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

Cytochemical analysis of cerebrospinal fluid in tuberculous meningitis versus other etiologies

BACKGROUND

Meningeal tuberculosis (TBM) is the most severe form of extrapulmonary tuberculosis due to its high mortality and long-term sequelae in survivors.

METHODS

A cross-sectional study of diagnostic tests was carried out in a private clinical laboratory in Lima, Peru. All cerebrospinal fluid (CSF) samples from patients with suspected meningitis were analyzed with cytochemical and biochemical studies, as well as smear microscopy, India ink, the FilmArray Meningitis/Encephalitis panel, Xpert® MTB/RIF or Xpert MTB/RIF Ultra, and culture for common bacterias, fungi or mycobacterial.

RESULTS

450 CSF samples were included. The main microorganisms detected were Mycobacterium tuberculosis (8.9%), Cryptococcus neoformans (6.0%), and Streptococcus pneumoniae (2.4%). 97.5% of patients with TBM presented positive Xpert MTB/RIF or Ultra. The median number of red blood cells, leukocytes, and percentage of mononuclear cells, glucose, and proteins in the CSF was 57.5 cells/μl, 91.5 cells/μl, 70%, 22.5 mg/dL and 218.3 mg/dL, respectively. Likewise, patients with TMB had the lowest glucose levels (median: 22.5, IQR: 11 - 35) compared to other etiologies of meningitis. While bacterial meningitis had the highest leukocyte (median: 173 μl; IQR: 17 - 520) and protein levels (median: 289.7; IQR: 92 - 556).

CONCLUSION

The characteristics of the cytochemical study of CSF can guide the differential diagnosis by identifying general trends of tuberculous meningitis and other meningitis etiologies. However, it remains necessary to establish methods with greater precision to properly define the etiological agent causing meningitis.

A Novel Diagnostic Prediction Model for Distinguishing Between Tuberculous and Cryptococcal Meningitis

Background and aim: Tuberculous meningitis (TBM) and cryptococcal meningitis (CM) are easily misdiagnosed due to atypical clinical symptoms. It is difficult for physcians to achieve a rapid and accurate differential diagnosis of TBM in the early stages of disease onset. The aim of this study was to construct a diagnostic prediction model for TBM and CM.Methods: In this retrospective study, 194 patients with TBM and CM were divided into two groups: training group (163 patients) and validation group (31 patients). Univariate and multivariate analyses were performed on the training group patients. The diagnostic factors were selected to construct the differential diagnostic prediction model for TBM and CM, and the prediction model was verified. A receiver operating characteristics curve (ROC) was constructed and used to evaluate the diagnostic value of the novel model.Results: Univariate analysis of clinical characteristics revealed differences in eight parameters (P<0.05) between tuberculous meningitis and cryptococcal meningitis. The multivariate analyses showed there were five independent differential factors including age, disease course, albumin-to-globulin ratio, cerebrospinal fluid protein, and cerebrospinal fluid sugar to blood sugar ratio in this study between TBM and CM, while there was no significant difference in the number of nucleated cells in CSF (P=0.088). A differential diagnosis model for TBM and CM was constructed based on those factors. A ROC was constructed with an area under curve [AUC] of 94.5%, a sensitivity of 85.71%, and specificity of 94.59% in the training group.Conclusion: The novel diagnostic scoring model for TBM and CM has greater differential diagnosis potential than previous reports, which can provide more reliable preliminary diagnosis results for primary hospitals, effectively reduce misdiagnosis, and provide references for early treatment.

Development and validation of a new model for the early diagnosis of tuberculous meningitis in adults based on simple clinical and laboratory parameters

BACKGROUND

The differential diagnosis between tuberculous meningitis (TBM) and viral meningitis (VM) or bacterial meningitis (BM) remains challenging in clinical practice, particularly in resource-limited settings. This study aimed to establish a diagnostic model that can accurately and early distinguish TBM from both VM and BM in adults based on simple clinical and laboratory parameters.

METHODS

Patients diagnosed with TBM or non-TBM (VM or BM) between January 2012 and October 2021 were retrospectively enrolled from the General Hospital (derivation cohort) and Branch Hospital (validation cohort) of Ningxia Medical University. Demographic characteristics, clinical symptoms, concomitant diseases, and cerebrospinal fluid (CSF) parameters were collated. Univariable logistic analysis was performed in the derivation cohort to identify significant variables (P < 0.05). A multivariable logistic regression model was constructed using these variables. We verified the performance including discrimination, calibration, and applicability of the model in both derivation and validation cohorts.

