A Diagnostic Formula for Discrimination of Tuberculous and Bacterial Meningitis Using Clinical and Laboratory Features

Yang, Yun; Qu, Xin-Hui; Kun-nan, Zhang; Wu, Xiao‐Mu; Wang, Xinrong; An, Wen; Li, Lingjuan

doi:10.3389/fcimb.2019.00448

Cited by 10 publications

(10 citation statements)

References 37 publications

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“…For example, Yang et al. ( 16 ) found a four-parameter signature that could discriminate TBM from BM with a sensitivity of 98% and a specificity of 82%. Another clinical prediction rule established by Vibha et al.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

et al. 2021

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“…This study consecutively included adult patients with acute meningitis admitted to Assiut Fever Hospital, Assiut, Egypt between March 2017 and December 2018. The diagnosis of meningitis was suspected when the patients had the following clinical features of meningeal irritation: headache, fever, neck stiffness, photophobia, altered conscious level and focal neurological signs (The diagnosis of meningitis was suspected when the patients had the following clinical features of meningeal irritation: headache, fever, neck stiffness, photophobia, altered mental status (assessed using the Glasgow come scale; where, altered mental status was defined as a score on the Glasgow coma scale ≤14) and focal neurological signs including Cranial nerve palsies, aphasia, monoparesis or hemiparesis) [ 12 , 13 ]. We excluded patients who received antibiotic therapy or immunomodulating agents and who were less than 18 years old.…”

Section: Methodsmentioning

confidence: 99%

Microbial aspects and potential markers for differentiation between bacterial and viral meningitis among adult patients

et al. 2021

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Objectives Meningitis is a medical emergency with permanent disabilities and high mortality worldwide. We aimed to determine causative microorganisms and potential markers for differentiation between bacterial and viral meningitis. Methodology Adult patients with acute meningitis were subjected to lumber puncture. Cerebrospinal fluid (CSF) microorganisms were identified using Real-time PCR. PCT and CRP levels, peripheral and CSF-leucocyte count, CSF-protein and CSF-glucose levels were assessed. Results Out of 80 patients, infectious meningitis was confirmed in 75 cases; 38 cases were bacterial meningitis, 34 cases were viral meningitis and three cases were mixed infection. Higher PCT, peripheral and CSF-leukocytosis, higher CSF-protein and lower CSF-glucose levels were more significant in bacterial than viral meningitis patients. Neisseria meningitides was the most frequent bacteria and varicella-zoster virus was the most common virus. Using ROC analyses, serum PCT and CSF-parameters can discriminate bacterial from viral meningitis. Combined ROC analyses of PCT and CSF-protein significantly improved the effectiveness in predicting bacterial meningitis (AUC of 0.998, 100%sensitivity and 97.1%specificity) than each parameter alone (AUC of 0.951 for PCT and 0.996 for CSF-protein). Conclusion CSF-protein and serum PCT are considered as potential markers for differentiating bacterial from viral meningitis and their combination improved their predictive accuracy to bacterial meningitis.

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“…The CSF of TBM is characterized by an increase in protein (>450 mg/L), a decrease in glucose (<2.50 mmol/L), and an increase in the number of leukocyte (>8 × 10 6 /L). It is generally believed that the decrease of chloride content in CSF is related to TBM [ 1 ]. However, with the development of mNGS technology for CSF pathogens, the final diagnosis was not TBM, which was found to be consistent with the clinical features of encephalitis or meningitis, and the CSF changes were consistent with classic “tuberculous meningitis”-like changes.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Cases with Cerebrospinal Fluid Characteristics Similar to Tuberculous Meningitis

Cao

Wang

et al. 2022

BioMed Research International

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Purpose. The diagnosis of tuberculous meningitis (TBM) is difficult and relies on the patient’s clinical presentation and initial cerebrospinal fluid testing. Treatment outcomes for some patients with early consideration of TBM meningitis are often poor. Patients and Methods. In this study, we retrospectively analyzed 24 non-TBM patients whose early changes of cerebrospinal fluid were similar to those of TBM through the second-generation cerebrospinal fluid sequencing technology. Results. All patients included in this study had an acute onset, including 5 patients with a history of upper respiratory tract infection, 9 patients with fever, 6 patients with headache, 5 patients with psychiatric symptoms, 6 patients with cognitive impairment, 9 patients with signs of meningeal irritation, and 6 patients with seizures. Sixteen patients presented with altered content and level of consciousness during their admission. The leukocyte counts (median, 124.0 × 10 6 / L ) and total protein concentrations (median, 1300 mg/L) were higher than normal reference values in all patients, whereas glucose (median, 1.345 mmol/L) and chloride concentration values (average, 111.7 ± 5.2 mmol / L ) were lower than normal reference values. The patients included 2 cases of Liszt’s meningitis, 2 cases of Brucella infection in the CNS, 4 cases of Varicella zoster virus encephalitis, 2 cases of human herpes simplex virus type 1, 2 cases of lupus encephalopathy, 2 cases of anti-NMDAR receptor encephalitis, 2 cases of meningeal carcinomatosis, 5 cases of cryptococcal meningitis, 2 cases of CNS sarcoidosis, and a case of invasive Rhizopus oryzae infection. All patients were tested for NGS in cerebrospinal fluid. Eight patients were diagnosed with anti-NMDAR encephalitis, meningeal carcinomatosis, lupus encephalopathy, and CNS sarcoidosis. Nine patients experienced death; 15 patients had a good prognosis and left no significant sequelae. Conclusion. The analysis of patients with TBM-like cerebrospinal fluid changes will help improve the diagnostic accuracy of the disease and reduce misdiagnosis and underdiagnosis.

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A Diagnostic Formula for Discrimination of Tuberculous and Bacterial Meningitis Using Clinical and Laboratory Features

Cited by 10 publications

References 37 publications

Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

Microbial aspects and potential markers for differentiation between bacterial and viral meningitis among adult patients

Analysis of Cases with Cerebrospinal Fluid Characteristics Similar to Tuberculous Meningitis

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