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Wen A, Liu SM, Cao WF, Zhou YL, Luo CQ, Xiang ZB, Hu F, Zhang P, Leng EL. A New Scoring System to Differentially Diagnose and Distinguish Tuberculous Meningitis and Bacterial Meningitis in South China. Front Neurol 2022; 13:830969. [PMID: 35432172 PMCID: PMC9006614 DOI: 10.3389/fneur.2022.830969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/08/2022] [Indexed: 11/15/2022] Open
Abstract
Background Tuberculous meningitis (TBM) is the most serious form of extrapulmonary tuberculosis caused by Mycobacterium tuberculosis, and is characterized by high morbidity and mortality. Unfortunately, it is difficult to distinguish TBM from bacterial meningitis (BM) based on clinical features alone. The latest diagnostic tests and neuroimaging methods are still not available in many developing countries. This study aimed to develop a simple diagnostic algorithm based on clinical and laboratory test results as an early predictor of TBM in South China. Methods A retrospective study was conducted to compare the clinical and laboratory characteristics of 114 patients with TBM and 47 with BM. Multivariate logistic regression analysis was performed on the characteristics of independently predicted TBM to develop a new diagnostic rule. Results Five characteristics were predictive of a diagnosis of TBM: duration of symptoms before admission; tuberculous symptoms; white blood cell (WBC) count, total cerebrospinal fluid WBC count, and cerebrospinal fluid chloride concentration. The sensitivity and specificity of the new scoring system developed in this study were 81.6 and 93.6%, respectively. Conclusion The new scoring system proposed in this study can help physicians empirically diagnose TBM and can be used in countries and regions with limited microbial and radiological resources.
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Affiliation(s)
- An Wen
- Department of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
- Institution of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
| | - Shi-Min Liu
- Department of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
- Institution of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
| | - Wen-Feng Cao
- Department of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
- Institution of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
| | - Yong-Liang Zhou
- Department of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
- Institution of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
| | - Chao-Qun Luo
- Department of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
- Institution of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
| | - Zheng-bing Xiang
- Department of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
- Institution of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
| | - Fan Hu
- Department of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
- Institution of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
| | - Ping Zhang
- Department of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
- Institution of Neurology, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
| | - Er-Ling Leng
- Department of Pediatrics, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, China
- *Correspondence: Er-Ling Leng
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Luo Y, Xue Y, Lin Q, Mao L, Tang G, Song H, Liu W, Wu S, Liu W, Zhou Y, Xu L, Xiong Z, Wang T, Yuan X, Gan Y, Sun Z, Wang F. Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis. Front Immunol 2021; 12:731876. [PMID: 34867952 PMCID: PMC8632769 DOI: 10.3389/fimmu.2021.731876] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 10/07/2021] [Indexed: 11/15/2022] Open
Abstract
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.
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Affiliation(s)
- Ying Luo
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Xue
- Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qun Lin
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Laboratory Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Liyan Mao
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guoxing Tang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huijuan Song
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Liu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shiji Wu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weiyong Liu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Zhou
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Lingqing Xu
- Qingyuan People's Hospital, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, China
| | - Zhigang Xiong
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xu Yuan
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yong Gan
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Boonyagars L, Sangketchon C, Pholtawornkulchai K. Presentation, Clinical Characteristics, and Treatment Outcomes among Tuberculous Meningitis Patients with and Without HIV Infection at Vajira Hospital, Thailand: A Retrospective Cohort Study. J Int Assoc Provid AIDS Care 2021; 20:23259582211045551. [PMID: 34613826 PMCID: PMC8725209 DOI: 10.1177/23259582211045551] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
To compare the characteristics, presentation, investigations, and outcomes in tuberculous meningitis (TBM) patients with and without human immunodeficiency virus (HIV) coinfection. A retrospective cohort study was conducted on adult (age > 18 years) patients whose final diagnosis was TBM and who were treated at Vajira Hospital, Navamindradhiraj University, Thailand, between January 2005 and December 2016. A final total of 174 individuals were included in the study. Of these, 97 (55.75%) were HIV positive. Treatment was successful in 53 (30.5%) individuals. In HIV-infected TBM patients, there were higher proportions of patients who were younger in age (≤40 years), patients with a low body mass index, history of previous tuberculosis infection, or hepatitis C virus coinfection. A successful treatment outcome rate was lower in HIV-infected TBM patients than in HIV-uninfected TBM patients. Since HIV infection decreases the chance of successful treatment outcomes of TBM patients, future studies are needed to determine the clinical indicators for poorer survival outcomes in HIV-positive TBM patients.
