Evaluation of cell population data on the UniCel DxH 800 Coulter Cellular Analysis system as a screening for viral infection in children

Detection of blasts using flags and cell population data rules on Beckman Coulter DxH 900 hematology analyzer in patients with hematologic diseases

OBJECTIVES

White blood cell (WBC)-related flags are essential for detecting abnormal cells including blasts in automated hematology analyzers (AHAs). Cell population data (CPD) may characterize each WBC population, and customized CPD rules can be also useful for detecting blasts. We evaluated the performance of WBC-related flags, customized CPD rules, and their combination for detecting blasts on the Beckman Coulter DxH 900 AHA (DxH 900, Beckman Coulter, Miami, Florida, USA).

METHODS

In a total of 239 samples from patients with hematologic diseases, complete blood count on DxH 900 and manual slide review (MSR) were conducted. The sensitivity, specificity, and efficiency of the five WBC-related flags, nine customized CPD rules, and their combination were evaluated for detecting blasts, in comparison with MSR.

RESULTS

Blasts were detected by MSR in 40 out of 239 (16.7 %) samples. The combination of flags and CPD rules showed the highest sensitivity compared with each of flags and CPD rules for detecting blasts (97.5 vs. 72.5 % vs. 92.5 %). Compared with any flag, the combination of flags and CPD rules significantly reduced false-negative samples from 11 to one for detecting blasts (27.5 vs. 2.5 %, p=0.002).

CONCLUSIONS

This is the first study that evaluated the performance of both flags and CPD rules on DxH 900. The customized CPD rules as well as the combination of flags and CPD rules outperformed WBC-related flags for detecting blasts on DxH 900. The customized CPD rules can play a complementary role for improving the capability of blast detection on DxH 900.

Revolutionizing chronic lymphocytic leukemia diagnosis: A deep dive into the diverse applications of machine learning

Chronic lymphocytic leukemia (CLL) is a B cell neoplasm characterized by the accumulation of aberrant monoclonal B lymphocytes. CLL is the predominant type of leukemia in Western countries, accounting for 25% of cases. Although many patients remain asymptomatic, a subset may exhibit typical lymphoma symptoms, acquired immunodeficiency disorders, or autoimmune complications. Diagnosis involves blood tests showing increased lymphocytes and further examination using peripheral blood smear and flow cytometry to confirm the disease. With the significant advancements in machine learning (ML) and artificial intelligence (AI) in recent years, numerous models and algorithms have been proposed to support the diagnosis and classification of CLL. In this review, we discuss the benefits and drawbacks of recent applications of ML algorithms in the diagnosis and evaluation of patients diagnosed with CLL.

Research use only and cell population data items obtained from the Beckman Coulter DxH800 automated hematology analyzer are useful in discriminating MDS patients from those with cytopenia without MDS

We investigated the performance of research use only/cell population data (RUO/CPD) items obtained from the Beckman Coulter DxH800 automated hematologic analyzer in discriminating MDS patients from cytopenic patients without MDS.Total of 14 routine CBC, 18 research use only (RUO) items, and 70 CPD items were obtained retrospectively at diagnosis. The results were then compared between 94 MDS patients and 100 cytopenic patients without MDS. In items with statistically significant differences, receiver operating characteristic (ROC) analysis was performed and the results were compared.Four CBC/RUO items [red cell distribution width-standard deviation (RDW-SD), immature reticulocyte fraction (IRF), mean sphered cell volume (MSCV), high light scatter reticulocytes (HLR)], and two CPD items [mean volume of neutrophils (NE-V-Mean) and mean volume of early granulated cells (EGC-V-Mean)] showed area-under the curve (AUC) scores > 0.750. Notably, four RUO/CPD items (MSCV > 81.4/HLR > 0.15%/NE-V-Mean > 145/EGC-V-Mean > 156) showed high sensitivity (91.9%/93.6%/88.1%/90.2%, respectively) in discriminating MDS patients from cytopenic patients without MDS. With these six items, scores ≥ 4 (defined as ≥ 4 items exceeding cutoff values out of six items) showed AUC scores/sensitivity/specificity/accuracy (0.891/87.3%/79.0%/83.0%, respectively).Six CBC/RUO/CPD items showed satisfactory AUC scores of > 0.750, and four RUO/CPD items showed high sensitivity in discriminating MDS patients from cytopenic patients without MDS. Scoring system with six items showed high sensitivity, specificity, and accuracy with decision criteria of ≥ 4 scores. Therefore, DxH800 RUO/CPD items would be useful in discriminating MDS patients from cytopenic patients without MDS.

