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Comparing Single vs. Combined Cerebrospinal Fluid Parameters for Diagnosing Full-Term Neonatal Bacterial Meningitis.

ABSTRACT: Objectives: To identify and compare the cerebrospinal fluid (CSF) parameters that predict the presence of neonatal bacterial meningitis using optimal cutoff values, and to derive and compare predictive profiles based on a combination of individual parameters for the same purpose. Study Design: The retrospective component of the Shanghai Neonate Meningitis Cohort included all term neonates who underwent lumbar puncture between 2000 and 2017. Those with severe neurological diseases, histories of ventricular drainage, or traumatic lumbar punctures were excluded. Reference ranges were determined for non-bacterial meningitis neonates based on the 5th, 25th, 50th, 75th, and 95th CSF parameter quantiles, and their relationships with age were calculated using generalized additive models that tested for linear relationships. The optimal cutoff value for each measured CSF parameter was calculated using receiver operating characteristic analysis and by deriving the Youden's index. Parameters with good diagnostic efficacies were combined to produce predictive profiles using logistic regression. The diagnostic efficacies of the single parameters and profiles were compared in neonates with confirmed bacterial meningitis. Results: White blood cells (WBCs) in CSF showed a higher diagnostic ability for neonatal bacterial meningitis than CSF protein, glucose, lactate dehydrogenase, or chloride. The sensitivity and specificity of the diagnostic cutoff value for WBCs (20 × 106/L) were 95.1 and 98.7%, respectively. Profiles based on CSF parameter combinations improved the specificities slightly to 99.0-99.7%. However, employing predictive profiles did not improve sensitivities, which remained at 95.1-96.0%. Conclusions: Profiles for predicting neonatal bacterial meningitis improve the sensitivity and specificity of diagnosis slightly, although not appreciably, compared to the single parameter of CSF WBC alone.

SUBMITTER: Huang H

PROVIDER: S-EPMC6351467 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Comparing Single vs. Combined Cerebrospinal Fluid Parameters for Diagnosing Full-Term Neonatal Bacterial Meningitis.

Huang Heyu H Tan Jintong J Gong Xiaohui X Li Jing J Wang Liping L Xu Min M Zhang Xi X Zhang Yongjun Y Huang Lisu L

Frontiers in neurology 20190123

<b>Objectives:</b> To identify and compare the cerebrospinal fluid (CSF) parameters that predict the presence of neonatal bacterial meningitis using optimal cutoff values, and to derive and compare predictive profiles based on a combination of individual parameters for the same purpose. <b>Study Design:</b> The retrospective component of the Shanghai Neonate Meningitis Cohort included all term neonates who underwent lumbar puncture between 2000 and 2017. Those with severe neurological diseases, ...[more]

PMID: 30728800

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Project description:BackgroundCryptococcal meningitis is a severe infectious disease associated with high morbidity and mortality. Rapidity and accuracy of diagnosis contribute to better prognosis, but readily available tools, such as microscopy, culture, and antigens do not perform well all the time. Our study attempted to diagnose and genotype cryptococcus in the cerebrospinal fluid (CSF) samples from patients with cryptococcal meningitis using the approach of metataxonomics of Internal Transcribed Spacer (ITS) amplicons.MethodsThe CSF samples were collected from 11 clinically suspected cryptococcal meningitis patients and four non-infectious controls. Samples were recruited from the First Affiliated Hospital of Fujian Medical University Hospital, Fuzhou Fourth Hospital and the 476th Hospital of Chinese People's Liberation Army from December 2017 to December 2018. ITS1 ribosomal deoxyribonucleic acid (rDNA) genes of 15 whole samples were amplified by universal forward primer ITS1 (CTTGGTCATTTAGAGGAAGTAA) and reverse primer ITS2 (GCTGCGTTCTTCATCGATGC), sequenced by Illumina MiSeq Benchtop Sequencer. The results were confirmed by sanger sequencing of ITS1 region and partial CAP59 gene of microbial isolates from 11 meningitic samples. Pair-wise comparison between infectious group and control group was conducted through permutational multivariate analysis (PERMANOVA) in R software.ResultsThe 30,000 to 340,000 high-quality clean reads were obtained from each of the positively stained or cultured CSF samples and 8 to 60 reads from each control. The samples from 11 infected patients yielded detectable cryptococcal-specific ITS1 DNA with top abundance (from 95.90% to 99.97%), followed by many other fungal groups (each <1.41%). ITS genotype was defined in 11 CSF samples, corresponding to ITS type 1, and confirmed by Sanger sequencing. A statistically significant difference (r = 0.65869, P = 0.0014) between infectious group and control group was observed.ConclusionsThe metataxonomics of ITS amplicons facilitates the diagnosis and genotype of cryptococcus in CSF samples, which may provide a better diagnostic approach of cryptococcal infection.