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Affiliation(s)
- Lakkana Boonyagars
- Department of infectious diseases, Faculty of Medicine, Vajira Hospital, 292577Navamindradhiraj University, Bangkok, Thailand
| | - Chunlanee Sangketchon
- Department of Disaster and Emergency Medical Operations, Faculty of Science and Health Technology, Vajira Hospital, 292577Navamindradhiraj University, Bangkok, Thailand
| | - Kittisak Pholtawornkulchai
- Department of infectious diseases, Faculty of Medicine, Vajira Hospital, 292577Navamindradhiraj University, Bangkok, Thailand
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He H, Zou Y, He J, Bu H, Liu Y. A Diagnostic Scoring System for Distinguishing between Tuberculous and Bacterial Meningitis Based on Clinical and Laboratory Findings. BIOMED RESEARCH INTERNATIONAL 2021; 2021:1220650. [PMID: 34355039 PMCID: PMC8331303 DOI: 10.1155/2021/1220650] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 06/29/2021] [Indexed: 11/17/2022]
Abstract
It is very difficult to diagnose and distinguish tuberculous meningitis, and the current laboratory methods are unsubstantial in developing countries. The study is aimed at creating a scoring system on the basis of basic laboratory and clinical achievements that could be used as diagnostic aid for tuberculous meningitis for Chinese patients. A retrospective study of cases was conducted for comparison between clinical characteristics and laboratory features of 241 patients on admission who conformed to inclusion criteria of tuberculous meningitis (n = 141) or bacterial meningitis (n = 100). Logistic regression was employed to establish a diagnostic formula to distinguish between tuberculous meningitis and bacterial meningitis. The receiver operating characteristic curve analysis was applied to determine the best diagnostic critical point of the diagnostic formula. It was found that five variables (disease course, white blood cell count, serum sodium, total white cell count of cerebrospinal fluid, and neutrophil proportion in cerebrospinal fluid) were independently associated with tuberculous meningitis. The 87% sensitivity and 94% specificity were included in the diagnostic scoring system derived from these variables. Especially in the case of limited microbial resources, doctors can use this diagnostic scoring system to distinguish tuberculous meningitis from bacterial meningitis.
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Affiliation(s)
- Hongyan He
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Neurological Laboratory of Hebei Province, Shijiazhuang Hebei, China
- Institute of Cardiocerebrovascular Disease, Shijiazhuang Hebei, China
- Department of Neurology, Hebei Chest Hospital, Shijiazhuang, Hebei, China
| | - Yueli Zou
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Neurological Laboratory of Hebei Province, Shijiazhuang Hebei, China
- Institute of Cardiocerebrovascular Disease, Shijiazhuang Hebei, China
| | - Junying He
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Neurological Laboratory of Hebei Province, Shijiazhuang Hebei, China
- Institute of Cardiocerebrovascular Disease, Shijiazhuang Hebei, China
| | - Hui Bu
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Neurological Laboratory of Hebei Province, Shijiazhuang Hebei, China
- Institute of Cardiocerebrovascular Disease, Shijiazhuang Hebei, China
| | - Yaling Liu
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Neurological Laboratory of Hebei Province, Shijiazhuang Hebei, China
- Institute of Cardiocerebrovascular Disease, Shijiazhuang Hebei, China
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Abd El-Wahab EW, Farrag T, Metwally M. A clinical rule for the prediction of meningitis in HIV patients in the era of combination antiretroviral therapy. Trans R Soc Trop Med Hyg 2021; 114:264-275. [PMID: 31768553 DOI: 10.1093/trstmh/trz107] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 09/14/2019] [Accepted: 09/30/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The diagnosis of meningitis in HIV patients is challenging due to altered immune responses. Diagnostic scoring systems were recently proposed for use in research settings to help prompt and easy differential diagnosis. The objective of this study was to create a clinical prediction rule (CPR) for meningitis in HIV-infected patients and to address the enigma of differentiating bacterial (BM), TB (TBM) and cryptococcal (CCM) meningitis based on clinical features alone, which may be enhanced by easy-to-obtain laboratory testing. METHODS We retrospectively enrolled 352 HIV patients presenting with neurological manifestations suggesting meningitis over the last 18 y (2000-2018). Relevant clinical and laboratory information were retrieved from inpatient records. The features independently predicting meningitis or its different types in microbiologically proven meningitis cases were modelled by multivariate logistic regression to create a CPR in an exploratory data set. The performance of the meningitis diagnostic score was assessed and validated in a subset of retrospective data. RESULTS AIDS clinical stage, injecting drug use, jaundice and cryptococcal antigen seropositivity were equally important as classic meningitic symptoms in predicting meningitis. Arthralgia and elevated cerebrospinal fluid Lactate dehydrogenase (LDH) were strong predictors of BM. Patients with cryptococcal antigenemia had 25 times the probability of having CCM, whereas neurological deficits were highly suggestive of TBM. CONCLUSION The proposed CPRs have good diagnostic potential and would support decision-making in resource-poor settings.
