Clinical significance of cell population data (CPD) on Sysmex XN-9000 in septic patients with our without liver impairment

A new hematological parameter model for the diagnosis and prognosis of sepsis in emergency department: A single-center retrospective study

INTRODUCTION

Sepsis, a syndrome of organ dysfunction caused by an unregulated host response to infection. This study aimed to develop a novel sepsis diagnostic model of hematological parameters and evaluate its effectiveness in the early identification and prognosis of sepsis in emergency departments.

METHODS

A retrospective study was conducted in Emergency Department. Cell population data parameters related to monocytes and neutrophils were obtained using the Mindary BC-6800 plus hematology analyzer. Receiver operating characteristic (ROC) curve analysis, logistic regression analysis was performed to assess the performance of the parameters and establish a diagnostic and prognostic model of sepsis, which was then verified with a validation cohort.

RESULTS

Mon_XW exhibited the best diagnostic performance (area under the ROC curve [AUC] = 0.848, 95% confidence interval [CI]: 0.810-0.885, p < 0.001), followed by Neu_Y and Neu_YW (AUC = 0.777 95% CI: 0.730-0.824, p < 0.001). Logistic regression analysis identified Mon_XW and Neu_Y as independent predictors, which were used to establish a diagnostic model named hematological parameter for sepsis (HPS). HPS demonstrated the best diagnostic performance with an AUC of 0.862 (95% CI: 0.826-0.898, p < 0.001), sensitivity of 70.0%, and specificity of 87.1%, compared to C-reactive protein (CRP) and procalcitonin (PCT). The validation cohort also found that the positive predictive value of HPS was 70.4% and the negative predictive value was 92.2%.

CONCLUSION

The developed HPS model showed promising diagnostic efficacy for sepsis in the emergency department, which outperformed CRP and PCT in terms of sensitivity and specificity. By enabling early identification and prognosis of sepsis, that contributes to reducing sepsis-related mortality.

Time-dependent changes in cell population data obtained using Sysmex XN-series hematology analyzer in bacterial infections

BACKGROUND

Time-dependent changes in cell populations during acute bacterial infections remain unclear. We assessed time-dependent changes in fluorescent light intensity of the neutrophil area (NE-SFL) and fluorescent light distribution width index of the neutrophil area (NE-WY) and their association with sepsis and bacteremia.

METHODS

Patients with acute bacterial infections were enrolled in this prospective, observational cohort study. Blood samples were collected from all patients at the onset of bacterial infections (day 0) and on days 1 and 3. Microbiological evaluation included the examination of blood bacterial load using PCR. Cell population data were assessed using an automated hematology analyzer (Sysmex series XN-2000).

RESULTS

Forty-three participants with acute bacterial infections were enrolled in the study. Twenty-five participants developed definite sepsis. All the participants improved after the onset of infection. NE-WY levels showed significant time-dependent changes in participants with sepsis, peaking on day 0 and significantly decreasing until day 3, whereas these changes were not statistically significant for NE-SFL. A significant correlation with the Sequential Organ Failure Assessment score was observed with NE-WY and NE-SFL in the entire cohort on days 0 and 1. However, only NE-WY showed a significant correlation with blood bacterial load on days 0 and 1.

CONCLUSION

This study demonstrated that NE-WY elevation in sepsis peaked earlier than NE-SFL, which may partly reflect the early bacterial invasion into circulation. These findings advocate caution in interpreting cell population data values as sepsis biomarkers and propose the potential of NE-WY as a therapeutic indicator.

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.

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.

Machine learning pipeline for blood culture outcome prediction using Sysmex XN-2000 blood sample results in Western Australia

BACKGROUND

Bloodstream infections (BSIs) are a significant burden on the global population and represent a key area of focus in the hospital environment. Blood culture (BC) testing is the standard diagnostic test utilised to confirm the presence of a BSI. However, current BC testing practices result in low positive yields and overuse of the diagnostic test. Diagnostic stewardship research regarding BC testing is increasing, and becoming more important to reduce unnecessary resource expenditure and antimicrobial use, especially as antimicrobial resistance continues to rise. This study aims to establish a machine learning (ML) pipeline for BC outcome prediction using data obtained from routinely analysed blood samples, including complete blood count (CBC), white blood cell differential (DIFF), and cell population data (CPD) produced by Sysmex XN-2000 analysers.

