Point-of-care neutrophil CD64 as a rule in diagnostic test for bacterial infections in the emergency department

Can nCD64 and mCD169 biomarkers improve the diagnosis of viral and bacterial respiratory syndromes in the emergency department? A prospective cohort pilot study

PURPOSE

Differentiating infectious from non-infectious respiratory syndromes is critical in emergency settings. This study aimed to assess whether nCD64 and mCD169 exhibit specific distributions in patients with respiratory infections (viral, bacterial, or co-infections) and to evaluate their diagnostic accuracy compared to non-infectious conditions.

METHODS

A prospective cohort study enrolled 443 consecutive emergency department patients with respiratory syndromes, categorized into four groups: no infection group (NOIG), bacterial infection group (BIG), viral infection group (VIG), and co-infection group (COING). Multinomial logistic regression was used to evaluate nCD64 and mCD169's association with diagnostic groups and estimate their predictive accuracy.

RESULTS

290 patients were included in VIG, 53 in BIG, 46 in COING, and 54 in NOIG. nCD64 was associated with bacterial infections and co-infections (p = 2.73 × 10- 16 and p = 8.83 × 10- 11, respectively), but not viral infections. mCD169 was associated with viral infections and co-infections (p = < 2 × 10- 16 and p = 2.45 × 10- 13, respectively), but not bacterial infections. The sensitivity and specificity of nCD64 for detecting bacterial infections were 0.75 and 0.84 (AUC = 0.83), respectively, while for mCD169 they were 0.87 and 0.91 (AUC = 0.92), respectively, for diagnosing viral infections. A diagnostic algorithm incorporating fever, nasopharyngeal swabs for the main respiratory virus, C-reactive protein, procalcitonin, and mCD169 reached an accuracy of 0.79 (95% CI 0.72-0.85) in distinguishing among the different groups.

CONCLUSIONS

nCD64 and MCD169 seem valuable for distinguishing between bacterial and viral respiratory infections. Integrating these biomarkers into diagnostic algorithms could enhance diagnostic accuracy aiding patient management in emergency settings.

Validation of monocyte CD169 expression as a valuable rapid diagnostic marker of SARS-CoV-2 and other acute viral infections

OBJECTIVES

Acute infectious diseases are some of the most common reasons for receiving medical care, and analysis of the host immune response is an attractive approach for their diagnosis. The present study aimed to evaluate the potential usefulness of CD169 expression on peripheral monocytes (mCD169) as a marker of viral-associated host immune response.

METHODS

In a large mono-institutional cohort of 4,025 patients evaluated for SARS-CoV-2 (CoV2) and other viral infections, mCD169 analysis was performed by rapid flow cytometry assay.

RESULTS

Increased mCD169 values (median, 17.50; IQR, 8.40-25.72) were found in 1,631 patients with CoV2+ acute infection compared to 2,394 in CoV2- patients (median, 2.35; IQR, 2.0-3.25) (odds ratio [OR], 21.84; 95% CI ,17.53-27.21; P < .001). Among CoV2- patients, 1,484 (62.0%) were assessed for other viral infections, and viral etiology was laboratory confirmed in 428 patients (CoV2- Vir+), with RNA viruses most frequently detected (94.6%). Higher levels of mCD169 were also confirmed in CoV2- Vir+ compared to CoV2- Vir- patients (OR, 10.05; 95% CI, 7.35-13.74; P < .001).

CONCLUSIONS

mCD169 analysis by rapid flow cytometry assay may be a sensitive broad marker useful for the rapid triage of patients with suspected acute viral infections and could potentially be directly applied to eventual new emergent viral outbreaks.

Current development of Fc gamma receptors (FcγRs) in diagnostics: a review

The ability of the immune system to fight against pathogens relies on the intricate collaboration between antibodies and Fc gamma receptors (FcγRs). These receptors are a group of transmembrane glycoprotein molecules, which can specifically detect and bind to the Fc portion of immunoglobulin G (IgG) molecules. They are distributed on a diverse array of immune cells, forming a strong defence system to eliminate invading threats. FcγRs have gained increasing attention as potential biomarkers for various diseases in recent years due to their ability to reflect immune dysregulation and disease pathogenesis. Increasing lines of evidence have shed new light on the remarkable association of FcγRs polymorphisms with the susceptibility of autoimmune diseases such as systemic lupus erythematosus (SLE) and lupus nephritis. Several studies have also reported the application of FcγR as a novel biomarker for the diagnosis of infection and cancer. Due to the surge in interest and concern regarding the potential of FcγRs as promising diagnostic biomarkers, this review, thereby, serves to provide a comprehensive overview of the structural characteristics, functional roles, and expression patterns of FcγRs, with a particular focus on their evolving role as diagnostic and prognostic biomarkers.

Point-of-care neutrophil and monocyte surface markers differentiate bacterial from viral infections at the emergency department within 30 min

Rapid discrimination between viral and bacterial infections in a point-of-care setting will improve clinical outcome. Expression of CD64 on neutrophils (neuCD64) increases during bacterial infections, whereas expression of CD169 on classical monocytes (cmCD169) increases during viral infections. The diagnostic value of automated point-of-care neuCD64 and cmCD169 analysis was assessed for detecting bacterial and viral infections at the emergency department. Additionally, their value as input for machine learning models was studied. A prospective observational cohort study in patients suspected of infection was performed at an emergency department. A fully automated point-of-care flow cytometer measured neuCD64, cmCD169, and additional leukocyte surface markers. Flow cytometry data were gated using the FlowSOM algorithm. Bacterial and viral infections were assessed in standardized clinical care. The sole and combined diagnostic value of the markers was investigated. Clustering based on unsupervised machine learning identified unique patient clusters. Eighty-six patients were included. Thirty-five had a bacterial infection, 30 had a viral infection, and 21 had no infection. neuCD64 was increased in bacterial infections (P < 0.001), with an area under the receiver operating characteristic curve (AUROC) of 0.73. cmCD169 was higher in virally infected patients (P < 0.001; AUROC 0.79). Multivariate analyses incorporating additional markers increased the AUROC for bacterial and viral infections to 0.86 and 0.93, respectively. The additional clustering identified 4 distinctive patient clusters based on infection type and outcome. Automated neuCD64 and cmCD169 determination can discriminate between bacterial and viral infections. These markers can be determined within 30 min, allowing fast infection diagnostics in the acute clinical setting.