Project description:Differential diagnosis of bacterial meningitis (BM) and viral meningitis (VM) is a critical clinical challenge, as the early and accurate identification of the causative agent determines the appropriate treatment regimen and markedly improves patient outcomes. Clinical and experimental studies have demonstrated that the pathogen and the host immune response contribute to mortality and neurological sequelae. As BM is associated with the activation of an inflammatory cascade, the patterns of pro- and anti-inflammatory cytokines/chemokines (CTs/CKs) present in the cerebrospinal fluid (CSF) in response to the immune assault may be useful as sensitive markers for differentiating BM from VM. In the present study, the ability of CTs/CKs in the CSF to differentiate between BM and VM was investigated. For this, biochemical markers and CT/CK profiles were analysed in 145 CSF samples, divided into three groups: BM (n=61), VM (n=58) and the control group (C; n=26) comprising patients with meningism. The CSF concentrations of monocyte chemoattractant protein-1, interleukin (IL)-8, IL-1β, IL-6, macrophage inflammatory protein-1α (MIP-1α), epithelial-neutrophil activating peptide, IL-10, tumour necrosis factor-α (TNF-α), proteins and white blood cells were significantly higher and the CSF glucose level was significantly lower in the BM group compared with the VM and C groups (P<0.01). Correlation analysis identified 28 significant correlations between various CTs/CKs in the BM group (P<0.01), with the strongest positive correlations being for TNF-α/IL-6 (r=0.75), TNF-α/MIP-1α (r=0.69), TNF-α/IL-1β (r=0.64) and IL-1β/MIP-1α (r=0.64). To identify the optimum CT/CK patterns for predicting and classifying BM and VM, a dataset of 119 BM and VM samples was divided into training (n=90) and testing (n=29) subsets for use as input for a Random Forest (RF) machine learning algorithm. For the 29 test samples (15 BM and 14 VM), the RF algorithm correctly classified 28 samples, with 92% sensitivity and 93% specificity. The results show that the patterns of CT/CK levels in the CSF can be used to aid discrimination of BM and VM.

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Project description:Background: In Africa, tuberculosis is generally regarded as persisting as one of the most devastating infectious diseases. The pediatric population is particularly vulnerable, with infection of the brain in the form of tuberculous meningitis (TBM) being the most severe manifestation. TBM is often difficult to diagnose in its early stages because of its non-specific clinical presentation. Of particular concern is that late diagnosis, and subsequent delayed treatment, leads to high risk of long-term neurological sequelae, and even death. Using advanced technology and scientific expertise, we are intent on further describing the biochemistry behind this devastating neuroinflammatory disease, with the goal of improving upon its early diagnosis. Method: We used the highly sensitive analytical platform of gas chromatography-mass spectrometry (GC-MS) to analyze amino acid profiles of cerebrospinal fluid (CSF) collected from a cohort of 33 South African pediatric TBM cases, compared to 34 controls. Results: Through the use of a stringent quality assurance procedure and various statistical techniques, we were able to confidently identify five amino acids as being significantly elevated in TBM cases, namely, alanine, asparagine, glycine, lysine, and proline. We found also in an earlier untargeted metabolomics investigation that alanine can be attributed to increased CSF lactate levels, and lysine as a marker of lipid peroxidation. Alanine, like glycine, is an inhibitory neurotransmitter in the brain. Asparagine, as with proline, is linked to the glutamate-glutamine cycle. Asparagine is associated with the removal of increased nitrites in the brain, whereas elevated proline coincides with the classic biochemical marker of increased CSF protein in TBM. All five discriminatory amino acids are linked to ammonia due to increased nitrites in TBM. Conclusion: A large amount of untapped biochemical information is present in CSF of TBM cases, of which amino acid profiling through GC-MS has potential in aiding in earlier diagnosis, and hence crucial earlier treatment.

Dataset Information

Comparing Single vs. Combined Cerebrospinal Fluid Parameters for Diagnosing Full-Term Neonatal Bacterial Meningitis.

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Comparing Single vs. Combined Cerebrospinal Fluid Parameters for Diagnosing Full-Term Neonatal Bacterial Meningitis.

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