Validation of the Uniform Case Definition Criteria for Differentiating Tuberculous Meningitis, Viral Meningitis, and Bacterial Meningitis in Adults

Sociodemographic and Clinical Factors Associated With Clinical Outcome in Neuroinfectious Diseases: A Multicenter Retrospective Cohort Study

Objective

To evaluate sociodemographic and clinical factors associated with clinical outcomes in patients hospitalized with neuroinfectious diseases at three tertiary care centers in New York City.

Methods

This retrospective cohort study was conducted at three large urban tertiary care centers between January 1, 2010 and December 31, 2017. Poor clinical outcome was defined as length of hospital stay (LOS) ≥2 weeks and/or discharge to a location other than home. Sociodemographic and clinical factors were obtained from electronic medical records and descriptively analyzed. Multivariate logistic regression analysis investigated relationships between sociodemographic and clinical factors, and outcomes.

Results

Among 205 patients with definitive neuroinfectious diagnoses, older patients were more likely to have a LOS ≥2 weeks (odds ratio [OR]: 1.03; 95% confidence interval [CI]: 1.01-1.05) and less likely to be discharged home (OR: 0.96; 95% CI: 0.94-0.98) than younger patients. Patients with an immunocompromised state were more likely to have a LOS ≥2 weeks (OR: 2.80; 95% CI: 1.17-6.69). Additionally, patients admitted to the intensive care unit (ICU) were more likely to have a LOS ≥2 weeks (OR: 4.65; 95% CI: 2.13-10.16) and less likely to be discharged home (OR: 0.14; 95% CI: 0.06-0.34). There were no statistically significant associations between sex, race, ethnicity, English proficiency, substance use, or poverty index, and clinical outcome.

Conclusions

In this multicenter cohort of hospitalized neuroinfectious diseases, older age, history of immunocompromised state, and admission to the ICU were significantly associated with poor clinical outcome.

Integrating central nervous system metagenomics and host response for diagnosis of tuberculosis meningitis and its mimics

The epidemiology of infectious causes of meningitis in sub-Saharan Africa is not well understood, and a common cause of meningitis in this region, Mycobacterium tuberculosis (TB), is notoriously hard to diagnose. Here we show that integrating cerebrospinal fluid (CSF) metagenomic next-generation sequencing (mNGS) with a host gene expression-based machine learning classifier (MLC) enhances diagnostic accuracy for TB meningitis (TBM) and its mimics. 368 HIV-infected Ugandan adults with subacute meningitis were prospectively enrolled. Total RNA and DNA CSF mNGS libraries were sequenced to identify meningitis pathogens. In parallel, a CSF host transcriptomic MLC to distinguish between TBM and other infections was trained and then evaluated in a blinded fashion on an independent dataset. mNGS identifies an array of infectious TBM mimics (and co-infections), including emerging, treatable, and vaccine-preventable pathogens including Wesselsbron virus, Toxoplasma gondii, Streptococcus pneumoniae, Nocardia brasiliensis, measles virus and cytomegalovirus. By leveraging the specificity of mNGS and the sensitivity of an MLC created from CSF host transcriptomes, the combined assay has high sensitivity (88.9%) and specificity (86.7%) for the detection of TBM and its many mimics. Furthermore, we achieve comparable combined assay performance at sequencing depths more amenable to performing diagnostic mNGS in low resource settings.