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Affiliation(s)
- Ekram W Abd El-Wahab
- Department of Tropical Health, High Institute of Public Health, Alexandria University, 165 El Horreya Road, 21561 Alexandria, Egypt
| | - Talaat Farrag
- Department of Endemic and Infectious Diseases, Alexandria Fever Hospital, 21568 Alexandria, Egypt.,Fellow of the Tropical Health Department, High Institute of Public Health, Alexandria University, 165 El Horreya Road, 21561 Alexandria, Egypt
| | - Mohammed Metwally
- Department of Endemic and Infectious Diseases, Alexandria Fever Hospital, 21568 Alexandria, Egypt
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Development and validation of a new scoring system for the early diagnosis of tuberculous meningitis in adults. Diagn Microbiol Infect Dis 2021; 101:115393. [PMID: 34237646 DOI: 10.1016/j.diagmicrobio.2021.115393] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 03/24/2021] [Accepted: 04/08/2021] [Indexed: 11/21/2022]
Abstract
We developed and validated a new diagnostic scoring system by simultaneously comparing 28 factors (including clinical, laboratory, and imaging) of HIV uninfected adult tuberculous meningitis (TBM) with viral meningitis (VM), bacterial meningitis (BM), and cryptococcal meningitis (CM). Predictors of TBM diagnosis obtained by logistic regression. A total of 382 patients with intracranial infection participated, and eight factors were independently associated with TBM diagnosis: symptom duration, evidence of extracranial tuberculosis, cerebrospinal fluid (CSF) leukocyte, CSF neutrophil, CSF protein, low serum sodium, meningeal enhancement, and tuberculomas. Factors are assigned according to weight, a score of ≥ 5 was suggestive of TBM with a sensitivity of 85.8% and a specificity of 87.7%, and the area under the receiver operating characteristic curve was 0.923. When applied to a prospective validation cohort, this scoring model showed robust performance. Our study suggests that the application of this score can help diagnose TBM more efficiently.
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Abstract
Tuberculous (TB) meningitis (TBM), accounting for 70-80% of cases of neurotuberculosis, is one of the most severe forms of extrapulmonary tuberculosis. Two-thirds of new TB cases come from eight countries. Polymorphisms in toll-interleukin-1 receptor domain and in leukotriene A4 hydrolase (LTA4H) gene, affect the risk of inflammation in TBM. The common site of tuberculoma in children is cerebellum, and they may rarely develop tuberculous encephalopathy which has a high mortality. Young females with a high cerebrospinal fluid (CSF) protein have an increased predisposition to develop optochiasmatic arachnoiditis. Spinal TB meningitis may mimic transverse myelitis or Guillain-Barre syndrome. An extra-neural focus of TB should be sought clinically and radiologically as it may indicate safer and more accessible sites for diagnostic samplings. Cartridge-based nucleic acid amplification test (CBNAAT), also known as Genexpert test, is a polymerase chain reaction (PCR)-based method for detection of TB which also detects rifampicin resistance as it targets the rpob gene of mycobacteria. Line probe assays, based on PCR and reverse hybridization methods, identify mutations associated with drug resistance within a week. TBM being a paucibacillary disease, often evades a definite diagnosis and empirical treatment for a minimum of 9 months is warranted based on clinical judgement. All TBM patients should receive adjunctive corticosteroids, even those with HIV infection. Drug resistance is strongly associated with previous treatment and bedaquiline as well as delamanid have received approvals for multidrug resistant (MDR) TB. The key principle of managing MDR TB is never to add a single drug to a failing regimen. Correct combination and duration of most effective second line drugs in MDR TB require further modifications. Early shunting should be considered in those with hydrocephalus failing medical management. The single most important determinant of outcome is the stage of TBM at which treatment has been started.
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Katrak SM. Central nervous system tuberculosis. J Neurol Sci 2020; 421:117278. [PMID: 33387702 DOI: 10.1016/j.jns.2020.117278] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/11/2020] [Accepted: 12/17/2020] [Indexed: 12/21/2022]
Abstract
The purpose of this article is to review the many facets of central nervous system tuberculosis (CNS-TB). The entities described are tuberculous meningitis (TBM) and its complications, spinal cord disorders, tuberculomas and co-infection with the human immune-deficiency virus (HIV). The latter has become a common problem worldwide becoming a more fulminant disease. The accuracy of the conventional and the modern molecular techniques for the diagnosis of TBM have a high specificity but a low to moderate sensitivity. Computerised tomographic scans and magnetic resonance imaging have many characteristic features which have vastly improved the diagnostic accuracy of CNS-TB. The recommended therapeutic regimens are an extrapolation of the regimen used for pulmonary TB, hence the optimal composition, dosage and duration of the therapy are not yet established. Multidrug resistant TB is emerging as a global threat and the delay in recognition of drug resistance combined with the lack of data on appropriate drug regimen adds to its high mortality.
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Affiliation(s)
- Sarosh M Katrak
- Prof. Emeritus, Grant Medical College & Sir JJ Group of Hospitals, Emeritus Director, Jaslok Hospital & Research Centre, Mumbai, India.
