Outcomes of a meningitis/encephalitis syndromic PCR algorithm at a regional UK hospital

Background

Meningitis and encephalitis are life-threatening conditions requiring rapid and accurate diagnosis to guide treatment. Traditional methods often have delayed turnaround times and limited pathogen coverage, complicating clinical decision-making and antimicrobial stewardship. Syndromic PCR testing offers rapid, comprehensive pathogen detection but presents cost challenges, particularly in resource-limited settings. At the Royal Cornwall Hospital, a targeted meningitis/encephalitis PCR algorithm using the QIAstat-Dx ME panel was implemented to balance diagnostic efficiency with cost-effectiveness.

Methods

The algorithm was introduced in late 2023, prioritising syndromic PCR testing for cerebrospinal fluid (CSF) samples meeting defined criteria, such as sufficient volume, elevated white blood cell counts, or Gram stain findings. Atypical cases underwent additional pathogen-specific testing. Key metrics included pathogen detection rates, turnaround times, and clinical impact, with comparisons to pre-implementation practices. Antimicrobial stewardship outcomes were tracked to assess the impact on treatment optimisation and healthcare sustainability.

Results

Samples Tested: 70 CSF samples.

Pathogen Detection: QIAstat-Dx identified pathogens in 51.4% of cases, including Enterovirus (13), HSV-1 (5), HSV-2 (2), Listeria monocytogenes (4), Neisseria meningitidis (3), Streptococcus pneumoniae (5), and Varicella-Zoster Virus (1). Two cases showed co-detection of multiple pathogens.

Turnaround Time: QIAstat-Dx delivered results in a median of 1 hour, compared to 21.6 hours for pre-implementation PCR and 48 hours for culture.

Clinical Impact: Rapid identification facilitated communication with the Health Protection Team (HPT), enabling targeted interventions such as optimized prophylaxis and contact tracing. Antimicrobial stewardship improved through early therapy adjustments in 32% of cases.

Conclusions

The QIAstat-Dx ME panel enhanced diagnostic efficiency, reduced turnaround times, and supported timely clinical and public health responses. This algorithm offers a scalable, cost-effective model for improving meningitis/encephalitis diagnostics, especially in resource-limited settings.