Abstract Background Diagnosing meningitis and encephalitis remains challenging due to non-specific clinical presentations and the limitations of traditional microbiological methods. Metagenomic next-generation sequencing (mNGS) offers a broad approach to detect pathogens, but its real-world impact on clinical decision-making remains undefined. Methods We used a cohort of patients with confirmed CNS infections and autoimmune encephalitis (AE) who underwent traditional microbiological CSF testing at CUIMC. Using published sensitivity and specificity data for mNGS, we applied Bayes’ theorem to calculate different etiology-specific pre-test probabilities and model the potential impact in the diagnostic workflows including the number of lumbar punctures (LPs), additional etiologic tests potentially avoided, and time to diagnosis. Results The cohort includes 54 patients in the infectious cohort and 29 patients with confirmed autoimmune encephalitis. In a modeled scenario, utilizing an mNGS test, such as Delve Detect, in patients with DNA viral infections (n=23) could lead to a reduction of up to 88 microbiological tests, 145 days to diagnosis, and 2 LPs in total. For bacterial infections (n=16), estimated impact include a reduction of 30 microbiological tests, 144 days to diagnosis and 12 LPs (Table 1). Although fungal, RNA viral and parasitic infections were less common, with adjusted PPVs of 92.8%, 89.5% and 84.6% respectively. In autoimmune cohort, a total of 2 LPs, 126 microbiological tests and 297 days to diagnosis could have been avoided through the use of mNGS. Conclusion Our analysis suggests that a mNGS test, such as Delve Detect, could potentially streamline diagnostic and treatment pathways in meningitis and encephalitis of infectious or autoimmune origin.
Vallejos et al. (Wed,) studied this question.