To Örebro University

BACKGROUND: Blood stream infection (BSI) and sepsis are serious clinical conditions and identification of the disease-causing pathogen is important for patient management. The RISE (Rapid Identification of SEpsis) study was carried out to collect a cohort allowing high-quality studies on different aspects of BSI and sepsis. The aim of this study was to identify patients at high risk for BSI who might benefit most from new, faster, etiological testing using neutrophil to lymphocyte count ratio (NLCR) and Shapiro score.

METHODS: Adult patients (≥ 18 years) presenting at the emergency department (ED) with suspected BSI were prospectively included between 2014 and 2016 at Örebro University Hospital. Besides extra blood sampling, all study patients were treated according to ED routines. Electronic patient charts were retrospectively reviewed. A modified Shapiro score (MSS) and NLCR were extracted and compiled. Continuous score variables were analysed with area under receiver operator characteristics curves (AUC) to evaluate the ability of BSI prediction.

RESULTS: The final cohort consisted of 484 patients where 84 (17%) had positive blood culture judged clinically significant. At optimal cut-offs, MSS (≥3 points) and NLCR (> 12) showed equal ability to predict BSI in the whole cohort (AUC 0.71/0.74; sensitivity 69%/67%; specificity 64%/68% respectively) and in a subgroup of 155 patients fulfilling Sepsis-3 criteria (AUC 0.71/0.66; sensitivity 81%/65%; specificity 46%/57% respectively). In BSI cases only predicted by NLCR> 12 the abundance of Gram-negative to Gram-positive pathogens (n = 13 to n = 4) differed significantly from those only predicted by MSS ≥3 p (n = 7 to n = 12 respectively) (p < 0.05).

CONCLUSIONS: MSS and NLCR predicted BSI in the RISE cohort with similar cut-offs as shown in previous studies. Combining the MSS and NLCR did not increase the predictive performance. Differences in BSI prediction between MSS and NLCR regarding etiology need further evaluation.

Bloodstream infections (BSI) are common, and identifying the causative organism is crucial for effective patient management. Shotgun metagenomics (SMg) has emerged as a promising diagnostic tool; however, standardized protocols are lacking. This study aimed to evaluate the use of SMg for diagnosing BSI in patients with confirmed or suspected infections, using stored samples collected at the time of blood culture (BC). DNA extraction was performed with Add-on 10 complement and SelectNA Blood Pathogen kit (Molzym) and SMg sequencing was performed on an Illumina MiSeq instrument (Illumina). The outputs from five taxonomic classification tools were compared with routine blood culture. Of the initial 51 samples (36 BCE-positive and 15 BCE-negative), 36 (71 %) were included in the taxonomic classification analysis. Fifteen samples were excluded due to a low DNA library yield (n = 8) or low sequencing output (n = 7). In two cases, SMg results matched BC findings involving one Cutibacterium acnes and one Staphylococcus aureus. These organisms could be clearly distinguished from the background level of bacterial DNA. Aside from these, SMg identified additional bacterial findings that overlapped with BC results but at low abundance making interpretation more difficult. Most SMg reads were suspected to represent contaminations, originating either from the patient or the laboratory. The output from the different taxonomic classification tools were overall similar but displayed notable differences related to their strategies for identifying bacterial findings. Based on these results, we discuss the challenges associated with SMg-based diagnosis of BSI and highlight key areas requiring further research to improve its clinical utility.

Shotgun metagenomics offers a broad detection of pathogens for rapid blood stream infection of pathogens but struggles with often low numbers of pathogens combined with high levels of human background DNA in clinical samples. This study aimed to develop a shotgun metagenomics protocol using blood spiked with various bacteria and to assess bacterial DNA extraction efficiency with human DNA depletion. The Blood Pathogen Kit (Molzym) was used to extract DNA from EDTA-whole blood (WB) and plasma samples, using contrived blood specimens spiked with bacteria for shotgun metagenomics diagnostics via Oxford Nanopore sequencing and PCR-based library preparation. Results showed that bacterial reads were higher in WB than plasma. Differences for Staphylococcus aureus and Streptococcus pneumoniae were more pronounced compared to Escherichia coli. Plasma samples exhibited better method reproducibility, with more consistent droplet digital PCR results for human DNA. The study found that extraction was more efficient for Gram-positive bacteria than Gram-negative, suggesting that the human DNA depletion exerts a negative effect on Gram-negative bacteria. Overall, shotgun metagenomics needs further optimisation to improve bacterial DNA recovery and enhance pathogen detection sensitivity. This study highlights some critical steps in the methodology of shotgun metagenomic-based diagnosis of blood stream infections using Nanopore sequencing.