A New Scoring System for the Differential Diagnosis between Tuberculous Meningitis and Viral Meningitis

Pediatric Tuberculosis: A Review of Evidence-Based Best Practices for Clinicians and Health Care Providers

Advances in pediatric TB care are promising, the result of decades of advocacy, operational and clinical trials research, and political will by national and local TB programs in high-burden countries. However, implementation challenges remain in linking policy to practice and scaling up innovations for prevention, diagnosis, and treatment of TB in children, especially in resource-limited settings. There is both need and opportunity to strengthen clinician confidence in making a TB diagnosis and managing the various manifestations of TB in children, which can facilitate the translation of evidence to action and expand access to new tools and strategies to address TB in this population. This review aims to summarize existing guidance and best practices for clinicians and health care providers in low-resource, TB-endemic settings and identify resources with more detailed and actionable information for decision-making along the clinical cascade to prevent, find, and cure TB in children.

Numerous Asymptomatic Brain Tuberculomas Complicated by Fatal Tuberculous Meningitis

Tuberculosis (TB) is still one of the most challenging infectious diseases worldwide. Coinfection with HIV increases the likelihood of extrapulmonary involvement, including the tuberculosis of the central nervous system (CNS-TB). CNS-TB often presents as tuberculomas or tuberculous meningitis. Although tuberculomas can be single or multiple, asymptomatic carriage of numerous tuberculomas is seldom reported. We present a case of a 55-year-old man who carried at least 34 tuberculomas of different sizes asymptomatically before developing and succumbing to tuberculous meningitis. Furthermore, we highlight several possible public health challenges that might have complicated his clinical course, suggesting that future studies also focus on these variables alongside more traditional clinical issues.

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

Background and Aims

This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis.

Methods

On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements.

Results

After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms.

Conclusion

The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers.

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.

Diagnostic challenges in tuberculous meningitis: a case report with negative genexpert result

Introduction

Tuberculous meningitis (TBM) is a severe form of tuberculosis affecting the meninges, primarily caused by Mycobacterium tuberculosis. Diagnosis of TBM poses numerous challenges due to its nonspecific clinical presentation and the limitations of diagnostic tests like GeneXpert.

Case presentation

The authors report a case of a 22-year-old female from Eastern Nepal presenting with acute-onset fever, headache, vomiting, and neck pain. Cerebrospinal fluid (CSF) analysis showed lymphocytic pleocytosis, elevated protein, low glucose levels, and cobweb coagulum indicative of TBM. However, the GeneXpert test revealed negative results.

Discussion

In resource-limited settings like Nepal, where access to GeneXpert MTB/Rif is limited, CSF analysis and clinical algorithms play a crucial role in diagnosing TBM. Relying solely on GeneXpert results may lead to false negatives, so a high level of suspicion based on patient risk factors is essential. Prompt initiation of empirical antitubercular therapy is vital for a favorable outcome in TBM cases.

Conclusion

Negative MTB PCR results from CSF can be misleading in diagnosis of tubercular meningitis. Therefore, comprehensive evaluations, including detailed patient history, physical examination, and CSF fluid analysis, are crucial in high tuberculous prevalence countries to ensure accurate and timely diagnosis.

Development of clinical-based scoring system to diagnose tuberculous meningitis in children

OBJECTIVE

Diagnosing tuberculous meningitis (TBM) in children is challenging due to the low sensitivity with time delay of bacterial culture techniques and the lack of brain imaging facilities in many low- and middle-income settings. This study aims to establish and test a scoring system consisting of clinical manifestations on history and examination for diagnosing TBM in children.

DESIGN

A retrospective study was conducted using a diagnostic multivariable prediction model.

PARTICIPANTS

167 children diagnosed with meningitis (tuberculous, bacterial, viral and others) aged 3 months to 18 years who were hospitalised from July 2011 until November 2021 in a national tertiary hospital in Indonesia.

RESULTS

Eight out of the 10 statistically significant clinical characteristics were used to develop a predictive model. These resulted in good discrimination and calibration variables, which divided into systemic features with a cut-off score of ≥3 (sensitivity 78.8%; specificity 86.6%; the area under the curve (AUC) value 0.89 (95% CI 0.85 to 0.95; p<0.001)) and neurological features with a cut-off score of ≥2 (sensitivity 61.2%; specificity 75.2%; the AUC value 0.73 (95% CI 0.66 to 0.81; p<0.001)). Combined together, this scoring system predicted the diagnosis of TBM with a sensitivity, specificity and positive predictive value of 47.1%, 95.1% and 90.9%, respectively.

CONCLUSION

The clinical scoring system consisting of systemic and neurological features can be used to predict the diagnosis of TBM in children with limited resource setting. The scoring system should be assessed in a prospective cohort.

