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.

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.

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.

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 thorn 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.

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.

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.

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. Am. J. Hematol. 89:369–374, 2014. © 2013 Wiley Periodicals, Inc.

Introduction Current hematology analysers can determine cell volume(V), conductivity for internal composition of cell(C) and light scatter for cytoplasmic granularity and nuclear structure(S) and standart deviations. Method We investigated these parameters in secreening of neonatal sepsis beyond the first day of life. We used LH780 hematological analyzer(Beckman Coulter, Fullerton, CA). We combined these parameters with interleukin-6(IL-6) and C-reactive protein(CRP), and developed models to diagnose sepsis by Effective Modelling of Moleculer Activity(EMMA). Results A total of 237 newborn, 61 proven sepsis, 108 clinical sepsis and 68 control, were enrolled the study. Mean neutrophil volume(MNV) and volume distribution width(VDW) were found to be statistically increased both in proven and clinical sepsis groups. We developed models using MNV, VDV, IL-6 and CRP. These models gave more sensitivity and specificity than usage of MNV, VDW, IL-6 and CRP alone. Conclusion We suggest to use combination of MNV and VDW with markers such as CRP and IL-6, and use diagnostic models created by using EMMA including these markers. Model 1: Sepsis= –1.17+0.015*[CRP]+0.009*[MNV]. Model 2: Sepsis= –1.35+0.0136*[CRP]+0.0074*[MNV]+0.0123* [VDW]. Model 3: Sepsis= –0.94+0.0043*[IL6]+0.011*[CRP]+0.0069*[MNV] Table 2. Test results and models’ performance of sepsis group Parameter Cut-off Sens Spec 95% Confidence Interval MNV (au) >157.1 78.64 81.63 0.807 to 0.890 VDW (au) >37.4 59.71 77.55 0.687 to 0.789 IL6 (pg/mL) >18 81.76 92.65 0.869 to 0.945 CRP (mg/dL) >7.5 71.57 98.53 0.852 to 0.928 Model 1 >0.3099 88.73 92.65 0.921 to 0.975 Model 2 >0.3615 87.75 92.65 0.912 to 0.970 Model 3 >0.2429 95.86 91.18 0.950 to 0.992

