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

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.

Leucocyte volume, conductivity, and scatter at presentation in COVID-19 patients

Background

In COVID-19 patients, besides changes in leucocyte count, morphological abnormalities of circulating blood cells have been reported.

Aim

This study aims to investigate the relationship between the morphological and functional properties of leucocytes and the severity of the disease in COVID-19 patients.

Materials and Methods

Blood samples were collected from COVID-19 patients (n = 130) at the time of admission. The patients were stratified according to the comorbidity, age, LDH, lymhocyte count score as mild, moderate, and severe. Complete blood count and the cell population data were analyzed by the Volume, conductivity, scatter (VCS) technology on Beckman Coulter LH-780 hematology analyzer. Kruskal-Wal'lis test was used to assess the differences between the groups with subsequent Bonferroni correction.

Results

Neutrophil count was increased, and lymphocyte count was decreased in severe patients compared to mild patients. The increase in the percent of neutrophils and the neutrophil/lymphocyte ratio in the severe patient group was significant in comparison to both the moderate and the mild group. The dispersion of the neutrophil volume and conductivity showed significant changes depending on the severity of the disease. The lymphocyte volume, lymphocyte-volume-SD and lymphocyte-conductivity as well as the monocyte-volume and monocyte-volume-SD were significantly increased in severe patients in comparison to mild patients. The increase of lymphocyte and monocyte volume in severe patients was also significant in comparison to moderate patients.

Conclusions

COVID-19 infection leads to important changes in cell population data of leucocytes. The volumetric changes in lymphocytes and monocytes are related to the severity of the disease.

Immune Dynamics Involved in Acute and Convalescent COVID-19 Patients

COVID-19 is a viral disease that has caused millions of deaths around the world since 2020. Many strategies have been developed to manage patients in critical conditions; however, comprehension of the immune system is a key factor in viral clearance, tissue repairment, and adaptive immunity stimulus. Participation of immunity has been identified as a major factor, along with biomarkers, prediction of clinical outcomes, and antibody production after infection. Immune cells have been proposed not only as a hallmark of severity, but also as a predictor of clinical outcomes, while dynamics of inflammatory molecules can also induce worse consequences for acute patients. For convalescent patients, mild disease was related to higher antibody production, although the factors related to the specific antibodies based on a diversity of antigens were not clear. COVID-19 was explored over time; however, the study of immunological predictors of outcomes is still lacking discussion, especially in convalescent patients. Here, we propose a review using previously published studies to identify immunological markers of COVID-19 outcomes and their relation to antibody production to further contribute to the clinical and laboratorial management of patients.

Validating a clinical laboratory parameter-based deisolation algorithm for patients with COVID-19 in the intensive care unit using viability PCR: the CoLaIC multicentre cohort study protocol

INTRODUCTION

To investigate whether biochemical and haematological changes due to the patient's host response (CoLab algorithm) in combination with a SARS-CoV-2 viability PCR (v-PCR) can be used to determine when a patient with COVID-19 is no longer infectious.We hypothesise that the CoLab algorithm in combination with v-PCR can be used to determine whether or not a patient with COVID-19 is infectious to facilitate the safe release of patients with COVID-19 from isolation.

METHODS AND ANALYSIS

This study consists of three parts using three different cohorts of patients. All three cohorts contain clinical, vital and laboratory parameters, as well as logistic data related to isolated patients with COVID-19, with a focus on intensive care unit (ICU) stay. The first cohort will be used to develop an algorithm for the course of the biochemical and haematological changes of the host response of the COVID-19 patient. Simultaneously, a second prospective cohort will be used to investigate the algorithm derived in the first cohort, with daily measured laboratory parameters, next to conventional SARS-CoV-2 reverse transcriptase PCRs, as well as v-PCR, to confirm the presence of intact SARS-CoV-2 particles in the patient. Finally, a third multicentre cohort, consisting of retrospectively collected data from patients with COVID-19 admitted to the ICU, will be used to validate the algorithm.

