Sepsis Biomarkers

Pancreatic Stone Protein in the Diagnosis of Sepsis in Children Admitted to High-Dependency Care: A Single-Center Prospective Cohort Study

OBJECTIVES

Blood level of pancreatic stone protein (PSP) is a promising biomarker of sepsis both in adults and children. The aim of our study was to investigate the diagnostic accuracy of PSP in children with suspected sepsis and to compare diagnostic performance with other sepsis biomarkers approved for clinical use, that is, procalcitonin (PCT) and C-reactive protein (CRP).

DESIGN

Prospective study.

SETTING

PICU and pediatric emergency department.

INTERVENTION

Blood levels of PSP were measured using a nanofluidic point-of-care immunoassay (abioSCOPE, Abionic SA, Switzerland) within 24 hours of admission.

MEASUREMENTS AND MAIN RESULTS

We studied 99 children aged between older than 1 month and younger than 18 years with signs and symptoms of systemic inflammatory response syndrome (irrespective of associated organ dysfunction). The prevalence of sepsis was 35 of 99 (35.4%). Patients with sepsis had higher PSP levels (p < 0.001) than patients with systemic inflammation of noninfectious cause. In this analysis, the optimal cutoff for the diagnosis of sepsis using PSP was 123 ng/mL, which resulted in a sensitivity of 0.63 (95% CI, 0.43-0.80), specificity of 0.89 (95% CI, 0.77-0.95). The PSP test area under the receiver operating characteristic curve (AUROC) was 0.82 (95% CI, 0.73-0.91) and, by comparison, procalcitonin and CRP AUROC were 0.70 (95% CI, 0.58-0.82) and 0.72 (95% CI, 0.60-0.84), respectively. Overall, the pretest to posttest probability of sepsis with a positive test changed from 0.35 to 0.73.

CONCLUSIONS

In this single-center prospective pediatric cohort, admitted to the high intensive care and to the PICU, our findings suggested the potential use of PSP as a sepsis biomarker. However, because of the clinical diagnostic uncertainty with a positive result, further investigation is needed particularly in combination with other biomarkers.

Establishment and assessment of mortality risk prediction model in patients with sepsis based on early-stage peripheral lymphocyte subsets

This study is aimed to explore the value of lymphocyte subsets in evaluating the severity and prognosis of sepsis. The counts of lymphocytes, CD3+ T cells, CD4+ T cells, CD8+ T cells, CD19+ B cells, and NK cells significantly decreased between day 1 and day 3 in both the survivor and the non-survivor groups. The peripheral lymphocyte subsets (PLS) at day 1 were not significantly different between the survivor and the non-survivor groups. However, at day 3, the counts of lymphocytes, CD3+ T cells, CD4+ T cells, and NK cells were remarkably lower in the non-survivor group. No significant differences in CD8+ T cells, or CD19+ B cells were observed. The PLS index was independently and significantly associated with the 28-day mortality risk in septic patients (OR: 3.08, 95% CI: 1.18-9.67). Based on these clinical parameters and the PLS index, we developed a nomograph for evaluating the individual mortality risk in sepsis. The area under the curve of prediction with the PLS index was significantly higher than that from the model with only clinical parameters (0.912 vs. 0.817). Our study suggests that the decline of PLS occurred in the early stage of sepsis. The new novel PLS index can be an independent predictor of 28-day mortality in septic patients. The prediction model based on clinical parameters and the PLS index has relatively high predicting ability.

Biomarkers for the Prediction and Judgement of Sepsis and Sepsis Complications: A Step towards precision medicine?

Sepsis and septic shock are a major public health concern and are still associated with high rates of morbidity and mortality. Whilst there is growing understanding of different phenotypes and endotypes of sepsis, all too often treatment strategies still only employ a “one-size-fits-all” approach. Biomarkers offer a unique opportunity to close this gap to more precise treatment approaches by providing insight into clinically hidden, yet complex, pathophysiology, or by individualizing treatment pathways. Predicting and evaluating systemic inflammation, sepsis or septic shock are essential to improve outcomes for these patients. Besides opportunities to improve patient care, employing biomarkers offers a unique opportunity to improve clinical research in patients with sepsis. The high rate of negative clinical trials in this field may partly be explained by a high degree of heterogeneity in patient cohorts and a lack of understanding of specific endotypes or phenotypes. Moving forward, biomarkers can support the selection of more homogeneous cohorts, thereby potentially improving study conditions of clinical trials. This may finally pave the way to a precision medicine approach to sepsis, septic shock and complication of sepsis in the future.

Biomarker combinations in predicting sepsis in hospitalized children with fever

Sepsis is among the leading causes of critical illness worldwide. It includes physiologic, pathologic, and biochemical abnormalities, induced by infection. Novel methods for recognizing a dysregulated inflammatory response and predicting associated mortality must be developed. Our aim was to investigate biomarkers that characterize a pro-inflammatory and anti-inflammatory response in patients with fever by comparing predictive validity for sepsis. 165 patients with fever were enrolled in this study, 55 of them had sepsis according to pSOFA criteria. All patients had blood samples drawn at the time of inclusion and after 24 h. CRP, PCT and also IL-6, IL-8 and sFAS levels were significantly higher in patients with sepsis. The AUC of CRP to predict sepsis was 0.799, all the other biomarkers had AUC's lower than that. Cytokines, when used as a single marker, did not show a significant diagnostic performance We analyzed various models of biomarker combinations. CRP combined with sFAS showed increase in sensitivity in predicting sepsis (88% vs. 83%). The highest AUC was achieved, when CRP, IL-6, sFAS and sVCAM-1 markers were combined 0.830 (95% CI 0.762-0.884) with a sensitivity of 70% and specificity of 84%. vs. 0.799 for CRP alone.

