Characteristics of peripheral blood differential counts in hospitalized patients with COVID-19

SARS-CoV-2 infection and multi-organ system damage: A review

The SARS-CoV-2 infection causes COVID-19, which has affected approximately six hundred million people globally as of August 2022. Organs and cells harboring angiotensin-converting enzyme 2 (ACE2) surface receptors are the primary targets of the virus. However, once it enters the body through the respiratory system, the virus can spread hematogenously to infect other body organs. Therefore, COVID-19 affects many organs, causing severe and long-term complications, even after the disease has ended, thus worsening the quality of life. Although it is known that the respiratory system is most affected by the SARS-CoV-2 infection, many organs/systems are affected in the short and long term. Since the COVID-19 disease simultaneously affects many organs, redesigning diagnostic and therapy policies to fit the damaged organs is strongly recommended. Even though the pathophysiology of many problems the infection causes is unknown, the frequency of COVID-19 cases rises with age and the existence of preexisting symptoms. This study aims to update our knowledge of SARS-CoV-2 infection and multi-organ dysfunction interaction based on clinical and theoretical evidence. For this purpose, the study comprehensively elucidates the most recent studies on the effects of SARS-CoV-2 infection on multiple organs and systems, including respiratory, cardiovascular, gastrointestinal, renal, nervous, endocrine, reproductive, immune, and parts of the integumentary system. Understanding the range of atypical COVID-19 symptoms could improve disease surveillance, limit transmission, and avoid additional multi-organ-system problems.

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.

Neural Network Training with Highly Incomplete Medical Datasets

Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Since the data to train the neural networks need to be complete, most studies discard the incomplete datapoints, which reduces the size of the training data, or impute the missing features, which can lead to artefacts. Alas, both approaches are inadequate when a large portion of the data is missing. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets without overfitting or introducing artefacts. First, the dataset is split into subsets of samples containing all values for a certain cluster of features. Then, these subsets are used to train individual neural networks. Finally, this ensemble of neural networks is combined into a single neural network whose training is fine-tuned using all complete datapoints. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer’s disease pathology and of patients at risk of hospitalization due to Covid-19. Compared to commonly used imputation methods, this improvement suggests that GapNet can become a general tool to handle incomplete medical datasets.

The Role of Hematological Parameters in Predicting the Death of Hospitalized Patients with COVID-19

Background

The role of the hematologic indicators in the identification of severe or critical patients requires further investigation. In this study, we focused on predicting Covid-19 patients at risk of progression using blood parameters.

Materials and Methods

We performed a retrospective study including 444 patients with confirmed Covid-19. Hematological parameters were evaluated. The logistic regression analysis was performed with step-wise method with dependent variables such as intensive care units admission, partial pressure of oxygen saturation, and mortality. Also, independent variables such as hematological parameters, age and sex to assess variables that are likely to predict patients at risk of progression.

Results

Patients in intensive care units had significantly higher mean absolute neutrophil count than outpatients (P < 0.001). There was a statistically significant difference in the mean absolute lymphocyte count between dead and survived patients (P = 0.015). Multivariate analysis confirmed the positive association of the white blood cells (P < 0.001), absolute neutrophil count (P < 0.004), red cell distribution width (P < 0.001), and lactate dehydrogenase (P = 0.007) to be positively associated with the admission of Covid-19 patients in the intensive care units and the absolute monocyte count (P = 0.012, Odds ratios = 0.100, CI95% = 0.066-0.605) to be negatively associated with mortality.

Conclusion

Based on the results of our study, it is recommended to use hematological data to make clinical decisions and evaluate the patient's prognosis.

S, Kumar T, Bharti S, Bhadani PP. Association of Peripheral Blood Parameters With Outcomes of COVID-19 Infection in a Tertiary Care Setting of Eastern India: An Institute-Based Study

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the Coronavirus DISEASE 2019 (COVID-19) pandemic. Blood investigations play a vital role in providing information regarding the inflammatory process. Previous studies have shown that complete blood count parameters have clinical importance in predicting disease outcomes. However, there is a scarcity of literature published from our region in India.

Aims

The present study was conducted to describe the epidemiological, clinical, and hematological characteristics and outcomes of COVID-19 confirmed cases.

