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.

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.

Introduction: Automatic cytological analysis of cerebrospinal fluid (CSF) by Sysmex XN‐Series represents a convenient laboratory platform for quantitative examination of nucleated CSF cells (monomorphonuclear (MN), polymorphonuclear (PMN), high‐fluorescent (HF)). HF cells (HFC), a research laboratory parameter so far, seem to be associated with certain clinical patterns. Hence, we aimed to determine the diagnostic HFC value for different clinical categories in neurological settings.Methods: Morphological classification of automatically detected HFC was carried out using manual light microscopy. Automatic method precision for cell differentiation was evaluated in comparison. In 284 cases, multiple correlation strategies and mathematical disease modellings enabled an explorative analysis of HFC suitability for case stratification into the categories Hemorrhage, Inflammation, Neoplasia, other, and unknown.Results: Manual microscopic reevaluation revealed plasma cells, macrophages, and malignant cells being HFC correlates in 80% of automatically detected HFC. Method correlation for automatic and manual CSF cell differentiation approaches was 95%, yielding a negative bias of 4.3% for MN and positive bias of 0.4% and 3.9% for PMN and HF, respectively. When HFC were used as a “stand‐alone” predicting tool, diagnostic accuracy, specificity, and sensitivity depended on the clinical condition, ranging from > 0.5 up to > 0.7 for Hemorrhage, Inflammation, and Neoplasia. However, multiparametric correlation analyses combining laboratory CSF diagnostics and mathematical methods defined HFC as a relevant laboratory parameter for adequate clinical case stratification. With an expected random distribution of 25% for four clinical categories, almost 70% of cases were correctly classified when the HFC‐based mathematical algorithm was applied.Conclusion: HFC has significant diagnostic and/or predictive value for neurological diseases.

Article Simplified preanalytical laboratory procedures for therapeutic drug monitoring (TDM) in patients treated with high-dose methotrexate (HD-MTX) and glucarpidase was published on July 15, 2024 in the journal Clinical Chemistry and Laboratory Medicine (CCLM) (volume 0, issue 0).