RESULTS

A total of 222 patients (70 TBM and 152 non-TBM [75 BM and 77 VM]) and 100 patients (32 TBM and 68 non-TBM [31 BM and 37 VM]) were enrolled as derivation and validation cohorts, respectively. The multivariable logistic regression model showed that disturbance of consciousness for > 5 days, weight loss > 5% of the original weight within 6 months, CSF lymphocyte ratio > 50%, CSF glucose concentration < 2.2 mmol/L, and secondary cerebral infarction were independently correlated with the diagnosis of TBM (P < 0.05). The nomogram model showed excellent discrimination (area under the curve 0.959 vs. 0.962) and great calibration (P-value in the Hosmer-Lemeshow test 0.128 vs. 0.863) in both derivation and validation cohorts. Clinical decision curve analysis showed that the model had good applicability in clinical practice and may benefit the entire population.

CONCLUSIONS

This multivariable diagnostic model may help clinicians in the early discrimination of TBM from VM and BM in adults based on simple clinical and laboratory parameters.

Misdiagnosis of Systemic Lupus Erythematosus Combined with Urinary Tuberculosis Leading to Tuberculous Meningitis: A Case Report and Literature Review

Purpose

To explore the lessons learned from the misdiagnosis of systemic lupus erythematosus (SLE) combined with urinary tuberculosis leading to tuberculous meningitis (TBM) and the diagnosis and treatment of TBM through case reports and review of the literature.

Methods

We report a case of an SLE patient presenting with urinary tuberculosis infection misdiagnosed as interstitial cystitis and complex urinary tract infection, who developed neurological infection after a cystocentesis biopsy and was eventually diagnosed with TBM. In addition, all cases of SLE combined with TBM from January 1975 to February 2022 were summarised and reviewed to compare current diagnostic and treatment strategies for the disease.

Results

The patient suddenly developed neurological symptoms after cystocentesis biopsy, and we detected Mycobacterium tuberculosis in the macrogenomic next-generation sequence (mNGS) of the cerebrospinal fluid. We therefore excluded interstitial cystitis and neuropsychiatric lupus to confirm the diagnosis of Mycobacterium tuberculosis infection leading to urinary tract tuberculosis and TBM.

Conclusion

SLE is complicated by urological tuberculosis, surgery triggering hematogenous dissemination leading to tuberculous meningitis. At the same time, the lack of specificity in the clinical presentation of patients makes it easy to misdiagnose neuropsychiatric lupus and delay treatment, so timely and accurate diagnosis and effective anti-tuberculosis treatment are essential.

Analysis of Cases with Cerebrospinal Fluid Characteristics Similar to Tuberculous Meningitis

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 × 10⁶/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.

Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

Background

The 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.

Methods

Patients 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.

Results

A 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.

Conclusions

The diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM.

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

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.

Tuberculosis Meningoencephalomyelitis in Good's Syndrome: a Case Report

Good's syndrome is a rare disease characterized by thymoma associated with combined B- and T-cell immunodeficiency in adults. Due to the lack of early onset of symptoms, it is difficult to diagnose this disorder. A 44-year-old man diagnosed with thymic carcinoma was admitted to the hospital with quadriplegia for 6 months. Brain abscess and meningoencephalitis were identified in the magnetic resonance imaging (MRI) of the brain. Antibiotics, steroid, and intravenous immunoglobulin treatment were provided for 3 months. Follow-up MRI showed progression to C7-level. The radiologic findings were consistent with tuberculosis infection and thus, the patient was treated with anti-tuberculosis medication. MRI of the brain and spine showed an improved state of meningoencephalomyelitis. In a laboratory study, there were decreased levels of peripheral B-cell and CD4 T-cell and decreased CD4:CD8 ratio; therefore, it confirmed that cellular immunity deteriorated. In addition to clinical findings, we were able to diagnose the patient with Good's syndrome. Good's syndrome is a highly suspicious disease in patients with thymoma who have recurrent unusual infections. Immunologic tests should be performed for diagnosis in which it can prevent delayed diagnosis and allow timely treatment.