Molecular diagnosis of sepsis in hospital patients: assessment of the Agata Sepsis^® Platform

Background: Sepsis remains a significant cause of morbidity and mortality worldwide, particularly in resource-limited settings where early and accurate pathogen identification is critical for timely treatment. Traditional diagnostic methods often fall short in identifying causative agents with sufficient speed and precision. This study aimed to assess the diagnostic accuracy of the Agata Sepsis^® Platform, developed by the companies Alfa S.A.B. de C.V. (Nuevo Leon, Mexico), and Labcitec (Estado de Mexico, Mexico) a Next-Generation Sequencing-based tool, for identifying sepsis-causing bacteria in clinical blood culture samples from patients diagnosed with sepsis in hospitals in Mexico. Methods: A retrospective analysis of 366 blood culture samples was conducted in two phases. Phase 1 employed biochemical tests to identify sepsis-causing bacteria, while Phase 2 assessed the performance of the Agata Sepsis^® Platform compared to traditional biochemical tests and the MALDI Biotyper^® system on clinical samples. Samples with inconsistent results underwent sequencing of the 16 S rDNA, rpoB, recA, and gyrB genes for definitive confirmation. Results: The Agata Sepsis^® Platform demonstrated complete agreement with biochemical tests at the genus level (100%) and outperformed biochemical tests at the species level, achieving an overall identification rate of 56.13%. The addition of MALDI Biotyper^® improved biochemical test accuracy to 96.73%, but 12 isolates remained unidentified. Gene sequencing confirmed that the Agata Sepsis^® Platform correctly identified all 12 isolates, including species often misclassified by biochemical tests, such as Staphylococcus epidermidis and Staphylococcus hominis. Conclusions: The Agata Sepsis^® Platform offers a reliable and highly accurate alternative to traditional methods for identifying sepsis-causing bacteria, specially difficult-to-identify pathogens. Its automated bioinformatic analysis of genomic data enhances taxonomic identification and shows strong potential for use as a complementary molecular tool in clinical diagnostics. Expanding its application and including antimicrobial resistance profiling could further increase its clinical value. Clinical trial: Not applicable.