Value of the Neutrophil-Lymphocyte Ratio in Predicting COVID-19 Severity: A Meta-analysis

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and multiple organ dysfunction. Although some hematological and biochemical parameters are altered during moderate and severe COVID-19, there is still a lack of tools to combine these parameters to predict the clinical outcome of a patient with COVID-19. Thus, this study aimed at employing hematological and biochemical parameters of patients diagnosed with COVID-19 in order to build machine learning algorithms for predicting COVID mortality or survival. Patients included in the study had a diagnosis of SARS-CoV-2 infection confirmed by RT-PCR and biochemical and hematological measurements were performed in three different time points upon hospital admission. Among the parameters evaluated, the ones that stand out the most are the important features of the T1 time point (urea, lymphocytes, glucose, basophils and age), which could be possible biomarkers for the severity of COVID-19 patients. This study shows that urea is the parameter that best classifies patient severity and rises over time, making it a crucial analyte to be used in machine learning algorithms to predict patient outcome. In this study optimal and medically interpretable machine learning algorithms for outcome prediction are presented for each time point. It was found that urea is the most paramount variable for outcome prediction over all three time points. However, the order of importance of other variables changes for each time point, demonstrating the importance of a dynamic approach for an effective patient's outcome prediction. All in all, the use of machine learning algorithms can be a defining tool for laboratory monitoring and clinical outcome prediction, which may bring benefits to public health in future pandemics with newly emerging and reemerging SARS-CoV-2 variants of concern.

Prognostic Significance of NLR and PLR in COVID-19: A Multi-Cohort Validation Study

INTRODUCTION

Recent studies have highlighted the prognostic value of easily accessible inflammatory markers, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) for predicting severe outcomes in patients affected by Coronavirus disease 2019 (COVID-19). Our study validates NLR and PLR cut-off values from a prior cohort at IRCCS Policlinico San Matteo (OSM) of Pavia, Italy, across two new cohorts from different hospitals. This aims to enhance the generalizability of these prognostic indicators.

METHODS

In this retrospective cohort study, conducted at Milan's Ospedale Luigi Sacco (OLS) and IRCCS Ospedale Maggiore Policlinico (OMP) hospitals, we assess the predictive capacity of NLR and PLR for three main outcomes-non-invasive ventilation (NIV) or continuous positive airway pressure (CPAP) usage, invasive ventilation (IV), and death-in patients with COVID-19 at admission. For each outcome, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed separately for male and female cohorts. Distinct NLR and PLR cut-off values were used for men (7.00, 7.29, 7.00 for NLR; 239.22, 248.00, 250.39 for PLR) and women (6.36, 7.00, 6.28 for NLR; 233.00, 246.45, 241.54 for PLR), retrieved from the first cohort at OSM.

RESULTS

A total of 3599 patients were included in our study, 1842 from OLS and 1757 from OMP. OLS and OMP sensitivity values for both NLR and PLR (NLR: 24-67%, PLR: 40-64%) were inferior to specificity values (NLR: 64-76%, PLR: 55-72%). Additionally, PPVs generally remained lower (< 63%), while NPVs consistently surpassed 68% for PLR and 72% for NLR. Finally, both PLR and NLR exhibited consistently higher NPVs for more severe outcomes (> 82%) compared to NPVs for CPAP/NIV.

CONCLUSIONS

Consistent findings across diverse patient populations validate the reliability and applicability of NLR and PLR cut-off values. High NPVs emphasize their role in identifying individuals less likely to experience severe outcomes. These markers not only aid in risk stratification but also guide resource allocation in emergencies or limited-resource situations.

