Vollständiger Abstract
Worum geht es in dieser Arbeit?
Meningitis and encephalitis necessitate rapid pathogen identification to guide therapy, as conventional methods are time-consuming. This study evaluated both the wet-lab analytical performance and the clinical performance evaluation of the Bioeksen Meningitis/Encephalitis Panel (BS-MEP) integrated onto the fully automated, high-throughput Sigmoida Lab platform. Analytical limits of detection (LoD) were defined via Probit analysis (95% threshold) by spiking negative cerebrospinal fluid (CSF) matrices. Target detection was verified using characterized reference materials for all 14 analytes, and in silico primer/probe coverage was assessed against taxon-specific sequence databases. Cross-reactivity was evaluated using high-prevalence non-target organisms. Clinical performance was evaluated retrospectively using 500 archived, anonymized CSF specimens from a single-center repository, with analyte-specific classifications compared with prespecified routine comparator methods. The automated platform provided an 80-min sample-to-result turnaround for up to 23 samples. Verified LoDs ranged from 472 to 2118 genome copies/mL. All inclusivity strains were successfully detected in 5/5 replicates. In silico coverage exceeded 98% combined, and zero wet-lab cross-reactivity was observed. Precision coefficients of variation (CVs) were consistently ≤1.38%. In the clinical evaluation, the pooled clinical agreement was high, with PPA ranging from 90.9% to 100% and NPA of 100% across the evaluated analytes. Analyte-level clinical sensitivity ranged from 90.9% to 100%, with zero false-positive results across all targets. The fully automated molecular system demonstrates excellent analytical robustness and strong clinical agreement for syndromic detection of major CNS pathogens. The automated workflow combines broad pathogen coverage with an approximately 80-min sample-to-result turnaround and warrants further prospective evaluation in routine clinical settings.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Mesut Yilmaz, Naim Mahroum
- Quelle
- Microorganisms
- Publikation
- 2026-01-01
- Band / Ausgabe
- Nicht angegeben
- Seiten
- Nicht angegeben
- ISSN / ISBN
- 2076-2607
- Zitationen
- 0 laut Crossref
- Referenzen
- 0 hinterlegt
Zitieren
Zitierfähiger Nachweis
Mesut Yilmaz, Naim Mahroum (2026). Analytical Performance and Clinical Evaluation of a Fully Automated Multiplex RT-qPCR Platform for Rapid Detection of Central Nervous System Pathogens. Microorganisms. https://doi.org/10.3390/microorganisms14091927
Kontext
Themen, Förderung und Nutzung
Lizenzhinweise: Lizenz 1