Reference intervals of cell population data parameters in Sysmex XN-Series and its patterns of changes from early adulthood to geriatric ages in South Korea

INTRODUCTION

Cell population data (CPD) parameters may be putative biomarkers for the screening of various diseases including some infections and myelodysplastic syndrome. This study aimed to establish the age- and sex-specific reference intervals (RIs) for the CPD parameters in the Korean population.

METHODS

The reference population for the RIs of CPD parameters comprised 124 856 subjects aged 20-99 years. CPD parameters were obtained from Sysmex XN-2000 (Kobe, Japan) datasets from 17 health promotion centers in 13 South Korean cities. We determined significant partitions for age and sex, and calculated RIs according to Clinical and Laboratory Standards Institute C28-A3 guidelines.

RESULTS

The side scattered light intensity in the neutrophil area and the lymphocyte area did not require sex-related partitioning except in those over the age of 50, among whom the lower limit (LL) and upper limit (UL) were lower in females. However, the side scattered light distribution width in the lymphocyte area required age- and sex-related partitioning, in which LL and UL were higher in females. The LL and UL of the fluorescent light distribution width were higher in males in the neutrophil area and higher in females in the lymphocyte area, but age-related partitioning was not required. The forward scattered light intensity in the neutrophil area, lymphocyte area, and monocyte area did not require age-related partitioning in males.

CONCLUSION

This study has determined comprehensive age- and sex-specific RIs for CPD parameters, which could help to prove the clinical significance of these parameters in the Sysmex XN-2000.

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.

The Clinical Performance of Cell Population Data for Diagnosis of Bloodstream Infection in Cancer Patients

Background Bloodstream infection (BSI) induces a change in the number and morphology of blood cells. In this study, we compared cell population data (CPD) parameters between cancer patients with or without BSI to determine whether these parameters could serve as biomarkers of BSI. Methods Between April and June 2021, 43 BSI-negative and 22 BSI-positive cancer patients were enrolled in this study. We compared 18 CPD parameters and biomarkers between cancer patients with BSI-positive and BSI-negative. Results There were significant differences in the levels of several CPD parameters, including MO-WZ (p=0.040), MO-X (p<0.01), MO-Y (p=0.012), NE-SFL (p<0.01), and NE-WX (p=0.037), but not C-reactive protein (p=0.347) and procalcitonin (p=0.237) between BSI-positive and BSI-negative patients. The areas under the receiver-operating characteristic curves (AUCs) were above 0.7 for MO-X (0.762; 95% confidence intervals (CI): 0.624-0.901), NE-SFL (0.766; 95% CI: 0.625-0.880). And LY-WY (p=0.024) showed a significant difference between gram-negative and gram-positive BSI patients with high AUC (0.883; 95% CI: 0.703-1). Conclusion CPD parameters (MO-X and NE-SFL) provide additional information for discriminating between BSI-negative and BSI-positive BSI. And LY-WY provides useful information for discriminating between cancer patients with gram-negative BSI and gram-positive BSI.

A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters

Monocytes and neutrophils play key roles in the cytokine storm triggered by SARS-CoV-2 infection, which changes their conformation and function. These changes are detectable at the cellular and molecular level and may be different to what is observed in other respiratory infections. Here, we applied machine learning (ML) to develop and validate an algorithm to diagnose COVID-19 using blood parameters. In this retrospective single-center study, 49 hemogram parameters from 12,321 patients with clinical suspicion of COVID-19 and tested by RT-PCR (4239 positive and 8082 negative) were analysed. The dataset was randomly divided into training and validation sets. Blood cell parameters and patient age were used to construct the predictive model with the support vector machine (SVM) tool. The model constructed from the training set (5936 patients) achieved an accuracy for diagnosis of SARS-CoV-2 infection of 0.952 (95% CI: 0.875-0.892). Test sensitivity and specificity was 0.868 and 0.899, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.896 and 0.872, respectively (prevalence 0.50). The validation set model (4964 patients) achieved an accuracy of 0.894 (95% CI: 0.883-0.903). Test sensitivity and specificity was 0.8922 and 0.8951, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.817 and 0.94, respectively (prevalence 0.34). The area under the receiver operating characteristic curve was 0.952 for the algorithm performance. This algorithm may allow to rule out COVID-19 diagnosis with 94% of probability. This represents a great advance for early diagnostic orientation and guiding clinical decisions.

