ArticleBMC neurology2020
Application of combined cerebrospinal fluid physicochemical parameters to detect intracranial infection in neurosurgery patients.
Article in BMC neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Predicting Post-Craniotomy Intracranial Infection Using Perioperative and Early Postoperative Variables: A Retrospective Machine Learning Study.International journal of general medicine · 2026Article
- Diagnostic and prognostic value of combined cerebrospinal fluid parameters for post-neurosurgical intracranial infection in brain tumor patients.Scientific reports · 2025Article
- A novel dynamic nomogram based on clinical features and laboratory indicators for diagnosis of post-neurosurgery intracranial infection.Translational neuroscience · 2025Article
- An Investigation into Diagnostic Strategies for Central Nervous System Infections Through the Integration of Metagenomic Next-Generation Sequencing and Conventional Diagnostic Methods.Infection and drug resistance · 2024Article
- Analysis of Cerebrospinal Fluid Routine Biochemical Level, Pathogenic Bacteria Distribution, and Risk Factors in Patients with Secondary Intracranial Infection after Brain Tumor Surgery.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- The risk factors of intracranial infection in patients with intracerebral hemorrhage undergone hematoma puncture: what should we care.BMC infectious diseases · 2020Article
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Authors and funding
6 authors.
Funding
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Abstract
Routine test of cerebrospinal fluid (CSF), such as glucose concentrations, chloride ion, protein and leukocyte, as well as color, turbidity and clot, were important indicators for intracranial infection. However, there were no models to predict the intracranial infection with these parameters. We collected data of 221 cases with CSF positive-culture and 50 cases with CSF negative culture from January 1, 2016 to December 31, 2018 in the First Affiliated Hospital of Nanchang University, China. SPSS17.0 software was used to establish the model by adopting seven described indicators, and P < 0.05 was considered as statistically significant. Meanwhile, 40 cases with positive-culture and 10 cases with negative-culture were selected to verify the sensitivity and specificity of the model. The results showed that each parameter was significant in the model establishment (P < 0.05). To extract the above seven parameters, the interpretation model C was established, and C = 0.952-0.183 × glucose value (mmol/L) - 0.024 × chloride ion value (mmol/L)- 0.000122 × protein value (mg/L) - 0.0000859 × number of leukocytes per microliter (× 10
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