Tuberculostearic acid incorporated predictive model contributes to the clinical diagnosis of tuberculous meningitis

The conventional confirmation tests of tuberculous meningitis (TBM) are usually low in sensitivity, leading to high TBM mortality. Hence, sensitive methods for indicating the presence of bacilli are required. Tuberculostearic acid (TBSA), a constituent from Mycobacterium tuberculosis had been evaluated as a promising marker, but fails to demonstrate consistent results for definite TBM. This study retrospectively reviewed medical records of 113 TBM suspects, constructing a TBSA-combined scoring system based on multiple factors, which show sensitivity and specificity of 0.8148 and 0.8814, respectively, and the area under the receiver operating characteristic curve of 0.9010. Multivariate analyses revealed four co-predictive factors strongly associated with TBSA: extra-neural tuberculosis, basal meningeal enhancement, CSF glucose/Serum glucose <0.595, and coinfection in CNS (Total). The subsequent machine learning-based validation showed correspondent importance to factors in the TBSA model. This study demonstrates a simple scoring system to facilitate TBM prediction, yield reliable diagnoses and allow timely treatment initiation.

Elusive tuberculous meningitis with rare neurological complication of longitudinally extensive transverse myelitis: a case report

INTRODUCTION

Longitudinally extensive transverse myelitis (LETM) is inflammation of the spinal cord that spans three or more spinal segments. LETM is a rare occurrence on its own and has seldom been reported with tuberculous meningitis (TBM), the rarest and deadliest of tuberculous manifestations. TBM is usually seen in children, the immunocompromised, or those with a previous history of tuberculosis infection.

CASE PRESENTATION

A 24-year-old healthy male with no co-morbidities or history of tuberculosis presented with fever and headache for the past 3 months. The patient's Kernig's and Brudzinski's signs were both negative, with bilateral abnormal plantar reflexes. The neurological level of injury was T8 and the patient was classified as AIS grade A. His CSF analysis showed a lymphocytic picture. However, both GeneXpert and Ziehl-Neelsen staining came back negative for Mycobacterium tuberculosis. MRI scans of the brain and thoracic spine revealed enhancing nodules and ring lesions in the brain and spinal cord, along with the rare complication of LETM, extending from T2 to T9.

DISCUSSION

Although Mycobacterium tuberculosis was never isolated, the patient started recovering as soon as antituberculous therapy was initiated. Hence, more emphasis needs to be placed on radiological imaging in the management of rare medical emergencies like tuberculous meningitis, especially in areas where tuberculosis is rampant and endemic, rather than waiting for a positive culture. This case report also demonstrates the growing evidence that transverse myelitis and/or LETM is associated with TBM.

Development and Validation of a LASSO Prediction Model for Better Identification of Ischemic Stroke: A Case-Control Study in China

Background

Timely diagnosis of ischemic stroke (IS) in the acute phase is extremely vital to achieve proper treatment and good prognosis. In this study, we developed a novel prediction model based on the easily obtained information at initial inspection to assist in the early identification of IS.

Methods

A total of 627 patients with IS and other intracranial hemorrhagic diseases from March 2017 to June 2018 were retrospectively enrolled in the derivation cohort. Based on their demographic information and initial laboratory examination results, the prediction model was constructed. The least absolute shrinkage and selection operator algorithm was used to select the important variables to form a laboratory panel. Combined with the demographic variables, multivariate logistic regression was performed for modeling, and the model was encapsulated within a visual and operable smartphone application. The performance of the model was evaluated on an independent validation cohort, formed by 304 prospectively enrolled patients from June 2018 to May 2019, by means of the area under the curve (AUC) and calibration.

Results

The prediction model showed good discrimination (AUC = 0.916, cut-off = 0.577), calibration, and clinical availability. The performance was reconfirmed in the more complex emergency department. It was encapsulated as the Stroke Diagnosis Aid app for smartphones. The user can obtain the identification result by entering the values of the variables in the graphical user interface of the application.

Conclusion

The prediction model based on laboratory and demographic variables could serve as a favorable supplementary tool to facilitate complex, time-critical acute stroke identification.

A clinical rule for the prediction of meningitis in HIV patients in the era of combination antiretroviral therapy

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.

Development and validation of a new scoring system for the early diagnosis of tuberculous meningitis in adults

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.

