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The cause of death in bacterial meningitis.

ABSTRACT: BACKGROUND:Death from bacterial meningitis is rarely attributed to the actual event causing death. The present study therefore categorized and characterized the cause and time of death due to bacterial meningitis. METHODS:In a cohort of patients >?15?years of age with community acquired bacterial meningitis the medical records were reviewed, and a clinical cause of death categorized into six main categories: 1) CNS complications, 2) Systemic complications, 3) Combination of systemic and CNS complications, 4) Sudden death, 5) Withdrawal of care, or 6) Unknown. RESULTS:We identified 358 patients of which 84 (23%) died in-hospital. Causes of death were ascribed to CNS complications in 43%, Systemic complications in 39%, Combined CNS and systemic complications in 4%, Sudden death in 7% and withdrawal of care in 5%. Brain herniation, circulatory failure, intractable seizures and other brain injury were the most common specific causes of death within 14?days from admission (55%). CONCLUSION:Fatal complications due to the primary infection - meningitis - is most common within 14?days of admission. The diversity of complications causing death in meningitis suggest that determining the clinical cause of death is essential to the evaluation of novel treatment strategies.

SUBMITTER: Sharew A

PROVIDER: S-EPMC7045616 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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The cause of death in bacterial meningitis.

Sharew A A Bodilsen J J Hansen B R BR Nielsen H H Brandt C T CT

BMC infectious diseases 20200227 1

<h4>Background</h4>Death from bacterial meningitis is rarely attributed to the actual event causing death. The present study therefore categorized and characterized the cause and time of death due to bacterial meningitis.<h4>Methods</h4>In a cohort of patients > 15 years of age with community acquired bacterial meningitis the medical records were reviewed, and a clinical cause of death categorized into six main categories: 1) CNS complications, 2) Systemic complications, 3) Combination of system ...[more]

PMID: 32106821

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Project description:BackgroundThe differential diagnosis between tuberculous meningitis (TBM) and bacterial meningitis (BM) remains challenging in clinical practice. This study aimed to establish a diagnostic model that could accurately distinguish TBM from BM.MethodsPatients with TBM or BM were recruited between January 2017 and January 2021 at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The detection for indicators involved in cerebrospinal fluid (CSF) and T-SPOT assay were performed simultaneously. Multivariate logistic regression was used to create a diagnostic model.ResultsA total of 174 patients (76 TBM and 98 BM) and another 105 cases (39 TBM and 66 BM) were enrolled from Qiaokou cohort and Caidian cohort, respectively. Significantly higher level of CSF lymphocyte proportion while significantly lower levels of CSF chlorine, nucleated cell count, and neutrophil proportion were observed in TBM group when comparing with those in BM group. However, receiver operating characteristic (ROC) curve analysis showed that the areas under the ROC curve (AUCs) produced by these indicators were all under 0.8. Meanwhile, tuberculosis-specific antigen/phytohemagglutinin (TBAg/PHA) ratio yielded an AUC of 0.889 (95% CI, 0.840-0.938) in distinguishing TBM from BM, with a sensitivity of 68.42% (95% CI, 57.30%-77.77%) and a specificity of 92.86% (95% CI, 85.98%-96.50%) when a cutoff value of 0.163 was used. Consequently, we successfully established a diagnostic model based on the combination of TBAg/PHA ratio, CSF chlorine, CSF nucleated cell count, and CSF lymphocyte proportion for discrimination between TBM and BM. The established model showed good performance in differentiating TBM from BM (AUC: 0.949; 95% CI, 0.921-0.978), with 81.58% (95% CI, 71.42%-88.70%) sensitivity and 91.84% (95% CI, 84.71%-95.81%) specificity. The performance of the diagnostic model obtained in Qiaokou cohort was further validated in Caidian cohort. The diagnostic model in Caidian cohort produced an AUC of 0.923 (95% CI, 0.867-0.980) with 79.49% (95% CI, 64.47%-89.22%) sensitivity and 90.91% (95% CI, 81.55%-95.77%) specificity.ConclusionsThe diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM.

Project description:Effective laboratory-based surveillance and public health response to bacterial meningitis depends on timely characterization of bacterial meningitis pathogens. Traditionally, characterizing bacterial meningitis pathogens such as Neisseria meningitidis (Nm) and Haemophilus influenzae (Hi) required several biochemical and molecular tests. Whole genome sequencing (WGS) has enabled the development of pipelines capable of characterizing the given pathogen with equivalent results to many of the traditional tests. Here, we present the Bacterial Meningitis Genomic Analysis Platform (BMGAP): a secure, web-accessible informatics platform that facilitates automated analysis of WGS data in public health laboratories. BMGAP is a pipeline comprised of several components, including both widely used, open-source third-party software and customized analysis modules for the specific target pathogens. BMGAP performs de novo draft genome assembly and identifies the bacterial species by whole-genome comparisons against a curated reference collection of 17 focal species including Nm, Hi, and other closely related species. Genomes identified as Nm or Hi undergo multi-locus sequence typing (MLST) and capsule characterization. Further typing information is captured from Nm genomes, such as peptides for the vaccine antigens FHbp, NadA, and NhbA. Assembled genomes are retained in the BMGAP database, serving as a repository for genomic comparisons. BMGAP's species identification and capsule characterization modules were validated using PCR and slide agglutination from 446 bacterial invasive isolates (273 Nm from nine different serogroups, 150 Hi from seven different serotypes, and 23 from nine other species) collected from 2017 to 2019 through surveillance programs. Among the validation isolates, BMGAP correctly identified the species for all 440 isolates (100% sensitivity and specificity) and accurately characterized all Nm serogroups (99% sensitivity and 98% specificity) and Hi serotypes (100% sensitivity and specificity). BMGAP provides an automated, multi-species analysis pipeline that can be extended to include additional analysis modules as needed. This provides easy-to-interpret and validated Nm and Hi genome analysis capacity to public health laboratories and collaborators. As the BMGAP database accumulates more genomic data, it grows as a valuable resource for rapid comparative genomic analyses during outbreak investigations.

Dataset Information

The cause of death in bacterial meningitis.

Publications

The cause of death in bacterial meningitis.

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