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ABSTRACT Rapid and accurate identification of gram-positive bacteria that cause bloodstream infections using molecular assays drives antibiotic optimization and patient management. This multisite study evaluated the performance of LIAISON PLEX Gram-Positive Blood Culture (BCP) Assay, a qualitative multiplexed diagnostic molecular test that detects and differentiates 13 gram-positive bacterial targets and 4 antibiotic resistance markers (AMRs) from blood culture bottles showing gram-positive bacterial growth. Clinical performance was examined on 509 prospective, 162 pre-selected, and 225 contrived blood culture samples. The overall valid rate of the tests was 99.9%. The overall sensitivity/PPA and specificity/NPA for all targets were 98.5% and 99.7%, respectively. In the inclusivity study, 183 of 184 tested strains were detected. Cross-reactivity was observed in five bacteria among a collection of 103 off-panel species. The LIAISON PLEX BCP Assay demonstrated compatibility with various types of blood culture bottles by three manufacturers. Target organism detection was not affected by the presence of six potential interfering substances. Both sensitivity and specificity were maintained in the setting of polymicrobial infections with different organisms. The LIAISON PLEX BCP Assay allows fast detection of gram-positive organisms and their AMR genes from positive blood cultures with high accuracy and reliability. IMPORTANCE The use of molecular assays has improved the diagnosis of bloodstream infections because of their much-reduced turnaround time and high performance compared to conventional culture workups. The early detection of clinically significant resistance markers has direct impacts on antibiotic optimization and patient isolation. This multisite study demonstrated excellent sensitivity and specificity of the LIAISON PLEX Gram-Positive Blood Culture Assay that detects and differentiates 13 gram-positive bacterial targets and 4 antibiotic resistance markers. The assay performance was assessed clinically and analytically using large and diverse sets of samples.
Chow et al. (Fri,) studied this question.