Hematological and biochemical abnormalities associated with severe forms of COVID-19: A retrospective single-center study from Morocco

Blood Features Associated with Viral Infection Severity: An Experience from COVID-19-Pandemic Patients Hospitalized in the Center of Iran, Yazd

Pandemics such as coronavirus disease 2019 (COVID-19) can manifest as systemic infections that affect multiple organs and show laboratory manifestations. We aimed to analyze laboratory findings to understand possible mechanisms of organ dysfunction and risk stratification of hospitalized patients in these epidemics. Methods. This retrospective study was conducted among patients admitted to COVID-19 referral treatment center, Shahid Sadoughi Hospital, Yazd, Iran, from April 21 to November 21, 2021. It was the fifth peak of COVID-19 in Iran, and Delta (VOC-21APR-02; B.1-617.2) was the dominant and most concerning strain. All cases were positive for COVID-19 by RT-PCR test. Lab information of included patients and association of sex, age, and outcome were analyzed, on admission. Results. A total of 466 COVID-19 patients were included in the study, the majority of whom were women (68.9%). The average age of hospitalized patients in male and female patients was 57.68 and 41.32 years, respectively (p < 0.01). During hospitalization, abnormality in hematological and biochemical parameters was significant and was associated with the outcome of death in patients. There was incidence of lymphopenia, neutrophilia, anemia, and thrombocytopenia. The changes in neutrophil/lymphocyte (N/L) and hematocrit/albumin (Het/Alb) ratio and potassium and calcium levels were significant. Conclusion. Based on these results, new biochemical and hematological parameters can be used to predict the spread of infection and the underlying molecular mechanism. Viral infection may spread through blood cells and the immune system.

Assessment of Some Risk Factors and Biological Predictors in the Post COVID-19 Syndrome in Asthmatic Patients

Long COVID-19 or post-COVID infection (PCI) refers to the prolongation of symptoms in people who have been infected with the SARS-CoV-2 virus. Some meta-analysis studies have shown that patients with comorbidities, such as diabetes, obesity or hypertension, have severe complications after infection with the SARS-CoV-2 virus. The presence of chronic respiratory diseases such as bronchial asthma, COPD, pulmonary hypertension or cystic fibrosis increases the risk of developing severe forms of the COVID-19 disease. The risk of developing the severe form of COVID-19 was observed in patients with bronchial asthma being treated with corticosteroids, but also in those hospitalized with severe asthma. The biological variables determined in patients with PCI infection showed changes, especially in the hematological parameters, but also in some inflammatory markers. The aim of this study was to investigate some biological predictors in post-COVID-19 infection in patients with asthma and various comorbidities. In the case of patients diagnosed with moderate and severe forms of COVID-19, the variation in biological tests has shown high concentrations for serum glucose, lactate dehydrogenase and C-reactive protein. Additionally, the calculation of the relative risk (RR) based on the associated comorbidities in patients with PCI points to higher values for patients with asthma, hypertension, diabetes and obesity (RR moderate/severe form = 0.98/1.52), compared to patients with PCI and asthma (RR moderate/severe form = 0.36/0.63). Based on the statistical results, it can be concluded that the alanine aminotransferase (ALT) activity (p = 0.006) and the age of patients (p = 0.001) are the variables that contribute the most to the separation of the four classes of comorbidities considered.

Association between biochemical and hematologic factors with COVID-19 using data mining methods

BACKGROUND AND AIM

Coronavirus disease (COVID-19) is an infectious disease that can spread very rapidly with important public health impacts. The prediction of the important factors related to the patient's infectious diseases is helpful to health care workers. The aim of this research was to select the critical feature of the relationship between demographic, biochemical, and hematological characteristics, in patients with and without COVID-19 infection.

METHOD

A total of 13,170 participants in the age range of 35-65 years were recruited. Decision Tree (DT), Logistic Regression (LR), and Bootstrap Forest (BF) techniques were fitted into data. Three models were considered in this study, in model I, the biochemical features, in model II, the hematological features, and in model II, both biochemical and homological features were studied.

RESULTS

In Model I, the BF, DT, and LR algorithms identified creatine phosphokinase (CPK), blood urea nitrogen (BUN), fasting blood glucose (FBG), total bilirubin, body mass index (BMI), sex, and age, as important predictors for COVID-19. In Model II, our BF, DT, and LR algorithms identified BMI, sex, mean platelet volume (MPV), and age as important predictors. In Model III, our BF, DT, and LR algorithms identified CPK, BMI, MPV, BUN, FBG, sex, creatinine (Cr), age, and total bilirubin as important predictors.

