Clinical and Health System Impact of Biofire Filmarray Meningitis/Encephalitis Routine Testing of CSF in a Pediatric Hospital: An Observational Study

Background: Rapid cartridge-based molecular test panels targeting multiple pathogens are increasingly available, improve pathogen detection and reduce turn-around-time but are more expensive than standard testing. Confirmation that these test panels contribute to improved patient or health service outcomes is required. Methods: In March 2021, our pediatric hospital laboratory implemented the BioFire Filmarray™ meningitis/encephalitis (M/E) panel as an additional routine test for all cerebrospinal fluid (CSF) samples collected from infants <90 days or from any patient in the emergency department. A retrospective chart review was done to ascertain changes in clinical outcomes, antimicrobial prescribing practices, and hospital length of stay, comparing two discrete 6-month periods: preimplementation (March–August 2019) and postimplementation (March-August 2021). Results: Both pre- and postimplementation groups were similar at baseline, except the preimplementation group had a higher proportion of infants with enterovirus and parechovirus meningitis. There was no significant difference between the groups in terms of median length of stay (2.94 vs 3.47 days, p = 0.41), duration of antibiotic treatment (2.0 vs 2.3 days, p = 0.25), need for central venous access (12.9% vs 17%, p = 0.38) or hospital-in-the-home admission (9.4% vs 9%, p = 0.92). A similar proportion of infants received aciclovir (33% vs 31%), however, a reduction in duration was observed (1.36 vs 0.90 days, p = 0.03) in the postimplementation period. Conclusions: Introduction of the Biofire Filmarray™ M/E panel for routine testing of CSF samples reduced the duration of antiviral prescribing but had only a minor impact on antibiotic prescribing practices or health service outcomes in our pediatric hospital. The introduction of new laboratory testing needs to be supported by a comprehensive stewardship program to see optimal outcomes from new testing platforms.