Machine learning of cell population data, complete blood count, and differential count parameters for early prediction of bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments

BACKGROUND

Bacteremia is a life-threatening complication of infectious diseases. Bacteremia can be predicted using machine learning (ML) models, but these models have not utilized cell population data (CPD).

METHODS

The derivation cohort from emergency department (ED) of China Medical University Hospital (CMUH) was used to develop the model and was prospectively validated in the same hospital. External validation was performed using cohorts from ED of Wei-Gong Memorial Hospital (WMH) and Tainan Municipal An-Nan Hospital (ANH). Adult patients who underwent complete blood count (CBC), differential count (DC), and blood culture tests were enrolled in the present study. The ML model was developed using CBC, DC, and CPD to predict bacteremia from positive blood cultures obtained within 4 h before or after the acquisition of CBC/DC blood samples.

RESULTS

This study included 20,636 patients from CMUH, 664 from WMH, and 1622 patients from ANH. Another 3143 patients were included in the prospective validation cohort of CMUH. The CatBoost model achieved an area under the receiver operating characteristic curve of 0.844 in the derivation cross-validation, 0.812 in the prospective validation, 0.844 in the WMH external validation, and 0.847 in the ANH external validation. The most valuable predictors of bacteremia in the CatBoost model were the mean conductivity of lymphocytes, nucleated red blood cell count, mean conductivity of monocytes, and neutrophil-to-lymphocyte ratio.

CONCLUSIONS

ML model that incorporated CBC, DC, and CPD showed excellent performance in predicting bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments.

Investigating the effects of maltreatment and acute stress on the concordance of blood and DNA methylation methods of estimating immune cell proportions

BACKGROUND

Immune cell proportions can be used to detect pathophysiological states and are also critical covariates in genomic analyses. The complete blood count (CBC) is the most common method of immune cell proportion estimation, but immune cell proportions can also be estimated using whole-genome DNA methylation (DNAm). Although the concordance of CBC and DNAm estimations has been validated in various adult and clinical populations, less is known about the concordance of existing estimators among stress-exposed individuals. As early life adversity and acute psychosocial stress have both been associated with unique DNAm alterations, the concordance of CBC and DNAm immune cell proportion needs to be validated in various states of stress.

RESULTS

We report the correlation and concordance between CBC and DNAm estimates of immune cell proportions using the Illumina EPIC DNAm array within two unique studies: Study 1, a high-risk pediatric cohort of children oversampled for exposure to maltreatment (N = 365, age 8 to 14 years), and Study 2, a sample of young adults who have participated in an acute laboratory stressor with four pre- and post-stress measurements (N = 28, number of observations = 100). Comparing CBC and DNAm proportions across both studies, estimates of neutrophils (r = 0.948, p < 0.001), lymphocytes (r = 0.916, p < 0.001), and eosinophils (r = 0.933, p < 0.001) were highly correlated, while monocyte estimates were moderately correlated (r = 0.766, p < 0.001) and basophil estimates were weakly correlated (r = 0.189, p < 0.001). In Study 1, we observed significant deviations in raw values between the two approaches for some immune cell subtypes; however, the observed differences were not significantly predicted by exposure to child maltreatment. In Study 2, while significant changes in immune cell proportions were observed in response to acute psychosocial stress for both CBC and DNAm estimates, the observed changes were similar for both approaches.

CONCLUSIONS

Although significant differences in immune cell proportion estimates between CBC and DNAm exist, as well as stress-induced changes in immune cell proportions, neither child maltreatment nor acute psychosocial stress alters the concordance of CBC and DNAm estimation methods. These results suggest that the agreement between CBC and DNAm estimators of immune cell proportions is robust to exposure to child maltreatment and acute psychosocial stress.