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Luo Y, Xue Y, Guo X, Lin Q, Mao L, Tang G, Song H, Wang F, Sun Z. Diagnostic Accuracy of T-SPOT.TB Assay for Tuberculous Meningitis: An Updated Meta-Analysis. Front Neurol 2020; 11:866. [PMID: 33013621 PMCID: PMC7494808 DOI: 10.3389/fneur.2020.00866] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 07/07/2020] [Indexed: 12/12/2022] Open
Abstract
Background: The role of T-SPOT.TB (T-SPOT) assay for tuberculous meningitis (TBM) diagnosis has not been fully assessed. Here, we conducted an updated meta-analysis to evaluate the diagnostic accuracy of peripheral blood (PB) T-SPOT and cerebrospinal fluid (CSF) T-SPOT for diagnosing TBM. Methods: Relevant studies in the PubMed database, EmBase database, Cochrane database, Scopus database, Google Scholar, China National Knowledge Internet, and Wan-Fang database were retrieved from August 1, 2005, to June 22, 2020. Statistical analysis was performed using Stata, Revman, and Meta-Disc software. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), summary receiver operating characteristic curves, and the area under the curve were determined and analyzed. Results: A total of 27 studies were eligible for inclusion within the meta-analysis. The pooled sensitivity and specificity of PB T-SPOT for TBM diagnosis were 0.78 (95% CI, 0.76-0.81) and 0.68 (95% CI, 0.66-0.71), respectively, whereas the pooled PLR, NLR, and DOR were 2.80 (95% CI, 2.29-3.42), 0.32 (95% CI, 0.27-0.38), and 10.08 (95% CI, 7.21-14.08), respectively. On the other hand, the pooled sensitivity and specificity of CSF T-SPOT on diagnosing TBM were 0.76 (95% CI, 0.72-0.80) and 0.88 (95% CI, 0.85-0.90), respectively, whereas the pooled PLR, NLR, and DOR were 5.92 (95% CI, 4.25-8.25), 0.28 (95% CI, 0.21-0.39), and 29.05 (95% CI, 16.40-51.45), respectively. The area under the summary receiver operating characteristic curve values of PB T-SPOT and CSF T-SPOT for TBM diagnosis were 0.83 (95% CI, 0.80-0.86) and 0.92 (95% CI, 0.89-0.94), respectively. Conclusions: CSF T-SPOT showed a higher specificity compared with PB T-SPOT for diagnosing TBM. Both two T-SPOT assays have considerable potential in improving the diagnosis of TBM. Furthermore, the standardization of the operating procedure is further needed when performing CSF T-SPOT.
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Affiliation(s)
- Ying Luo
- Department of Laboratory Medicine, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Xue
- Department of Clinical Immunology, Tongji Medical College, Tongji Hospital, Huazhong University of Sciences and Technology, Wuhan, China
| | - Xueyun Guo
- Department of Dermatology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Qun Lin
- Department of Laboratory Medicine, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Liyan Mao
- Department of Laboratory Medicine, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Guoxing Tang
- Department of Laboratory Medicine, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Huijuan Song
- Department of Laboratory Medicine, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Wang
- Department of Laboratory Medicine, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
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Boyles TH, Lynen L, Seddon JA. Decision-making in the diagnosis of tuberculous meningitis. Wellcome Open Res 2020; 5:11. [PMID: 32964134 PMCID: PMC7490569 DOI: 10.12688/wellcomeopenres.15611.1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/14/2020] [Indexed: 12/23/2022] Open
Abstract
Tuberculous meningitis (TBM) is the most devastating form of tuberculosis (TB) but diagnosis is difficult and delays in initiating therapy increase mortality. All currently available tests are imperfect; culture of Mycobacterium tuberculosis from the cerebrospinal fluid (CSF) is considered the most accurate test but is often negative, even when disease is present, and takes too long to be useful for immediate decision making. Rapid tests that are frequently used are conventional Ziehl-Neelsen staining and nucleic acid amplification tests such as Xpert MTB/RIF and Xpert MTB/RIF Ultra. While positive results will often confirm the diagnosis, negative tests frequently provide insufficient evidence to withhold therapy. The conventional diagnostic approach is to determine the probability of TBM using experience and intuition, based on prevalence of TB, history, examination, analysis of basic blood and CSF parameters, imaging, and rapid test results. Treatment decisions may therefore be both variable and inaccurate, depend on the experience of the clinician, and requests for tests may be inappropriate. In this article we discuss the use of Bayes' theorem and the threshold model of decision making as ways to improve testing and treatment decisions in TBM. Bayes' theorem describes the process of converting the pre-test probability of disease to the post-test probability based on test results and the threshold model guides clinicians to make rational test and treatment decisions. We discuss the advantages and limitations of using these methods and suggest that new diagnostic strategies should ultimately be tested in randomised trials.