METHODS

ML models were trained using retrospective data produced between 2018 and 2019, from patients at Sir Charles Gairdner hospital, Nedlands, Western Australia, and processed at Pathwest Laboratory Medicine, Nedlands. Trained ML models were evaluated using stratified 10-fold cross validation.

RESULTS

Two ML models, an XGBoost model using CBC/DIFF/CPD features with boruta feature selection (BFS) , and a random forest model trained using CBC/DIFF features with BFS were selected for further validation after obtaining AUC scores of [Formula: see text] and [Formula: see text] respectively using stratified 10-fold cross validation. The XGBoost model obtained an AUC score of 0.76 on a internal validation set. The random forest model obtained AUC scores of 0.82 and 0.76 on internal and external validation datasets respectively.

CONCLUSIONS

We have demonstrated the utility of using an ML pipeline combined with CBC/DIFF, and CBC/DIFF/CPD feature spaces for BC outcome prediction. This builds on the growing body of research in the area of BC outcome prediction, and provides opportunity for further research.

A genome-wide association study of blood cell morphology identifies cellular proteins implicated in disease aetiology

Blood cells contain functionally important intracellular structures, such as granules, critical to immunity and thrombosis. Quantitative variation in these structures has not been subjected previously to large-scale genetic analysis. We perform genome-wide association studies of 63 flow-cytometry derived cellular phenotypes-including cell-type specific measures of granularity, nucleic acid content and reactivity-in 41,515 participants in the INTERVAL study. We identify 2172 distinct variant-trait associations, including associations near genes coding for proteins in organelles implicated in inflammatory and thrombotic diseases. By integrating with epigenetic data we show that many intracellular structures are likely to be determined in immature precursor cells. By integrating with proteomic data we identify the transcription factor FOG2 as an early regulator of platelet formation and α-granularity. Finally, we show that colocalisation of our associations with disease risk signals can suggest aetiological cell-types-variants in IL2RA and ITGA4 respectively mirror the known effects of daclizumab in multiple sclerosis and vedolizumab in inflammatory bowel disease.

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.

Effects of Different Types of Recombinant SARS-CoV-2 Spike Protein on Circulating Monocytes' Structure

This study investigated the biological effects on circulating monocytes after challenge with SARS-CoV-2 recombinant spike protein. Whole blood collected from seven ostensibly healthy healthcare workers was incubated for 15 min with 2 and 20 ng/mL final concentration of recombinant spike protein of Ancestral, Alpha, Delta, and Omicron variants. Samples were analyzed with Sysmex XN and DI-60 analyzers. Cellular complexity (i.e., the presence of granules, vacuoles and other cytoplasmic inclusions) increased in all samples challenged with the recombinant spike protein of the Ancestral, Alpha, and Delta variants, but not in those containing Omicron. The cellular content of nucleic acids was constantly decreased in most samples, achieving statistical significance in those containing 20 ng/mL of Alpha and Delta recombinant spike proteins. The heterogeneity of monocyte volumes significantly increased in all samples, achieving statistical significance in those containing 20 ng/mL of recombinant spike protein of the Ancestral, Alpha and Delta variants. The monocyte morphological abnormalities after spike protein challenge included dysmorphia, granulation, intense vacuolization, platelet phagocytosis, development of aberrant nuclei, and cytoplasmic extrusions. The SARS-CoV-2 spike protein triggers important monocyte morphological abnormalities, more evident in cells challenged with recombinant spike protein of the more clinically severe Alpha and Delta variants.

Monocyte Distribution Width as a predictor of community acquired sepsis in patients prospectively enrolled at the Emergency Department

Background

Monocyte Distribution Width (MDW), a simple cellular marker of innate monocyte activation, can be used for the early recognition of sepsis. We performed an observational prospective monocentric study to assess the predictive role of MDW in detecting sepsis in a sample of consecutive patients presenting at the Emergency Department.

Methods

Prospective observational study using demographic and clinical characteristics, past medical history and other laboratory measurements to predict confirmed sepsis using multivariate logistic regression.

