Rapid screening of COVID-19 patients using white blood cell scattergrams, a study on 381 patients

White blood cells scattergram as a valuable tool for COVID-19 screening: A multicentric study

INTRODUCTION

New tools have been developed to distinguish the COVID-19 diagnosis from other viral infections presenting similar symptomatology and mitigate the lack of sensitivity of molecular testing. We previously identified a specific "sandglass" aspect on the white blood cells (WBC) scattergram of COVID-19 patients, as a highly reliable COVID-19 screening test (sensitivity: 85.9%, specificity: 83.5% and positive predictive value: 94.3%). We then decided to validate our previous data in a multicentric study.

METHODS

This retrospective study involved 817 patients with flu-like illness, among 20 centers, using the same CBC instrument (XN analyzer, SYSMEX, Japan). After training, one specialist per center independently evaluated, under the same conditions, the presence of the "sandglass" aspect of the WDF scattergram, likely representing plasmacytoid lymphocytes.

RESULTS

Overall, this approach showed sensitivity: 59.0%, specificity: 72.9% and positive predictive value: 77.7%. Sensitivity improved with subgroup analysis, including in patients with lymphopenia (65.2%), patients presenting symptoms for more than 5 days (72.3%) and in patients with ARDS (70.1%). COVID-19 patients with larger plasmacytoid lymphocyte cluster (>15 cells) more often have severe outcomes (70% vs. 15% in the control group).

CONCLUSION

Our findings confirm that the WBC scattergram analysis could be added to a diagnostic algorithm for screening and quickly categorizing symptomatic patients as either COVID-19 probable or improbable, especially during COVID-19 resurgence and overlapping with future influenza epidemics. The observed large size of the plasmacytoid lymphocytes cluster appears to be a hallmark of COVID-19 patients and was indicative of a severe outcome. Furthers studies are ongoing to evaluate the value of the new hematological parameters in combination with WDF analysis.

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.

A convolutional neural network-based, quantitative complete blood count scattergram-mapping framework promptly screens acute promyelocytic leukemia with high sensitivity

BACKGROUND

Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML) characterized by its rapidly progressive and fatal clinical course if untreated, although it is curable if treated in a timely manner. Promptly screening patients who have results that are suspicious for APL is vital to overcome early death.

METHODS

The authors developed an innovative framework consisting of ResNet-18, a convolutional neural network architecture, with the objective of quantitatively mapping a complete blood count (CBC) scattergram to quickly and robustly indicate a probable susceptibility to APL. Three hundred and twenty scattergrams of the white blood cell differential channel from 51 patients with APL, 510 scattergrams from 105 patients who had non-APL AML, and 320 scattergrams from 320 healthy controls were randomly stratified at a ratio of 4:1 and split into training and testing data sets to accomplish five-fold cross-validation.

RESULTS

Both the area under the curve and the average precision of >0.99 were achieved in each fold. Three hundred four of the 320 APL scattergrams (95%) were correctly flagged by the model, which outcompeted the CBC review rules recommended by the International Society of Laboratory Hematology (all p < .001). External validation based on an independent testing data set that included 56 scattergrams from 31 patients with APL, 56 scattergrams from 55 patients with non-APL AML, and 64 scattergrams from 64 healthy controls also confirmed the sensitivity and specificity of the framework.

CONCLUSIONS

To the authors' knowledge, their convolutional neural network-based framework is the first to use scattergram output from routine CBC analysis to map suspicious APL early with outstanding sensitivity, specificity, and precision. The authors also describe a new CBC workflow incorporating this framework upstream of the morphologic review, which would provide the earliest flag for APL.

PLAIN LANGUAGE SUMMARY

The authors propose an innovative way to visualize complete blood counts (CBCs) by mapping the difference in white blood cell counts using automated CBC analysis to identify potential acute promyelocytic leukemia (APL) using a convolutional neural network (CNN), which can eliminate the potential pitfalls of manual observation. Analyses of an unprecedented, realistic data set validated that the quantitative relationship between the CBC scattergram and an APL abnormality is highly consistent. This is the first study to date focusing on screening for APL using scattergrams of the difference in white blood cell counts from routine CBC tests and has significant clinical relevance. The authors recommend using this method even before analyzing cell images, which could provide the earliest way to screen for APL in a sensitive and accurate way.

Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is related to new coronavirus disease (COVID-19) has mobilized several scientifics to explore clinical data using soft-computing approaches. In the context of machine learning, previous studies have explored supervised algorithms to predict and support diagnosis based on several clinical parameters from patients diagnosed with and without COVID-19. However, in most of them the decision is based on a "black-box" method, making it impossible to discover the variable relevance in decision making. Hence, in this study, we introduce a non-supervised clustering analysis with neural network self-organizing maps (SOM) as a strategy of decision-making. We propose to identify potential variables in routine blood tests that can support clinician decision-making during COVID-19 diagnosis at hospital admission, facilitating rapid medical intervention. Based on SOM features (visual relationships between clusters and identification of patterns and behaviors), and using linear discriminant analysis , it was possible to detect a group of units of the map with a discrimination power around 83% to SARS-CoV-2-positive patients. In addition, we identified some variables in admission blood tests (Leukocytes, Basophils, Eosinophils, and Red cell Distribution Width) that, in combination had strong influence in the clustering performance, which could assist a possible clinical decision. Thus, although with limitations, we believe that SOM can be used as a soft-computing approach to support clinician decision-making in the context of COVID-19.

Coronavirus disease 2019 (COVID-19): Focus on peripheral blood cell morphology

Numerous studies have shown peculiar morphological anomalies in COVID-19 patients' smears. We searched all the peer-reviewed scientific publications that explicitly reference the cytomorphological alterations on peripheral blood smears of patients with COVID-19. We extracted data from sixty-five publications (case reports, patient group studies, reviews, and erythrocyte morphology studies). The results show that frequent alterations concern the morphology of lymphocytes (large lymphocytes with weakly basophilic cytoplasm, plasmacytoid lymphocytes, large granular lymphocytes). Neutrophils display abnormal nuclei and cytoplasm in a distinctive cytomorphological picture. Besides a left shift in maturation, granulations can be increased (toxic type) or decreased with areas of basophilia. Nuclei are often hyposegmented (pseudo-Pelger-Huёt anomaly). Apoptotic or pycnotic cells are not uncommon. Monocytes typically have a large cytoplasm loaded with heterogeneous and coalescing vacuoles. Platelets show large and giant shapes. The presence of erythrocyte fragments and schistocytes is especially evident in the forms of COVID-19 that are associated with thrombotic microangiopathies. Such atypia of blood cells reflects the generalized activation in severe COVID-19, which has been demonstrated with immunophenotypic, molecular, genetic, and functional methods. Neutrophils, in particular, are involved in the pathophysiology of hyperinflammation with cytokine storm, which characterizes the most unfavorable evolution.

Utility of Differential White Cell Count and Cell Population Data for Ruling Out COVID-19 Infection in Patients With Community-Acquired Pneumonia

INTRODUCTION

The main aim of this study was to assess the utility of differential white cell count and cell population data (CPD) for the detection of COVID-19 in patients admitted for community-acquired pneumonia (CAP) of different etiologies.

METHODS

This was a multicenter, observational, prospective study of adults aged ≥18 years admitted to three teaching hospitals in Spain from November 2019 to November 2021 with a diagnosis of CAP. At baseline, a Sysmex XN-20 analyzer was used to obtain detailed information related to the activation status and functional activity of white cells.

RESULTS

The sample was split into derivation and validation cohorts of 1065 and 717 patients, respectively. In the derivation cohort, COVID-19 was confirmed in 791 patients and ruled out in 274 patients, with mean ages of 62.13 (14.37) and 65.42 (16.62) years, respectively (p<0.001). There were significant differences in all CPD parameters except MO-Y. The multivariate prediction model showed that lower NE-X, NE-WY, LY-Z, LY-WY, MO-WX, MO-WY, and MO-Z values and neutrophil-to-lymphocyte ratio were related to COVID-19 etiology with an AUC of 0.819 (0.790, 0.846). No significant differences were found comparing this model to another including biomarkers (p=0.18).

CONCLUSIONS

Abnormalities in white blood cell morphology based on a few cell population data values as well as NLR were able to accurately identify COVID-19 etiology. Moreover, systemic inflammation biomarkers currently used were unable to improve the predictive ability. We conclude that new peripheral blood biomarkers can help determine the etiology of CAP fast and inexpensively.

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.