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Affiliation(s)
- Tom H. Boyles
- ANOVA Health Institute, Johannesburg, South Africa
- Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Lutgarde Lynen
- Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
| | - James A. Seddon
- Department of Infectious Diseases, Imperial College London, London, UK
- Desmond Tutu TB Centre Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, South Africa
| | - Tuberculous Meningitis International Research Consortium
- ANOVA Health Institute, Johannesburg, South Africa
- Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Department of Infectious Diseases, Imperial College London, London, UK
- Desmond Tutu TB Centre Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, South Africa
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Yang Y, Qu XH, Zhang KN, Wu XM, Wang XR, Wen A, Li LJ. A Diagnostic Formula for Discrimination of Tuberculous and Bacterial Meningitis Using Clinical and Laboratory Features. Front Cell Infect Microbiol 2020; 9:448. [PMID: 32010636 PMCID: PMC6978638 DOI: 10.3389/fcimb.2019.00448] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 12/12/2019] [Indexed: 11/24/2022] Open
Abstract
Background: The discrimination of tuberculous meningitis and bacterial meningitis remains difficult at present, even with the introduction of advanced diagnostic tools. This study aims to differentiate these two kinds of meningitis by using the rule of clinical and laboratory features. Methods: A prospective observational study was conducted to collect the clinical and laboratory parameters of patients with tuberculous meningitis or bacterial meningitis. Logistic regression was used to define the diagnostic formula for the discrimination of tuberculous meningitis and bacterial meningitis. A receiver operator characteristic curve was established to determine the best cutoff point for the diagnostic formula. Results: Five parameters (duration of illness, coughing for two or more weeks, meningeal signs, blood sodium, and percentage of neutrophils in cerebrospinal fluid) were predictive of tuberculous meningitis. The diagnostic formula developed from these parameters was 98% sensitive and 82% specific, while these were 95% sensitive and 91% specific when prospectively applied to another 70 patients. Conclusion: The diagnostic formula developed in the present study can help physicians to differentiate tuberculous meningitis from bacterial meningitis in high-tuberculosis-incidence-areas, particularly in settings with limited microbiological and radiological resources.
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Affiliation(s)
- Yun Yang
- Department of Neurology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China
| | - Xin-Hui Qu
- Department of Neurology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China
| | - Kun-Nan Zhang
- Department of Neurology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China
| | - Xiao-Mu Wu
- Department of Neurology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China
| | - Xin-Rong Wang
- Department of Prenatal Diagnosis, Jiangxi Maternal and Child Health Hospital, Nanchang, China
| | - An Wen
- Department of Neurology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China
| | - Ling-Juan Li
- Department of Neurology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China
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Solari L, Soto A, Van der Stuyft P. Development of a clinical prediction rule for tuberculous meningitis in adults in Lima, Peru. Trop Med Int Health 2018; 23:367-374. [PMID: 29446860 DOI: 10.1111/tmi.13041] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
OBJECTIVES Diagnosis of tuberculous meningitis (TM) is a challenge in countries with a high burden of the disease and constrained resources and clinical prediction rules (CPRs) could be of assistance. We aimed at developing a CPR for diagnosis of TM in a Latin American setting with high tuberculosis incidence and a concentrated HIV epidemic. METHODS We enrolled adult patients with clinical suspicion of TM attending two hospitals in Lima, Peru. We obtained information on potential anamnestic, clinical and laboratory predictive findings that are easy to collect and promptly available. We independently diagnosed TM according to a composite reference standard that included a series of microbiological tests. We performed bivariate analysis and constructed a logistic regression model to select the predictive findings associated with TM. With the selected predictors included in the model, we developed a score-based CPR. We assessed its internal validity and diagnostic performance. RESULTS Of 155 analysed patients, 59 (38%) had TM. The CPR we derived includes three predictors: cough for 14 days or more, 10-500 cells in CSF and adenosine deaminase ≥ 6 U/l in CSF. It classifies patients into high-, moderate- or low-score groups and has an overall area under the ROC curve of 0.87. 59% of patients were assigned to either the high- or the low-score group, permitting prompt decision-making. In patients in the high-score group, it attains a positive likelihood ratio for TM of 10.6 and in patients with low scores, a negative likelihood ratio of 0.10. Bootstrap analysis indicated high internal validity. CONCLUSION This CPR could support decision-making in patients with clinical suspicion of TM. External validation and further assessment of its clinical impact are necessary before application in other settings.
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Affiliation(s)
- Lely Solari
- Unit of General Epidemiology and Disease Control, Institute of Tropical Medicine of Antwerp, Antwerp, Belgium.,Escuela de Medicina, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
| | - Alonso Soto
- Escuela de Medicina, Universidad Peruana de Ciencias Aplicadas, Lima, Peru.,Departamento de Medicina, Hospital Nacional Hipólito Unanue, Lima, Peru
| | - Patrick Van der Stuyft
- Unit of General Epidemiology and Disease Control, Institute of Tropical Medicine of Antwerp, Antwerp, Belgium.,Department of Public Health, Gent University, Gent, Belgium
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Wilkinson RJ, Rohlwink U, Misra UK, van Crevel R, Mai NTH, Dooley KE, Caws M, Figaji A, Savic R, Solomons R, Thwaites GE. Tuberculous meningitis. Nat Rev Neurol 2017; 13:581-598. [PMID: 28884751 DOI: 10.1038/nrneurol.2017.120] [Citation(s) in RCA: 285] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Tuberculosis remains a global health problem, with an estimated 10.4 million cases and 1.8 million deaths resulting from the disease in 2015. The most lethal and disabling form of tuberculosis is tuberculous meningitis (TBM), for which more than 100,000 new cases are estimated to occur per year. In patients who are co-infected with HIV-1, TBM has a mortality approaching 50%. Study of TBM pathogenesis is hampered by a lack of experimental models that recapitulate all the features of the human disease. Diagnosis of TBM is often delayed by the insensitive and lengthy culture technique required for disease confirmation. Antibiotic regimens for TBM are based on those used to treat pulmonary tuberculosis, which probably results in suboptimal drug levels in the cerebrospinal fluid, owing to poor blood-brain barrier penetrance. The role of adjunctive anti-inflammatory, host-directed therapies - including corticosteroids, aspirin and thalidomide - has not been extensively explored. To address this deficit, two expert meetings were held in 2009 and 2015 to share findings and define research priorities. This Review summarizes historical and current research into TBM and identifies important gaps in our knowledge. We will discuss advances in the understanding of inflammation in TBM and its potential modulation; vascular and hypoxia-mediated tissue injury; the role of intensified antibiotic treatment; and the importance of rapid and accurate diagnostics and supportive care in TBM.