Results

A total of 2724 patients were included in the study, of which 272 (10%) had sepsis or septic shock. After adjusting for known and potential risk factors, logistic regression found the following independent predictors of sepsis: SIRS equal to 1 (OR: 2.32, 1.16–4.89) and 2 or more (OR: 27.8, 14.8–56.4), MDW > 22 (OR: 3.73, 2.46–5.70), smoking (OR: 3.0, 1.22–7.31), end stage renal function (OR: 2.3, 1.25–4.22), neurodegenerative disease (OR: 2.2, 1.31–3.68), Neutrophils ≥ 8.9 × 10³/µL (OR: 2.73, 1.82–4.11), Lymphocytes < 1.3 × 10³/µL (OR: 1.72, 1.17–2.53) and CRP ≥ 19.1 mg/L (OR: 2.57, 1.63–4.08). A risk score derived from predictive models achieved high accuracy by using an optimal threshold (AUC: 95%; 93–97%).

Conclusions

The study suggests that incorporating MDW in the clinical decision process may improve the early identification of sepsis, with minimal additional effort on the standard procedures adopted during emergency care.

Recent advances in laboratory hematology reflected by a decade of CCLM publications

On the occasion of the 60th anniversary of Clinical Chemistry and Laboratory Medicine (CCLM) we present a review of recent developments in the discipline of laboratory hematology as these are reflected by papers published in CCLM in the period 2012–2022. Since data on CCLM publications from 1963 to 2012 are also available, we were able to make a comparison between the two periods. This interestingly revealed that the share of laboratory hematology papers has steadily increased and reached now 16% of all papers published in CCLM. It also became evident that blood coagulation and fibrinolysis, erythrocytes, platelets and instrument and method evaluation constituted the ‘hottest’ topics with regard to number of publications. Some traditional, characteristic CCLM categories like reference intervals, standardization and harmonization, were more stable and probably will remain so in the future. With the advent of important newer topics, like new coagulation assays and drugs and cell population data generated by hematology analyzers, laboratory hematology is anticipated to remain a significant discipline in CCLM publications.

Value of hematological parameters for predicting patients with severe coronavirus disease 2019: a real-world cohort from Morocco

Objective

Coronavirus disease 2019 (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2. The clinical manifestations and the evolution of patients with COVID-19 are variable. In addition to respiratory involvement, COVID-19 leads to systemic involvement and can affect the hematopoietic system. This study aimed to evaluate the prognostic value of hematological and hemocytometric parameters in predicting the severity of patients with COVID-19.

Methods

We performed a retrospective study at Mohammed VI university Hospital from 1 March to 11 November 2020. We collected demographic characteristics and hematological findings of incident COVID-19 cases.

Results

A total of 245 patients were included in our study. We found that the rate of lymphopenia was significantly reduced in patients who were severely affected by COVID-19. Additionally, the rate of neutrophilia, the neutrophil side fluorescence light signal, monocyte fluorescent intensity, monocyte size, the neutrophil-to-lymphocyte ratio, the platelet-to-lymphocyte ratio, and the lymphocyte-to-monocyte ratio were significantly elevated in patients who were severely affected by COVID-19.

Conclusions

These results are consistent with the literature regarding the predictive value of these markers. A prospective validation in a large population with a longer follow-up is required.

Cardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors

BACKGROUND

Researchers routinely evaluate novel biomarkers for incorporation into clinical risk models, weighing tradeoffs between cost, availability, and ease of deployment. For risk assessment in population health initiatives, ideal inputs would be those already available for most patients. We hypothesized that common hematologic markers (eg, hematocrit), available in an outpatient complete blood count without differential, would be useful to develop risk models for cardiovascular events.

METHODS

We developed Cox proportional hazards models for predicting heart attack, ischemic stroke, heart failure hospitalization, revascularization, and all-cause mortality. For predictors, we used 10 hematologic indices (eg, hematocrit) from routine laboratory measurements, collected March 2016 to May 2017 along with demographic data and diagnostic codes. As outcomes, we used neural network-based automated event adjudication of 1 028 294 discharge summaries. We trained models on 23 238 patients from one hospital in Boston and evaluated them on 29 671 patients from a second one. We assessed calibration using Brier score and discrimination using Harrell's concordance index. In addition, to determine the utility of high-dimensional interactions, we compared our proportional hazards models to random survival forest models.

RESULTS

Event rates in our cohort ranged from 0.0067 to 0.075 per person-year. Models using only hematology indices had concordance index ranging from 0.60 to 0.80 on an external validation set and showed the best discrimination when predicting heart failure (0.80 [95% CI, 0.79-0.82]) and all-cause mortality (0.78 [0.77-0.80]). Compared with models trained only on demographic data and diagnostic codes, models that also used hematology indices had better discrimination and calibration. The concordance index of the resulting models ranged from 0.75 to 0.85 and the improvement in concordance index ranged up to 0.072. Random survival forests had minimal improvement over proportional hazards models.