ETHICS AND DISSEMINATION

This study was approved by the Medical Ethics Committee from Maastricht University Medical Centre+ (cohort I: 2020-1565/300523) and Zuyderland MC (cohorts II and III: METCZ20200057). All patients will be required to provide informed consent. Results from this study will be disseminated via peer-reviewed journals and congress/consortium presentations.

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.

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.

Use of an algorithm based on routine blood laboratory tests to exclude COVID-19 in a screening-setting of healthcare workers

Background

COVID-19 is an ongoing pandemic leading to exhaustion of the hospital care system. Our health care system has to deal with a high level of sick leave of health care workers (HCWs) with COVID-19 related complaints, in whom an infection with SARS-CoV-2 has to be ruled out before they can return back to work. The aim of the present study is to investigate if the recently described CoLab-algorithm can be used to exclude COVID-19 in a screening setting of HCWs.

Methods

In the period from January 2021 till March 2021, HCWs with COVID-19-related complaints were prospectively collected and included in this study. Next to the routinely performed SARS-CoV-2 RT-PCR, using a set of naso- and oropharyngeal swab samples, two blood tubes (one EDTA- and one heparin-tube) were drawn for analysing the 10 laboratory parameters required for running the CoLab-algorithm.

Results

In total, 726 HCWs with a complete CoLab-laboratory panel were included in this study. In this group, 684 HCWs were tested SARS-CoV-2 RT-PCR negative and 42 cases RT-PCR positive. ROC curve analysis showed an area under the curve (AUC) of 0.853 (95% CI: 0.801–0.904). At a safe cut-off value for excluding COVID-19 of -6.525, the sensitivity was 100% with a specificity of 34% (95% CI: 21 to 49%). No SARS-CoV-2 RT-PCR cases were missed with this cut-off and COVID-19 could be safely ruled out in more than one third of HCWs.

Conclusion

The CoLab-score is an easy and reliable algorithm that can be used for screening HCWs with COVID-19 related complaints. A major advantage of this approach is that the results of the score are available within 1 hour after collecting the samples. This results in a faster return to labour process of a large part of the COVID-19 negative HCWs (34%), next to a reduction in RT-PCR tests (reagents and labour costs) that can be saved.

Definition of the Immune Parameters Related to COVID-19 Severity

A relevant portion of patients with disease caused by the severe acute respiratory syndrome coronavirus 2 (COVID-19) experience negative outcome, and several laboratory tests have been proposed to predict disease severity. Among others, dramatic changes in peripheral blood cells have been described. We developed and validated a laboratory score solely based on blood cell parameters to predict survival in hospitalized COVID-19 patients. We retrospectively analyzed 1,619 blood cell count from 226 consecutively hospitalized COVID-19 patients to select parameters for inclusion in a laboratory score predicting severity of disease and survival. The score was derived from lymphocyte- and granulocyte-associated parameters and validated on a separate cohort of 140 consecutive COVID-19 patients. Using ROC curve analysis, a best cutoff for score of 30.6 was derived, which was associated to an overall 82.0% sensitivity (95% CI: 78–84) and 82.5% specificity (95% CI: 80–84) for detecting outcome. The scoring trend effectively separated survivor and non-survivor groups, starting 2 weeks before the end of the hospitalization period. Patients’ score time points were also classified into mild, moderate, severe, and critical according to the symptomatic oxygen therapy administered. Fluctuations of the score should be recorded to highlight a favorable or unfortunate trend of the disease. The predictive score was found to reflect and anticipate the disease gravity, defined by the type of the oxygen support used, giving a proof of its clinical relevance. It offers a fast and reliable tool for supporting clinical decisions and, most important, triage in terms of not only prioritization but also allocation of limited medical resources, especially in the period when therapies are still symptomatic and many are under development. In fact, a prolonged and progressive increase of the score can suggest impaired chances of survival and/or an urgent need for intensive care unit admission.

Hematological changes associated with COVID-19 infection

BACKGROUND

The unresolved COVID-19 pandemic considerably impacts the health services in Iraq and worldwide. Consecutive waves of mutated virus increased virus spread and further constrained health systems. Although molecular identification of the virus by polymerase chain reaction is the only recommended method in diagnosing COVID-19 infection, radiological, biochemical, and hematological studies are substantially important in risk stratification, patient follow-up, and outcome prediction.

