Evidence map›Paper›PMID 39755624›Full record

ArticleBMC pediatrics2025

Predicting purulent meningitis in very preterm infants: a novel clinical model.

Xiaowei Sun, Rui Jing, Yang Li

Abstract read
In one paragraph

Article in BMC pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xiaowei SunDepartment of Pediatrics, Qilu Hospital, Shandong University, No.107, West Culture Road, Lixia District, Jinan City, Shandong Province, 250000, China.
Rui JingDepartment of Pediatrics, Weifang People's Hospital, No.151 Guangwen Street, Kuiwen District, Weifang City, Shandong Province, 261000, China.
Yang LiDepartment of Pediatrics, Qilu Hospital, Shandong University, No.107, West Culture Road, Lixia District, Jinan City, Shandong Province, 250000, China. liyang10248@qiluhospital.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPurulent meningitis (PM) is a commonly encountered infectious condition in newborns, which unfortunately can result in infant mortality. Newborns with PM often present nonspecific symptoms. The success of lumbar puncture, an invasive test, relies on the operator's expertise. Preterm infants pose diagnostic challenges compared to full-term babies. The objective of this study is to establish a convenient and effective clinical prediction model based on perinatal factors to assess the risk of PM in very preterm infants, thereby assisting clinicians in developing new diagnostic and treatment strategies.

methodsThis study involved very preterm infants (gestational age < 32 weeks) admitted to the Qilu Hospital of Shandong University from January 2020 to December 2023. All included infants underwent lumbar puncture. We gathered comprehensive data that included information on maternal health conditions and the clinical features of very preterm infants. The PM was diagnosed according to the diagnostic criteria. This study conducted data analysis and processing using R version 4.1.2. A stepwise regression method was applied for multivariate Logistic regression analysis to select the best predictors for PM and to develop a predictive model. Differences were considered statistically significant at P < 0.05.

resultsThis study enrolled a total of 201 preterm infants, including 117 boys and 84 girls. The gestational age was 28.7 ± 1.7 weeks, and the weight was 1166.2 ± 302.7 g. Ninety infants were diagnosed with PM, while 111 did not have PM. The influencing factors include birth weight, PCT within 24 h after birth, cesarean delivery, and premature rupture of membranes. These were used to construct a risk prediction nomogram and verified its accuracy. The Brier score was 0.157, the calibration slope was 1.0, and the concordance index was 0.849.

conclusionsWe developed and validated a personalized nomogram to identify high-risk individuals for early prediction of purulent meningitis in very preterm infants. This practical predictive model may help reduce unnecessary lumbar puncture procedures.

Indexed as

Meningitis, BacterialFemaleGestational AgeHumansInfant, NewbornInfant, PrematureInfant, Premature, DiseasesLogistic ModelsMaleRetrospective StudiesRisk AssessmentRisk FactorsSpinal PuncturePredictive modelPurulent meningitisRisk factorsVery preterm infants

Identifiers

PMID39755624
PMCPMC11700457

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.