CONCLUSIONS

We conclude that low-cost, ubiquitous inputs, if biologically informative, can provide population-level readouts of risk.

Haematology laboratory parameters to assess efficacy of CD19-, CD22-, CD33-, and CD123-directed chimeric antigen receptor T-cell therapy in haematological malignancies

INTRODUCTION

Chimeric antigen receptor (CAR) T cell products are available to treat relapsed/refractory B-lymphoblastic leukaemia/lymphoma (B-ALL), diffuse large B-cell lymphoma, mantle-cell lymphoma, and myeloma. CAR products vary by their target epitope and constituent molecules. Hence, there are no common laboratory assays to assess CAR T cell expansion in the clinical setting. We investigated the utility of common haematology laboratory parameters to measure CAR T cell expansion and response.

METHODS

Archived CellaVision images, absolute lymphocyte counts, and Sysmex CPD parameters spanning 1 month after CD19-CAR, UCAR19, CD22-CAR, CD33-CAR, and UCAR123 therapy were compared against donor lymphocyte infused control patients. Additionally, CellaVision images gathered during acute EBV infection were analysed.

RESULTS

CellaVision images revealed a distinct sequence of three lymphocyte morphologies, common among CD19-CAR, CD22-CAR and UCAR19. This lymphocyte sequence was notably absent in CAR T cell non-responders and stem-cell transplantation controls, but shared some features seen during acute EBV infection. CD19-CAR engraftment kinetics monitored by quantitative PCR show an expansion and persistence phase and mirror CD19-CAR ALC kinetics. We show other novel CAR T cell therapies (UCAR19, CD22-CAR, CD33-CAR and UCAR123) display similar ALC expansion in responders and diminished ALC expansion in non-responders. Furthermore, the CPD parameter LY_WY fluorescence increased within the first week after CD19-CAR infusion, preceding the peak absolute lymphocyte count (ALC) by 3.7 days.

CONCLUSION

Autologous and allogeneic CAR T cell therapy produce unique changes in common haematology laboratory parameters and could be a useful surrogate to follow CAR T-cell expansion after infusion.

The Value of a Complete Blood Count (CBC) for Sepsis Diagnosis and Prognosis

Sepsis represents an important global health burden due to its high mortality and morbidity. The rapid detection of sepsis is crucial in order to prevent adverse outcomes and reduce mortality. However, the diagnosis of sepsis is still challenging and many efforts have been made to identify reliable biomarkers. Unfortunately, many investigated biomarkers have several limitations that do not support their introduction in clinical practice, such as moderate diagnostic and prognostic accuracy, long turn-around time, and high-costs. Complete blood count represents instead a precious test that provides a wealth of information on individual health status. It can guide clinicians to early-identify patients at high risk of developing sepsis and to predict adverse outcomes. It has several advantages, being cheap, easy-to-perform, and available in all wards, from the emergency department to the intensive care unit. Noteworthy, it represents a first-level test and an alteration of its parameters must always be considered within the clinical context, and the eventual suspect of sepsis must be confirmed by more specific investigations. In this review, we describe the usefulness of basic and new complete blood count parameters as diagnostic and prognostic biomarkers of sepsis.

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.

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.

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.

Reviewing the value of leukocytes cell population data (CPD) in the management of sepsis

Sepsis is a medical emergency that describes the body's systemic immune response to an infection and can lead to end-stage organic dysfunction and death. Despite the advances in understanding the pathophysiology of this syndrome and therapies, sepsis remains one of the leading causes of morbidity and mortality in critically ill patients. Early diagnosis and rapid intervention are essential to improve outcomes, which inspired the concept "golden hour," during which the correction of shock and organic dysfunction can improve the patients' outcomes. But the initial presentation of sepsis is often nonspecific and its severity is difficult to assess. Anomalies in temperature, heart and respiratory rates and leukocyte counts are manifestations of systemic inflammatory response syndrome (SIRS). Diagnosis, management and follow-up of patients with sepsis remains a challenge, and diverse biomarkers have been proposed for the timely diagnosis and prognosis of septic patients: lactic acid, procalcitonin (PCT), C-reactive protein, immature granulocytes. The host's initial response to infection is a humoral, cellular and neuroendocrine reaction to infection, and leukocytes interact with endothelial cells. The new generation of hematological analyzers incorporates technological innovations allowing to expand the information derived from the complete blood count: new leukocyte derived parameters are emerging as potentially useful markers in different clinical situations. Additional research parameters cell population data (CPD), characterizing different leukocyte populations have become available, and preliminary observations suggest their utility in the diagnosis of sepsis. This review emphasizes the value of CPD, reported by modern cellular counters for early recognition of sepsis, and therefore the potential improvement in patient outcomes.

