to identify neonates with low bacterial meningitis risk: a multicenter study.
Article in Annals of clinical and translational neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Pooled it
- Establishment of a risk prediction model for hydrocephalus complicated by neonatal bacterial meningitis.BMC infectious diseases · 2025Article
- Early prognostic value of repeat CSF parameter measurements in neonatal bacterial meningitis following initial antimicrobial therapy.Italian journal of pediatrics · 2025Article
- Predicting purulent meningitis in very preterm infants: a novel clinical model.BMC pediatrics · 2025Article
- Construction and Validation of a Nomogram Model for Predicting the Risk of Neonatal Sepsis Complicated by Purulent Meningitis.Journal of inflammation research · 2025Article
- Current Status and Problems of Newborn Infection Management at a Regional Hospital in Japan.The Kobe journal of medical sciences · 2023Article
- Establishment of a predictive model for purulent meningitis in preterm infants.Translational pediatrics · 2022Article
Corrections and comments
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Authors and funding
12 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo derive and validate a predictive algorithm integrating clinical and laboratory parameters to stratify a full-term neonate's risk level of having bacterial meningitis (BM).
methodsA multicentered dataset was categorized into derivation (689 full-term neonates aged ≤28 days with a lumbar puncture [LP]) and external validation (383 neonates) datasets. A sequential algorithm with risk stratification for neonatal BM was constructed.
resultsIn the derivation dataset, 102 neonates had BM (14.8%). Using stepwise regression analysis, fever, infection source absence, neurological manifestation, C-reactive protein (CRP), and procalcitonin were selected as optimal predictive sets for neonatal BM and introduced to a sequential algorithm. Based on the algorithm, 96.1% of BM cases (98 of 102) were identified, and 50.7% of the neonates (349 of 689) were classified as low risk. The algorithm's sensitivity and negative predictive value (NPV) in identifying neonates at low risk of BM were 96.2% (95% CI 91.7%-98.9%) and 98.9% (95% CI 97.6%-99.6%), respectively. In the validation dataset, sensitivity and NPV were 95.9% (95% CI 91.0%-100%) and 98.8% (95% CI 97.7%-100%).
interpretationThe sequential algorithm can risk stratify neonates for BM with excellent predictive performance and prove helpful to clinicians in LP-related decision-making.
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