CONCLUSION

The proposed BF, DT, and LR models appear to be able to predict and classify infected and non-infected people based on CPK, BUN, BMI, MPV, FBG, Sex, Cr, and Age which had a high association with COVID-19.

Characteristics of COVID-19 Patients Admitted to Intensive Care Unit in Multispecialty Hospital of Riyadh, Saudi Arabia: A Retrospective Study

During the early stages of the COVID-19 pandemic, infection rates were high and symptoms were severe. Medical resources, including healthcare experts and hospital facilities, were put to the test to ensure their readiness to deal with this unique event. An intensive care unit (ICU) is expected to be required by many hospitalized patients. Many hospitals worldwide lacked resources during the pandemic's peak stages, particularly in critical care treatment. Because of this, there were issues with capacity, as well as an excessive influx of patients. Additionally, even though the research location provides medical care to a sizable population, there is a paucity of scientific data detailing the situation as it pertains to COVID-19 patients during the height of the outbreak. Therefore, this study aimed to identify and describe the features of COVID-19 patients hospitalized in the ICU of one of the multispecialty hospitals in Riyadh, Saudi Arabia. An observational retrospective study was conducted using a chart review of COVID-19 patients admitted to the ICU between March 2020 and December 2020. To characterize the patients, descriptive statistics were utilized. An exploratory multivariate regression analysis was carried out on the study cohort to investigate the factors that were shown to be predictors of death and intubation. Only 333 (29.33%) of the 1135 samples from the hospital's medical records were used for the final analysis and interpretation. More than 76% of the patients in the study were male, with a mean BMI of 22.07 and an average age of around 49 years. The most frequent chronic condition found among the patients who participated in the study was diabetes (39.34%), followed by hypertension (31.53%). At the time of admission, 63 of the total 333 patients needed to have intubation performed. In total, 22 of the 333 patients died while undergoing therapy. People with both diabetes and hypertension had a 7.85-fold higher risk of death, whereas those with only diabetes or hypertension had a 5.43-fold and 4.21-fold higher risk of death, respectively. At admission, intubation was necessary for many male patients (49 out of 63). Most intubated patients had hypertension, diabetes, or both conditions. Only 13 of the 63 patients who had been intubated died, with the vast majority being extubated. Diabetes and hypertension were significant contributors to the severity of illness experienced by COVID-19 participants. The presence of multiple comorbidities had the highest risk for intubation and mortality among ICU-admitted patients. Although more intubated patients died, the fatality rate was lower than in other countries due to enhanced healthcare management at the ICU of the study center. However, large-scale trials are needed to determine how effective various strategies were in preventing ICU admission, intubation, and death rates.

An investigation of the correlation of vitamin D status and management outcomes in patients with severe COVID-19 at a South African tertiary hospital

Background

Severe COVID-19 has a poor prognosis, and biomarkers may predict disease severity. This study aimed to assess the effect of baseline Vitamin D (VitD) inadequacy on outcome of patients with severe COVID-19 admitted to intensive care unit (ICU) in a tertiary hospital in South Africa.

Methods

Patients with confirmed SARS-CoV-2 were recruited during wave II of the pandemic in Cape Town. Eighty-six patients were included in the study. They were categorized into three groups "VitD deficient, VitD insufficient and VitD sufficient". We combined the VitD deficient with insufficient group to form "VitD inadequate'' group. Cox regression analysis was done to assess the association between VitD status and mortality. Factors with p< 0.05 in adjusted multivariable cox regression were considered statistically significant.

Results

The proportion of VitD inadequacy was 64% (55/86), with significantly higher proportion of hypertension (66%; p 0.012). Kaplan Meir curve showed no significant difference in the probability of survival among the COVID-19 patients admitted in the ICU with or without VitD inadequacy. However, patients with elevated serum creatinine were significantly more at risk of dying (Adjusted Hazard Ratio 1.008 (1.002 - 1.030, p<0.017).