A Cross-Sectional Comparative Characterization of Hematological Changes in Patients with COVID-19 Infection, Non-COVID Influenza-like Illnesses and Healthy Controls

INTRODUCTION

Studies have documented the role of the "neutrophil-to-lymphocyte ratio" (NLR) in influenza virus infection. In addition, morphometric parameters derived from automated analyzers on the volume, scatter and conductivity of monocytes, neutrophils and lymphocytes in many viral etiologies have helped with their early differentiation. With this background, we aimed to characterize the hematological changes of coronavirus-positive cases and also compare them with the healthy controls and patients affected by non-COVID Influenza-like illnesses so that early isolation could be considered.

MATERIAL AND METHODS

This was a cross-sectional analytical study carried out in the years 2020-2022. All cases with COVID-19 and non-COVID-19 Influenza-like illnesses and healthy controls above 18 years were included. Cases were diagnosed according to the WHO guidelines. All samples were processed on a Unicel DxH 800 (Beckman Coulter, California, USA) automated hematology analyzer. The demographic, clinical and regular hematological parameters along with additional parameters such as volume, conductivity and scatter (VCS) of the three groups were compared.

RESULTS

The 169 COVID-19 cases were in the moderate to severe category. Compared with 140 healthy controls, the majority of the routine hematological values including the NLR (neutrophil-to-lymphocyte ratio) and PLR (platelet-to-lymphocyte ratio) showed statistically significant differences. A cutoff of an absolute neutrophil count of 4350 cell/cumm was found to have a sensitivity of 76% and specificity of 70% in differentiating moderate and severe COVID-19 cases from healthy controls. COVID-19 and the non-COVID-19 Influenza-like illnesses were similar statistically in all parameters except the PLR, mean neutrophilic and monocytic volume, scatter parameters in neutrophils, axial light loss in monocytes and NLR. Interestingly, there was a trend of higher mean volumes and scatter in neutrophils and monocytes in COVID-19 cases as compared to non-COVID-19 Influenza-like illnesses.

CONCLUSION

We demonstrated morphological changes in neutrophils, monocytes and lymphocytes in COVID-19 infection and also non-COVID-19 Influenza-like illnesses with the help of VCS parameters. A cutoff for the absolute neutrophils count was able to differentiate COVID-19 infection requiring hospitalization from healthy controls and eosinopenia was a characteristic finding in cases with COVID-19 infection.

Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias

A targeted and timely treatment can be a beneficial tool for patients with hematological emergencies (particularly acute leukemias). The key challenges in the early diagnosis of leukemias and related hematological disorders are their symptom-sharing nature and prolonged turnaround time as well as the expertise needed in reporting confirmatory tests. The present study made use of the potential morphological and immature fraction-related parameters (research items or cell population data) generated during complete blood cell count (CBC), through artificial intelligence (AI)/machine learning (ML) predictive modeling for early (at the pre-microscopic level) differentiation of various types of leukemias: acute from chronic as well as myeloid from lymphoid. The routine CBC parameters along with research CBC items from a hematology analyzer in the diagnosis of 1577 study subjects with hematological neoplasms were collected. The statistical and data visualization tools, including heat-map and principal component analysis (PCA,) helped in the evaluation of the predictive capacity of research CBC items. Next, research CBC parameter-driven artificial neural network (ANN) predictive modeling was developed to use the hidden trend (disease’s signature) by increasing the auguring accuracy of these potential morphometric parameters in differentiation of leukemias. The classical statistics for routine and research CBC parameters showed that as a whole, all study items are significantly deviated among various types of leukemias (study groups). The CPD parameter-driven heat-map gave clustering (separation) of myeloid from lymphoid leukemias, followed by the segregation (nodding) of the acute from the chronic class of that particular lineage. Furthermore, acute promyelocytic leukemia (APML) was also well individuated from other types of acute myeloid leukemia (AML). The PCA plot guided by research CBC items at notable variance vindicated the aforementioned findings of the CPD-driven heat-map. Through training of ANN predictive modeling, the CPD parameters successfully differentiate the chronic myeloid leukemia (CML), AML, APML, acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL), and other related hematological neoplasms with AUC values of 0.937, 0.905, 0.805, 0.829, 0.870, and 0.789, respectively, at an agreeably significant (10.6%) false prediction rate. Overall practical results of using our ANN model were found quite satisfactory with values of 83.1% and 89.4.7% for training and testing datasets, respectively. We proposed that research CBC parameters could potentially be used for early differentiation of leukemias in the hematology–oncology unit. The CPD-driven ANN modeling is a novel practice that substantially strengthens the predictive potential of CPD items, allowing the clinicians to be confident about the typical trend of the “disease fingerprint” shown by these automated potential morphometric items.