AIM

This narrative review summarized the hematological changes including the blood indices, coagulative indicators, and other associated biochemical laboratory markers in different stages of COVID-19 infection, highlighting the diagnostic and prognostic significance.

METHODS

Literature search was conducted for multiple combinations of different hematological tests and manifestations with novel COVID-19 using the following key words: "hematological," "complete blood count," "lymphopenia," "blood indices," "markers" "platelet" OR "thrombocytopenia" AND "COVID-19," "coronavirus2019," "2019-nCoV," OR "SARS-CoV-2." Articles written in the English language and conducted on human samples between December 2019 and January 2021 were included.

RESULTS

Hematological changes are not reported in asymptomatic or presymptomatic COVID-19 patients. In nonsevere cases, hematological changes are subtle, included mainly lymphocytopenia (80.4%). In severe, critically ill patients and those with cytokine storm, neutrophilia, lymphocytopenia, elevated D-dimer, prolonged PT, and reduced fibrinogen are predictors of disease progression and adverse outcome.

CONCLUSION

Monitoring hematological changes in patients with COVID-19 can predict patients needing additional care and stratify the risk for severe course of the disease. More studies are required in Iraq to reflect the hematological changes in COVID-19 as compared to global data.

Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes, and Brescia chest X-ray score were the variables processed using a random forests classification algorithm to build and validate the model. Receiver operating characteristic (ROC) analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, neutrophil/lymphocyte ratio, C-reactive protein, lymphocyte %, ferritin std, and monocyte %), and Brescia chest X-ray score (https://bdbiomed.shinyapps.io/covid19score/). The areas under the ROC curve obtained for the three groups (training, validating, and testing) were 0.98, 0.83, and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

Diagnostic value of white blood cell parameters for COVID-19: Is there a role for HFLC and IG?

Introduction

As the Coronavirus disease 2019 (COVID‐19) pandemic is still ongoing with patients overwhelming healthcare facilities, we aimed to investigate the ability of white blood cell count (WBC) and their subsets, high fluorescence lymphocyte cells (HFLC), immature granulocyte count (IG), and C‐reactive protein (CRP) to aid diagnosis of COVID‐19 during the triage process and as indicators of disease progression to serious and critical condition.

Methods

We collected clinical and laboratory data of patients, suspected COVID‐19 cases, admitted at the emergency department of University General Hospital of Ioannina (Ioannina, Greece). We selected 197 negative and 368 positive cases, confirmed by polymerase chain reaction test for severe acute respiratory syndrome coronavirus 2. COVID‐19 cases were classified into mild, serious, and critical disease. Receiver operating characteristic curve and binary logistic regression analysis were utilized for assessing the diagnosing ability of biomarkers.

Results

WBC, neutrophil count (NEUT), and HFLC can discriminate efficiently negative cases from mild and serious COVID‐19, whereas eosinopenia and basopenia are early indicators of the disease. The combined WBC‐HFLC marker is the best diagnostic marker for both mild (sensitivity: 90.6% and specificity: 64.1%) and serious (sensitivity: 90.3% and specificity: 73.4%) disease. CRP and Lymphocyte count are early indicators of progression to serious disease whereas WBC, NEUT, IG, and neutrophil‐to‐lymphocyte ratio are the best indicators of critical disease.

Conclusion

Lymphopenia is not useful in screening patients with COVID‐19. HFLC is a good diagnostic marker for mild and serious disease either as a single marker or combined with WBC whereas IG is a good indicator of progression to critical disease.

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.

