Biomarker profiling in sepsis diagnostics

Abstract

Effective and timely antibiotic therapy for sepsis requires a thorough understanding of the types and molecular characteristics of bacterial strains. Therefore, we investigated diagnostic strategies to facilitate faster classification of bacteria and identification of their molecular features. We benchmarked the 1928 Diagnostic platform (1928 Diagnostics, Gothenburg, Sweden) for characterizing Staphylococcus aureus (S. aureus) strains against an in-house bioinformatics (INH) pipeline and reference clinical laboratory methods, including MALDI-TOF MS and phenotypic antibiotic susceptibility testing. We observed a high agreement between the 1928 platform and the INH pipeline in predicting laboratory results. Notably, the 1928 platform exhibited a lower rate of false negative while showing slightly higher rates of false positive (Paper I). Additionally, our findings revealed that clindamycin, erythromycin, and fusidic acid exhibited efficacy against all methicillin resistance S. aureus strains, and vancomycin demonstrated susceptibility in all tested strains (Paper II). The challenge remains in predicting the bacterial type. Several studies highlighted the differences between blood markers of gram-positive and gram-negative bacterial sepsis. Using machine learning algorithms and Proximity Extension Assay (PEA), we discovered a set of informative proteins comprising 55 proteins, including 5 potential biomarkers, which distinguish patients with gram-positive or gram-negative bacteria from other cases, achieving AUCs of 0.66 and 0.69, respectively (Paper III). However, while the analysis of 55 proteins offered insights into classifying bacterial types, our method did not distinguish between specific bacterial strains. Employing a more comprehended approach utilizing whole blood microarray technology on septic patients infected with either S. aureus or Escherichia coli revealed 25 genes with high AUC values (0.98 and 0.96, respectively) that effectively distinguished these infections from other cases. These findings were consistent across two separate independent datasets, with AUC values ranging from 0.72 to 0.87 (Paper IV). In conclusion, efforts to improve diagnostic strategies and understand bacterial characteristics in sepsis continue. Platforms like 1928 Diagnostics and technologies such as the PEA show promise, with machine learning offering opportunities to tackle bacterial typing challenges. These advancements are crucial for evolving clinical practices in sepsis diagnosis and management.