Assessing the Diagnostic Values of the Neutrophil-to-Lymphocyte Ratio (NLR) and Systematic Immunoinflammatory Index (SII) as Biomarkers in Predicting COVID-19 Severity: A Multicentre Comparative Study

COVID-19 has been notoriously unpredictable in its clinical course. Such unpredictability poses a challenge to clinicians in predicting patients who will develop severe cases and possibly die from the infection. This study aims to assess and compare the diagnostic value of the NLR and SII as biomarkers in predicting COVID-19 severity, represented by mortality, with a multicentre comparative study including 855 patients in Saudi Arabia. Descriptive and analytical statistics were used to compare haematological indices between survivors and non-survivors. The median age of patients included was 41 years old, with an almost equal ratio of men to women. Most participants were Saudis, and the mortality rate in the study cohort was 13.22%. Non-survivors, as compared to survivors, were significantly older, had lower RBC counts, haemoglobin and haematocrit levels, as well as significantly higher WBC and neutrophil counts. Both the NLR and SII were capable of differentiating between survivors and non-survivors, with the latter having significantly higher values. However, the NLR was superior to the SII in such differentiation, as it had a larger area under the curve. This study further confirms the diagnostic values of the NLR and SII as biomarkers in predicting COVID-19 severity and mortality, with the NLR being more sensitive and specific. Clinical guidelines on managing COVID-19 cases should benefit from these findings by harnessing the value of the NLR in COVID-19 management.

UBA1-CDK16 : A Sex-Specific Chimeric RNA and Its Role in Immune Sexual Dimorphism

RNA processing mechanisms, such as alternative splicing and RNA editing, have been recognized as critical means to expand the transcriptome. Chimeric RNAs formed by intergenic splicing provide another potential layer of RNA diversification. By analyzing a large set of RNA-Seq data and validating results in over 1,200 blood samples, we identified UBA1-CDK16 , a female-specific chimeric transcript. Intriguingly, both parental genes, are expressed in males and females. Mechanistically, UBA1-CDK16 is produced by cis-splicing between the two adjacent X-linked genes, originating from the inactive X chromosome. A female-specific chromatin loop, formed between the junction sites, facilitates the alternative splicing of its readthrough precursor. This unique chimeric transcript exhibits evolutionary conservation, evolving to be female-specific from non-human primates to humans. Furthermore, our investigation reveals that UBA1-CDK16 is enriched in the myeloid lineage and plays a regulatory role in myeloid differentiation. Notably, female COVID-19 patients who tested negative for this chimeric transcript displayed higher counts of neutrophils, highlighting its potential role in disease pathogenesis. These findings support the notion that chimeric RNAs represent a new repertoire of transcripts that can be regulated independently from the parental genes, and a new class of RNA variance with potential implications in sexual dimorphism and immune responses.

Haematological sequelae in the post-acute phase of symptomatic SARS-CoV-2 infection

Many patients surviving SARS-CoV-2 infection suffer from long-term symptoms (long COVID or post COVID) such as shortness of breath, fatigue, loss of taste or smell and cognitive deterioration. However, few data are available concerning blood cell counts and haematological parameters during the post-COVID period. We analysed haematological data from 83 patients previously admitted to the internal medicine unit of our institution because of symptomatic SARS-CoV-2 infection; all data were obtained within 1-12 months from disease onset. A control group of 70 apparently healthy, age- and sex-matched COVID-19 negative individuals was assessed for comparison. Blood cell counts improved in the post-COVID period, but 81% of patients had persistent abnormalities, compared with 50% in the control group, p < 0.001. Most common haematological findings included anaemia (40%), reduced lymphocyte (43%) or eosinophil counts (38%) and low IgM memory B cells and correlated with advanced age, number of chronic comorbidities, female gender, altered renal function, reduced baseline Hb and procalcitonin concentrations and increased RDW. Data on lymphocytes and IgM memory B cells show that impaired immune responses may persist for up to one year in the post-COVID period, possibly contributing to long-term symptoms, especially in female patients.

Developing a COVID-19 Mortality Prediction (CoMPred) Indicator for ICU Diabetic Patients Treated with Tocilizumab in Saudi Arabia: A Proof-of-Concept Study

Since the beginning of the COVID-19 pandemic, efforts have been made to underline its discourse and identify factors contributing to its severe forms. Clinically, many physicians depended on subjective criteria to determine its severe forms, which varied significantly between practices. However, they did not rely on objective laboratory findings. This study aimed to present a novel and objective laboratory-based indicator to predict mortality among COVID-19 patients. The study included 249 COVID-19 patients who were admitted to the ICU, of which 80 did not survive. The COVID-19 Mortality Prediction (CoMPred) indicator was developed by including the age and the following lab investigations: neutrophil-to-lymphocyte ratio (NLR), D-Dimer, PT, aPTT, ESR, CRP, and urea levels. A CoMPred score of 7.5 or higher carries a sensitivity of 81.10% in predicting mortality, i.e., a patient with a CoMPred score of 7.5 or higher has an 81.10% chance of dying. The CoMPred indicator score directly correlates with mortality, i.e., the higher the score, the higher the possibility of the patient dying. In conclusion, the CoMPred indicator is an objective tool that is affordable and widely available, will assist physicians, and limit the burden on clinical decisions on an unpredicted course of COVID-19 in patients.

