Biomarker could be objective and reliable tools to predict mortality in sepsis. We explored the prognostic utilities of emerging biomarkers in septic patients and questioned whether adding biomarkers to the clinical variables would improve the prediction of mortality in sepsis. This retrospective study included 157 septic patients (112 patients with sepsis; 45 patients with septic shock). Procalcitonin (PCT), presepsin, galectin-3, and soluble suppression of tumorigenicity 2 (sST2) concentrations were analyzed in relation to the 30-day all-cause mortality. Their value added on top of Sequential (Sepsis-related) Organ Failure Assessment (SOFA) score, high-sensitivity C-reactive protein, and white blood cells was also analyzed. PCT could not predict 30-day mortality. Univariate hazard ratio [HR with 95% confidence interval (CI)] of the other dichotomized variables was: 1.33 (0.55–3.194) for presepsin; 7.87 (2.29–26.96) for galectin-3; 1.55 (0.71–3.38) for sST2; and 2.18 (1.01–4.75) for SOFA score. The risk of 30-day mortality increased stepwise as the number of biomarkers above optimal cutoff values increased, and the highest risk was observed when all four biomarkers and SOFA score increased (HR = 14.5). Multi-marker approach predicted 30-day mortality better than SOFA score [area under the curves (95% CI), 0.769 (0.695–0.833) vs. 0.615 (0.535–0.692)]. In reclassification analyses, adding biomarkers to clinical variables improved the prediction of mortality. This study demonstrated a possible prognostic utility of PCT, presepsin, galectin-3, and sST2 in sepsis. Multi-marker approach could be beneficial for an optimized management of patients with sepsis.

Presepsin is a novel biomarker to diagnose sepsis but its prognostic value has not been comprehensively reviewed. We conducted this meta-analysis to evaluate the mortality prediction value of presepsin in sepsis. We searched comprehensive electronic databases from PubMed, EMBASE, and Cochrane Library through September 2017 using the key words of (‘presepsin’ or ‘sCD14-ST’ or ‘soluble CD14 subtype’) and (‘sepsis’ or ‘septic shock’) and (‘prognosis’ or ‘prognostic value’ or ‘prognostic biomarker’ or ‘mortality’). We extracted the presepsin levels in survivors and non-survivors from each individual study and evaluated the standardized mean difference (SMD) using a web-based meta-analysis with the R statistical analysis program. A total of 10 studies and 1617 patients were included. Presepsin levels in the first sampling (within 24 hours) were significantly lower among survivors as compared with non-survivors: the pooled SMD between survivors and non-survivors was 0.92 (95% CI: 0.62–1.22) in the random effects model (I² = 79%, P< 0.01). In subgroups, divided by the sepsis severity or study site, pooled SMD was consistently noting higher presepsin levels in non-survivals (P< 0.05). This meta-analysis demonstrates some mortality prediction value in presepsin in patients with sepsis. Further studies are needed to define the optimal cut-off point to predict mortality in sepsis.

Kim H, et al. Ann Lab Med. 2017 Sep;37(5):388-397. https://doi.org/10.3343/alm.2017.37.5.388

Background: The Sysmex DI-60 system (DI-60, Sysmex, Kobe, Japan) is a new automated digital cell imaging analyzer. We explored the performance of DI-60 in comparison with Sysmex XN analyzer (XN, Sysmex) and manual count. Methods: In a total of 276 samples (176 abnormal and 100 normal samples), white blood cell (WBC) differentials, red blood cell (RBC) classification and platelet (PLT) estimation by DI-60 were compared with the results by XN and/or manual count. RBC morphology between pre-classification and verification was compared according to the ICSH grading criteria. The manual count was performed according to the Clinical and Laboratory Standards Institute guidelines (H20-A2). Results: The overall concordance between DI-60 and manual count for WBCs was 86.0%. The agreement between DI-60 pre-classification and verification was excellent (weighted κ=0.963) for WBC five-part differentials. The correlation with manual count was very strong for neutrophils (r=0.955), lymphocytes (r=0.871), immature granulocytes (r=0.820), and blasts (r=0.879). RBC grading showed notable differences between DI-60 and manual counting on the basis of the ICSH grading criteria. Platelet count by DI-60 highly correlated with that by XN (r=0.945). However, DI-60 underestimated platelet counts in samples with marked thrombocytosis. Conclusions: The performance of DI-60 for WBC differential, RBC classification, and platelet estimation seems to be acceptable even in abnormal samples with improvement after verification. DI-60 would help optimize the workflow in hematology laboratory with reduced manual workload.

Hur M, et al. Ann Lab Med. 2015 Nov;35(6):570-577. https://doi.org/10.3343/alm.2015.35.6.570