Leukocyte cell population data as potential markers of COVID-19 disease characterization

Background

The usefulness of leukocyte cell population data (CPD) is currently being investigated. In COVID-19 pandemic several reports showed the clinical importance of hematological parameters. Our study aimed to assess CPDs in Sars CoV-2 patients as new disease markers.

Methods

From February to April 2020 (1st wave) 540 and from September to December 2020 (2nd wave) 2821 patients respectively were enrolled. SARS CoV-2 infection diagnosis was carried out by Multiplex rRT-PCR from nasopharyngeal swabs. CPDs were detected by XN 2000 hematology analyzer (Sysmex Corporation). A comparison between two disease waves was performed. Additionally, C-reactive protein (CRP) and lactate dehydrogenase (LDH) were assayed.

Results

CPDs were classified into: cell complextity, DNA/RNA content and abnormal sized cells. We detected parameters increased from the reference population for all cell types for both 1st and 2nd wave (p<0.05). However, in the 2nd vs 1st wave 5 CPDs vs 9 CPDs were found. In addition we observed higher CPD values of the 1st compared to 2nd wave: (NE-SFL) (p<0.001), (LY-Y) (p<0.0001), (LY-Z) (p<0.0001), (MO-X) (p<0.0001), (MO-Y) (p<0.0001). These findings were confirmed by the higher concentrations of CRP and LDH in the 1st vs 2nd wave: 17.3 mg/L (8.5-59.3) vs 6.3 mg/L (2.3-17.6) (p<0.001) and 241.5 IU/L (201-345) vs 195 IU/L (174-228) (p< 0.001) (median, interquartile range) respectively.

Conclusions

CPDs showed increased cell activation in 1st wave patients confirmed by clinical and biochemical data, associated with worse clinical conditions. Results highlighted the CPDs as disease characterization markers or useful for a risk model.

Exploring Extended White Blood Cell Parameters for the Evaluation of Sepsis among Patients Admitted to Intensive Care Units

Sepsis is a major cause of mortality and morbidity in intensive care units. This case-control study aimed to investigate the haematology cell population data and extended inflammatory parameters for sepsis management. The study included three groups of patients: sepsis, non-sepsis, and healthy controls. Patients suspected of having sepsis underwent a Sequential Organ Failure Assessment (SOFA) evaluation and had blood drawn for blood cultures, complete peripheral blood counts (CBC), and measurements of various markers such as C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6). We observed significant changes in numerous CBC parameters and extended inflammation parameters (EIPs), in addition to significant biochemical analysis markers CRP and IL-6 in sepsis cohorts. Multiple logistic regression analyses showed that combining different CBC parameters and EIPs were effective to profile these patients. Two different models have been developed using white blood cell counts and their extended parameters. Our findings indicate that the absolute counts of white blood cells, and the EIPs which reflect their activation states, are important for the prediction and assessment of sepsis, as the body responds to an insult that triggers an immune response. In an emergency situation, having timely updates on patient conditions becomes crucial for guiding the management process. Identifying trends in these specific patient groups will aid early diagnosis, complementing clinical signs and symptoms, especially as CBC is the most commonly ordered test in a diagnostic workup.

Predictive value of cell population data with Sysmex XN-series hematology analyzer for culture-proven bacteremia

Background

Cell population data (CPD) parameters related to neutrophils, such as fluorescent light intensity (NE-SFL) and fluorescent light distribution width index (NE-WY), have emerged as potential biomarkers for sepsis. However, the diagnostic implication in acute bacterial infection remains unclear. This study assessed the diagnostic value of NE-WY and NE-SFL for bacteremia in patients with acute bacterial infections, and those associations with other sepsis biomarkers.

Methods

Patients with acute bacterial infections were enrolled in this prospective observational cohort study. For all patients, a blood sample, with at least two sets of blood cultures, were collected at the onset of infection. Microbiological evaluation included examination of the blood bacterial load using PCR. CPD was assessed using Automated Hematology analyzer Sysmex series XN-2000. Serum levels of procalcitonin (PCT), interleukin-6 (IL-6), presepsin, and CRP were also assessed.

Results

Of 93 patients with acute bacterial infection, 24 developed culture-proven bacteremia and 69 did not. NE-SFL and NE-WY were significantly higher in patients with bacteremia than in those without bacteremia (p < 0.005, respectively), and were significantly correlated with the bacterial load determined by PCR (r = 0.384 and r = 0.374, p < 0.005, respectively). To assess the diagnostic value for bacteremia, receiver operating characteristic curve analysis was used. NE-SFL and NE-WY showed an area under the curve of 0.685 and 0.708, respectively, while those of PCT, IL-6, presepsin, and CRP were 0.744, 0.778, 0.685, and 0.528, respectively. Correlation analysis showed that the levels of NE-WY and NE-SFL were strongly correlated with PCT and IL-6 levels.

