Source-specific host response and outcomes in critically ill patients with sepsis: a prospective cohort study

Abstract

Purpose: There is limited knowledge on how the source of infection impacts the host response to sepsis. We aimed to compare the host response in sepsis patients with a single, known source at admission (< 24 h) to the intensive care unit.

Methods: From the molecular diagnosis and risk stratification of sepsis (MARS) prospective cohort, we measured 16 plasma host response biomarkers reflective of key host response pathways in 621 sepsis patients. In a subgroup (n = 335), blood leukocyte transcriptomes were compared between the sources. Differences in clinical patient profiles and survival were compared in the whole sepsis cohort (n = 2019).

Results: The plasma biomarker cohort was categorized into sepsis originating from the respiratory tract (n = 334, 53.8%), abdomen (n = 159, 25.6%), urinary tract (n = 44, 7.1%), cardiovascular (n = 41, 6.6%), central nervous system (CNS) (n = 18, 2.9%), or skin (n = 25, 4%). This analysis revealed stronger inflammatory and cytokine responses, loss of vascular integrity and coagulation activation in abdominal sepsis relative to respiratory. Endothelial cell activation was prominent in urinary, cardiovascular and skin infections, while CNS infection was associated with the least host response aberrations. The leukocyte transcriptional response showed the largest overlap between abdominal and pulmonary infections (76% in common); notable differences between the sources were detected regarding hemostasis, cytokine signaling, innate and adaptive immune, and metabolic transcriptional pathways. After adjustment for confounders, the source of infection remained an independent contributor to 30-day mortality (unadjusted p = 0.001, adjusted p = 0.028).

Conclusion: Sepsis heterogeneity is partly explained by source-specific host response dysregulations and should be considered when selecting patients for trials testing immune modulatory drugs.

Trial registration: ClinicalTrials.gov NCT01905033.

Keywords: Host response; Intensive care unit; Sepsis; Site of infection; Source of infection.

Conflict of interest statement

The authors disclose that they do not have any potential conflicts of interest.

© 2021. The Author(s).

Figures

Fig. 1

30-day mortality incidence (%) among sepsis patients with different sources of infection; p values calculated by type-III overall Wald test. Data are presented as bars with 95% confidence bands. Adjusted model included age, sex, ethnicity, BMI, Charlson comorbidity score (without age), admission type, hospital site, blood culture positivity, type of causative pathogen, mSOFA score, Acute Physiology Score and shock

Fig. 2

Host response biomarkers in patients with sepsis on admission stratified according to the source of infection. Biological parameters are classified as A inflammatory responses, B endothelial cell activation, and C coagulation activation biomarkers. Data are presented as principal component analysis (PCA) plots (far left side of each row), and box and whiskers (with dotted lines in box plots representing median values obtained in 27 healthy age-matched healthy subjects). Ellipse circles of infection groups in PCA plots are drawn around patient data points (not shown here for clarity), wherein the centroid is the barycenter of the patient data points belonging to the same source of infection; arrows in PCA plots indicate direction of correlation of plasma markers with loadings of PCA components. p values in box plots represent type-III Wald tests for the source of infection groups derived from linear regression models, wherein the adjusted model included age, sex, ethnicity, BMI, Charlson comorbidity score (without age), admission type, hospital site, blood culture positivity, type of causative pathogen, mSOFA score, Acute Physiology Score and shock. ANG angiopoietin, aPTT activated partial thromboplastin time, IL interleukin, MMP matrix metalloproteinase, PT prothrombin time, sE-Selectin soluble E-selectin, sICAM soluble intercellular adhesion molecule

Fig. 3

Blood transcriptomics response in patients with sepsis on admission stratified according to the source of infection. A Venn–Euler diagram illustrating the shared and distinct leukocyte transcriptional responses between source of infection groups relative to health (with differential expressed genes according to high effect size > 0.8 with Hedges g). Number of overlapping genes are shown if above 65. B Pairwise comparison of source of infection groups showing common transcriptional response (with differential expressed genes according to medium effect size > 0.4 with Hedges g). C Pathway analysis of the common response to sepsis (4126 genes common to all sources) relative to health. Canonical signaling sub-pathways were grouped into their parent pathway according to Reactome pathway database. D Comparing the blood transcriptional responses between the source of infection groups for targeted pathways. For every gene, expression values were scaled across all sepsis samples. Then, for each source of infection, the vector of mean expression values was ordered for gene set enrichment analysis implementing 1000 permutations. For each selected pathway, we summarize its enrichment by magnitude, using the BH adjusted p value correcting for all existing Reactome database pathways, and direction using the normalized enrichment score (red =  + ve, blue = −ve)