Abstract Abstract 4729 Sepsis and its sequelae remain leading causes of death in critically ill patients. Early identification and subsequently prompt treatment of sepsis can have a major impact on the outcome of septic patients. White Blood Cell (WBC) count, neutrophils count or percentage and percentage of band and immature neutrophils have been included in the diagnostic criteria of sepsis d. Previous studies have shown that morphological changes of leukocytes during sepsis can be quantified using Volume, Conductivity and Scatter (VCS) technology by some hematology analyzers. DxH800 (Beckman Coulter, Fullerton, CA) has the potential to report, in addition to Volume and Conductivity, five extra-laser diffraction angles for each cellular event. The aim of the study was to evaluate the diagnostic accuracy of VCS parameters of leukocytes in detecting sepsis in critically ill adult patients. This prospective observational study involved all consecutive adult patients admitted to a 31-bed medico-surgical department of intensive care (ICU) for more than 4 hours in a 3.5-month period. Diagnosis of sepsis was based on standard definitions. Blood samples were run by DxH800. Complete blood count (CBC) and WBC differential, but not VCS parameters were reported to attending physicians. Of a total of 722 patients admitted during the observation period 422 had WBC differential (and thus VCS parameters) determined at admission, of whom 125 had sepsis Septic and non-septic patients had a median age (upper and lower quartile) of 60 (51-71) and 58 (45-72) years and APACHE II scores of 19 (14-25) and 11 (7-16). VCS parameters had higher Areas Under the Receiver Operating Characteristic Curve (AUC) than WBC or percentage of neutrophils for diagnosis of sepsis at admission (Table 1.).Table 1.DxH800 VCS parameters. Results as median values (upper and lower quartile)VCS parametersSepsis N=125No Sepsis N=317p valueAUC95%CI of AUCMean Volume of Neutrophils (MNVNE)153 (146–163)144 (140–148)<0.0010.780.72–0.83SD of Volume of Neutrophils (SDVNE)20 (18–23)17 (16–18)<0.0010.810.77–0.86Mean Axial Light loss of Neutrophils (MNAL2NE)155 (150–160)148 (144–151)<0.0010.780.73–0.84SD of Axial Light loss of Neutrophils (SDAL2NE)13 (12–16)11 (10–12)<0.0010.800.75–0.84Mean Volume of Monocytes (MNVMO)179 (169–189)163 (159–167)<0.0010.880.84–0.91SD of Volume of Monocytes (SDVMO)24 (21–28)18 (16–20)<0.0010.880.84–0.91SD of Axial Light loss of Monocytes (SDAL2MO)16 (15–20)13 (12–14)<0.0010.850.81–0.89WBC, 103/ml10.9 (7.3–15.3)10.5 (7.8–14.2)NS0.510.44–0.57Percent of Neutrophils, %85 (76–91)80 (68–88)<0.0010.610.55–0.67 On ICU admission, MNVMO had the highest discriminant values in detecting sepsis with sensitivity (Se) of 84%, specificity (Sp) 76%, positive predictive value (PPV)58%, negative predictive value (NPV) 92% and odds ratio (OR) 17 for a cutoff of 168. MNVMO<168 (59% of patients) gave reasonable evidence against sepsis (likelihood ratio (LR) of 0.21, pre- and post-test probability, 0.28 and 0.076,) and MNVMO≥175 (25% of patients) gave reasonable evidence for sepsis (LR of 6.85, pre- and post-test probability, 0.28 and 0.73, respectively). Only 16% of patients, in the intermediate range, had a rather uninformative test result (LR of 1.32). Combining monocyte or neutrophil VCS parameters with CRP slightly increases AUC to 0.91. During the ICU stay, VCS parameters were obtained in 316 initially non-septic patients, of whom 20 became septic. MNVMO < 174 excluded ICU-acquired infection with NPV of 99% and MNVMO > 174 predicted ICU-acquired infection 1.7 days earlier than clinical diagnosis (Se: 85%, Sp 68%, OR 12 and AUC=0.78, 95% CI: 66–90). CONCLUSIONS: This large prospective study in unselected critically ill adult patients demonstrates the diagnostic utility of VCS parameters, especially MNVMO, in detecting and, more importantly, excluding sepsis at admission and during ICU stay. VCS parameters are obtained automatically, routinely, within minutes, requiring neither additional sampling nor additional cost to that of complete blood count thus making their prospects very promising. Disclosures: Pradier: Beckman Coulter: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Abstract Abstract 4731 Introduction: Malaria and dengue are highly prevalent mosquito-borne tropical diseases that share several clinical and haematological features: a peak incidence in the rainy season, presentations with malaise, body ache and high fever, almost invariable thrombocytopenia and a propensity to develop DIC with bleeding and adverse outcomes if neglected or misdiagnosed. They are challenging to differentiate clinically from each other and also from the more common viral infections. Current laboratory tests are relatively time consuming and/or require specialized expertise or equipment. Expected findings of leukopenia and thrombocytopenia may be absent, or the parasites missed, thus delaying the ordering of confirmatory testing and definitive therapy. Beckman Coulter analyzers incorporate VCS technology to quantify morphological characteristics of neutrophils, lymphocytes, monocytes and eosinophils: cell volume by voltage impedance (V); cytoplasmic/nuclear ratio by radiofrequency conductivity (C); and cytoplasmic granularity/nuclear complexity by laser light scatter (S). All these measurements (Mean and Standard Deviations (SD)) are reported as numerical values, called Cell Population Data (CPD) for every sample.VCS has helped improve the diagnosis of malaria and septicaemia in adults and neonates. We compared the VCS parameters in dengue fever, malaria and other febrile illnesses with a view to generate automated factors for the identification of these illnesses. Methods: We studied CBC parameters and VCS indices from 115 malaria patients (diagnosed by blood smear examination and immunochromatographic strip test for the plasmodium LDH and histidine rich protein-2) along with 105 patients with dengue fever diagnosed on positive NS1 antigen or IgM ELISA assays along with 105 control patients referred for testing for febrile illnesses who were negative for both malaria and dengue by all the above tests. We used the originally developed program “EMMA” [Sukhachev DV, Zefirov NS. 10th European Symposium on Structure-Activity relationships: QSAR and molecular modeling. Barcelona, 4–9. 1994:A104] to generate discriminant functions to differentiate between the groups. The program uses combinatorial algorithms of selected parameters for regression equations by a modified stepwise procedure. It allows computation of a number of “best” regression equations with different parameter combinations. Only parameters statistically significantly different between the groups (checked with Mann-Whitney U test) are included in the discriminant functions. The diagnostic performances of various functions thus generated were assessed by ROC curve analysis with calculation of areas-under-the-curves (AUC). Results: The following equations were generated: To discriminate Malaria from Controls: Improved Malaria Factor =-0.473-0.00163*PLT+0.0524*LySDV+0.0302*LySDC To discriminate Dengue from Controls: Dengue Factor =0.3-0.00183*PLT+0.00619*LY%+0.0335*LySDC To discriminate Malaria versus Dengue: =5.51+0.0579*MCHC+0.00549*NE%+0.0138*LyMV+0.00956MoMV +0.027*MoSDV The performance characteristics of these 3 factors are shown in Table 1 . Table 1 . Performance characteristics of the discriminant factors generated for diagnosis of malaria and dengue and distinguishing them from other febrile illnesses. Factor Cut-off Sens Spec AUC 95% Confidence Interval Significance level P (Area=0.5) Improved malaria factor >0.556 90.4 88.6 0.931 0.889 to 0.961 Dengue factor >0.478 81.0 77.1 0.837 0.780 to 0.884 Malaria versus Dengue Factor >0.601 85.1 91.4 0.937 0.896 to 0.965 Conclusions: The parasitic/virological stimuli elicit strong immune responses resulting in leukocyte abnormalities in malaria and dengue that permit their distinction from other causes of fever. Our discriminant functions are easily calculable by LIS and flags thus generated are likely to have a high sensitivity and specificity for dengue and malaria. Our Improved Malaria Factor represents an enhancement over the original result by Briggs et al (In the ROC curves analysis Malaria Factor has AUC = 0.864, cut-off >4.25, Sens 86%, Spec 78%). Since the VCS data are obtained automatically as part of any CBC-diff, these results can greatly improve the detection of serious febrile illnesses in a timely and cost effective manner. Disclosures: No relevant conflicts of interest to declare.