Clinical Utility and Optimization Strategies of Rapid Molecular Testing in Central Nervous System Infections

Background: Central nervous system (CNS) infections pose a significant threat due to high mortality and morbidity. Rapid molecular tests (RMTs) offer early pathogen identification, but in resource-limited settings like India, high cost and distinct epidemiology, such as a higher prevalence of CNS-tuberculosis, may hinder their utility. The present study investigates the clinical utility and optimal utilization strategies for RMTs like meningitis/encephalitis (ME) syndromic panels and Gene-Xpert MTB/RIF Ultra, covering major pathogens prevalent in Indian setup and impact on clinical outcomes. Methods: Adult patients with community-acquired CNS infections were included. Based on the cerebrospinal fluid (CSF) profile, one of or both the BioFire® ME panel and the Gene-Xpert® MTB/RIF Ultra were performed, prioritizing either test accordingly. Diagnostic yield of RMTs, clinical outcomes, and duration of hospital stay were compared between RMT-positive and RMT-negative cases. Results: In a total of 114 cases, RMTs identified pathogens in 28 (24.5%) cases, with 27 (23.6%) results being clinically and/or microbiologically concordant. The CSF profile had good predictability for the outcome, with only few deviations from expected results. The diagnostic yield of RMTs was significantly higher (31.3%) compared to reference tests (13.9%, P = 0.0065). Rapid molecular test–positive cases had significantly better outcomes, complete recovery, antibiotics downgrading, and hospital stay as compared to RMT-negative cases. Conclusion: This study highlights the importance of combined use of both Gene Xpert-Ultra and ME syndromic panels for early and comprehensive etiological diagnosis of CNS infections, including tuberculosis in Indian context. Optimization using CSF profiles and clinical scores prevents overuse of these tests and significantly reduces unnecessary antimicrobial use and hospital stays. Ultimately, accurate pathogen identification is crucial for improving patient outcomes.