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD. The data collection was done at Konkuk University Medical Center, Seoul. A total of (882 cases: 457 hematologic malignancy and 425 hematologic non-malignancy) were used for analysis. In our study, seven machine learning models, i.e., SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN, were used. In order to measure the performance of our ML models, stratified 10-fold cross validation was performed, and metrics, such as accuracy, precision, recall, and AUC were used. We observed outstanding performance by the ANN model as compared to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall, and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5% ± 2.6 respectively. ANN algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic malignancies. Our results encourage further work of applying ML to wider field of clinical practice.

Comparison of Monocyte Distribution Width (MDW) and Procalcitonin for early recognition of sepsis

We carried out a prospective observational study to evaluate whether Monocyte Distribution Width (MDW) may play a role in identifying patients with sepsis in comparison with Procalcitonin (PCT). We prospectively enrolled all consecutive patients hospitalized at the Infectious Diseases Unit of Pescara General Hospital for bacterial infection or sepsis. MDW values were collected for all patients. Clinical characteristics, demographic data, past and present medical history, microbiological results, PCT, as well as neutrophil and monocytes indices at entry were compared in the 2 groups. Two-hundred-sixty patients were enrolled, 63.5% males, aged 59.1±19.5 years. Sepsis was diagnosed in 105 (40.4%); in 60 (57.1%) at least 1 microorganism was isolated from blood cultures. In multivariate models, MDW as a continuous variable (OR:1.57 for each unit increase; 95%CI: 1.31–1.87, p<0.001) and PCT˃1 ng/mL (OR: 48.5; 95%CI: 14.7–160.1, p<0.001) were independently associated with sepsis. Statistical best cut points associated with sepsis were 22.0 for MDW and 1.0 ng/mL for PCT whereas MDW values<20 were invariably associated with negative blood cultures. At ROC curve analysis, the AUC of MDW (0.87) was nearly overlapping that of PCT (0.88). Our data suggest that incorporating MDW within current routine WBC counts and indices may be of remarkable use for detection of sepsis. Further research is warranted.

Improvement in detecting sepsis using leukocyte cell population data (CPD)

Background

The cell population data (CPD) parameters reported by XN analyzers (Sysmex Corporation, Kobe, Japan) reflect the size and internal structure of leukocytes. We explored whether CPD values could contribute to recognize those patients with fever at risk to develop sepsis. A profile of sepsis was developed combining CPD parameters and other markers.

Methods

We recruited 295 patients at the onset of fever, with infection confirmed by positive cultures. We studied the diagnostic performance of the CPD parameters in the differential diagnosis of sepsis vs. non-systemic bacterial infection using receiver operating characteristic (ROC) curve analysis. Additionally, the K-means unsupervised clustering method was applied. Once the clusters had been defined, the relationship between them and the CPD parameter values was assessed with the non-parametric Wilcoxon test. Lastly, the relationship between the clusters obtained and the categorical variables was examined with the χ2-test (or Fisher’s exact test).

Results

ROC analysis demonstrated that NE-FSL, NE-WY, NE-WZ and MO-WZ had areas under the curve (AUCs) >0.700 for predicting sepsis. Using the K-means clustering algorithm, 80 patients (66.67%) were assigned to Cluster 1 and the others to Cluster 2. Out of 80 of patients in Cluster 1, 45 (56.25%) presented a PCT value ≥2 ng/mL, whereas almost 80% of Cluster 2 patients had a PCT <2 ng/mL. Cluster 1 was characterized by high NE-SFL, NE-WY, MO-X, MO-WX and MO-Z values (p<0.05).

Conclusions

CPD related to monocyte complexity and neutrophil activation were found to be significant, with high values suggesting sepsis.

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.

Usefulness of the lymphocyte positional parameters in the Sysmex XN haematology analyser in lymphoproliferative disorders and mononucleosis syndrome

INTRODUCTION

The lymphocyte positional parameters included in Sysmex XN have been suggested as useful means to differentiate lymphoproliferative disorders (LPD), mononucleosis syndrome (MNS) and other lymphocytoses.