A Novel Algorithm Using Cell Population Data (VCS Parameters) as a Screening Discriminant between Alpha and Beta Thalassemia Traits

Thalassemia is one of the major inherited haematological disorders in the Southeast Asia region. This study explored the potential utility of red blood cell (RBC) parameters and reticulocyte cell population data (CPD) parameters in the differential diagnosis of α and β-thalassaemia traits as a rapid and cost-effective tool for screening of thalassemia traits. In this study, a total of 1597 subjects (1394 apparently healthy subjects, 155 subjects with α-thalassaemia trait, and 48 subjects with β-thalassaemia trait) were accrued. The parameters studied were the RBC parameters and reticulocyte CPD parameters derived from Unicel DxH800. A novel algorithm named αβ-algorithm was developed: (MN-LMALS-RET × RDW) − MCH) to discriminate α from β-thalassaemia trait with a cut-off value of 1742.5 [AUC = 0.966, sensitivity = 92%, specificity = 90%, 95% CI = 0.94–0.99]. Two prospective studies were carried: an in-house cohort to assess the specificity of this algorithm in 310 samples comprising various RBC disorders and in an interlaboratory cohort of 65 α-thalassemia trait, and 30 β-thalassaemia trait subjects to assess the reproducibility of the findings. We propose the αβ-algorithm to serve as a rapid, inexpensive surrogate evaluation tool of α and β-thalassaemia in the population screening of thalassemia traits in geographic regions with a high burden of these inherited blood disorders.

Neoteric Algorithm Using Cell Population Data (VCS Parameters) as a Rapid Screening Tool for Haematological Disorders

Hitherto, there has been no comprehensive study on the usefulness of cell population data (CPD) parameters as a screening tool in the discrimination of non-neoplastic and neoplastic haematological disorders. Hence, we aimed to develop an algorithm derived from CPD parameters to enable robust screening of neoplastic from non-neoplastic samples and subsequently to aid in differentiating various neoplastic haematological disorders. In this study, the CPD parameters from 245 subtypes of leukaemia and lymphoma were compared against 1103 non-neoplastic cases, and those CPD parameters that were vigorous discriminants were selected for algorithm development. We devised a novel algorithm: [(SD-V-NE*MN-UMALS-LY*SD-AL2-MO)/MN-C-NE] to distinguish neoplastic from non-neoplastic cases. Following that, the single parameter MN-AL2-NE was used as a discriminant to rule out reactive cases from neoplastic cases. We then assessed CPD parameters that were useful in delineating leukaemia subtypes as follows: AML (SD-MALS-NE and SD-UMALS-NE), APL (MN-V-NE and SD-V-MO), ALL (MN-MALS-NE and MN-LMALS-NE) and CLL (SD-C-MO). Prospective studies were carried out to validate the algorithm and single parameter, MN-AL2-NE. We propose these CPD parameter-based discriminant strategies to be adopted as an initial screening and flagging system in the preliminary evaluation of leukocyte morphology.