Conclusion

This study demonstrated that NE-WY and NE-SFL could predict bacteremia in a manner that may be different from that of other indicators. These findings suggest there are potential benefits of NE-WY/NE-SFL in predicting severe bacterial infections.

Combination of NeuX and NeuZ can predict neutrophil dysplasia features of myelodysplastic neoplasms in peripheral blood

INTRODUCTION

The assessment of neutrophil dysplasia features in peripheral blood is very helpful for the early screening and diagnosis of myelodysplastic neoplasms (MDS). Cell population data (CPD) parameters generated by automated hematology analyzers can reflect morphological characteristics of blood cells. This study aimed to investigate the clinical significance of CPD parameters neutrophil (Neu) X, NeuY and NeuZ in assessing neutrophil dysplasia.

METHODS

218 MDS patients were divided into two subgroups according to neutrophil morphology. The differences of neutrophil research parameters between the two MDS subgroups and the control group, consisting of 210 healthy individuals, were compared, the correlation among neutrophil research parameters and the relationship between these parameters and cell morphology in MDS patients were analyzed, and receiver operating characteristic analysis were performed.

RESULTS

The median values of neutrophil research parameters NeuX and NeuZ in MDS with granulocyte dysplasia group were significantly lower than those in MDS without granulocyte dysplasia group and control group (p < 0.001), and they were positively correlated (r = 0.878, p < 0.001). The area under the receiver operating characteristic curve of NeuX and NeuZ was 0.720 (95% CI: 0.643-0.796, p < 0.001) and 0.738 (95% CI: 0.665-0.811, p < 0.001), respectively. In addition, with the decrease of NeuX value, neutrophils gradually show decreased nuclear segment and/or cytoplasmic granules.

CONCLUSIONS

Combining NeuX and NeuZ can predict neutrophil dysplasia features of MDS in peripheral blood, and this can be an easier method to screen for the neutrophil dysplasia cases, as compared with the microscopic examination of peripheral blood and/or bone marrow smears.

Diagnostic performance of machine learning models using cell population data for the detection of sepsis: a comparative study

OBJECTIVES

To compare the artificial intelligence algorithms as powerful machine learning methods for evaluating patients with suspected sepsis using data from routinely available blood tests performed on arrival at the hospital. Results were compared with those obtained from the classical logistic regression method.

METHODS

The study group consisted of consecutive patients with fever and suspected infection admitted to the Emergency Department. The complete blood counts (CBC) were acquired using the Mindray BC-6800 Plus analyser (Mindray Diagnostics, Shenzhen, China). Cell Population Data (CPD) were also recorded. The ML and artificial intelligence (AI) models were developed; their performance was evaluated using several indicators, such as the area under the receiver operating curve (AUC), calibration plots and decision curve analysis (DCA).

RESULTS

Overall, all the tested approaches obtained an AUC>0.90. The logistic regression (LR) performed well compared to the ML/AI models. The naïve Bayes and the K-nearest neighbour (KNN) methods did not show good calibration properties. The multi-layer perceptron (MLP) model was the best in terms of discrimination, calibration and clinical usefulness.

CONCLUSIONS

The best performance in the early detection of sepsis was achieved using the ML and AI models. However, external validation studies are needed to strengthen model derivation and procedure updating.

Leukocyte cell population data in patients with cardiac surgery and cardiopulmonary bypass: A potential readily available tool to monitor immunity

Purpose

Cardiac surgery with cardiopulmonary bypass triggers sterile inflammation that is responsible for post-operative morbidity. Automated flow cytometry devices used for leucocyte count provide cell population data (CPD) regarding fluorescence intensity, size and granularity of leukocytes that have never been studied in the context of sterile inflammation. Our objective was to explore leukocyte cell population data in patients undergoing cardiac surgery with cardiopulmonary bypass in order to determine whether CPD could be used to monitor immune cell activation.

Methods

This is an ancillary study of a cohort of patients undergoing cardiac surgery with cardiopulmonary bypass. Cell population data (CPD) extracted from a routine automated flow cytometer were analyzed (Fluorescence targeted to nucleic acids). The time points of interest were: pre-operative, postoperative and 5 days after surgery. The variations in those parameters were studied. Data were then compared between patients according to the occurrence of a composite criteria (supra-ventricular arrythmia, stroke, acute renal failure, and/or death).

