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

Abstract

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.

Keywords: COVID-19; Cellular population data; Prognosis.

Fig. 1

Selection of study patients. Patients were recruited from the emergency department. To be included, they had to have both a positive SARS-CoV-2 RT-qPCR and a complete blood count. Exclusion criteria were pregnancy, because a possible influence of pregnancy on cellular population data (CPD) can be hypothesized, immunosuppressive and antineoplastic treatments, which modify leukocyte counts and hemopathies, because abnormal blood cells interfere with the determination of the CPD of the normal lineages. CT-scan: computed tomography-scan.

Fig. 2

Correlations between cellular population data (CPD) of 825 patients with proven COVID-19 and C-reactive protein (CRP), procalcitonin (PCT), ferritin and D-dimers For each CPD, correlations with the 4 classical markers of severity of COVID-19 were indicated by a colored rectangle which indicated the p value, calculated by the rank correlation test. The exact value of “p” as well as the Pearson's correlation coefficient are indicated in the appendix (see Supplementary Table 2). CRP, PCT, ferritin, and D-dimer levels were available for 761, 253, 415 and 480 patients, respectively.

Fig. 3

Variations of different cellular population data according (A) to lung injury quantification on computed tomography-scan of 493 patients with COVID-19 or (B) needs in oxygen. Abscissa indicates the percentages of lung injury on CT-Scan (A) or the volume of oxygen in L/min distributed (B). Abs: absence, OTI a/o ECMO: orotracheal intubation and/or extracorporeal membrane oxygenation * p < 0.05; **p < 0.01; ***p < 0.001 (Mann-Whitney test) The gray zone corresponds to the normal range (5^th – 95^th percentile). The dotted lines correspond to the cutoffs determined by ROC curve analysis to identify patients with more than 75% injury or requiring orotracheal intubation and/or ECMO. Only significant statistical differences between the groups with a low (<10%) or critical (>75%) lung injuries were indicated, or without the need for oxygen or orotracheal intubation and/or ECMO.

Fig. 4

Variations of @MN-V-MO, SD-V-MO and @SD-V-NE according to the need for hospitalization in 362 patients with COVID-19, and the type of hospitalization. DIS: discharged, HOS: Hospitalization in conventional wards, ICU: Intensive care unit * p < 0.05; **p < 0.01; ***p < 0.001 (Mann-Whitney test) The gray zone corresponds to the normal range (5^th – 95^th percentile). The dotted lines correspond to the cutoffs determined by ROC curve analysis to identify patients that can be discharged (small dots) or require immediate ICU admission (large dots). Only statistical differences between the groups of discharged and ICU patients are indicated.

Fig. 5

(A) ROC curve analysis of the ICU factor obtained by logistic regression to identify patients hospitalized in conventional wards who will worsen and require transfer to the Intensive Care Unit. The clinical performance of different cut-offs of the “ICU factor” are indicated in the table (B). The ICU factor is composed of the following parameters: mean light scatter at the low angle of neutrophils (@MN-LALS-NE) and of lymphocytes (@MN-LALS-LY), mean of the monocyte conductivity (@MN-C-MO), mean of the light scatter at upper median angle of monocytes (@MN-UMALS-MO) and standard deviation at the median angle of the light scatter of neutrophils (@SD-MALS-NE), the standard deviation of light scatter at upper median angle of lymphocytes (@SD-UMALS-LY) and the standard deviation of the volume and conductivity of monocytes(@SD-V-MO and @SD-C-MO), the absolute count of platelets and basophils, and the existence of a diabetes and obesity.