Insight into prognostics, diagnostics, and management strategies for SARS CoV-2

The foremost challenge in countering infectious diseases is the shortage of effective therapeutics. The emergence of coronavirus disease (COVID-19) outbreak has posed a great menace to the public health system globally, prompting unprecedented endeavors to contain the virus. Many countries have organized research programs for therapeutics and management development. However, the longstanding process has forced authorities to implement widespread infrastructures for detailed prognostic and diagnostics study of severe acute respiratory syndrome (SARS CoV-2). This review discussed nearly all the globally developed diagnostic methodologies reported for SARS CoV-2 detection. We have highlighted in detail the approaches for evaluating COVID-19 biomarkers along with the most employed nucleic acid- and protein-based detection methodologies and the causes of their severe downfall and rejection. As the variable variants of SARS CoV-2 came into the picture, we captured the breadth of newly integrated digital sensing prototypes comprised of plasmonic and field-effect transistor-based sensors along with commercially available food and drug administration (FDA) approved detection kits. However, more efforts are required to exploit the available resources to manufacture cheap and robust diagnostic methodologies. Likewise, the visualization and characterization tools along with the current challenges associated with waste-water surveillance, food security, contact tracing, and their role during this intense period of the pandemic have also been discussed. We expect that the integrated data will be supportive and aid in the evaluation of sensing technologies not only in current but also future pandemics.

Risk-Assessment of Hospitalized Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infected Patients Using Laboratory Data and Immune Cell Morphological Assessment

CONTEXT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious agent, with the propensity to cause severe illness. While vaccine uptake has been increasing in recent months, many regions remain at risk of significant coronavirus disease 19 (COVID-19) related healthcare burden. Health systems will continue to benefit from the availability of a variety of clinical and laboratory model when other triaging models are equivocal.

OBJECTIVE

To validate previously reported clinical laboratory abnormalities seen in COVID-19 patients and identify what laboratory parameters might be outcome-predictive.

DESIGN

We undertook an observational study of hospital-admitted COVID-19 patients (n=113), looking at a broad selection of clinical, laboratory, peripheral blood smear, and outcome data over discrete discovery and validation periods from March 2020 to November 2020.

RESULTS

We confirmed the findings of previous studies noting derangement of a variety of laboratory parameters in COVID-19 patients, including peripheral blood morphological changes. We also devised a simple-to-use decision tree by which patients could be risk stratified on the basis of Red Blood Cell count, creatinine, urea, and atypical plasmacytoid lymphocyte ("covidocyte") count. This outcome classifier performed comparably to the World Health Organization clinical classifier and the neutrophil-lymphocyte ratio.

CONCLUSIONS

Our data add to the increasing number of studies cataloguing laboratory changes in COVID-19, and support the clinical utility of incorporating blood morphological assessment in the workup of hospitalized COVID-19 patients.

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.

Leukocyte differential and reactive lymphocyte counts from Sysmex XN analyzer in the evaluation of SARS-CoV-2 infection

BACKGROUND

Leukocyte differential present certain features in COVID 19 patients. RE-LYMP (reactive lymphocytes) is an extended inflammation parameter (EIP) reported by XN analyzer (Sysmex Corporation, Kobe, Japan) reflect the activation of lymphocytes triggered by infections. We aimed to assess the clinical utility of these parameters as biomarkers for the rapid detection of COVID 19.

METHODS

The study group included 200 COVID 19 and 167 patients with other infections at admission. Differences of leukocyte differential, neutrophil/lymphocyte ratio (NLR) and EIP among groups were assessed with the Kruskal-Wallis test; parameters statiscally different in the groups were tested with Receiver operating characteristic (ROC) curve analysis to assess their diagnostic performance in distinguishing SARS-CoV-2 infections. The reliability of the selected parameters was evaluated in a validation group of 347 patients (160 COVID 19 and 187 other infections).

RESULTS

NLR performed well to discard viral infections, area under curve (AUC) 0.988 (95%CI 0.973 - 0.991) and RE-LYMP was useful to distinguish COVID 19 and bacterial infections AUC 0.920 (95%CI 0.884 - 0.948); the two conditions NLR> 3.3 RE-LYMP> 0.6% was applied to the validation group and 153 out of 160 COVID 19 patients were correctly distinguished (95.6%).

CONCLUSIONS

Early diagnosis of SARS-CoV-2 infection is critical for better caring of patients and to reduce the threat of nosocomial infection for professionals. Leukocyte differential and RE-LYMPH could assist in a preliminary differential diagnosis of the disease.

Overview of the Haematological Effects of COVID-19 Infection

From its early origins, COVID-19 has spread extensively and was declared a global pandemic by the World Health Organization in March of 2020. Although initially thought to be predominantly a respiratory infection, more recent evidence points to a multisystem systemic disease which is associated with numerous haematological and immunological disturbances in addition to its other effects. Here we review the current knowledge on the haematological effects of COVID-19.

