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

It's in the blood: a review of the hematological system in SARS-CoV-2-associated COVID-19

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global healthcare crisis. While SARS-CoV-2-associated COVID-19 affects primarily the respiratory system, patients with COVID-19 frequently develop extrapulmonary manifestations. Notably, changes in the hematological system, including lymphocytopenia, neutrophilia and significant abnormalities of hemostatic markers, were observed early in the pandemic. Hematological manifestations have since been recognized as important parameters in the pathophysiology of SARS-CoV-2 and in the management of patients with COVID-19. In this narrative review, we summarize the state-of-the-art regarding the hematological and hemostatic abnormalities observed in patients with SARS-CoV-2-associated COVID-19, as well as the current understanding of the hematological system in the pathophysiology of acute and chronic SARS-CoV-2-associated COVID-19.

Differential diagnosis of lymphocytosis in routine laboratory practice: Contribution of lymphocyte parameters using the Sysmex-XN9000 haematology analyzer

INTRODUCTION

This study investigates the potential contribution of structural and dispersion parameters, as well as alarms provided by Sysmex XN9000 haematology analyzer. The objective was to assess the need for a microscopic examination in the context of lymphocytosis. It also aims to contribute in differentiating rapidly lymphoproliferative disorders such as chronic lymphocytic leukaemia (CLL), non-chronic lymphocytic leukaemia (NON-CLL) and non-infectious reactive lymphocytosis (REAC).

METHODS

We prospectively assessed lymphocyte parameters (Ly-X, Ly-Y, Ly-Z, Ly-WX, Ly-WY, Ly-WZ) provided by the Sysmex XN9000 analyzer; they were measured in the white blood cell differential (WDF) channel which also provides alarms via the precursor/pathological cellular channel (WPC). Blood samples from 71 subjects with CLL, NON-CLL lymphoproliferative and REAC non-infectious reactive lymphocytosis, as well as a control (NORM) group of 12 subjects without any abnormalities were analyzed.

RESULTS

The most discriminating parameters to distinguish the different groups were Ly-X, Ly-Z and Ly-WZ. The lymphoid structural parameters Ly-X and Ly-Z significantly discriminated the CLL group from the other groups (p < 0.001), and the CLL group from the REAC group (p < 0.01), respectively. The Ly-WZ parameter discriminated the CLL group from the NON-CLL, REAC (p < 0.001) and NORM (p < 0.01) groups. Alarms were higher in all study groups compared to the NORM group. An algorithm integrating these structural and alarm parameters is proposed.

CONCLUSION

This study demonstrated that Ly-X, Ly-Z, and Ly-WZ lymphocyte parameters are useful for detecting morphological changes in lymphocytes; they provide useful information for the differential diagnosis of lymphocytosis, and this before the examination of the blood smear. An algorithm combining the WDF (parameters) and the WPC (alarms) makes it possible to decide whether or not to use a microscopic examination or flow cytometry immunophenotyping.

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.

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.

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.

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.