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.

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.

Unique characteristics of leukocyte volume, conductivity and scatter in chronic myeloid leukemia.

BACKGROUND: Modern automated hematology analyzers provide quantitative data on leukocyte size and structure that may be useful to distinguish reactive from neoplastic cellular proliferations. We compared leukocyte volume, conductivity and scatter (VCS) characteristics of chronic myeloid leukemia (CML), bcr-abl1-positive patients with those of non-neoplastic neutrophilia. MATERIALS AND METHODS: Complete blood counts and VCS data (LH750 hematology analyzers, Beckman Coulter) from 38 newly-diagnosed CML patients, 65 CML on imatinib mesylate therapy, 58 patients with elevated age-specific neutrophil counts due to varied causes, 100 pregnant women and 99 healthy controls were collated and compared. Receiver-operating-characteristic curves, logistic regression models and classification trees were studied for their abilities to distinguish various groups. RESULTS: Untreated CML had higher mean neutrophil volume and mean monocyte volume (MNV and MMV), mean lymphocyte scatter (MLS) and higher standard deviations of the mean neutrophil volume and conductivity (MNV-SD and MNC-SD) over all other groups (p < 0.0001 for all). MNV, MNC-SD and MLS distinguished CML from reactive neutrophilia + pregnancy groups (sensitivities 89.5%, 94.7%, 94.7% and specificities 90.6%, 95.6% and 94.0% respectively). Combination of MNV>163.0 AND MNC-SD>12.69 was 89.5% sensitive and 100% specific for CML. Two algorithmic classification-tree approaches using VCS parameters alone (i.e. without the aid of blood count parameters) correctly separated 100% cases of untreated CML from all others. CONCLUSION: Successful distinction of untreated but not post-imatinib CML patients from subjects who were either normal, pregnant or had reactive neutrophilia by automated analyzer-derived cell-population data opens possibilities for their applications in diagnosing and understanding the pathogenesis of CML.

Utility of cell population data (VCS parameters) as a rapid screening tool for Acute Myeloid Leukemia (AML) in resource-constrained laboratories.

INTRODUCTION: Despite advances in diagnostic techniques, many cases of acute myeloid leukemia (AML) remain underdiagnosed in remote centers unequipped with these technologies. We hypothesized that the automated cellular indices with scatter plots and flags may aid in rapid and cost-effective screening of AML. METHODS: Cell population data (CPD) parameters from 100 de novo AML samples were analyzed by Coulter LH 780 automated analyzer and were compared with 100 age-matched controls. Similar parameters were also compared with 100 and 50 reactive cases of neutrophilia and monocytosis, respectively. System-generated flags and scatter plot patterns were also analyzed. RESULTS: Results were compared between AML cases and normal controls; AML FAB M2, M3, M4 vs reactive neutrophilia; AML FAB M4, M5 vs reactive monocytosis. Significant parameters were selected from all comparison groups. Using appropriate statistical tools, we calculated the cutoff values of these parameters and were able to screen out AML cases with 94% sensitivity and 95% specificity. Three statistical equations were generated using two of the most significant parameters which improved the sensitivity to 98% and specificity to 99%. Five hypothetical scatter plot patterns were devised and were classified according to FAB categories of AML. Most common pattern was selected in AML which was seen in 56% of the cases. Output was analyzed combining these patterns and flags with CPD parameters. CONCLUSION: CPD either alone or in the form of statistical equations along with scatter plots and flags can provide rapid and economic tool in preliminary diagnosis of AML in cost-constrained settings.

Leukocyte Cell Population Data for Hematology Analyzer-Based Distinction of Clonal-versus-Non-Clonal Lymphocytosis: A Real-World Testing Experience.

Automated blood counts revealing lymphocytosis necessitate smear reviews. Even expert morphological evaluation may however, fail to differentiate a benign-versus-malignant etiology without further testing. Automated analyser-derived quantitative data on leukocyte cell populations remain undertested for distinguishing such etiologies. Instrument manufacturers claim that if successful, they may be used to generate software flags that help under-resourced laboratories better triage hemogram specimens requiring further testing. We tested the diagnostic accuracy of volume-conductivity-scatter (VCS) indices together with complete blood count (CBC) parameters in such scenarios. We compared LH780-derived (Beckman Coulter, FL, USA) CBC + VCS parameters from patients with clonal lymphoproliferations (n = 42, including 30 chronic lymphocytic leukemia cases) versus 83 controls with absolute or relative lymphocytosis (derivation cohort). Diagnostic performances of 11 logistic regression equations derived were subsequently evaluated on two specific validation cohorts (n = 130 and n = 1465). Clonal lymphocytoses showed significantly lower hemoglobin and higher leukocyte counts but similar lymphocyte percentages (LY %) vis-à-vis controls. The most significant, albeit overlapping predictor of clonality was the absolute lymphocyte count, LY# (47.8 ± 48.4 × 109/L vs. 2.9 ± 1.4 × 109/L in clonal vs. benign cases). In eleven logistic regression equations constructed using four combinatorial approaches, only the models with LY# (highest sensitivity/specificity of 99.3%/100%) and the lymphocytic VCS parameters alone (highest sensitivity/specificity of 76.2%/90.2%) performed consistently in both validation cohorts. Lymphocytic VCS parameters were moderately successful in distinguishing benign-versus-malignant lymphocytes. Other approaches of CBC-plus-VCS parameters did not sustain their initial excellent performances in the validation cohorts, highlighting a need for careful appraisal and better standardization of automated cellular analysis technologies.

Automated screening for myelodysplastic syndromes through analysis of complete blood count and cell population data parameters.

The diagnosis of myelodysplastic syndromes (MDS) requires a high clinical index of suspicion to prompt bone marrow studies as well as subjective assessment of dysplastic morphology. We sought to determine if data collected by automated hematology analyzers during complete blood count (CBC) analysis might help to identify MDS in a routine clinical setting. We collected CBC parameters (including those for research use only and cell population data) and demographic information in a large (>5,000), unselected sequential cohort of outpatients. The cohort was divided into independent training and test groups to develop and validate a random forest classifier that identifies MDS. The classifier effectively identified MDS and had a receiver operating characteristic area under the curve (AUC) of 0.942. Platelet distribution width and the standard deviation of red blood cell distribution width were the most discriminating variables within the classifier. Additionally, a similar classifier was validated with an additional, independent set of >200 patients from a second institution with an AUC of 0.93. This retrospective study demonstrates the feasibility of identifying MDS in an unselected outpatient population using data routinely collected during CBC analysis with a classifier that has been validated using two independent data sets from different institutions.