Results

Data from 1453 patients were analyzed. The neutrophil count, fluorescence granularity (NE-SCC), intensity (NE-SFL) and size (NE-FSC) increased with surgery. Heterogeneity of neutrophils decreased in terms of fluorescence granularity (NE-WX) and size (NE-WZ) but increased in terms of intensity (NE-WY). The lymphocyte count decreased with surgery. While fluorescence granularity (LY-X) and size increased (LY-Z), Lymphocyte intensity decreased (LY-Y). Lymphocytes were less heterogeneous in terms of their granularity, size and intensity after surgery (LY-WX, LY-WY, LY-WZ). Patients who developed the composite complication criteria had a higher pre-operative neutrophil count (5.08 [3.89;6.95] vs 4.76 [3.60;6.13], p = 0.02; AUC = 0.56 [0.51;0.60]), and more heterogeneous neutrophils in terms of fluorescence granularity (NE-WX, AUC = 0.57 [0.52;0.62]) and intensity (NE-WY, AUC 0.61 [0.56;0.65]). Those patients also had lower pre-operative lymphocyte count (1.49 [1.10;1.14] vs 1.81 [1.39;2.39], p<0.01, AUC = 0.61 [0.57;0.66]) and fluorescence granularity (LY-X, AUC = 0.57 [0.53;0.62]). NE-WX, NE-WY and LY-X were associated with post-operative complications after adjustment on the EuroSCORE 2 (adjusted odd ratio of 1.01 [1.00;1.02]; 1.01 [1.00;1.01] and 1.08 [1.02;1.15] respectively).

Conclusion

Cardiac surgery with cardiopulmonary bypass was associated with substantial alterations of CPD probably reflecting leukocytes activation in sterile inflammation. Pre-operative NE-WX, NE-WY and LY-X biomarkers levels were associated with post-operative complications, independently of the EuroSCORE 2. Such routine, unexploited and low cost parameters might represent useful tools likely to monitor immune function and predict outcomes for patients undergoing cardiac surgery. Our findings requires validation on a larger external cohort.

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.

Cell population data in identifying active tuberculosis and community-acquired pneumonia

Objective

Leukocyte morphological parameters known as CPD (cell population data) is detected by hematology analyzer UniCel DxH800 with VCS technology. This study aimed to investigate the diagnostic efficacy of morphological changes in CPD parameters in distinguishing active tuberculosis from community-acquired pneumonia.

Methods

From October 2018 to February 2019, 88 patients with active tuberculosis, 78 patients with community-acquired pneumonia, and 89 healthy controls were enrolled in this study. CPD was obtained using Unicel DxH800 analyzer for all whole blood samples, one-way ANOVA (non-parametric) and area analysis under ROC curve were performed.

Results

The neutrophil mean conductivity (NMC), monocyte mean volume (MMV), monocyte mean conductivity (MMC), lymphocyte percentage (LY%), and monocyte percentage (MO%) were significantly higher in the active tuberculosis group than in the community-acquired pneumonia group. The white blood cell (WBC) count and neutrophil percentage (NE%) were significantly lower in the active tuberculosis group than in the community-acquired pneumonia group. The analysis of the area under the ROC curve proved that WBC count, neutrophil percentage (NE%), lymphocyte percentage (LY%), and monocyte percentage (MO%) did not achieve a higher area under the curve (AUC: 0.63, 0.71, 0.62, and 0.7, respectively). However, the AUC of NMC, MMV, and MMC in the CPD parameters was 0.951, 0.877, 0.98, respectively, and the simultaneous measurement of the three parameters was 0.99. The sensitivity and specificity were 98.5% and 91.1%, respectively.

Conclusion

The combined diagnosis of NMC, MMV, and MMC could assist the clinical diagnosis of active tuberculosis and community-acquired pneumonia.

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.

Aiding clinical assessment of neonatal sepsis using hematological analyzer data with machine learning techniques

INTRODUCTION

Early diagnosis and antibiotic administration are essential for reducing sepsis morbidity and mortality; however, diagnosis remains difficult due to complex pathogenesis and presentation. We created a machine learning model for bacterial sepsis identification in the neonatal intensive care unit (NICU) using hematological analyzer data.

