Adenosine deaminase activity in cerebrospinal fluid and serum for the diagnosis of tuberculous meningitis

Cho, Bang‐Hoon; Kim, Byeong C.; Yoon, Geum-Jin; Choi, Seong-Min; Chang, Jane; Lee, Seung-Han; Park, Man‐Seok; Shin, Jong Hee; Kim, Myeong-Kyu; Cho, Ki-Hyun

doi:10.1016/j.clineuro.2013.05.017

Cited by 17 publications

(12 citation statements)

References 29 publications

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“…For diagnosing TB pleurisy, sensitivity and specificity are reportedly 92% and 89%, respectively, using a cut-off value of 40 IU/L 20 . There is a wide range of CSF ADA activity in TB meningitis 24 , 25 . An ADA cutoff value of 8 IU/L showed a sensitivity of 59% and a specificity of 96% in the diagnosis of TB meningitis 25 .…”

Section: Diagnosismentioning

confidence: 99%

Diagnosis and Treatment of Extrapulmonary Tuberculosis

2015

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Extrapulmonary tuberculosis (EPTB) constitutes about 20% of all cases of tuberculosis (TB) in Korea. Diagnosing EPTB remains challenging because clinical samples obtained from relatively inaccessible sites may be paucibacillary, thus decreasing the sensitivity of diagnostic tests. Whenever practical, every effort should be made to obtain appropriate specimens for both mycobacteriologic and histopathologic examinations. The measurement of biochemical markers in TB-affected serosal fluids (adenosine deaminase or gamma interferon) and molecular biology techniques such as polymerase chain reaction may be useful adjuncts in the diagnosis of EPTB. Although the disease usually responds to standard anti-TB drug therapy, the ideal regimen and duration of treatment have not yet been established. A paradoxical response frequently occurs during anti-TB therapy. It should be distinguished from other causes of clinical deterioration. Surgery is required mainly to obtain valid diagnostic specimens and to manage complications. Because smear microscopy or culture is not available to monitor patients with EPTB, clinical monitoring is the usual way to assess the response to treatment.

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Section: Diagnosismentioning

confidence: 99%

Diagnosis and Treatment of Extrapulmonary Tuberculosis

2015

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“…Meningitis was defined as CSF white blood cell (WBC) count >5 cells/mm 3 [9] were collected as discriminative features for machine-learning. Neurologic symptoms and signs were defined according to one of the following symptoms or signs: lethargy, confusion, cranial nerve palsy, hemiparesis, delirium, stupor, coma, seizures, hemiplegia, or paraparesis [6].…”

Section: Definition and Data Collectionmentioning

confidence: 99%

Machine-Learning-Based Approach to Differential Diagnosis in Tuberculous and Viral Meningitis

et al. 2021

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Background Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM. Material and Methods For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machine-learning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. Results The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machine-learning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03). Conclusion The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.

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“…As an important enzyme involved in nucleic acid metabolism, adenosine deaminase (ADA) is associated with the activity of cellular immunity. Reported data showed that ADA could be used as a biomarker in the diagnosis of TBM [ 18 ]. Previous studies have found purine metabolism related-molecules, such as xanthine, urate, malonate and malonic acid, increased in TBM, rather than other types of meningitis [ 19 , 20 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of potential metabolic biomarkers of cerebrospinal fluids that differentiate tuberculous meningitis from other types of meningitis by a metabolomics study

et al. 2017

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Tuberculous meningitis (TBM) is caused by tuberculosis infection of of the meninges, which are the membrane systems that encircle the brain, with a high morbidity and mortality rate. It is challenging to diagnose TBM among other types of meningitis, such as viral meningitis, bacterial meningitis and cryptococcal meningitis. We aimed to identify metabolites that are differentially expressed between TBM and the other types of meningitis by a global metabolomics analysis. The cerebrospinal fluids (CSF) from 50 patients with TBM, 17 with viral meningitis, 17 with bacterial meningitis, and 16 with cryptococcal meningitis were analyzed using ultra high performance liquid chromatography coupled with quadrupole time of flight mass spectrometry (UHPLC-QTOF-MS). A total of 1161 and 512 features were determined in positive and negative electrospray ionization mode, respectively. A clear separation between TBM and viral, bacterial or cryptococcal meningitis was achieved by orthogonal projections to latent structures-discriminate analysis (OPLS-DA) analysis. Potential metabolic markers and related pathways were identified, which were mainly involved in the metabolism of amino acid, lipids and nucleosides. In summary, differential metabolic profiles of the CSF exist between TBM and other types of meningitis, and potential metabolic biomarkers were identified to differentiate TBM from other types of meningitis.

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Adenosine deaminase activity in cerebrospinal fluid and serum for the diagnosis of tuberculous meningitis

Cited by 17 publications

References 29 publications

Diagnosis and Treatment of Extrapulmonary Tuberculosis

Diagnosis and Treatment of Extrapulmonary Tuberculosis

Machine-Learning-Based Approach to Differential Diagnosis in Tuberculous and Viral Meningitis

Identification of potential metabolic biomarkers of cerebrospinal fluids that differentiate tuberculous meningitis from other types of meningitis by a metabolomics study

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