Age-dependent immune responses in COVID-19-mediated liver injury: focus on cytokines

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is potentially pathogenic and causes severe symptoms; in addition to respiratory syndromes, patients might experience other severe conditions such as digestive complications and liver complications injury. The abnormality in the liver is manifested by hepatobiliary dysfunction and enzymatic elevation, which is associated with morbidity and mortality. The direct cytopathic effect, immune dysfunction, cytokine storm, and adverse effects of therapeutic regimens have a crucial role in the severity of liver injury. According to aging and immune system alterations, cytokine patterns may also change in the elderly. Moreover, hyperproduction of cytokines in the inflammatory response to SARS-CoV-2 can lead to multi-organ dysfunction. The mortality rate in elderly patients, particularly those with other comorbidities, is also higher than in adults. Although the pathogenic effect of SARS-CoV-2 on the liver has been widely studied, the impact of age and immune-mediated responses at different ages remain unclear. This review discusses the association between immune system responses in coronavirus disease 2019 (COVID-19) patients of different ages and liver injury, focusing on cytokine alterations.

Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis

BACKGROUND

Deep learning (DL) prediction models hold great promise in the triage of COVID-19.

OBJECTIVE

We aimed to evaluate the diagnostic test accuracy of DL prediction models for assessing and predicting the severity of COVID-19.

METHODS

We searched PubMed, Scopus, LitCovid, Embase, Ovid, and the Cochrane Library for studies published from December 1, 2019, to April 30, 2022. Studies that used DL prediction models to assess or predict COVID-19 severity were included, while those without diagnostic test accuracy analysis or severity dichotomies were excluded. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2), PROBAST (Prediction Model Risk of Bias Assessment Tool), and funnel plots were used to estimate the bias and applicability.

RESULTS

A total of 12 retrospective studies involving 2006 patients reported the cross-sectionally assessed value of DL on COVID-19 severity. The pooled sensitivity and area under the curve were 0.92 (95% CI 0.89-0.94; I²=0.00%) and 0.95 (95% CI 0.92-0.96), respectively. A total of 13 retrospective studies involving 3951 patients reported the longitudinal predictive value of DL for disease severity. The pooled sensitivity and area under the curve were 0.76 (95% CI 0.74-0.79; I²=0.00%) and 0.80 (95% CI 0.76-0.83), respectively.

CONCLUSIONS

DL prediction models can help clinicians identify potentially severe cases for early triage. However, high-quality research is lacking.

TRIAL REGISTRATION

PROSPERO CRD42022329252; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD 42022329252.

Differential Characteristics of Patients for Hospitalized Severe COVID-19 Infected by the Omicron Variants and Wild Type of SARS-CoV-2 in China

Background

As multiple mutations of SARS-Cov-2 exist, there are now many viral variants with regional differences in distribution. The clinical characteristics of patients hospitalized with the virus also vary significantly, with those of the Omicron variants being strikingly different from those of the earliest wild-type variant. However, comprehensive data on this subject is lacking. It is therefore crucial to explore these differences to develop better clinical strategies for the management of COVID-19.

Methods

A total of 554 confirmed COVID-19 cases in China were clinically classified as mild, moderate, severe, and critical according to their diagnoses and treatment plans. We compared the demographics and clinical characteristics of patients infected with the Omicron vs wild-type strains, between severe and non-severe cases. Bacterial co-infections with SARS-CoV-2 and correlation between inflammatory factors and T cells were analyzed.