Material and methods

All real-time reverse transcriptase-polymerase chain reaction (RT-PCR) confirmed SARS-CoV-2 cases admitted in our institute over three months, from July to September 2020, were included in the study population. The blood samples of SARS-CoV-2 positive cases were analyzed for complete blood counts and coagulation profile on admission and at the time of discharge (most recent in case of mortality).

Results

A total of 252 RT-PCR confirmed SARS-CoV-2 cases were included in the study. The most common age group affected was 46 to 60 years, and the male-to-female ratio was 2.45:1. The most common clinical symptom was dyspnea, and the commonest comorbidity was hypertension. The statistical analysis showed a significant association of age, absolute neutrophil count (ANC) D-dimer, neutrophil-to-lymphocyte ratio (NLR), and platelets-to-lymphocyte ratio (PLR) with intensive care unit (ICU) admission and death. Gender, dyspnea, and absolute eosinophil count (AEC) showed significant association with ICU patients only, while liver disease and absolute lymphocyte count (ALC) had a significant association with death.

Conclusion

There are many notable clinical and hematological manifestations of COVID-19. Age, gender, dyspnea, comorbid liver disease, ANC, ALC AEC, NLR, PLR, and D- dimer may help clinicians predict the disease progression and reduce mortality risk.

Comparative study of hematological and radiological feature of severe/critically ill patients with COVID-19, influenza A H7N9, and H1N1 pneumonia

OBJECTIVES

This study aimed to explore clinical indexes for management of severe/critically ill patients with COVID-19, influenza A H7N9, and H1N1 pneumonia by comparing hematological and radiological characteristics.

METHODS

Severe/critically ill patients with COVID-19, H7N9, and H1N1 pneumonia were retrospectively enrolled. The demographic data, clinical manifestations, hematological parameters, and radiological characteristics were compared.

RESULTS

In this study, 16 cases of COVID-19, 10 cases of H7N9, and 13 cases of H1N1 who met severe/critically ill criteria were included. Compared with COVID-19, H7N9 and H1N1 groups had more chronic diseases (80% and 92.3% vs. 25%, p < 0.05), higher APACHE Ⅱ scores (16.00 ± 8.63 and 15.08 ± 6.24, vs. 5.50 ± 2.58, p < 0.05), higher mortality rates (40% and 46.2% vs. 0%, p < 0.05), significant lymphocytopenia (0.59 ± 0.31 × 10⁹ /L and 0.56 ± 0.35 × 10⁹ /L vs. 0.97 ± 0.33 × 10⁹ /L, p < 0.05), and elevated neutrophil-to-lymphocyte ratio (NLR; 14.67 ± 6.10 and 14.64 ± 10.36 vs. 6.29 ± 3.72, p < 0.05). Compared with the H7N9 group, ground-glass opacity (GGO) on chest CT was common in the COVID-19 group (p = 0.028), while pleural effusion was rare (p = 0.001).

CONCLUSIONS

The NLR can be used as a clinical parameter for the predication of risk stratification and outcome in COVID-19 and influenza A pneumonia. Manifestations of pleural effusion or GGO in chest CT may be helpful for the identification of different viral pneumonia.

Prognostic value of neutrophil-to-lymphocyte ratio in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

Neutrophil-to-lymphocyte ratio (NLR) is an accessible and widely used biomarker. NLR may be used as an early marker of poor prognosis in patients with COVID-19.

OBJECTIVE

To evaluate the prognostic value of the NLR in patients diagnosed with COVID-19.

METHODS

We conducted a systematic review and meta-analysis. Observational studies that reported the association between baseline NLR values (ie, at hospital admission) and severity or all-cause mortality in COVID-19 patients were included. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). Random effects models and inverse variance method were used for meta-analyses. The effects were expressed as odds ratios (ORs) and their 95% confidence intervals (CIs). Small study effects were assessed with the Egger's test.

RESULTS

We analysed 61 studies (n = 15 522 patients), 58 cohorts, and 3 case-control studies. An increase of one unit of NLR was associated with higher odds of severity (OR 6.22; 95%CI 4.93 to 7.84; P < .001) and higher odds of all-cause mortality (OR 12.6; 95%CI 6.88 to 23.06; P < .001). In our sensitivity analysis, we found that 41 studies with low risk of bias and moderate heterogeneity (I2  = 53% and 58%) maintained strong association between NLR values and both outcomes (severity: OR 5.36; 95% CI 4.45 to 6.45; P < .001; mortality: OR 10.42 95% CI 7.73 to 14.06; P = .005).

