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. 2022 Mar 9;10(1):13.
doi: 10.1186/s40560-022-00602-x.

A prospective observational cohort study to identify inflammatory biomarkers for the diagnosis and prognosis of patients with sepsis

Valentino D'Onofrio  1   2   3 , Dries Heylen  4   5 , Murih Pusparum  4   5 , Inge Grondman  6 , Johan Vanwalleghem  7 , Agnes Meersman  8 , Reinoud Cartuyvels  9 , Peter Messiaen  10   11 , Leo A B Joosten  6 , Mihai G Netea  6   12 , Dirk Valkenborg  5 , Gökhan Ertaylan  4 , Inge C Gyssens  13   14
Affiliations

A prospective observational cohort study to identify inflammatory biomarkers for the diagnosis and prognosis of patients with sepsis

Valentino D'Onofrio et al. J Intensive Care. .

Abstract

Background: Sepsis is a life-threatening organ dysfunction. A fast diagnosis is crucial for patient management. Proteins that are synthesized during the inflammatory response can be used as biomarkers, helping in a rapid clinical assessment or an early diagnosis of infection. The aim of this study was to identify biomarkers of inflammation for the diagnosis and prognosis of infection in patients with suspected sepsis.

Methods: In total 406 episodes were included in a prospective cohort study. Plasma was collected from all patients with suspected sepsis, for whom blood cultures were drawn, in the emergency department (ED), the department of infectious diseases, or the haemodialysis unit on the first day of a new episode. Samples were analysed using a 92-plex proteomic panel based on a proximity extension assay with oligonucleotide-labelled antibody probe pairs (OLink, Uppsala, Sweden). Supervised and unsupervised differential expression analyses and pathway enrichment analyses were performed to search for inflammatory proteins that were different between patients with viral or bacterial sepsis and between patients with worse or less severe outcome.

Results: Supervised differential expression analysis revealed 21 proteins that were significantly lower in circulation of patients with viral infections compared to patients with bacterial infections. More strongly, higher expression levels were observed for 38 proteins in patients with high SOFA scores (> 4), and for 21 proteins in patients with worse outcome. These proteins are mostly involved in pathways known to be activated early in the inflammatory response. Unsupervised, hierarchical clustering confirmed that inflammatory response was more strongly related to disease severity than to aetiology.

Conclusion: Several differentially expressed inflammatory proteins were identified that could be used as biomarkers for sepsis. These proteins are mostly related to disease severity. Within the setting of an emergency department, they could be used for outcome prediction, patient monitoring, and directing diagnostics.

Trail registration number: clinicaltrial.gov identifier NCT03841162.

Keywords: Biomarkers; Disease severity; Inflammation; Sepsis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Venn diagram showing all differentially expressed proteins between three different groupings, together with overlapping findings. Three different groupings are: aetiology (influenza versus bacterial), severity (SOFA score < 2 versus > 4), and outcome (less severe outcome versus worse outcome)
Fig. 2
Fig. 2
A principal component analysis to detect clustering between patients with influenza (pink), bacterial pneumonia (blue) and other bacterial infections (green). B principal component analysis to detect clustering based on SOFA score
Fig. 3
Fig. 3
Hierarchical clustering plot
Fig. 4
Fig. 4
HSP60 and HSP70/TLR signalling pathway involved in the activation of the inflammatory response formula image
Fig. 5
Fig. 5
Model optimization for aetiology, disease severity and outcome, starting from the maximum number of parameters resulting from the elastic net regression models. The most optimal model was chosen based on the minimum decline of 5% in area under the ROC curve (AUROC) and sensitivity with the lowest number of proteins in the model. Aetiology: viral vs. bacterial sepsis, the most optimal model had an AUROC of 94% and sensitivity of 86% with six proteins in the model. Disease severity: SOFA score > 4 vs. SOFA score < 2, the most optimal model had an AUROC of 80% and a sensitivity of 83% with six proteins in the model. Outcome: worse vs. less severe outcome, the most optimal model had an AUROC of 76% and a sensitivity of 68% with ten proteins in the model. The most optimal models for aetiology and outcome were chosen after SMOTE procedure. AUROC, PR-AUC, sensitivity and specificity are shown
Fig. 6
Fig. 6
Proteins in the most optimal models, accurately predicting differences in aetiology, disease severity and outcome
See this image and copyright information in PMC

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