Results

Compared to the wild-type cases, the severe Omicron cases were older (median age 48.36 vs 73.24), and had more upper-respiratory symptoms and comorbidities. Decreased leukocyte counts were less pronounced, although more instances of significantly decreased CD4+ and CD8+ T-cell counts, elevated infection-related biomarkers (eg procalcitonin and C-reactive protein), and abnormal coagulation factors (including increased D-dimer and fibrinogen levels) were detected in the severe Omicron cases. The mean length of hospital stay was significantly shorter in the severe Omicron cases. CD4+ and CD8+ T cell numbers were negatively correlated with neutrophil-to-lymphocyte ratios, as well as serum interleukin-6, procalcitonin, and C-reactive protein levels.

Conclusion

There were significant clinical differences between patients hospitalized with severe cases of Omicron- variant COVID-19 vs wild-type. The Omicron cases tended to be older and had more upper respiratory tract symptoms, comorbidities and bacterial co-infections. Elevated levels of inflammatory cytokines with T-cell depletion correlated with poor disease progression and prognosis. We hope these data provide a theoretical basis for future integrated prevention and control plans for COVID-19.

The Fatal Clinical Outcome of Severe COVID-19 in Hospitalized Patients: Findings from a Prospective Cohort Study in Dhaka, Bangladesh

Background and Objectives: The morbidity and mortality associated with COVID-19 have burdened worldwide healthcare systems beyond their capacities, forcing them to promptly investigate the virus characteristics and its associated outcomes. This clinical analysis aimed to explore the key factors related to the fatal outcome of severe COVID-19 cases. Materials and Methods: Thirty-five adult severe COVID-19 patients were enrolled from two COVID-19 hospitals in Dhaka, Bangladesh. Clinical manifestation, comorbid conditions, medications, SARS-CoV-2 RT-PCR related cycle threshold (CT) value, hematology, biochemical parameters with SARS-CoV-2 specific IgG and IgM responses at enrollment were compared between the survivors and deceased participants. Results: Total 27 patients survived and 8 patients died within 3 months of disease onset. Deceased patients suffered longer from shortness of breath than the survived (p = 0.049). Among the severe cases, 62% of the deceased patients had multiple comorbid condition compared to 48% of those who survived. Interestingly, the anti-viral was initiated earlier among the deceased patients [median day of 1 (IQR: 0, 1.5) versus 6.5 (IQR: 6.25, 6.75)]. Most of the survivors (55%) received a combination of anticoagulant (p = 0.034). Liver enzymes, creatinine kinase, and procalcitonin were higher among the deceased patients during enrollment. The median CT value among the deceased was significantly lower than the survivors (p = 0.025). A significant difference for initial IgG (p = 0.013) and IgM (p = 0.030) responses was found between the survivor and the deceased groups. Conclusions: The factors including older age, male gender, early onset of respiratory distress, multiple comorbidities, low CT value, and poor antibody response may contribute to the fatal outcome in severe COVID-19 patients. Early initiation of anti-viral and a combination of anticoagulant treatment may prevent or lower the fatality among severe COVID-19 cases.

Impact of genotype on clinical course in sickle cell disease and the utility of neutrophil-lymphocyte ratio: a ten-year single-institution experience

BACKGROUND

Sickle cell disease (SCD) is a diverse group of blood disorders with significant global disease burden. Contemporary interest in the underlying inflammatory paradigm of SCD has emphasized the role of the neutrophil-lymphocyte ratio (NLR) as a prognostic inflammatory marker.

METHODS

We retrospectively reviewed 268 hospitalized patients with SCDs of different genotypes (HbSS, HbSβ0 thalassemia, HbSβ+ thalassemia, and HbSC), totaling 3329 hospital admissions over a 10-year period. Patients were stratified into SS/Sβ0 and Sβ+/SC groups for statistical analysis of parameters collected at steady state and at hospital admission.

RESULTS

At steady state, per unit increase of hemoglobin values was associated with reduced odds of ≥ 2 hospital admissions per year in SS/Sβ0 and Sβ+/SC groups; per unit increase in platelet count and white blood cell count was associated with increased odds only in the SS/Sβ0 group. The NLR had no association in either group. During admission, a cutoff of NLR = 3.5 discerned infection with a sensitivity of 60% and specificity of 57%. Performance improved when excluding patients on outpatient hydroxyurea therapy (cutoff of NLR = 3.5; sensitivity of 68% and specificity of 64%).

