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Updated: Aug 11, 2025
Non-Invasive Model of Neuropathogenic Escherichia coli Infection in the Neonatal Rat
Published on: October 29, 2014
Development a prediction model for identifying bacterial meningitis in young infants aged 29-90 days: a retrospective
Jiahui Wu1, Ting Shi1, Yongfei Yue2
1Department of Infectious Diseases, Children's Hospital of Soochow University, No. 92, Zhongnan Street, Suzhou, 215025, China.
Background:
The early diagnosis and treatment of bacterial meningitis (BM) in young infants was very critical. But, it was difficult to make a definite diagnosis in the early stage due to nonspecific clinical symptoms. Our objectives were to find the risk factors associated with BM and develop a prediction model of BM especially for young infants.
Methods:
We retrospectively reviewed the clinical data of young infants with meningitis between January 2011 and December 2020 in Children's Hospital of Soochow University. The independent risk factors of young infants with BM were screened using univariate and multivariate logistic regression analyses. The independent risk factors were used to construct a new scoring model and compared with Bacterial Meningitis Score (BMS) and Meningitis Score for Emergencies (MSE) models.
Results:
Among the 102 young infants included, there were 44 cases of BM and 58 of aseptic meningitis. Group B Streptococcus (22, 50.0%) and Escherichia coli (14, 31.8%) were the main pathogens of BM in the young infants. Multivariate logistic regression analysis identified procalcitonin (PCT), cerebrospinal fluid (CSF) glucose, CSF protein as independent risk factors for young infants with BM. We assigned one point for CSF glucose ≤ 1.86 mmol/L, two points were assigned for PCT ≥ 3.80 ng/ml and CSF protein ≥ 1269 mg/L. Using the not low risk criterion (score ≥ 1) with our new prediction model, we identified the young infantile BM with 100% (95% CI 91.9%-100%) sensitivity and 60.3% (95% CI 46.4%-72.9%) specificity. Compared with BMS and MSE model, our prediction model had larger area under receiver operating characteristic curve and higher specificity, the differences were statistically significant.
Conclusion:
Our new scoring model for young infants can facilitate early identification of BM and has a better performance than BMS and MSE models.
Insights
Early diagnosis of bacterial meningitis (BM) in infants is critical. A new scoring model using procalcitonin, CSF glucose, and CSF protein aids in early BM identification, outperforming existing methods.
Area of Science:
- Pediatrics
- Infectious Diseases
- Clinical Diagnostics
Background:
- Early diagnosis of bacterial meningitis (BM) in young infants is challenging due to nonspecific symptoms.
- Identifying risk factors and developing predictive models are crucial for timely intervention.
Purpose of the Study:
- To identify independent risk factors for BM in young infants.
- To develop and validate a new prediction model for BM in this population.
- To compare the performance of the new model against existing scores (BMS, MSE).
Main Methods:
- Retrospective review of clinical data from young infants with meningitis (2011-2020).
- Univariate and multivariate logistic regression analyses to identify independent risk factors.
- Construction of a novel scoring model based on identified risk factors.
Main Results:
- Procalcitonin (PCT), cerebrospinal fluid (CSF) glucose, and CSF protein were identified as independent risk factors for BM.
- The new prediction model demonstrated 100% sensitivity and 60.3% specificity for identifying BM.
- The developed model showed superior performance (AUC, specificity) compared to BMS and MSE models.
Conclusions:
- A new scoring model effectively facilitates early identification of BM in young infants.
- The novel model exhibits improved diagnostic performance over existing Bacterial Meningitis Score (BMS) and Meningitis Score for Emergencies (MSE) models.