Conclusion

Our study found a high prevalence of VitD inadequacy (combined deficiency and insufficiency) in COVID-19 patients admitted to the ICU. This may indicate a possible risk of severe disease. Whilst there was no statistically significant relationship between VitD status and mortality in this cohort, baseline VitD may be an important prognostic biomarker in COVID-19 patients admitted to the ICU, particularly in those with comorbidities that predispose to VitD deficiency.

Exploration of Sex and Age-Based Associations in Clinical Characteristics, Predictors of Severity, and Duration of Stay among COVID-19 Patients at the University Hospital of Saudi Arabia

COVID-19 infection has a spectrum of variable clinical severity between populations because of their characteristic demographic features, co-morbidities, and immune system reactions. This pandemic tested the healthcare system's preparedness, which depends on predictors of severity and factors related to the duration of hospital stays. Therefore, we carried out a single-center, retrospective cohort study in a tertiary academic hospital to investigate these clinical features and predictors of severe disease and study the different factors that affect hospital stay. We utilized medical records from March 2020 to July 2021, which included 443 confirmed (positive RT-PCR) cases. The data were explained using descriptive statistics and analyzed via multivariate models. Among the patients, 65.4% were female and 34.5% were male, with a mean age of 45.7 years (SD ± 17.2). We presented seven age groups with ranges of 10 years and noticed that patients aged 30-39 years old comprised 23.02% of the records, while patients aged 70 and above comprised 10%. Nearly 47% were diagnosed as having mild, 25% as moderate, 18% as asymptomatic, and 11% as having a severe case of COVID-19 disease. Diabetes was the most common co-morbidity factor in 27.6% of patients, followed by hypertension (26.4%). Our population's predictors of severity included pneumonia, identified on a chest X-ray, and co-morbid conditions such as cardiovascular disease, stroke, ICU stay, and mechanical ventilation. The median length of hospital stay was six days. It was significantly longer in patients with a severe disease and who were administered systemic intravenous steroids. An empirical assessment of various clinical parameters could assist in effectively measuring the disease progression and follow-up with patients.

A Comparative Analysis of Laboratory Parameters of Hospitalized COVID-19 Patients by Disease Severity and Mortality at a Facility in Ibadan, Nigeria

Background

COVID-19, which is caused by the SARS-CoV-2 virus, is associated with abnormalities of biochemical parameters. There are indications that some of these biochemical parameters can differ according to the severity of the disease and the outcome of the infection. This study describes and compares laboratory findings among COVID-19 patients hospitalized at a facility in Southwestern Nigeria according to disease severity and mortality.

Methodology

Records of 223 patients with COVID-19 disease admitted between March 2020 and May 2021 were retrospectively collected. Socio-demographic characteristics, laboratory parameters, and patient outcomes were obtained. Patients were classified according to COVID-19 severity. Laboratory parameters were compared between patients with severe and non-severe disease and between survivors and non-survivors.

Results

Of the 215 patients with some laboratory data included in the analysis, there were 133 (62%) males, and 56.7% were aged above 60 years. A total of 71.6% had severe COVID-19 and 48.4% died during hospitalization. The severe disease occurred significantly more frequently among non-survivors (P0.01). Higher leukocyte and neutrophil counts, urea levels, D-Dimer, and fasting blood glucose levels occurred significantly more frequently in both severe disease and mortality categories. Additionally, elevated GGT and CRP were significantly more common in those with severe than non-severe disease while lower hemoglobin, hematocrit, albumin, and higher creatinine levels were significantly more common in non-survivors.

Conclusion

Our study found that certain readily obtainable biochemical parameters occur more frequently with severe disease and/or mortality amongst patients hospitalized with COVID-19 in sub-Saharan Africa and might be useful for prognostication and allocation of resources.

Biochemical and hematological factors associated with COVID-19 severity among Gabonese patients: A retrospective cohort study