Omics of endothelial cell dysfunction in sepsis

During sepsis, defined as life-threatening organ dysfunction due to dysregulated host response to infection, systemic inflammation activates endothelial cells and initiates a multifaceted cascade of pro-inflammatory signaling events, resulting in increased permeability and excessive recruitment of leukocytes. Vascular endothelial cells share many common properties but have organ-specific phenotypes with unique structure and function. Thus, therapies directed against endothelial cell phenotypes are needed to address organ-specific endothelial cell dysfunction. Omics allow for the study of expressed genes, proteins and/or metabolites in biological systems and provide insight on temporal and spatial evolution of signals during normal and diseased conditions. Proteomics quantifies protein expression, identifies protein-protein interactions and can reveal mechanistic changes in endothelial cells that would not be possible to study via reductionist methods alone. In this review, we provide an overview of how sepsis pathophysiology impacts omics with a focus on proteomic analysis of mouse endothelial cells during sepsis/inflammation and its relationship with the more clinically relevant omics of human endothelial cells. We discuss how omics has been used to define septic endotype signatures in different populations with a focus on proteomic analysis in organ-specific microvascular endothelial cells during sepsis or septic-like inflammation. We believe that studies defining septic endotypes based on proteomic expression in endothelial cell phenotypes are urgently needed to complement omic profiling of whole blood and better define sepsis subphenotypes. Lastly, we provide a discussion of how in silico modeling can be used to leverage the large volume of omics data to map response pathways in sepsis.

Cell Population Data (CPD) for Early Recognition of Sepsis and Septic Shock in Children: A Pilot Study

Objectives: Innovative Cell Population Data (CPD) have been used as early biomarkers for diagnosing sepsis in adults. We assessed the usefulness of CPD in pediatric patients with sepsis/septic shock, in terms of early recognition and outcome prediction. We revised 54 patients (0-15 y) admitted to our Pediatric Intensive Care Unit (PICU) for sepsis/septic shock during a 4-year period. Twenty-eight patients were excluded, 26 septic patients were enrolled (G1). Forty children admitted for elective surgery served as controls (G2). Data on five selected CPD parameters, namely neutrophils fluorescence intensity (NE-SFL), monocytes cells complexity (MO-X), monocytes fluorescence intensity (MO-Y), monocytes complexity and width of dispersion of events measured (MO-WX), and monocytes cells size and width dispersion (MO-WZ), were obtained at time of PICU admission (t0) by a hematological analyzer (Sysmex XN 9000®). As the primary outcome we evaluated the relevance of CPD for diagnosing sepsis/septic shock on PICU admission. Furthermore, we investigated if CPD at t0 were correlated with C-reactive protein (CRP), patient survival, or complicated sepsis course. Results: On PICU admission (t0), NE-SFL, MO-WX, and MO-Y were higher in sepsis/septic shock patients compared to controls. NE-SFL values were correlated with CRP values in G1 patients (r = 0.83). None of the five CPD parameters was correlated with survival or complicated sepsis course. Conclusion: We found higher values of NE-SFL, MO-WX, and MO-Y in children with sepsis/septic shock upon PICU admission. These parameters may be a promising adjunct for early sepsis diagnosis in pediatric populations. Larger, prospective studies are needed to confirm our preliminary observations.

Procalcitonin Levels in Post TACE Infection

Purpose

This study aimed to evaluate the value of serum procalcitonin (PCT) levels in the diagnosis of abscess and sepsis following transarterial chemoembolization (TACE) therapy among patients with hepatocellular carcinoma (HCC).

Patients and Methods

In this study, a retrospective review of patient charts was performed in 2221 patients who suffered from hepatocellular carcinoma and had undergone 8656 TACE procedures from January 2012 to January 2018. According to the diagnosis of infection and abscess after TACE, these participants were divided into infection group (group A, n=48) and abscess group (group B, n=35). Group B included subgroup B1 (suffered from liver abscess but no sepsis, n=16) and subgroup B2 (suffered from liver abscess and sepsis, n=19). The main observational indexes included sociodemographic characteristics and laboratory and clinical parameters.

Results

The results showed that the mean PCT and C-reactive protein (CRP) levels were higher in group B, but receiver-operating characteristic (ROC) analysis showed low sensitivity and specificity. Only the mean PCT level was higher in subgroup B2 than in subgroup B1 (P<0.001); the ROC analysis had high sensitivity and specificity. However, all other data such as NEUT (neutrophil count) and NEUTP (neutrophil percentage) showed no significant differences.

Conclusion

Serum PCT level was a promising inexpensive marker for the diagnosis of liver abscess and sepsis following TACE therapy among patients with primary liver cancer. A cutoff level of 5.1 ng/mL for PCT had high sensitivity and specificity in predicting liver abscess with sepsis.

Machine learning based refined differential gene expression analysis of pediatric sepsis

Background

Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches.

Methods

In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure.

Results

Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89.

Conclusions

Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.