Prognostic value of cellular population data in patients with COVID-19.

Background and aims: Beckman Coulter hematology analysers identify leukocytes by their volume (V), conductivity (C) and scatter (S) of a laser beam at different angles. Each leukocyte sub-population [neutrophils (NE), lymphocytes (LY), monocytes (MO)] is characterized by the mean (MN) and the standard deviation (SD) of 7 measurements called "cellular population data" (@CPD), corresponding to morphological analysis of the leukocytes. As severe forms of infections to SARS-CoV-2 are characterized by a functional activation of mononuclear cells, leading to a cytokine storm, we evaluated whether CPD variations are correlated to the inflammation state, oxygen requirement and lung damage and whether CPD analysis could be useful for a triage of patients with COVID-19 in the Emergency Department (ED) and could help to identify patients with a high risk of worsening. Materials and method: The CPD of 825 consecutive patients with proven COVID-19 presenting to the ED were recorded and compared to classical biochemical parameters, the need for hospitalization in the ward or ICU, the need for oxygen, or lung injury on CT-scan. Results: 40 of the 42 CPD were significantly modified in COVID-19 patients in comparison to 245 controls. @MN-V-MO and @SD-V-MO were highly correlated with C-reactive protein, procalcitonin, ferritin and D-dimers. SD-UMALS-LY > 21.45 and > 23.92 identified, respectively, patients with critical lung injuries (>75%) and requiring tracheal intubation. @SD-V-MO > 25.03 and @SD-V-NE > 19.4 identified patients required immediate ICU admission, whereas a @MN-V-MO < 183 suggested that the patient could be immediately discharged. Using logistic regression, the combination of 8 CPD with platelet and basophil counts and the existence of diabetes or obesity could identify patients requiring ICU after a first stay in conventional wards (area under the curve = 0.843). Conclusion: CPD analysis constitutes an easy and inexpensive tool for triage and prognosis of COVID-19 patients in the ED.

Can Haematological Parameters Discriminate COVID-19 from Influenza?

Symptoms of COVID-19 are similar to the influenza virus, but because treatments and prognoses are different, it is important to accurately and rapidly differentiate these diseases. The aim of this study was to evaluate whether the analysis of complete blood count (CBC), including cellular population (CPD) data of leukocytes and automated flow cytometry analysis, could discriminate these pathologies. In total, 350 patients with COVID-19 and 102 patients with influenza were included between September 2021 and April 2022 in the tertiary hospital of Suresnes (France). Platelets were lower in patients with influenza than in patients with COVID-19, whereas the CD16pos monocyte count and the ratio of the CD16pos monocytes/total monocyte count were higher. Significant differences were observed for 9/56 CPD of COVID-19 and flu patients. A logistic regression model with 17 parameters, including among them 11 CPD, the haemoglobin level, the haematocrit, the red cell distribution width, and B-lymphocyte and CD16pos monocyte levels, discriminates COVID-19 patients from flu patients. The sensitivity and efficiency of the model were 96.2 and 86.6%, respectively, with an area under the curve of 0.862. Classical parameters of CBC are very similar among the three infections, but CPD, CD16pos monocytes, and B-lymphocyte levels can discriminate patients with COVID-19.

A low level of CD16pos monocytes in SARS-CoV-2 infected patients is a marker of severity.

OBJECTIVES: Severe forms of coronavirus disease 2019 (COVID-19) are characterized by an excessive production of inflammatory cytokines. Activated monocytes secrete high levels of cytokines. Human monocytes are divided into three major populations: conventional (CD14posCD16neg), non-classical (CD14dimCD16pos), and intermediate (CD14posCD16pos) monocytes. The aim of this study was to analyze whether the distribution of conventional (CD16neg) and CD16pos monocytes is different in patients with COVID-19 and whether the variations could be predictive of the outcome of the disease. METHODS: We performed a prospective study on 390 consecutive patients referred to the Emergency Unit, with a proven diagnosis of SARS-CoV 2 infection by RT-PCR. Using the CytoDiff™ reagent, an automated routine leukocyte differential, we quantified CD16neg and CD16pos monocytes. RESULTS: In the entire population, median CD16neg and CD16pos monocyte levels (0.398 and 0.054×109/L, respectively) were in the normal range [(0.3-0.7×109/L) and (0.015-0.065×109/L), respectively], but the 35 patients in the intensive care unit (ICU) had a significantly (p<0.001) lower CD16pos monocyte count (0.018 × 109/L) in comparison to the 70 patients who were discharged (0.064 × 109/L) or were hospitalized in conventional units (0.058 × 109/L). By ROC curve analysis, the ratio [absolute neutrophil count/CD16pos monocyte count] was highly discriminant to identify patients requiring ICU hospitalization: with a cut-off 193.1, the sensitivity and the specificity were 74.3 and 81.8%, respectively (area under the curve=0.817). CONCLUSIONS: Quantification of CD16pos monocytes and the ratio [absolute neutrophil count/CD16pos monocyte count] could constitute a marker of the severity of disease in COVID-19 patients.

Interest of the cellular population data analysis as an aid in the early diagnosis of SARS-CoV-2 infection.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is characterized by a high contagiousness requiring isolation measures. At this time, diagnosis is based on the positivity of specific RT-PCR and/or chest computed tomography scan, which are time-consuming and may delay diagnosis. Complete blood count (CBC) can potentially contribute to the diagnosis of COVID-19. We studied whether the analysis of cellular population data (CPD), provided as part of CBC-Diff analysis by the DxH 800 analyzers (Beckman Coulter), can help to identify SARS-CoV-2 infection. METHODS: Cellular population data of the different leukocyte subpopulations were analyzed in 137 controls, 322 patients with proven COVID-19 (COVID+), and 285 patients for whom investigations were negative for SARS-CoV-2 infection (COVID-). When CPD of COVID+ were different from controls and COVID- patients, we used receiver operating characteristic analysis to test the discriminating capacity of the individual parameters. Using a random forest classifier, we developed the algorithm based on the combination of 4 monocyte CPD to discriminate COVID+ from COVID- patients. This algorithm was tested prospectively in a series of 222 patients referred to the emergency unit. RESULTS: Among the 222 patients, 86 were diagnosed as COVID-19 and 60.5% were correctly identified using the discriminating protocol. Among the 136 COVID- patients, 10.3% were misclassified (specificity 89.7%, sensitivity 60.5%). False negatives were observed mainly in patients with a low inflammatory state whereas false positives were mainly seen in patients with sepsis. CONCLUSION: Consideration of CPD could constitute a first step and potentially aid in the early diagnosis of COVID-19.