Evaluation of the QIAstat-Dx Meningitis/Encephalitis Panel, a multiplex PCR platform for the detection of community-acquired meningoencephalitis

From cytokines to chemokines: Understanding inflammatory signaling in bacterial meningitis

Bacterial meningitis is a serious central nervous system (CNS) infection, claiming millions of human lives annually around the globe. The deadly infection involves severe inflammation of the protective sheath of the brain, i.e., meninges, and sometimes also consists of the brain tissue, called meningoencephalitis. Several inflammatory pathways involved in the pathogenesis of meningitis caused by Streptococcus pneumoniae, Neisseria meningitidis, Escherichia coli, Haemophilus influenzae, Mycobacterium tuberculosis, Streptococcus suis, etc. are mentioned in the scientific literature. Many in-vitro and in-vivo analyses have shown that after the disruption of the blood-brain barrier (BBB), these pathogens trigger several inflammatory pathways including Toll-Like Receptor (TLR) signaling in response to Pathogen-Associated Molecular Patterns (PAMPs), Nucleotide oligomerization domain (NOD)-like receptor-mediated signaling, pneumolysin related signaling, NF-κB signaling and many other pathways that lead to pro-inflammatory cascade and subsequent cytokine release including interleukine (IL)-1β, tumor necrosis factor(TNF)-α, IL-6, IL-8, chemokine (C-X-C motif) ligand 1 (CXCL1) along with other mediators, leading to neuroinflammation. The activation of another protein complex, nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3 (NLRP3) inflammasome, also takes place resulting in the maturation and release of IL-1β and IL-18, hence potentiating neuroinflammation. This review aims to outline the inflammatory signaling pathways associated with the pathogenesis of bacterial meningitis leading to extensive pathological changes in neurons, astrocytes, oligodendrocytes, and other central nervous system cells.

Towards improved point-of-care (POC) testing for patients with suspected sepsis: POC tests for host biomarkers and possible microbial pathogens

INTRODUCTION

Sepsis is a heterogeneous syndrome often misdiagnosed. Point-of-care (POC) diagnostic tests are commonly used to guide decision and include host biomarkers and molecular diagnostics.

AREAS COVERED

The diagnostic and prognostic accuracy of established and emerging biomarkers for sepsis, including procalcitonin (PCT) soluble urokinase plasminogen activator receptor (suPAR), presepsin, TRAIL/IP-10/CRP, MxA, and MxA-CRP, are analyzed in this review. The clinical utility of the two prevalent molecular techniques for pathogens identification using polymerase chain reaction (PCR) assays is also presented: FILMARRAY and QIAstat-Dx RP.

EXPERT OPINION

The rising benefits of the combined use of POC biomarkers with molecular diagnostics in daily clinical routine appear to outperform conventional practices in terms of reduced turnaround time, timely diagnosis, and prompt administration of the appropriate treatment. Yet, this must be further demonstrated in future investigations. However, the cost-effectiveness of POC tests and the high rate of false positive and negative results, indicate the need for a comprehensive clinical evaluation.

An Assessment of a New Rapid Multiplex PCR Assay for the Diagnosis of Meningoencephalitis

The rapid and broad microbiological diagnosis of meningoencephalitis (ME) has been possible thanks to the development of multiplex PCR tests applied to cerebrospinal fluid (CSF). We aimed to assess a new multiplex PCR panel (the QIAstat-Dx ME panel), which we compared to conventional diagnostic tools and the Biofire FilmArray ME Panel. The pathogens analyzed using both methods were Escherichia coli K1, Haemophilus influenzae, Listeria monocytogenes, Neisseria meningitidis, Streptococcus agalactiae, Streptococcus pneumoniae, Enterovirus, herpes simplex virus 1-2, human herpesvirus 6, human parechovirus, varicella zoster virus, and Cryptococcus neoformans/gattii. We used sensitivity, specificity, PPV, NPV, and kappa correlation index parameters to achieve our objective. Fifty CSF samples from patients with suspected ME were included. When conventional methods were used, 28 CSF samples (56%) were positive. The sensitivity and specificity for QIAstat-Dx/ME were 96.43% (CI95%, 79.8-99.8) and 95.24% (75.2-99.7), respectively, whereas the PPV and NPV were 96.43% (79.8-99.8) and 95.24% (75.1-99.7), respectively. The kappa value was 91.67%. Conclusions: A high correlation of the QIAstat-Dx ME panel with reference methods was shown. QIAstat-Dx ME is a rapid-PCR technique to be applied in patients with suspected ME with a high accuracy.

A multicenter evaluation of the QIAstat-Dx meningitis-encephalitis syndromic test kit as compared to the conventional diagnostic microbiology workflow

PURPOSE

Rapid diagnosis and treatment of infectious meningitis and encephalitis (ME) is critical to minimize morbidity and mortality. Recently, Qiagen introduced the CE-IVD QIAstat-Dx ME panel (QS-ME) for syndromic diagnostic testing of meningitis and encephalitis. Some data on the performance of the QS-ME in comparison to the BioFire FilmArray ME panel are available. In this study, the performance of the QS-ME is compared to the current diagnostic workflow in two academic medical centers in the Netherlands.

METHODS

A total of 110 cerebrospinal fluid samples were retrospectively tested with the QS-ME. The results obtained were compared to the results of laboratory-developed real-time PCR assays (LDTs), IS-pro, bacterial culture, and cryptococcal antigen (CrAg) testing. In addition, the accuracy of the QS-ME was also investigated using an external quality assessment (EQA) panel consisting of ten samples.

RESULTS

Four of the 110 samples tested failed to produce a valid QS-ME result. In the remaining 106 samples, the QS-ME detected 53/53 viral targets, 38/40 bacterial targets, and 7/13 Cryptococcus neoformans targets. The discrepant bacterial results consisted of two samples that were previously tested positive for Listeria monocytogenes (CT 35.8) and Streptococcus pneumoniae (CT 40), respectively. The QS-ME detected one additional result, consisting of a varicella-zoster virus signal (CT 35.9), in a sample in which both techniques detected Streptococcus pyogenes. Finally, 100% concordance was achieved in testing a blinded bacterial ME EQA panel.

CONCLUSION

The QS-ME is a relevant addition to the syndromic testing landscape to assist in diagnosing infectious ME.