CONCLUSION

This study supports the utility of NLR as an accessible adjunctive clinical tool in SCD prognostication.

Changes and Rate of Change in Neutrophil-Lymphocyte Ratio (∆NLR) as an Early Prognostic Marker for the Severity of Outcomes in Patients With COVID-19 and Its Applicability in Other Viral and Bacterial Diseases

INTRODUCTION

COVID-19 is a global pandemic that has spread rapidly and resulted in numerous deaths worldwide. Many inflammatory markers such as neutrophil-lymphocyte ratio (NLR), D-dimer, serum ferritin, C-reactive protein (CRP), and interleukin-6 (IL-6) were used for the diagnosis and prognosis of COVID-19.

METHODS

We have proposed using Delta NLR (0-48 hours) (∆NLR) as an early diagnostic marker for COVID-19 and other inflammatory disorders. We have created a prediction model based on six variables: overall severity, death, shifting to the ICU, length of stay, oxygen requirement, and ventilator support. Prediction models help us prepare for future pandemics through early diagnosis and management.

RESULTS

A total of 1,865 patient records were retrieved from the database. The final sample available for analysis was 461. Change in NLR or ∆NLR was significant for all the models (except for length of stay) created by logistic regression.

CONCLUSION

An independent predictor of the poor prognosis of COVID-19 is the severity of the disease in the initial one or two days. ∆NLR is a unique marker, and its scope of use in other disorders' prognoses must be further researched. The prediction models also help us in decision-making strategies and also prepare us for future pandemics.

Determining clinical biomarkers to predict long-term SARS-CoV-2 antibody response among COVID-19 patients in Bangladesh

Background

Information on antibody responses following SARS-CoV-2 infection, including the magnitude and duration of responses, is limited. In this analysis, we aimed to identify clinical biomarkers that can predict long-term antibody responses following natural SARS-CoV-2 infection.

Methodology

In this prospective study, we enrolled 100 COVID-19 patients between November 2020 and February 2021 and followed them for 6 months. The association of clinical laboratory parameters on enrollment, including lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and D-dimer, with predicting the geometric mean (GM) concentration of SARS-CoV-2 receptor-binding domain (RBD)-specific IgG antibody at 3 and 6 months post-infection was assessed in multivariable linear regression models.

Result

The mean ± SD age of patients in the cohort was 46.8 ± 14 years, and 58.8% were male. Data from 68 patients at 3 months follow-up and 55 patients at 6 months follow-up were analyzed. Over 90% of patients were seropositive against RBD-specific IgG till 6 months post-infection. At 3 months, for any 10% increase in absolute lymphocyte count and NLR, there was a 6.28% (95% CI: 9.68, -2.77) decrease and 4.93% (95% CI: 2.43, 7.50) increase, respectively, in GM of IgG concentration, while any 10% increase for LDH, CRP, ferritin, and procalcitonin was associated with a 10.63, 2.87, 2.54, and 3.11% increase in the GM of IgG concentration, respectively. Any 10% increase in LDH, CRP, and ferritin was similarly associated with an 11.28, 2.48, and 3.0% increase in GM of IgG concentration at 6 months post-infection.

Conclusion

Several clinical biomarkers in the acute phase of SARS-CoV-2 infection are associated with enhanced IgG antibody response detected after 6 months of disease onset. The measurement of SARS-CoV-2 specific antibody responses requires improved techniques and is not feasible in all settings. Baseline clinical biomarkers can be a useful alternative as they can predict antibody response during the convalescence period. Individuals with an increased level of NLR, CRP, LDH, ferritin, and procalcitonin may benefit from the boosting effect of vaccines. Further analyses will determine whether biochemical parameters can predict RBD-specific IgG antibody responses at later time points and the association of neutralizing antibody responses.

Dynamic NLR and PLR in Predicting COVID-19 Severity: A Retrospective Cohort Study

INTRODUCTION

The hyperinflammation phase of severe SARS-CoV-2 is characterised by complete blood count alterations. In this context, the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) can be used as prognostic factors. We studied NLR and PLR trends at different timepoints and computed optimal cutoffs to predict four outcomes: use of continuous positive airways pressure (CPAP), intensive care unit (ICU) admission, invasive ventilation and death.