METHODS

We evaluated Sysmex XN analysers, which supply 6 lymphocyte positional parameters that can be measured in the WDF scattergram: LY-X, LY-Y, LY-Z, LY-WX, LY-WY and LY-WZ.

RESULTS

We collected 301 samples from normal controls, polyclonal lymphocytosis, MNS and LPD. MNS and monoclonal expansion of T granular lymphocyte (T-GL) diagnostic groups accumulated higher numbers of significant differences in the mean values in comparison with the other groups. We propose a new algorithm that can differentiate T-GL cases from other diagnostic groups with an SE of 67.5%, an SP of 98.2%, a PPV of 87.1% and an NPV of 94.3%. Another algorithm showed its efficiency to differentiate MNS cases from other diagnostic groups with an SE of 63.6%, an SP of 97.5%, a PPV of 70.0% and an NPV of 96.7%. In 38.5% of all cases, the analyser did not generate any morphologic flag. Abnormal results in lymphocyte positional parameters were useful to detect 72.5% of these samples.

CONCLUSION

The lymphocyte positional parameters provided by Sysmex XN analysers are useful to differentiate expansions of T-GLs from other LPD and to differentiate MNS cases from other diagnostic groups. In addition, these parameters are very useful for detecting changes in the lymphocytes that make it necessary to review blood smears in samples without morphological flagging.

Innovative haematological parameters for early diagnosis of sepsis in adult patients admitted in intensive care unit

Aims

This study was aimed to investigate the role of erythrocyte, platelet and reticulocyte (RET) parameters, measured by new haematological analyser Sysmex XN and C reactive protein (CRP), for early diagnosis of sepsis during intensive care unit (ICU) stay.

Methods

The study population consisted of 62 ICU patients, 21 of whom developed sepsis during ICU stay and 41 who did not. The performance for early diagnosing of sepsis was calculated as area under the curve (AUC) of receiver operating characteristics curves analysis.

Results

Compared with CRP (AUC 0.81), immature platelet fraction (IPF) (AUC 0.82) showed comparable efficiency for identifying the onset of sepsis. The association with the risk of developing sepsis during ICU stay was also assessed. One day before the onset of sepsis, a decreased of RET% was significantly associated with the risk of developing sepsis (OR=0.35, 95% CI 0.14 to 0.87), whereas an increased of IPF absolute value (IPF#) was significantly associated with the risk of developing sepsis (OR=1.13, 95% CI 1.03 to 1.24) 2 days before the onset of sepsis. The value of CRP was not predictive of sepsis at either time points.

Conclusions

IPF# and RET% may provide valuable clinical information for predicting the risk of developing sepsis, thus allowing early management of patients before the onset of clinically evident systemic infections.

Role of leucocytes cell population data in the early detection of sepsis

AIMS

The cell population data (CPD) parameters reported by XN analyser (Sysmex, Kobe, Japan) reflect the size and internal structure of leucocytes. We aimed to assess the clinical utility of these parameters as biomarkers for the early diagnosis of sepsis.

METHODS

The study group (G1) included 586 controls (no quantitative or morphological alterations in the complete blood count) and 137 patients diagnosed with sepsis. The reliability of the model was evaluated using a validation group (G2) of 212 controls and 60 patients with sepsis. The optimal cut-off for the diagnosis of sepsis and the OR for CPD were established using a univariate logistic regression. A multivariate logistic regression model was then created. The OR and area under the curve were recorded. A risk stratification scale (neutrophils and monocytes (NEMO)) for diagnosing sepsis was established on the basis of the coefficients of the multivariate model.

RESULTS

MO-X and neutrophils fluorescence intensity (NE-SFL) were found to be the most relevant of the CPD in predicting sepsis applying multivariate analysis to G1.NEMO score was composed using the above-mentioned CPD and subsequently stratified into three risk groups: mild (≤3), moderate (4≤NEMO≤5) and high (≥6). The OR for patients with a score of 4-5 was 10 and 249 for a score of ≥6. When applied to G2, the positive predictive value was 84.8 % and the negative predictive value was 96.0%.

CONCLUSIONS

CPD are potentially useful for the early diagnosis of sepsis. Their values were used to compose in NEMO score can help in rapid and reliable decision making.