Mechanism, spectrum, consequences and management of hyponatremia in tuberculous meningitis

Hyponatremia is the commonest electrolyte abnormality in hospitalized patients and is associated with poor outcome. Hyponatremia is categorized on the basis of serum sodium into severe (< 120 mEq/L), moderate (120-129 mEq/L) and mild (130-134mEq/L) groups. Serum sodium has an important role in maintaining serum osmolality, which is maintained by the action of antidiuretic hormone (ADH) secreted from the posterior pituitary, and natriuretic peptides such as atrial natriuretic peptide and brain natriuretic peptide. These peptides act on kidney tubules via the renin angiotensin aldosterone system. Hyponatremia <120mEq/L or a rapid decline in serum sodium can result in neurological manifestations, ranging from confusion to coma and seizure. Cerebral salt wasting (CSW) and syndrome of inappropriate secretion of ADH (SIADH) are important causes of hyponatremia in tuberculosis meningitis (TBM). CSW is more common than SIADH. The differentiation between CSW and SIADH is important because treatment of one may be detrimental for the other; evidence of hypovolemia in CSW and euvolemia or hypervolemia in SIADH is used for differentiation. In addition, evidence of dehydration, polyuria, negative fluid balance as assessed by intake output chart, weight loss, laboratory evidence and sometimes central venous pressure are helpful in the diagnosis of these disorders. Volume contraction in CSW may be more protracted than hyponatremia and may contribute to border zone infarctions in TBM. Hyponatremia should be promptly and carefully treated by saline and oral salt, while 3% saline should be used in severe hyponatremia with coma and seizure. In refractory patients with hyponatremia, fludrocortisone helps in early normalization of serum sodium without affecting polyuria or functional outcome. In SIADH, V2 receptor antagonist conivaptan or tolvaptan may be used if the patient is not responding to fluid restriction. Fluid restriction in SIADH has not been found to be beneficial in TBM and should be avoided.

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

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.

Principal component analysis of blood microRNA datasets facilitates diagnosis of diverse diseases

Early, ideally pre-symptomatic, recognition of common diseases (e.g., heart disease, cancer, diabetes, Alzheimer’s disease) facilitates early treatment or lifestyle modifications, such as diet and exercise. Sensitive, specific identification of diseases using blood samples would facilitate early recognition. We explored the potential of disease identification in high dimensional blood microRNA (miRNA) datasets using a powerful data reduction method: principal component analysis (PCA). Using Qlucore Omics Explorer (QOE), a dynamic, interactive visualization-guided bioinformatics program with a built-in statistical platform, we analyzed publicly available blood miRNA datasets from the Gene Expression Omnibus (GEO) maintained at the National Center for Biotechnology Information at the National Institutes of Health (NIH). The miRNA expression profiles were generated from real time PCR arrays, microarrays or next generation sequencing of biologic materials (e.g., blood, serum or blood components such as platelets). PCA identified the top three principal components that distinguished cohorts of patients with specific diseases (e.g., heart disease, stroke, hypertension, sepsis, diabetes, specific types of cancer, HIV, hemophilia, subtypes of meningitis, multiple sclerosis, amyotrophic lateral sclerosis, Alzheimer’s disease, mild cognitive impairment, aging, and autism), from healthy subjects. Literature searches verified the functional relevance of the discriminating miRNAs. Our goal is to assemble PCA and heatmap analyses of existing and future blood miRNA datasets into a clinical reference database to facilitate the diagnosis of diseases using routine blood draws.

Decision-making in the diagnosis of tuberculous meningitis

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.

Mechanism, spectrum, consequences and management of hyponatremia in tuberculous meningitis

Hyponatremia is the commonest electrolyte abnormality in hospitalized patients and is associated with poor outcome. Hyponatremia is categorized on the basis of serum sodium into severe (< 120 mEq/L), moderate (120-129 mEq/L) and mild (130-134mEq/L) groups. Serum sodium has an important role in maintaining serum osmolality, which is maintained by the action of antidiuretic hormone (ADH) secreted from the posterior pituitary, and natriuretic peptides such as atrial natriuretic peptide and brain natriuretic peptide. These peptides act on kidney tubules via the renin angiotensin aldosterone system. Hyponatremia <120mEq/L or a rapid decline in serum sodium can result in neurological manifestations, ranging from confusion to coma and seizure. Cerebral salt wasting (CSW) and syndrome of inappropriate secretion of ADH (SIADH) are important causes of hyponatremia in tuberculosis meningitis (TBM). CSW is more common than SIADH. The differentiation between CSW and SIADH is important because treatment of one may be detrimental for the other; evidence of hypovolemia in CSW and euvolemia or hypervolemia in SIADH is used for differentiation. In addition, evidence of dehydration, polyuria, negative fluid balance as assessed by intake output chart, weight loss, laboratory evidence and sometimes central venous pressure are helpful in the diagnosis of these disorders. Volume contraction in CSW may be more protracted than hyponatremia and may contribute to border zone infarctions in TBM. Hyponatremia should be promptly and carefully treated by saline and oral salt, while 3% saline should be used in severe hyponatremia with coma and seizure. In refractory patients with hyponatremia, fludrocortisone helps in early normalization of serum sodium without affecting polyuria or functional outcome. In SIADH, V2 receptor antagonist conivaptan or tolvaptan may be used if the patient is not responding to fluid restriction. Fluid restriction in SIADH has not been found to be beneficial in TBM and should be avoided.