Combination of Leukocyte Cell Surface Biomarkers Measured by Cytometry, to Differentiate Bacterial From Viral Infections in Emergency Department: a Multicentric Cohort for the Validation of Diagnostic Performances

Overview

The characterization of the bacterial or viral etiology of an infectious event is required for both isolation decisions and rationale use of antibiotics. In emergency room (ER), the direct identification of the causal pathogen is rarely available in real-time. Alternative is the identification of the host-response to either a bacterial or viral infection. One of this host-response is the expression of peripheral leukocytes cell surface markers, measured by flow cytometry. Investigators and others have reported the high diagnostic performances of combination of cell surface biomarkers to differentiate bacterial from viral infection. The CYTOBACT study aims to confirm on a 500 patients multicentric cohort (200 having already been collected during another study: SEPTIMET), the best combinations for this diagnostic issue. The study will be conducted in 3 emergency departments of APHP hospitals network in Paris, France. Patients with a suspicion of infection will be proposed to participate. No intervention will be introduced during the routine care in the (ER) which will be let at the discretion of the treating emergency physician. During the routine blood sampling in the ER, an additional 30 ml volume of whole blood will be collected, centrifugated, aliquoted and stored at -80°C for further measurement of the expression of a panel of cell surface markers. The participants will be followed up during their hospitalization (if any) and no longer than 28 days. Clinical data at admission, usual blood tests and all microbiological investigations performed during the hospital stay will be recorded into an electronic case record form (eCRF). Based on all those recorded data (excepted the results of flow cytometry for cell surface biomarkers) 2 independent adjudicators will qualify the infectious episode into bacterial,viral or no infection, and (if any) into infection, sepsis or septic shock (according to Sepsis 3.0 definitions). Using different "machine learning" statistical tools, all the combination of the cell surface biomarkers will be tested to select those with the highest performance to differentiate bacterial from viral infection.

Patients attending the ED of one of the participant centers for a suspicion of infection will be informed and asked to participate. After obtaining a non-opposition to participate, during the routine blood sampling performed in the ER, an additional volume of 30 ml of whole blood will be collected, aliquoted and stored at -80°C, comprising notably 12 ml of whole blood to which 1 ml of Cytodelics® Stabiliser will be added and incubated à room temperature for 10 mn before being aliquoted and stored at -80°C. The remaining whole blood will be collected on EDTA and Paxgen tubes, centrifugated, aliquoted and stored at -80°C for blood collection. Clinical data at admission (past medical history, vital parameters, infectious source (if any), treatments delivered in the ER) will be recorded into an eCRF. The participants will be followed up until leaving the hospital and no longer than day-28. All the microbiology tests performed during the hospital stay will be also recorded into eCRF. The diagnostic performance of the combinations of cell surface markers will be evaluated against a diagnostic reference on the bacterial of viral qualification of each infectious event. This diagnostic reference will be established by an independant expert comitee after reviewing all the clinical, and biological data recorded (excepted the results of flow cytometry), in order to adjudicate between bacterial, viral or no infection, and among infected patients to classify into infection, sepsis or septic shock (sepsis 3.0). After completing the recruitement of participants, a panel of cell surface markers will be measured by batch on a spectral cytometer, comprising notably the biomarkers of interest already published : HLA-DR, CD169 and CX3CR1 on monocytes, and MerTk, CD64 and CD24 on neutrophils. The performances of the combinations of cell surface markers already identified in the littérature will be tested prioritarily. However, in order to refine the best combinations of biomarkers to discriminate bacterial from viral infection, machine learning algorithms like gradient boosting tree and support vector machine tools will be applied on the entire results of cell surface markers measured. The diagnostic performance will be evaluated calculating the sensitivity, specificity, area under the ROC curve of the biomarkers combinations selected.