Neutrophil Maturation, Reactivity and Granularity Research Parameters to Characterize and Differentiate Convalescent Patients from Active SARS-CoV-2 Infection

Studying the dynamics changes of neutrophils during innate immune response in coronavirus 2019 (COVID-19) can help understand the pathogenesis of this disease. The aim of the study was to assess the usefulness of new neutrophil activation parameters: Immature Granulocyte (IG), Neutrophil Reactivity Intensity (NEUT-RI), Neutrophil Granularity Intensity (NEUT-GI), and data relating to granularity, activity, and neutrophil volume (NE-WX, NE-WY, NE-WZ) available in hematology analyzers to distinguish convalescent patients from patients with active SARS-CoV-2 infection and healthy controls (HC). The study group consisted of 79 patients with a confirmed positive RT-PCR test for SARS-CoV2 infection, 71 convalescent patients, and 20 HC. We observed leukopenia with neutrophilia in patients with active infection compared to convalescents and HC. The IG median absolute count was higher in convalescent patients than in COVID-19 and HC (respectively, 0.08 vs. 0.03 vs. 0.02, p < 0.0001). The value of the NEUT-RI parameter was the highest in HC and the lowest in convalescents (48.3 vs. 43.7, p < 0.0001). We observed the highest proportion of NE-WX, NE-WY, and NE-WZ parameters in HC, without differences between the COVID-19 and convalescent groups. New neutrophil parameters can be useful tools to assess neutrophils’ activity and functionalities in the immune response during infection and recovery from COVID-19 disease.

Leukocyte cell population data from the blood cell analyzer as a predictive marker for severity of acute pancreatitis

BACKGROUND

The prediction for severe acute pancreatitis (SAP) is the key to give timely targeted treatment. Leukocyte cell population data (CPD) have been widely applied in early prediction and diagnosis of many diseases, but their predictive ability for SAP remains unexplored. We aim to testify whether CPD could be an indicator of AP severity in the early stage of the disease.

METHODS

The prospective observational study was conducted in the emergency department ward of a territory hospital in Shanghai. The enrolled AP patients should meet 2012 Atlanta guideline.

RESULTS

Totally, 103 AP patients and 62 healthy controls were enrolled and patients were classified into mild AP (n = 30), moderate SAP (n = 42), and SAP (n = 31). Forty-two CPD parameters were examined in first 3 days of admission. Four CPD parameters were highest in SAP on admission and were constantly different among 3 groups during first 3 days of hospital stay. Eighteen CPD parameters were found correlated with the occurrence of SAP. Stepwise multivariate logistic regression analysis identified a scoring system of 4 parameters (SD_LALS_NE, MN_LALS_LY, SD_LMALS_MO, and SD_AL2_MO) with a sensitivity of 96.8%, specificity of 65.3%, and AUC of 0.87 for diagnostic accuracy on early identification of SAP. AUC of this scoring system was comparable with MCTSI, SOFA, APACHE II, MMS, BISAP, or biomarkers as CRP, PCT, and WBC in prediction of SAP and ICU transfer or death.

CONCLUSIONS

Several leukocyte CPD parameters have been identified different among MAP, MSAP, and SAP. They might be ultimately incorporated into a predictive system marker for severity of AP.

Automated Early Detection of Myelodysplastic Syndrome within the General Population Using the Research Parameters of Beckman-Coulter DxH 800 Hematology Analyzer

Simple Summary

A substantial fraction of the elderly population suffers from moderate anemia, and blood smear analysis can guide towards a diagnosis of myelodysplastic syndrome (MDS). Nevertheless, in medical laboratories, blood smear review is only performed when quantitative or qualitative flags occur upon complete blood count (CBC). Consequently, the suspicion of MDS can be delayed in the absence of systematic blood smear observation, which is crucial to initiate a full diagnosis process by cytological analysis of bone marrow aspiration. The Beckman Coulter DxH 800 hematology analyzer (Beckman-Coulter, Brea, CA) is widely used over the world. We propose in this study the clinical use of 10 unexploited “research parameters” for early detection of subclinical MDS by selective triggering of blood smear examination.

Abstract

The incidence of myelodysplastic syndrome increases with aging and the early diagnosis enables optimal care of these diseases. The DxH 800 hematology analyzer measures and calculates 126 cytological parameters, but only 23 are used for routine CBC assessment. The goal of this study was to use the 103 unexploited “research parameters” to develop an algorithm allowing for an early detection of subclinical MDS patients by triggering morphological analysis. Blood sample parameters from 101 MDS patients and 88 healthy volunteers were analyzed to identify the critical “research parameters” with: (i) the most significant differences between MDS patients and healthy volunteers, (ii) the best contributions to principal component analysis (PCA), first axis, and (iii) the best correlations with PCA, first two axes (cos² > 0.6). Ten critical “research parameters” of white blood cells were identified, allowing for the calculation of an MDS-likelihood score (MDS-LS), based on logistic regression. Automatic calculation of the MDS-LS is easily implementable on the middleware system of the DxH 800 to generate a flag for blood smear review, and possibly early detection of MDS patients in the general population.