METHODS

Hematological analyzer data were gathered from NICU patients up to 48 hours prior to clinical evaluation for bacterial sepsis. Five models, Support Vector Machine, K-nearest-neighbors, Logistic Regression, Random Forest (RF), and Extreme Gradient boosting (XGBoost), were trained on 60 hematological and nine clinical variables for 2357 cases (1692 control, 665 septic). Clinical feature only models (nine variables) were additionally trained and compared with models including hematological variables. Feature importance was used to assess relative contributions of parameters to performance.

RESULTS

The three best performing models were RF, Logistic Regression, and XGBoost. RF achieved an average accuracy of 0.74, AUC-ROC of 0.73, Sensitivity of 0.38, and Specificity of 0.88. Logistic Regression achieved an average accuracy of 0.70, AUC-ROC of 0.74, Sensitivity of 0.62, and Specificity of 0.73. XGBoost achieved an average accuracy of 0.72, AUC-ROC of 0.71, Sensitivity of 0.40, and Specificity of 0.85. All models with hematological variables had significantly stronger performance than models trained on only clinical features. Neutrophil parameters had the highest average feature importance.

CONCLUSIONS

Machine learning models using hematological analyzer data can classify NICU patients as sepsis positive or negative with stronger performance compared to clinical feature only models. Hematological analyzer variables could augment current sepsis classification machine learning algorithms.

Discriminant value of automated leucocyte VCS parameters in the detection of tropical infections

Introduction

In India, infectious diseases are a leading treatable cause of morbidity and mortality. Mangalore being endemic to many vector‐borne diseases, their incidence is known to show seasonal variations with sharp increase during monsoon. Leucocytes have substantial role in the immunological pathogenesis of infections.

Methods

The present series was a hospital‐based cross‐sectional study performed in a tertiary care hospital for a period of three months from June‐August wherein the cell population data of cases of malaria, dengue, leptospirosis, typhoid and rickettsial infections along with equal number of healthy controls were collected and analysed. Effectiveness of leucocyte‐related volume (V), conductivity (C) and scatter (S) parameters by Coulter®DXH800 haematology analyser in predicting these infections was appraised.

Results

A total of 324 cases comprising of malaria (50%), dengue (30.9%), leptospirosis (13.9%), typhoid (4.0%) and rickettsial infections (1.2%) were included. There was statistically significant differences (P < 0.05) in the mean values of complete blood count parameters—haemoglobin, total leucocyte count, red blood cell count, haematocrit, red cell distribution width, differential leucocyte count, platelet count and plateletcrit between cases and controls and also between specific infections. The mean volumes of neutrophil, monocyte and lymphocyte were considerably increased in malaria and dengue fever compared to leptospirosis, typhoid and rickettsial infections. VCS parameters were the least altered in typhoid fever, except for a strikingly high conductivity and scatter of eosinophils.

Conclusions

Haematological analysis is a part of routine evaluation of any case of febrile illness. This study showed that there are specific alterations in VCS parameters in different types of infections such as malaria, dengue, leptospira, typhoid and rickettsia, the information and analysis of which comes without any additional cost.

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.

Hemocytometric characteristics of COVID-19 patients with and without cytokine Storm syndrome on the Sysmex XN-10 hematology analyzer

OBJECTIVES

COVID-19 is an ongoing global pandemic. There is an urgent need for identification and understanding of clinical and laboratory parameters related to progression towards a severe and fatal form of this illness, often preceded by a so-called cytokine-storm syndrome (CSS). Therefore, we explored the hemocytometric characteristics of COVID-19 patients in relation to the deteriorating clinical condition CSS, using the Sysmex XN-10 hematology analyzer.

METHODS

From March 1st till May 16th, 2020, all patients admitted to our hospital with respiratory complaints and suspected for COVID-19 were included (n=1,140 of whom n=533 COVID-19 positive). The hemocytometric parameters of immunocompetent cells in peripheral blood (neutrophils [NE], lymphocytes [LY] and monocytes [MO]) obtained upon admission to the emergency department (ED) of COVID-19 positive patients were compared with those of the COVID-19 negative ones. Moreover, patients with CSS (n=169) were compared with COVID-19 positive patients without CSS, as well as with COVID-19 negative ones.

RESULTS

In addition to a significant reduction in leukocytes, thrombocytes and absolute neutrophils, it appeared that lymphocytes-forward scatter (LY-FSC), and reactive lymphocytes (RE-LYMPHO)/leukocytes were higher in COVID-19-positive than negative patients. At the moment of presentation, COVID-19 positive patients with CSS had different neutrophils-side fluorescence (NE-SFL), neutrophils-forward scatter (NE-FSC), LY-FSC, RE-LYMPHO/lymphocytes, antibody-synthesizing (AS)-LYMPHOs, high fluorescence lymphocytes (HFLC), MO-SSC, MO-SFL, and Reactive (RE)-MONOs. Finally, absolute eosinophils, basophils, lymphocytes, monocytes and MO-FSC were lower in patients with CSS.