Ruling Out Coronavirus Disease 2019 in Patients with Pneumonia: The Role of Blood Cell Count and Lung Ultrasound

Coronavirus disease 2019 (COVID-19) is characterized by a distinctive blood leucocyte pattern and B-lines on lung ultrasound (LUS) as marker of alveolar-interstitial syndrome. We aimed to evaluate the accuracy of blood leucocyte count alone or in combination with LUS for COVID-19 diagnosis. We retrospectively enrolled consecutive patients diagnosed with community acquired pneumonia (CAP) at hospital admission to derive and validate cutoff values for blood cell count that could be predictive of COVID-19 before confirmation by the nucleic acid amplification test (NAAT). Cutoff values, generated and confirmed in inception (41/115, positive/negative patients) and validation (100/180, positive/negative patients) cohorts, were ≤17 and ≤10 cells/mm³ for basophils and eosinophils, respectively. Basophils and/or eosinophils below cutoff were associated with sensitivity of 98% (95%CI, 94–100) and negative likelihood ratio of 0.04 (95%CI, 0.01–0.11). In a subgroup of 265 subjects, the sensitivity of B-line on LUS was 15% lower (p < 0.001) than that of basophils and/or eosinophils below cutoff. The combination of B-lines with basophils and eosinophils below cutoff was associated with a moderate increase of the positive likelihood ratio: 5.0 (95%CI, 3.2–7.7). In conclusion, basophil and eosinophil counts above the generated cutoff virtually rule out COVID-19 in patients with CAP. Our findings can help optimize patient triage pending the NAAT results.

Cell Population Data and Serum Polyclonal Immunoglobulin Free Light Chains in the Assessment of COVID-19 Severity

Distinguishing between severe and nonsevere COVID-19 to ensure adequate healthcare quality and efficiency is a challenge for the healthcare system. The aim of this study was to assess the usefulness of CBC parameters together with analysis of FLC serum concentration in risk stratification of COVID-19. Materials and methods: CBC was analyzed in 735 COVID ICU, COVID non-ICU, and non-COVID ICU cases. FLC concentration was analyzed in 133 of them. Results: COVID ICU had neutrophils and lymphocytes with the greatest size, granularity, and nucleic acid content. Significant differences in concentrations of κ and λ FLCs were shown between COVID ICU and COVID non-ICU. However, no difference was found in the κ/λ ratio between these groups, and the ratio stayed within the reference value, which indicates the presence of polyclonal FLCs. FLC κ measurement has significant power to distinguish between severe COVID-19 and nonsevere COVID-19 (AUC = 0.7669), with a sensitivity of 86.67% and specificity of 93.33%. The κ coefficients’ odds ratio of 3.0401 was estimated. Conclusion: It can be concluded that the results obtained from the measure of free light immunoglobulin concentration in serum are useful in distinguishing between severe and nonsevere COVID-19.

Coronavirus disease 2019 (COVID-19) and autoimmunity

The coronavirus 2019 pandemic (coronavirus disease, COVID-19), etiologically related to the SARS-CoV-2 virus (severe acute respiratory syndrome coronavirus-2), has once again reawakened healthcare professionals’ interest towards new clinical and conceptual issues of human immunology and immunopathology. An unprecedented number of clinical trials and fundamental studies of epidemiology, virology, immunology and molecular biology, of the COVID-19 clinical course polymorphism and pharmacotherapy have been conducted within one year since the outbreak of 2019 pandemic, bringing together scientists of almost all biological and physicians of almost all medical specialties. Their joint efforts have resulted in elaboration of several types of vaccines against SARS-CoV-2 infection and, in general, fashioning of more rational approaches to patient management. Also important for COVID-19 management were all clinical trials of biologics and “targeted” anti-inflammatory drugs modulating intracellular cytokine signaling, which have been specifically developed for treatment immune-mediated inflammatory rheumatic disease (IMIRDs) over the past 20 years. It became obvious after a comprehensive analysis of the entire spectrum of clinical manifestations and immunopathological disorders in COVID-19 is accompanied by a wide range of extrapulmonary clinical and laboratory disorders, some of which are characteristic of IMIRDs and other autoimmune and auto-in-flammatory human diseases. All these phenomena substantiated the practice of anti-inflammatory drugs repurposing with off-label use of specific antirheumatic agents for treatment of COVID-19. This paper discusses potential use of glucocorticoids, biologics, JAK inhibitors, etc., blocking the effects of pro-inflammatory cytokines for treatment of COVID-19.