Cell Population Data (CPD) for Early Recognition of Sepsis and Septic Shock in Children: A Pilot Study

Objectives: Innovative Cell Population Data (CPD) have been used as early biomarkers for diagnosing sepsis in adults. We assessed the usefulness of CPD in pediatric patients with sepsis/septic shock, in terms of early recognition and outcome prediction. We revised 54 patients (0-15 y) admitted to our Pediatric Intensive Care Unit (PICU) for sepsis/septic shock during a 4-year period. Twenty-eight patients were excluded, 26 septic patients were enrolled (G1). Forty children admitted for elective surgery served as controls (G2). Data on five selected CPD parameters, namely neutrophils fluorescence intensity (NE-SFL), monocytes cells complexity (MO-X), monocytes fluorescence intensity (MO-Y), monocytes complexity and width of dispersion of events measured (MO-WX), and monocytes cells size and width dispersion (MO-WZ), were obtained at time of PICU admission (t0) by a hematological analyzer (Sysmex XN 9000®). As the primary outcome we evaluated the relevance of CPD for diagnosing sepsis/septic shock on PICU admission. Furthermore, we investigated if CPD at t0 were correlated with C-reactive protein (CRP), patient survival, or complicated sepsis course. Results: On PICU admission (t0), NE-SFL, MO-WX, and MO-Y were higher in sepsis/septic shock patients compared to controls. NE-SFL values were correlated with CRP values in G1 patients (r = 0.83). None of the five CPD parameters was correlated with survival or complicated sepsis course. Conclusion: We found higher values of NE-SFL, MO-WX, and MO-Y in children with sepsis/septic shock upon PICU admission. These parameters may be a promising adjunct for early sepsis diagnosis in pediatric populations. Larger, prospective studies are needed to confirm our preliminary observations.

Diagnostic and prognostic utility of WBC counts and cell population data in patients with COVID-19

Introduction

Early diagnosis and identification of potential critical cases for timely treatment are crucial for COVID‐19 patients. The aim of this study was to analyze the diagnostic and prognostic implications of WBC and cell population data (CPD) abnormalities related to COVID‐19 at disease onset.

Methods

Baseline WBC counts and CPD data were analyzed in one hundred COVID‐19 patients presenting to emergency department and subsequently discharged (n=49), admitted (n=51) or deceased (n=22), and in 47 healthy subjects.

Results

Lymphopenia and eosinopenia were observed in all COVID‐19 patients, with more intensity in the admitted and deceased groups, that also presented increased WBC and neutrophil counts. On CPD analysis, COVID‐19 was associated with increased volume of neutrophils, lymphocytes, and monocytes, whereas conductivity was decreased for neutrophils and increased for lymphocytes. The ROC curve analysis showed good performance for lymphocyte counts in predicting COVID‐19 diagnosis (AUC=0.858), for neutrophil counts in predicting admission for COVID‐19 (AUC=0.744) and for monocytes volume in predicting COVID‐19 diagnosis (AUC=0.837).

Conclusion

WBC counts and CPD parameters at disease onset in COVID‐19 patients can improve diagnostic characterization and aid in the discrimination between severe and nonsevere presentations.

Abnormal characteristic "round bottom flask" shape volume-based scattergram as a trigger to suspect persistent polyclonal B-cell lymphocytosis

BACKGROUND AND AIMS

The diagnosis of persistent polyclonal B-cell lymphocytosis (PPBL) is often challenging because of the lack of features and the overlap with the peripheral expression of splenic marginal zone lymphomas (SMZL). To obtain new clues for PPBL detection and diagnosis, all data provided by the DxH 800 analyzer (including scatter and cell population data (CPD)) was exploited and combined using a machine learning (ML) approach.

