Diagnostics in suspected central nervous system infections

Infections of the central nervous system (CNS) encompass a wide variety of diseases, and differ in presentation, treatment, and prognosis depending on localization and causative pathogen. An important factor in the prognosis of CNS infections is the initial diagnosis and subsequent treatment. However, patients suspected of a CNS infection can pose a diagnostic challenge to the treating physician. Clinical symptoms are often nonspecific, and while cerebrospinal fluid (CSF) examination is the cornerstone of the initial diagnostic process, it can lack specificity. The gold standard is detection of the pathogen in the CSF; however, conventional microbiological tests are not able to identify the causative pathogen in a substantial proportion of cases. This highlights the need for new methods to diagnose CNS infections and determine cause-specific diagnosis. This thesis evaluates the accuracy of conventional and emerging diagnostics for CNS infections within the I-PACE study (Improving Prognosis by using innovAtive methods to diagnose Causes of Encephalitis), a prospective multicentre biobank study in the Netherlands in which clinical data and samples from patients undergoing CSF examination are collected. We aimed to review existing diagnostic tools and prediction models, and explore the potential of novel biomarkers as well as molecular-based techniques, such as metagenomic next generation sequencing, to improve diagnostics and thereby prognosis in patients suspected of a CNS infection.