The COVID-19 disease presents a large range of clinical manifestations and includes asymptomatic, mild, and severe cases. The level of severity is related to parameters associated with immunity, genetics, and biochemistry. Africa shows one of the lowest COVID-19 fatality rates but very few data on the biochemical markers of COVID-19 in patients and the factors associated with disease severity are available for the continent. In Gabon, the COVID-19 fatality rate is only 0.63% but almost no data on biomarkers in COVID-19 patients have been published. Both the number of COVID-19 cases and the mortality rate reported in Africa in general, and in Gabon in particular, are lower than in non-African countries. As such, understanding the factors associated with disease severity in Gabonese patients is a crucial step to better understand the disease in the African context and prepare for future COVID-19 waves and other epidemics of emerging diseases. Here, we compared biochemical and hematological markers among 753 Gabonese COVID-19 patients with asymptomatic (184/753), mild/moderate (420/753), and severe/critical (149/753) forms of the disease using an Analysis of Variance (ANOVA) or a Kruskal-Wallis (KW) test. We modeled these parameters together with comorbidities, age, and sex to predict factors associated with disease severity by using a "binomial generalized linear model" utilizing the "package" stats of R software version 4.0.2. Our results showed that almost all the biochemical and hematological parameters (except creatinine, phosphorus, D-dimers, platelets, and monocytes) varied according to disease severity. However, age and the dysfunction of organs like the kidney, liver, and lung together with the decrease of electrolytes (chloride, potassium, and sodium) are the best predictors of disease severity in Gabonese patients.

Risk of Death in Comorbidity Subgroups of Hospitalized COVID-19 Patients Inferred by Routine Laboratory Markers of Systemic Inflammation on Admission: A Retrospective Study

Our study objective was to construct models using 20 routine laboratory parameters on admission to predict disease severity and mortality risk in a group of 254 hospitalized COVID-19 patients. Considering the influence of confounding factors in this single-center study, we also retrospectively assessed the correlations between the risk of death and the routine laboratory parameters within individual comorbidity subgroups. In multivariate regression models and by ROC curve analysis, a model of three routine laboratory parameters (AUC 0.85; 95% CI: 0.79–0.91) and a model of six laboratory factors (AUC 0.86; 95% CI: 0.81–0.91) were able to predict severity and mortality of COVID-19, respectively, compared with any other individual parameter. Hierarchical cluster analysis showed that inflammatory laboratory markers grouped together in three distinct clusters including positive correlations: WBC with NEU, NEU with neutrophil-to-lymphocyte ratio (NLR), NEU with systemic immune-inflammation index (SII), NLR with SII and platelet-to-lymphocyte ratio (PLR) with SII. When analyzing the routine laboratory parameters in the subgroups of comorbidities, the risk of death was associated with a common set of laboratory markers of systemic inflammation. Our results have shown that a panel of several routine laboratory parameters recorded on admission could be helpful for early evaluation of the risk of disease severity and mortality in COVID-19 patients. Inflammatory markers for mortality risk were similar in the subgroups of comorbidities, suggesting the limited effect of confounding factors in predicting COVID-19 mortality at admission.

The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Pandemic: Are Africa's Prevalence and Mortality Rates Relatively Low?

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the cause of coronavirus disease 19 (COVID-19), has been rapidly spreading since December 2019, and within a few months, it turned out to be a global pandemic. The disease affects primarily the lungs, but its pathogenesis spreads to other organs as well. However, its mortality rates vary, and in the majority of infected people, there are no serious consequences. Many factors including advanced age, preexisting health conditions, and genetic predispositions are believed to exacerbate outcomes of COVID-19. The virus contains several structural proteins including the spike (S) protein with subunits for binding, fusion, and internalization into host cells following interaction with host cell receptors and proteases (ACE2 and TMPRSS2, respectively) to cause the subsequent pathology. Although the pandemic has spread into all countries, most of Africa is thought of as having relatively less prevalence and mortality. Several hypotheses have been forwarded as reasons for this and include warmer weather conditions, vaccination with BCG (i.e., trained immunity), and previous malaria infection. From genetics or metabolic points of view, it has been proposed that most African populations could be protected to some degree because they lack some genetic susceptibility risk factors or have low-level expression of allelic variants, such as ACE2 and TMPRSS2 that are thought to be involved in increased infection risk or disease severity. The frequency of occurrence of α-1 antitrypsin (an inhibitor of a tissue-degrading protease, thereby protecting target host tissues including the lung) deficiency is also reported to be low in most African populations. More recently, infections in Africa appear to be on the rise. In general, there are few studies on the epidemiology and pathogenesis of the disease in African contexts, and the overall costs and human life losses due to the pandemic in Africa will be determined by all factors and conditions interacting in complex ways.

