Background: We evaluated how plasma proteomic signatures in patients with suspected COVID-19 can unravel the pathophysiology, and determine kinetics and clinical outcome of the infection. Methods: Plasma samples from patients presenting to the emergency department (ED) with symptoms of COVID-19 were stratified into: (1) patients with suspected COVID-19 that was not confirmed (n = 44); (2) non-hospitalized patients with confirmed COVID-19 (n = 44); (3) hospitalized patients with confirmed COVID-19 (n = 53) with variable outcome; and (4) patients presenting to the ED with minor diseases unrelated to SARS-CoV-2 infection (n = 20). Besides standard of care diagnostics, 177 circulating proteins related to inflammation and cardiovascular disease were analyzed using proximity extension assay (PEA, Olink) technology. Results: Comparative proteome analysis revealed 14 distinct proteins as highly associated with SARS-CoV-2 infection and 12 proteins with subsequent hospitalization (p < 0.001). ADM, IL-6, MCP-3, TRAIL-R2, and PD-L1 were each predictive for death (AUROC curve 0.80–0.87). The consistent increase of these markers, from hospital admission to intensive care and fatality, supported the concept that these proteins are of major clinical relevance. Conclusions: We identified distinct plasma proteins linked to the presence and course of COVID-19. These plasma proteomic findings may translate to a protein fingerprint, helping to assist clinical management decisions.

Accurate diagnosis of sepsis is needed to initiate life-saving treatment decisions. Biomarkers capable of identifying both acute infection and sepsis are required to assist clinicians. A real-life heterogeneous cohort of 388 patients with suspected acute infections was recruited at presentation to the ED. Nine emerging host-response biomarkers (MyD88, MMP-8, leptin, ENA-78, fractalkine, PD- L1, pentraxin-3, TRAIL, and GLP-1) were quantified using a multiparameter assay. We performed AUROC analysis for the endpoints bacterial infection, sepsis, and 30-day mortality. We further assessed diagnostic performance when combining these biomarkers using a machine learning algorithm. Particularly, MyD88, PD-L1, and pentraxin-3 presented high AUROCs for the endpoints bacterial infection (≥0.87), sepsis (≥0.81), and 30-day mortality (≥0.71). Seven out of the nine investigated biomarkers showed statistically significant discrimination for all three endpoints. A combined algorithm via the XGBoost model using pentraxin-3, MyD88, and GLP-1 was used for sepsis prediction, with an AUROC of 0.89, higher than clinical assessment via NEWS-2 (0.83) or procalcitonin (0.81). Pentraxin-3, MyD88, GLP-1, and PD-L1 are a promising complementary set of biomarkers for risk assessment and stratification. When a trained multiparameter classifier is used, the combination of biomarkers results in a valid tool for sepsis diagnosis. DRKS00020521, DRKS00017395.

Background: Acute infections and sepsis are a leading cause of death. These patients are primarily encountered at the emergency department (ED), where early assessment for sepsis is necessary to improve outcome. In sepsis, the inflammatory response causes several characteristic pathophysiological changes, including a dysregulated and generalized activation of the endothelium. This study aimed to analyze endothelial markers released to the blood as diagnostic biomarkers for acute infection and sepsis in the ED, as smaller studies have previously shown promising results in other settings. Methods: Serum samples from n = 312 adult patients with suspected acute infections at presentation to the ED were utilized. Patients’ courses of disease and outcomes were assessed by clinical adjudication. E-selectin, P-selectin, ICAM-1, and VCAM-1 were measured by ELISAs. The accuracy of each marker for predicting bacterial infection, sepsis, and in-hospital mortality was evaluated. Results: For sepsis, E-selectin and ICAM-1 both showed an area under the receiver operating characteristic (AUROC) of 0.62, lower than procalcitonin with 0.77 (both P < 0.01) and lactate with 0.73 (P = 0.030 and 0.046, respectively), but similar to CRP with 0.60 (P = 0.758 and 0.876, respectively). For 28-day in-hospital mortality among patients with infection, ICAM-1 performed best with an AUROC of 0.75. Conclusions: Despite promising results in small studies and specific cohorts, particularly in intensive care units, this large-scale evaluation of four endothelial biomarkers highlights their limited diagnostic utility in a broader inclusion setup design at the earliest possible time point of evaluation.

Soluble thrombomodulin (TM) has been proposed as an endothelial injury marker with potential diagnostic value, particularly for severe pneumococcal community-acquired pneumonia (CAP). Its validation could enable clinically meaningful patient stratification and support biomarker-guided therapy. This retrospective study evaluated the associations between serum TM levels and disease severity/origin in 80 patients with suspected acute infection. Serum TM concentrations were measured in patients presenting to the emergency department and adjudicated by an expert panel as pneumococcal CAP, non-pneumococcal CAP, other bacterial infections (non-pneumonic), viral pneumonia, or patients with clinically ruled-out infection (controls). TM levels did not differ significantly between CAP patients and controls or those with other bacterial infections. However, highest TM concentrations were observed in patients with bacterial infections without pneumonia, significantly exceeding control levels. TM levels were not correlated with disease severity in CAP, determined by National Early Warning Score 2 (NEWS2), but were significantly elevated in non-pneumonic bacterial infections of moderate severity (NEWS2 5–6). In this cohort, TM was elevated in bacterial non-pneumonic infections but was not a specific marker for pneumococcal CAP. These findings indicate that TM predominantly reflects systemic endothelial injury, a hallmark of more severe bacterial infections, particularly those of non-pneumonic origin.