Ratios between circulating myeloid cells and lymphocytes are associated with mortality in severe COVID-19 patients

Recent studies indicate that host immune responses are dysregulated with either myeloid cell compartment or lymphocyte composition being disturbed in COVID-19. This study aimed to assess the impact of SARS-CoV-2 viral infection on the composition of circulating immune cells in severe COVID-19 patients. In this retrospective single-center cohort, 71 out of 87 COVID-19 patients admitted to the intense care unit for oxygen treatment were included in this study. Demographics, clinical features, comorbidities, and laboratory findings were collected on admission. Out of the 71 patients, 5 died from COVID-19. Compared with survived patients, deceased patients showed higher blood cell counts of neutrophils and monocytes but lower cell counts of lymphocytes. Intriguingly, the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and basophil-to-lymphocyte ratio (BLR) were markedly higher in deceased patients compared to survived patients. Furthermore, the lymphocyte counts were negatively correlated with D-dimer levels, while the ratios between myeloid cells and lymphocyte (NLR, MLR, and BLR) were positively correlated with D-dimer levels. Our findings revealed that the ratios between myeloid cells and lymphocytes were highly correlated with coagulation status and patient mortality in severe COVID-19.

Hematological indicators for lung damage caused by COVID-19 infection

During the pandemic of a new coronavirus infection one of the most serious complications is pneumonia. When entering a specialized pulmonology Department, the presence of pneumonia is confirmed by computed tomography (CT), the etiology is confirmed by the detection of SARS-CoV-2 RNA by PCR, the article analyzes the indicators of blood analysis when patients are admitted to the hospital. All admitted patients were divided into 2 groups. Group 1 of the study - with a new coronavirus infection Covid-19, confirmed by the detection of PCR-RNA SARS-CoV-2 (n=27). Group 2 consisted of 65 patients (70.7%) with lung tissue damage characteristic of Covid-19 according to CT, who had a negative analysis of PCR-RNA SARS-CoV-2. Statistically significant deviations were shown in a decrease in the percentage and absolute content of lymphocytes, monocytes and platelets, and an increase in neutrophils. The comparative analysis in the groups with positive and negative PCR analysis for the detection of SARS-CoV-2 RNA showed no statistically significant differences. It can be assumed that with the similarity of clinical manifestations and CT-data, a negative result of PCR-RNA SARS-CoV-2 can be diagnosed as «lung damage caused by COVID-19 infection».

Predicting severe or critical symptoms in hospitalized patients with COVID-19 from Yichang, China

Objectives: We aimed to identify potential risk factors for severe or critical coronavirus disease 2019 (COVID-19) and establish a prediction model based on significant factors.

Methods: A total of 370 patients with COVID-19 were consecutively enrolled at The Third People’s Hospital of Yichang from January to March 2020. COVID-19 was diagnosed according to the COVID-19 diagnosis and treatment plan released by the National Health and Health Committee of China. Effect-size estimates are summarized as odds ratio (OR) and 95% confidence interval (CI).

Results: 326 patients were diagnosed with mild or ordinary COVID-19, and 44 with severe or critical COVID-19. After propensity score matching and statistical adjustment, eight factors were significantly associated with severe or critical COVID-19 (p <0.05) relative to mild or ordinary COVID-19. Due to strong pairwise correlations, only five factors, including diagnostic delay (OR, 95% CI, p: 1.08, 1.02 to 1.17, 0.048), albumin (0.82, 0.75 to 0.91, <0.001), lactate dehydrogenase (1.56, 1.14 to 2.13, 0.011), white blood cell (1.27, 1.08 to 1.50, 0.004), and neutrophil (1.40, 1.16 to 1.70, <0.001), were retained for model construction and performance assessment. The nomogram model based on the five factors had good prediction capability and accuracy (C-index: 90.6%).

Conclusions: Our findings provide evidence for the significant contribution of five independent factors to the risk of severe or critical COVID-19, and their prediction was reinforced in a nomogram model.

A novel haemocytometric COVID-19 prognostic score developed and validated in an observational multicentre European hospital-based study

COVID-19 induces haemocytometric changes. Complete blood count changes, including new cell activation parameters, from 982 confirmed COVID-19 adult patients from 11 European hospitals were retrospectively analysed for distinctive patterns based on age, gender, clinical severity, symptom duration, and hospital days. The observed haemocytometric patterns formed the basis to develop a multi-haemocytometric-parameter prognostic score to predict, during the first three days after presentation, which patients will recover without ventilation or deteriorate within a two-week timeframe, needing intensive care or with fatal outcome. The prognostic score, with ROC curve AUC at baseline of 0.753 (95% CI 0.723-0.781) increasing to 0.875 (95% CI 0.806-0.926) on day 3, was superior to any individual parameter at distinguishing between clinical severity. Findings were confirmed in a validation cohort. Aim is that the score and haemocytometry results are simultaneously provided by analyser software, enabling wide applicability of the score as haemocytometry is commonly requested in COVID-19 patients.