MATERIALS AND METHODS

A total 211 samples from 101 normal controls and 110 patients (PPBL and SMZL) were assessed. Age, gender, full blood count, CPD, scatter, flags and CellaVision differentials were also considered. A ML model was built for true classification purposes.

RESULTS

PPBL and SMZL shared increased absolute lymphoid counts, atypical lymphoid flag presence and CPD values (8 out of 14). A typical "round-bottom-flask" shape scattergram was described for the first time for PPBL which was also present in 51.4% of SMZL cases. The developed ML model render a global classification accuracy of 93.4%, allowing the detection of all pathological cases, with mean misclassification rates of 12% among PPBL and SMZL.

CONCLUSION

Our ML model represents a new unbiased tool than can be widely applied in the laboratory as an aid for detection of PPBL.

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.

Discriminating myelodysplastic syndrome and other myeloid malignancies from non-clonal disorders by multiparametric analysis of automated cell data

BACKGROUND

We investigated the usefulness of novel complete blood count (CBC) data for discriminating myeloid malignancies from non-clonal CBC abnormalities.

METHODS

Data were obtained during routine CBC tests of 119 samples from 37 myelodysplastic syndrome (MDS) patients, 92 samples from 45 myeloproliferative neoplasm (MPN) patients, and 15 samples from 11 chronic myelogenous leukemia (CML) patients using a DxH800 (Beckman Coulter). Data obtained from patients with hypocellular bone marrow and from those with other non-clonal diseases with CBC abnormalities were included in the comparisons.

RESULTS

For cell population data of neutrophils, the means of median, upper median, lower median, and low angle light scatters were significantly lower in MDS patients than in patients without hematological malignancies. Low hemoglobin density (LHD) did not significantly differ between the MDS and non-clonal cytopenia patients, but it was significantly higher in the MPN and CML patients. We selected 13 parameters and scored the MDS diagnosis using cut-off values obtained from receiver operating characteristic (ROC) curve analysis. Using a score > 9, MDS was distinguished from non-clonal cytopenia with a sensitivity of 92.4% and a specificity of 85.4%.

CONCLUSIONS

Multiparametric analyses of new automated parameters are useful for discriminating MDS from non-clonal cytopenia.

Flow cytometric white blood differential using CytoDiff™ in the diagnosis of neonatal early-onset infection

OBJECTIVE

Neonatal early-onset infection is a life-threatening disease, requiring early diagnosis and treatment. Newborns at risk are identified by a combination of risk factors, clinical signs of infection and laboratory parameters such as white blood cell count and C-reactive protein (CRP). This method is labor-intensive, time consuming and has a variable reproducibility. New reliable diagnostic markers are needed to identify neonatal infection. This study presents additional leukocyte differential parameters produced by the automated flow cytometry and processing software using CytoDiff™ reagent (Beckman Coulter) in newborns suspected for early-onset infection.

METHODS

An analytic prospective observational case-control study was performed in which 185 newborns were included and retrospectively allocated into two groups, "infection likely" and "infection unlikely". Leukocyte parameters of the CytoDiff™ technique were compared with microscopic slide differentiation and routine tests.

RESULTS

We showed significant lower numbers of monocytes, CD16(-) monocytes and lymphocytes (including T+/NK-lymphocytes) in neonates suspected for early-onset infection using CytoDiff™ technique. The manual counting did not demonstrate changes with respect to the number of monocytes in these neonates.

CONCLUSIONS

The automated routine CytoDiff™ leukocyte differential provides an interesting additional diagnostic tool, next to routine laboratory diagnostics, in the diagnosis of neonatal early-onset infection.

Evaluation of new laboratory tests to discriminate bacterial from nonbacterial chronic obstructive pulmonary disease exacerbations

INTRODUCTIONS

Discriminating bacterial from nonbacterial acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is difficult, causing antibiotics overuse and bacterial resistance. Sputum cultures are of limited use because results take time. In our hospital, only leukocyte concentration and CRP are laboratory parameters evaluated in AECOPD. We evaluated additional tests to discriminate bacterial vs. nonbacterial AECOPD: 5-part leukocyte differentiation (hematology analyzer), leukocyte differentiation using flow cytometry (Leukoflow, Cytodiff), Leuko64 kit, and procalcitonin.

