Background: Sepsis remains a major challenge in intensive care units, causing unacceptably high mortality rates due to the lack of rapid diagnostic tools with sufficient sensitivity. Therefore, there is an urgent need to replace time-consuming blood cultures with a new method. Ideally, such a method also provides comprehensive profiling of pathogenic bacteria to facilitate the treatment decision.
Methods: We developed a Random Forest with balanced subsampling to screen for pathogenic bacteria and diagnose sepsis based on cell-free DNA (cfDNA) sequencing data in a small blood sample. In addition, we constructed a bacterial co-occurrence network, based on a set of normal and sepsis samples, to infer unobserved bacteria.
Results: Based solely on cfDNA sequencing information from three independent datasets of sepsis, we distinguish sepsis from healthy samples with a satisfactory performance. This strategy also provides comprehensive bacteria profiling, permitting doctors to choose the best treatment strategy for a sepsis case.
Conclusions: The combination of sepsis identification and bacteria-inferring strategies is a success for noninvasive cfDNA-based diagnosis, which has the potential to greatly enhance efficiency in disease detection and provide a comprehensive understanding of pathogens. For comparison, where a culture-based analysis of pathogens takes up to 5 days and is effective for only a third to a half of patients, cfDNA sequencing can be completed in just 1 day and our method can identify the majority of pathogens in all patients.
Keywords: Bacteremia; Bacteria profiling; Bacterial co-occurrence network; Cell-free DNA sequence; Rapid diagnosis; Sepsis.