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Affiliation(s)
- Robert J Wilkinson
- Department of Medicine, Imperial College London, Norfolk Place, London W2 1PG, UK
- The Francis Crick Institute, Midland Road, London NW1 2AT, UK
- Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine and Department of Medicine, University of Cape Town, Republic of South Africa
| | - Ursula Rohlwink
- Division of Neurosurgery, University of Cape Town, Anzio Road, Observatory 7925, Republic of South Africa
| | - Usha Kant Misra
- Department of Neurology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Rae Bareli Road, Lucknow, Uttar Pradesh 226014, India
| | - Reinout van Crevel
- Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Nguyen Thi Hoang Mai
- Oxford University Clinical Research Unit, 764 Vo Van Kiet, Quan 5, Ho Chi Minh City, Vietnam
| | - Kelly E Dooley
- Johns Hopkins University School of Medicine, The Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, Maryland 21287, USA
| | - Maxine Caws
- Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Anthony Figaji
- Division of Neurosurgery, University of Cape Town, Anzio Road, Observatory 7925, Republic of South Africa
| | - Rada Savic
- UCSF School of Pharmacy, Department, Bioengineering, 1700 4th Street, San Francisco, California 94158, UA
| | - Regan Solomons
- Faculty of Health Sciences, Stellenbosch University, Tygerberg Hospital, Francie van Zijl Drive, Tygerberg 7505, Cape Town, Republic of South Africa
| | - Guy E Thwaites
- Oxford University Clinical Research Unit, 764 Vo Van Kiet, Quan 5, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Old Road, Oxford OX3 9FZ, UK
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Fillatre P, Crabol Y, Morand P, Piroth L, Honnorat J, Stahl JP, Lecuit M. Infectious encephalitis: Management without etiological diagnosis 48hours after onset. Med Mal Infect 2017; 47:236-251. [PMID: 28314470 PMCID: PMC7131623 DOI: 10.1016/j.medmal.2017.02.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Accepted: 02/22/2017] [Indexed: 12/18/2022]
Abstract
Introduction The etiological diagnosis of infectious encephalitis is often not established 48 hours after onset. We aimed to review existing literature data before providing management guidelines. Method We performed a literature search on PubMed using filters such as “since 01/01/2000”, “human”, “adults”, “English or French”, and “clinical trial/review/guidelines”. We also used the Mesh search terms “encephalitis/therapy” and “encephalitis/diagnosis”. Results With Mesh search terms “encephalitis/therapy” and “encephalitis/diagnosis”, we retrieved 223 and 258 articles, respectively. With search terms “encephalitis and corticosteroid”, we identified 38 articles, and with “encephalitis and doxycycline” without the above-mentioned filters we identified 85 articles. A total of 210 articles were included in the analysis. Discussion Etiological investigations must focus on recent travels, animal exposures, age, immunodeficiency, neurological damage characteristics, and potential extra-neurological signs. The interest of a diagnosis of encephalitis for which there is no specific treatment is also to discontinue any empirical treatments initially prescribed. Physicians must consider and search for autoimmune encephalitis.
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Affiliation(s)
- P Fillatre
- Service de maladies infectieuses et réanimation médicale, CHU Pontchaillou, 35000 Rennes, France
| | - Y Crabol
- Médecine interne, CHBUA site de Vannes, 56017 Vannes, France
| | - P Morand
- Virologie, CHU Grenoble Alpes, 38043 Grenoble cedex 9, France
| | - L Piroth
- Infectiologie, CHU de Dijon, 21000 Dijon, France
| | - J Honnorat
- Inserm U1028, CNRS UMR5292, équipe neuro-oncologie et neuro-inflammation (Oncoflam), centre de recherche en neurosciences (CRNL), université Lyon 1, 69500 Bron, France
| | - J P Stahl
- Service d'infectiologie, CHU de Grenoble, 38043 Grenoble cedex 9, France.
