Clinical predictors, biomarkers and pathogen discovery sequencing in suspected central nervous system infections

Central nervous system (CNS) infections are associated with high morbidity and mortality, depending on the causative pathogen. Virtually all pathogens can infect the brain or meninges surrounding it, including viruses, bacteria, fungi and parasites. The differential diagnosis in patients suspected of CNS infections is broad, and the diagnosis can be difficult to establish. Also, in a substantial part of the patients in which the diagnosis of a CNS infection is extremely likely, no pathogen can be identified by currently available diagnostic methods. Treatment and prognosis of patients with CNS infections do, however, depend on the type of CNS infection. Also, empirical treatment without confirmation of the pathogen has its drawbacks, like antimicrobial resistance or kidney damage caused by acyclovir. For this reason, development and improvement of diagnostic methods to diagnose CNS infections is needed to improve prognosis in these patients. The aim of this thesis was to assess and improve diagnostic accuracy of clinical predictors, biomarkers and pathogen discovery sequencing techniques to diagnose CNS infections. To address this aim, several methods were studied or validated in patients included in the PACEM and/or IPACE studies, which included consecutive patients in whom a lumbar puncture was performed because of the suspicion of a CNS infection.