The Usefulness of Peripheral Blood Cell Counts to Distinguish COVID-19 from Dengue during Acute Infection

COVID-19 and dengue disease are challenging to tell apart because they have similarities in clinical and laboratory features during the acute phase of infection, leading to misdiagnosis and delayed treatment. The present study evaluated peripheral blood cell count accuracy to distinguish COVID-19 non-critical patients from non-severe dengue cases between the second and eleventh day after symptom onset. A total of 288 patients infected with SARS-CoV-2 (n = 105) or dengue virus (n = 183) were included in this study. Neutrophil, platelet, and lymphocyte counts were used to calculate the neutrophil–lymphocyte ratio (NLR), the platelet–lymphocyte ratio (PLR), and the neutrophil–lymphocyte*platelet ratio (NLPR). The logistic regression and ROC curves analysis revealed that neutrophil and platelet counts, NLR, LPR, and NLPR were higher in COVID-19 than dengue. The multivariate predictive model showed that the neutrophils, platelets, and NLPR were independently associated with COVID-19 with a good fit predictive value (p = 0.1041). The neutrophil (AUC = 0.95, 95% CI = 0.84–0.91), platelet (AUC = 0.89, 95% CI = 0.85–0.93) counts, and NLR (AUC = 0.88, 95% CI = 0.84–0.91) were able to discriminate COVID-19 from dengue with high sensitivity and specificity values (above 80%). Finally, based on predicted probabilities on combining neutrophils and platelets with NLR or NLPR, the adjusted AUC was 0.97 (95% CI = 0.94–0.98) to differentiate COVID-19 from dengue during the acute phase of infection with outstanding accuracy. These findings might suggest that the neutrophil, platelet counts, and NLR or NLPR provide a quick and cost-effective way to distinguish between dengue and COVID-19 in the context of co-epidemics in low-income tropical regions.

Identification of hematological and inflammatory parameters associated with disease severity in hospitalized patients of COVID-19

Background and Objectives:

India has emerged as the second worst hit nation by the Coronavirus pandemic in terms of total number of cases. Our data on hematological and inflammatory markers associated with COVID-19 is very limited. This study was conducted to assess the utility of various biomarkers in identifying severe disease.

Materials and Methods:

All confirmed cases of COVID-19 admitted in our tertiary care centre from 1^st March 2021 to 31^st March 2021 were enrolled in the study. They were categorized into severe and non-severe disease categories based on pre-decided criteria. Their complete blood count parameters, D-dimer levels, serum C-reactive protein (CRP), ferritin and Lactate dehydrogenase (LDH) values were retrieved.

Statistical Analysis:

All parameters were expressed as Mean ± Standard deviation for the two groups of patients. Student's t-test was used to test significance of the above markers between severe and non- severe disease. (P value < 0.05 was taken as statistically significant).

Results:

A total of 150 COVID RT-PCR positive patients were evaluated. The patients with higher Absolute Neutrophil Count (ANC), Neutrophil to lymphocyte Ratio (NLR), Platelet to lymphocyte ratio (PLR), D-dimer levels and raised serum CRP, LDH, ferritin along with lymphocytopenia were associated with severe disease (P < 0.05). Hemoglobin, total leucocyte count and platelet count showed no correlation with disease severity.

Conclusion:

These biomarkers associated with disease severity especially NLR, PLR, D-dimer and serum CRP levels could be used to triage patients at the time of admission thereby identifying those requiring intensive care and enabling optimal resource utilization.

Significance of Laboratory Markers in Predicting the Severity of COVID-19 in the Central Reserve Police Force Front-line Workers with a Review of Literature

COVID-19 disease has variable clinical presentations, ranging from asymptomatic to mild symptoms to severe manifestation with pneumonia, acute respiratory distress syndrome, septic shock, disseminated intravascular coagulation, and/or multiple organ failure. The real-time reverse transcription-polymerase chain reaction is gold standard test for severe acute respiratory syndrome-coronavirus-2 detection. In the present study, we aimed to predict the significance of various hematological and biochemical markers for early identification of complications and assessing the severity of the disease. A total of cases were divided into two study groups, namely, severe and nonsevere based on clinical presentation. Out of 210 cases, 186 (88.5%) cases were nonsevere and 24 (11.5%) cases were severe. Among various hematological and biochemical markers studied, hemoglobin, total leukocyte count, neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, albumin, lactate dehydrogenase, C-reactive protein, ferritin, D-dimer, and interleukin-6 are found to have P < 0.05 and significantly correlated with the severity of disease.