| | - M Lecuit
- Institut Pasteur, Biology of Infection Unit, CNR CCOMS Listeria, Inserm U1117, Paris, France; Paris Descartes University, Sorbonne Paris Cité, Department of Infectious Diseases and Tropical Medicine, Necker-Enfants-Malades University Hospital, Institut Imagine, Assistance Publique-Hôpitaux de Paris, Paris, France
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Saavedra JS, Urrego S, Toro ME, Uribe CS, García J, Hernández O, Arango JC, Pérez ÁB, Franco A, Vélez IC, Del Corral H. Validation of Thwaites Index for diagnosing tuberculous meningitis in a Colombian population. J Neurol Sci 2016; 370:112-118. [PMID: 27772738 DOI: 10.1016/j.jns.2016.09.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Revised: 08/21/2016] [Accepted: 09/06/2016] [Indexed: 01/12/2023]
Abstract
OBJECTIVE To determine the diagnostic accuracy of Thwaites Index (TI) in a Colombian population to distinguish meningeal tuberculosis (MTB) from bacterial meningitis (BM) and from non-tuberculous meningitis. Exploratory analyses were conducted to assess the TI's validity for patients with human immunodeficiency virus (HIV) and children above six-years-old. METHODS The study included 527 patients, the TI was calculated and results compared with those of a reference standard established by expert neurologists. Sensitivity, specificity, area under the curve of receiver-operator characteristics (AUC-ROC) and likelihood ratios were calculated. RESULTS The AUC-ROC to distinguish MTB from non-tuberculous meningitis was 0.72 (95% CI: 0.67-0.77) for HIV negative adults. AUC-ROC was 0.62 (95% CI: 0.50-0.74) for HIV positive adults and 0.83 (95% CI: 0.68-0.97) for children. For distinguishing MTB from BM the AUC-ROC was 0.78 (95% CI: 0.73-0.83); furthermore, the AUC-ROC was 0.57 (95% CI: 0.31-0.83) for HIV positive adults and 0.86 (95% CI: 0.73-0.99) for children. CONCLUSION The TI was sensitive but not specific when used to distinguish MTB from BM in HIV negative adults. In HIV positive adults the index had low diagnostic accuracy. Moreover, the TI showed discrimination capability for children over 6years; however, research with larger samples is required in these.
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Affiliation(s)
- Juan Sebastián Saavedra
- Neurology Section, Department of Internal Medicine, Faculty of Medicine, University of Antioquia, Bloque 6, Calle 64 # 51 D - 154, Medellín, Colombia
| | - Sebastián Urrego
- Neurology Section, Department of Internal Medicine, Faculty of Medicine, University of Antioquia, Bloque 6, Calle 64 # 51 D - 154, Medellín, Colombia
| | - María Eugenia Toro
- Neurology Section, Department of Internal Medicine, Faculty of Medicine, University of Antioquia, Bloque 6, Calle 64 # 51 D - 154, Medellín, Colombia
| | - Carlos Santiago Uribe
- Neurology Section, Department of Internal Medicine, Faculty of Medicine, University of Antioquia, Bloque 6, Calle 64 # 51 D - 154, Medellín, Colombia
| | - Jenny García
- Clinical Epidemiology Academic Group (GRAEPIC), Faculty of Medicine, University of Antioquia, Carrera 51 D # 62-29, Edificio Manuel Uribe Ángel, tercer piso, Medellín, Colombia
| | - Olga Hernández
- Instituto Neurológico de Colombia, Calle 55 # 46-36, Medellín, Colombia
| | - Juan Carlos Arango
- Department of Pathology, Faculty of Medicine, University of Antioquia, Bloque 13, Calle 64 N°51 D - 154, Medellín, Colombia
| | | | - Andrés Franco
- Hospital Pablo Tobón Uribe, Calle 78B # 69-240, Medellín, Colombia
| | - Isabel Cristina Vélez
- Neurology Section, Department of Internal Medicine, Faculty of Medicine, University of Antioquia, Bloque 6, Calle 64 # 51 D - 154, Medellín, Colombia
| | - Helena Del Corral
- MICROBA, School of Microbiology, University of Antioquia, Calle 67 # 53-108 Bloque 5, Medellín, Colombia.
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Zhang YL, Lin S, Shao LY, Zhang WH, Weng XH. Validation of thwaites' diagnostic scoring system for the differential diagnosis of tuberculous meningitis and bacterial meningitis. Jpn J Infect Dis 2014; 67:428-31. [PMID: 25410556 DOI: 10.7883/yoken.67.428] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
To compare the clinical features of patients with tuberculous meningitis (TBM) and bacterial meningitis (BM) and to validate Thwaites' diagnostic scoring system for the differential diagnosis of TBM and BM, a retrospective review of 211 patients with TBM or BM who were admitted to Huashan Hospital, Fudan University, from 2007 to 2012 was conducted. The clinical characteristics and laboratory data were compared, and Thwaites' diagnostic scores were assessed at the time of admission for the differential diagnosis of TBM and BM. Significant differences were observed between the 2 groups in general information, clinical features, and cerebrospinal fluid characteristics. The sensitivity and specificity of Thwaites' diagnostic scoring system for the differential diagnosis of TBM and BM were found to be 98.2% and 43.6%, respectively, with positive and negative predictive values being 65.9% and 95.8%, respectively. The sensitivity and specificity for the differential diagnosis of TBM and initially treated BM were 98.2% and 82.9%, respectively, but were only 98.2% and 24.2% for that of TBM and partially treated BM, respectively. Thus, Thwaites' diagnostic scoring system was found to be highly effective for the differential diagnosis of TBM and initially treated BM but was found to be less effective for that of TBM and partially treated BM.