CONCLUSIONS

Hemocytometric parameters indicative of changes in immunocompetent peripheral blood cells and measured at admission to the ED were associated with COVID-19 with and without CSS.

Red Blood Cells and Platelets Conventional and Research Parameters: Stability Remarks Before Their Interpretation

OBJECTIVES

To analyze the stability of red blood cells, platelets, and reticulocytes of the research parameters, in combination with the respective conventional parameters, for each analyte; and to quantify the morphological changes in these analytes, to propose a correction factor for each.

METHODS

Ethylenediaminetetraacetic acid (EDTA) blood specimens from patients were reanalyzed in 2-hour intervals and then, the mean percentage (X¯t%) changes were calculated. To evaluate the stability of the analyzed material, we used different criteria according to within-run and between-batch analytical variation, as well as intraindividual biological variation. Next, the mean deviation percentage of the parameters that undergo time-dependent significant changes was calculated, to obtain a correction factor.

RESULTS

Several conventional and research parameters showed significant alterations in the stability at an early time after arrival at the laboratory.

CONCLUSION

Cell variations over time can be quantified and corrected by applying a multiplying factor to the signal obtained in the analyzer.

Machine learning algorithms for the detection of spurious white blood cell differentials due to erythrocyte lysis resistance

AIMS

Red blood cell (RBC) lysis resistance interferes with white blood cell (WBC) count and differential; still, its detection relies on the identification of an abnormal scattergram, and this is not clearly adverted by specific flags in the Beckman-Coulter DXH-800. The aims were to analyse precisely the effect of RBC lysis resistance interference in WBC counts, differentials and cell population data (CPD) and then to design, develop and implement a novel diagnostic machine learning (ML) model to optimise the detection of samples presenting this phenomenon.

METHODS

WBC counts, differentials and CPD from 232 patients (anaemia or liver disease) were compared with 100 healthy controls (HC) using analysis of variance. The data were analysed after a corrective action, and the analyser differentials were also compared with the digital leucocyte differentials. The ML support vector machine (SVM) algorithm was trained with 70% of the samples (n=233) and the 30% remaining (n=99) were employed exclusively during the validation phase.

RESULTS

We identified that impedance WBC was not affected by the RBC lysis resistance interference while the DXH-800 differentials overestimated lymphoid subpopulations (17.6%), sometimes even yielding spurious lymphocytosis, and the latter were corrected when sample dilution was performed. The ML-SVM algorithm allowed the classification of the pathological groups when compared with HC with validation accuracies corresponding to 97.98%, 100% and 88.78% for the global, anaemia and liver disease groups, respectively.

CONCLUSIONS

The proposed algorithm has an impressive discriminatory potential and its application would be a valuable support system to detect spurious results due to RBC lysis resistance.

A Preliminary Proposal for Quality Control Assessment and Harmonization of Leukocytes Morphology-structural Parameters (cell Population Data Parameters)

BACKGROUND

The cell population data (CPD) measured by Sysmex XN-9000 can be used for screening many hematological and non-hematological disorders. Since little information is available on harmonization of CPD among different instrumentation and clinical laboratories, this study aimed at assessing the current degree of CPD harmonization between separate Sysmex XN modules allocated to the same laboratory.

METHODS

A total number of 78291 data were used for verification of within-run imprecision, analyzers harmonization, reference ranges and assessment of blood sample stability of CPD parameters, including results of daily quality control testing and those generated in samples collected from blood donors and healthy volunteers.

RESULTS

Within-run imprecision of CPD parameters ranged between 0.4 and 14.1%. Good agreement was found among five different XN-modules, especially when values were adjusted after calculation of instrument-specific alignment factors. The bias of all parameters remained always lower than the reference change values in samples stored for up to 8 hours, regardless of storage temperature.

CONCLUSIONS

The imprecision of CPD parameters was acceptable, except for those reflecting the dispersion of cellular clusters. Due to the lack of reference control materials, we showed that the use of data generated on a large number of normal routine samples (i.e., a Moving Average population) may be a reliable approach for testing analyzers harmonization. Nevertheless, availability of both calibration and quality control materials for these parameters is highly advisable in the future. We finally showed that whole blood samples may be stable for up to 2-4 hours for most CPD parameters.

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.