METHODS

Retrospectively, patients were classified as bacterial or nonbacterial AECOPD. ROC analyses tested how the additional tests discriminate these groups.

RESULTS

Twenty-two AECOPD were classified as bacterial and 23 as nonbacterial. From the additional tests, basophil percentage (Cytodiff) has superior AUC (0.800). At a cutoff resulting in ≥90% sensitivity, neutrophil/lymphocyte ratio (AUC:0.755) and CD4-positive T cells (Leukoflow, AUC:0.747) have the highest specificity (57%). Both neutrophil mean volume and standard deviation (Cell Population Data, DxH800 hematology analyzer) had good combined sensitivity and specificity (AUC:0.846/0.804, 91% sensitivity, 69% specificity). Addition of leukocyte populations and procalcitonin to CRP in regression models (AUC: 0.907/0.876/0.890) increased specificity compared to CRP alone (71% or 73% vs. 39%).

CONCLUSION

No additional test has sufficient accuracy on its own to predict bacterial AECOPD. Combining CRP with several parameters from the additional tests may improve this.

Cell population data in neonates: differences by age group and associations with perinatal factors

INTRODUCTION

Cell population data (CPD) describe physical parameters of white blood cell subpopulations and are reported to be of some value in the diagnosis of sepsis in neonates. Before using the CPD for diagnosing sepsis, the baseline features of the CPD distribution in healthy neonates should be clarified. The aim of this study was to compare the CPD distributions of healthy neonates and other age groups and to identify perinatal factors that are associated with changes in the CPD distribution of healthy neonates.

METHODS

The CPD distribution of 69 samples from term neonates was compared with adolescents and adults. The CPD distribution of 163 samples from healthy neonates was analyzed in association with perinatal factors, including gestational age, chronologic age, birthweight, delivery mode, premature rupture of membranes, diabetes, and pregnancy-induced hypertension.

RESULTS

The CPD distribution for term neonates was significantly different from those in adolescents and adults. The mean lymphocyte volume showed a negative correlation with gestational age at birth (r = -0.305; P < 0.01). The mean neutrophil volume was smaller in the cesarean section group than in the normal delivery group. The small for gestational age (SGA) group had smaller mean neutrophil volume and mean monocyte volume than the appropriate for gestational age group.

CONCLUSION

The CPD distribution of healthy neonates differed from those of adolescents or adults, and the differences were associated with gestational age, delivery mode, and being SGA.

Automated screening for tuberculosis by multiparametric analysis of data obtained during routine complete blood count

INTRODUCTION

The main goal of this study was to develop a multiparametric cell population data (CPD) model that combines information from several morphologic parameters generated by DxH800, in addition to the traditional parameters regularly reported in the CBC-diff, and to test the performance of this model in screening the general population for primary tuberculosis (TB).

METHODS

A total of 3741 study cases were divided into two groups, test and validation set at cut-off value of 6000 WBCs/μL. We developed multiparametric model for primary TB screening (TB hemeprint), selected CPD, and calculated parameters which could discriminate primary TB from other non-TB diseases and normal control in test set. We applied it to the validation set, which was a set of completely different samples, to test its reproducibility if applied to a routine laboratory test.

RESULTS

After screening primary TB using TB hemeprint, sensitivity, specificity, PPV, and NPV were 85.4%, 89.6%, 31.1%, and 99.1%, respectively, in primary TB with lower than 6000 WBCs/μL of test set (test set-L). In primary TB with higher than 6000 WBCs/μL of test set (test set-H), those values were 83.1%, 85.6%, 29.7%, and 98.6%, respectively. There were only 0.4% (2/461) and 0.6% (2/326) of normal control samples included in test set-L and -H, respectively. Diagnostic efficiencies except sensitivity in each validation set were very comparable with those in each test set.

CONCLUSION

Tuberculosis hemeprint may allow us to screen primary TB with acceptable sensitivity and specificity using combination of TB-specific CPD and calculated parameters.