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Affiliation(s)
- Yan-liang Zhang
- Department of Infectious Diseases, Nanjing Hospital Affiliated to Nanjing Medical University
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Abstract
Tuberculous meningitis is especially common in young children and people with untreated HIV infection, and it kills or disables roughly half of everyone affected. Childhood disease can be prevented by vaccination and by giving prophylactic isoniazid to children exposed to infectious adults, although improvements in worldwide tuberculosis control would lead to more effective prevention. Diagnosis is difficult because clinical features are non-specific and laboratory tests are insensitive, and treatment delay is the strongest risk factor for death. Large doses of rifampicin and fluoroquinolones might improve outcome, and the beneficial effect of adjunctive corticosteroids on survival might be augmented by aspirin and could be predicted by screening for a polymorphism in LTA4H, which encodes an enzyme involved in eicosanoid synthesis. However, these advances are insufficient in the face of drug-resistant tuberculosis and HIV co-infection. Many questions remain about the best approaches to prevent, diagnose, and treat tuberculous meningitis, and there are still too few answers.
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Cunha BA. The clinical and laboratory diagnosis of acute meningitis and acute encephalitis. ACTA ACUST UNITED AC 2013; 7:343-64. [PMID: 23721048 DOI: 10.1517/17530059.2013.804508] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Acute bacterial meningitis (ABM) is a life-threatening infectious disease requiring prompt antimicrobial therapy. ABM must be differentiated from systemic disorders with CNS manifestations that may mimic ABM. ABM should also be differentiated from acute meningoencephalitis (AME) and acute viral encephalitis (AVE). Nonviral causes of AME are treatable. Among the causes of AVE, Herpes simplex encephalitis (HSE) is treatable. This article reviews the clinical diagnostic approach to ABM, AME and AVE. AREAS COVERED The differential diagnostic (DDx) approach to ABM, AME and AVE is based on clinical and laboratory findings. A specific pathogen diagnosis is based on serum/cerebrospinal fluid (CSF) tests. This overview presents the diagnostic approach to ABM, AME and AVE in normal hosts (excluding brain abscesses and chronic CNS infections). EXPERT OPINION It is time critical to diagnose ABM and begin empiric antimicrobial therapy based on the known/most likely pathogen. The diagnosis of ABM depends on clinical features and the CSF profile. The CSF Gram stain and CSF lactic acid (LA) levels provide the most rapid, reliable and cost-effective tests to diagnose ABM. CSF LA levels are also the best way to diagnose partially treated acute bacterial meningitis (PTABM). In those cases of AME/AVE due to viruses with a CSF profile mimicking ABM, for example, HSE, unelevated CSF LA levels rapidly/reliably rule out ABM as a diagnostic possibility.
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Affiliation(s)
- Burke A Cunha
- Winthrop-University Hospital, Infectious Disease Division, Mineola, NY, USA.
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Dendane T, Madani N, Zekraoui A, Belayachi J, Abidi K, Zeggwagh AA, Abouqal R. A simple diagnostic aid for tuberculous meningitis in adults in Morocco by use of clinical and laboratory features. Int J Infect Dis 2013; 17:e461-5. [PMID: 23535301 DOI: 10.1016/j.ijid.2013.01.026] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2012] [Revised: 01/23/2013] [Accepted: 01/31/2013] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The delay in diagnosis and treatment of tuberculous meningitis (TBM) is a major factor in the high mortality observed with this pathology. The distinction between bacterial meningitis (BM) and TBM by clinical features alone is often impossible, and the available biological resources remain inadequate or inaccessible, especially in developing countries. We attempted to develop a simple diagnostic algorithm on the basis of clinical and laboratory findings that could be used as an early predictor of TBM in adult patients in Morocco. METHODS We compared the clinical and laboratory features on admission of 508 adults in a medical intensive care unit in Morocco who satisfied diagnostic criteria for tuberculous (n=274) or bacterial (n=234) meningitis. Features independently predictive of TBM were modeled by multivariate logistic regression to create a diagnostic rule, and by a classification and regression tree (CART). RESULTS Six features were predictive of a diagnosis of TBM: female gender, duration of symptoms, the presence of localizing signs, white blood cell (WBC) count, the level of serum sodium, and the total cerebrospinal fluid WBC count. The sensitivity for CART was 87% and for a score >7 was 88%; specificity was 96% and 95%, respectively. The internal validation was excellent for both diagnostic methods, with a receiver operating characteristic (ROC) area of 0.906 bootstrap samples for a score >7 and 0.910 for CART. CONCLUSIONS The clinical and laboratory parameters identified in this study may help the clinician with the empiric diagnosis of TBM and could be used in settings with limited microbiological diagnostic support.
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Affiliation(s)
- Tarek Dendane
- Medical Intensive Care Unit Department, Ibn Sina University Hospital, Mohamed V Souissi University, 10000 Rabat, Morocco.
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