CONCLUSIONS

Higher values of NLR were associated with severity and all-cause mortality in hospitalised COVID-19 patients.

The Impact of Neutrophil-Lymphocyte Count Ratio in COVID-19: A Systematic Review and Meta-Analysis

Background: The neutrophil–lymphocyte count ratio (NLR) has emerged as a potential prognostic tool for different diseases. In the current coronavirus disease (COVID-19) pandemic, the NLR may be a useful tool for risk scarification and the optimal utilization of limited healthcare resources. However, there is no consensus regarding the optimal value of NLR, and the association with disease severity and mortality. Thus, this study aims to systematically analyze the current evidence of the utility of baseline NLR as a predictive tool for mortality, disease severity in COVID-19 patients. Methods: A compendious screening of electronic databases up to June 15, 2021, was done after enlisting the protocol in PROSPERO (CRD42020202659). Studies evaluating the utility of baseline NLR in COVID-19 are included for this review as per the PRISMA statement. Results: We retrieved a total of 13112 and 12986 COVID-19 patients for survivability and severity over 90 studies. The expired and critically sick patients had elevated baseline NLR on admission, in comparison to survivors and noncritical patients. (SMD = 3.82; 95% CI: 2.79-4.85; I² = 100% and SMD = 1.42; 95% CI: 1.22-1.63; I² = 95%, respectively). The summary receiver operating curve analysis for mortality (AUC = 0.87; 95% CI: 0.86-0.87; I² = 94.7%), and severity (AUC = 0.82; 95% CI: 0.80-0.84; I² = 79.7%) were also suggestive of its significant predictive value. Conclusions: The elevated NLR on admission in COVID-19 patients is associated with poor outcomes.

Correlation Analysis Between Serum Uric Acid, Prealbumin Level, Lactate Dehydrogenase, and Severity of COVID-19

Objective: To analyze the correlation between serum uric acid, prealbumin levels, lactate dehydrogenase (LDH), and the severity of COVID-19.

Methods: The data from 135 patients with COVID-19 was collected, and the patients were divided into a non-severe group (110 cases) and a severe group (25 cases), according to the severity of illness. Sixty cases with normal physical examinations over the same period and 17 cases diagnosed with other viral pneumonia in the past five years were selected as the control group to analyze the correlation between the detection index and the severity of COVID-19.

Results: Serum albumin and prealbumin in the severe group were significantly lower than those in the non-severe group (p < 0.01); serum uric acid in the severe group was lower than that in the non-severe group (p < 0.05). LDH and C-reaction protein (CRP) in the severe group were higher than those in non-severe group (p < 0.01); the levels of albumin, prealbumin, serum uric acid, and LDH in the severe group were significantly different from those in healthy control group (p < 0.01) and the levels of prealbumin, serum uric acid, LDH, and CRP in the severe group were significantly different from those in the other viral pneumonia group (p < 0.01). Serum albumin and prealbumin were positively correlated with the oxygenation index (p < 0.001), while LDH was negatively correlated with oxygenation index (p < 0.001).

Conclusion: Serum albumin, prealbumin, the oxygenation index, and LDH are risk factors of COVID-19.

Association of hyperglycaemia with hospital mortality in nondiabetic COVID-19 patients: A cohort study

Objective

Diabetes is a known risk factor for mortality in Coronavirus disease 2019 (COVID-19) patients. Our objective was to identify prevalence of hyperglycaemia in COVID-19 patients with and without prior diabetes and quantify its association with COVID-19 disease course.

Research design and methods

This observational cohort study included all consecutive COVID-19 patients admitted to John H Stroger Jr. Hospital, Chicago, IL from March 15, 2020 to May 3, 2020 and followed till May 15, 2020. The primary outcome was hospital mortality, and the studied predictor was hyperglycaemia [any blood glucose ≥7.78 mmol/L (140 mg/dL) during hospitalization].

Results

Of the 403 COVID-19 patients studied, 51 (12.7%) died; 335 (83.1%) were discharged while 17 (4%) were still in hospital. Hyperglycaemia occurred in 228 (56.6%) patients; 83 of these hyperglycaemic patients (36.4%) had no prior history of diabetes. Compared to the reference group no-diabetes/no-hyperglycaemia patients the no-diabetes/hyperglycaemia patients showed higher mortality [1.8% versus 20.5%, adjusted odds ratio 21.94 (95% confidence interval 4.04–119.0), P < 0.001]; improved prediction of death (P = 0.01) and faster progression to death (P < 0.01). Hyperglycaemia within the first 24 and 48 h was also significantly associated with mortality (odds ratio 2.15 and 3.31, respectively).

Conclusions

Hyperglycaemia without prior diabetes was common (20.6% of hospitalized COVID-19 patients) and was associated with an increased risk of and faster progression to death. Development of hyperglycaemia in COVID-19 patients who do not have diabetes is an early indicator of progressive disease.

Protection against COVID-19 in African population: Immunology, genetics, and malaria clues for therapeutic targets

BACKGROUND

There is a marked discrepancy between SARS-CoV-2 seroprevalence and COVID-19 cases and deaths in Africa. MAIN: SARS-CoV-2 stimulates humoral and cellular immunity systems, as well as mitogen-activated protein kinase (MAPK) and nuclear NF-kB signalling pathways, which regulate inflammatory gene expression and immune cell differentiation. The result is pro-inflammatory cytokines release, hyperinflammatory condition, and cytokine storm, which provoke severe lung alterations that can lead to multi-organ failure in COVID-19. Multiple genetic and immunologic factors may contribute to the severity of COVID-19 in African individuals when compared to the rest of the global population. In this article, the role of malaria, NF-kB and MAPK pathways, caspase-12 expression, high level of LAIR-1-containing antibodies, and differential glycophorins (GYPA/B) expression in COVID-19 are discussed.

CONCLUSION

Understanding pathophysiological mechanisms can help identify target points for drugs and vaccines development against COVID-19. To our knowledge, this is the first study that explores this link and proposes a biological and molecular answer to the epidemiologic discrepancy in COVID-19 in Africa.

Thrombocytopenia in Virus Infections

Thrombocytopenia, which signifies a low platelet count usually below 150 × 10⁹/L, is a common finding following or during many viral infections. In clinical medicine, mild thrombocytopenia, combined with lymphopenia in a patient with signs and symptoms of an infectious disease, raises the suspicion of a viral infection. This phenomenon is classically attributed to platelet consumption due to inflammation-induced coagulation, sequestration from the circulation by phagocytosis and hypersplenism, and impaired platelet production due to defective megakaryopoiesis or cytokine-induced myelosuppression. All these mechanisms, while plausible and supported by substantial evidence, regard platelets as passive bystanders during viral infection. However, platelets are increasingly recognized as active players in the (antiviral) immune response and have been shown to interact with cells of the innate and adaptive immune system as well as directly with viruses. These findings can be of interest both for understanding the pathogenesis of viral infectious diseases and predicting outcome. In this review, we will summarize and discuss the literature currently available on various mechanisms within the relationship between thrombocytopenia and virus infections.

COVID-19 and multiorgan failure: A narrative review on potential mechanisms

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) in December 2019 form Wuhan, China leads to coronavirus disease 2019 (COVID-19) pandemic. While the common cold symptoms are observed in mild cases, COVID-19 is accompanied by multiorgan failure in severe patients. The involvement of different organs in severe patients results in lengthening the hospitalization duration and increasing the mortality rate. In this review, we aimed to investigate the involvement of different organs in COVID-19 patients, particularly in severe cases. Also, we tried to define the potential underlying mechanisms of SARS-CoV2 induced multiorgan failure. The multi-organ dysfunction is characterized by acute lung failure, acute liver failure, acute kidney injury, cardiovascular disease, and as well as a wide spectrum of hematological abnormalities and neurological disorders. The most important mechanisms are related to the direct and indirect pathogenic features of SARS-CoV2. Although the presence of angiotensin-converting enzyme 2, a receptor of SARS-CoV2 in the lung, heart, kidney, testis, liver, lymphocytes, and nervous system was confirmed, there are controversial findings to about the observation of SARS-CoV2 RNA in these organs. Moreover, the organ failure may be induced by the cytokine storm, a result of increased levels of inflammatory mediators, endothelial dysfunction, coagulation abnormalities, and infiltration of inflammatory cells into the organs. Therefore, further investigations are needed to detect the exact mechanisms of pathogenesis. Since the involvement of several organs in COVID-19 patients is important for clinicians, increasing their knowledge may help to improve the outcomes and decrease the rate of mortality and morbidity.