METHODS

We retrospectively included all adult patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia admitted from 23 January 2020 to 18 May 2021. Analyses included non-parametric tests to study the ability of NLR and PLR to distinguish the patients' outcomes at each timepoint. Receiver operating characteristic (ROC) curves were built for NLR and PLR at each timepoint (minus discharge) to identify cutoffs to distinguish severe and non-severe disease. Their statistical significance was assessed with the chi-square test. Collection of data under the SMACORE database was approved with protocol number 20200046877.

RESULTS

We included 2169 patients. NLR and PLR were higher in severe coronavirus disease 2019 (COVID-19). Both ratios were able to distinguish the outcomes at each timepoint. For NLR, the areas under the receiver operating characteristic curve (AUROC) ranged between 0.59 and 0.81, and for PLR between 0.53 and 0.67. From each ROC curve we computed an optimal cutoff value.

CONCLUSION

NLR and PLR cutoffs are able to distinguish severity grades and mortality at different timepoints during the course of disease, and, as such, they allow a tailored approach. Future prospects include validating our cutoffs in a prospective cohort and comparing their performance against other COVID-19 scores.

In the SARS-CoV-2 Pandora Pandemic: Can the Stance of Premorbid Intestinal Innate Immune System as Measured by Fecal Adnab-9 Binding of p87:Blood Ferritin, Yielding the FERAD Ratio, Predict COVID-19 Susceptibility and Survival in a Prospective Population Database?

SARS-CoV-2 severity predictions are feasible, though individual susceptibility is not. The latter prediction allows for planning vaccination strategies and the quarantine of vulnerable targets. Ironically, the innate immune response (InImS) is both an antiviral defense and the potential cause of adverse immune outcomes. The competition for iron has been recognized between both the immune system and invading pathogens and expressed in a ratio of ferritin divided by p87 (as defined by the Adnab-9 ELISA stool-binding optical density, minus the background), known as the FERAD ratio. Associations with the FERAD ratio may allow predictive modeling for the susceptibility and severity of disease. We evaluated other potential COVID-19 biomarkers prospectively. Patients with PCR+ COVID-19 tests (Group 1; n = 28) were compared to three other groups. In Group 2 (n = 36), and 13 patients displayed COVID-19-like symptoms but had negative PCR or negative antibody tests. Group 3 (n = 90) had no symptoms and were negative when routinely PCR-tested before medical procedures. Group 4 (n = 2129) comprised a pool of patients who had stool tests and symptoms, but their COVID-19 diagnoses were unknown; therefore, they were chosen to represent the general population. Twenty percent of the Group 4 patients (n = 432) had sufficient data to calculate their FERAD ratios, which were inversely correlated with the risk of COVID-19 in the future. In a case report of a neonate, we studied three biomarkers implicated in COVID-19, including p87, Src (cellular-p60-sarcoma antigen), and Abl (ABL-proto-oncogene 2). The InImS of the first two were positively correlated. An inverse correlation was found between ferritin and lysozyme in serum (p < 0.05), suggesting that iron could have impaired an important innate immune system anti-viral effector and could partially explain future COVID-19 susceptibility.

The Role of Hemogram-derived Ratios in COVID-19 Severity Stratification in a Primary Healthcare Facility

Background

Coronavirus disease 2019 (COVID-19) can cause a wide clinical spectrum, ranging from asymptomatic to severe disease with a high mortality rate. In view of the current pandemic and the increasing influx of patients into healthcare facilities, there is a need to identify simple and reliable tools for stratifying patients.

Objective

Study aimed to analyze whether hemogram-derived ratios (HDRs) can be used to identify patients with a risk of developing a severe clinical form and admission to hospital.

Methods

This cross-sectional and observational study included 500 patients with a confirmed diagnosis of COVID-19. Data on clinical features and laboratory parameters were collected from medical records and 13 HDRs were calculated and analyzed. Descriptive and inferential statistics were included in the analysis.

Results

Of the 500 patients, 43.8% had a severe form of the disease. Lymphocytopenia, monocytopenia, higher C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR) were found in severe patients (p < 0.05). Significantly higher neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), neutrophil-to-platelet ratio (NPR), neutrophil-to-lymphocyte-to-platelet ratio (NLPR) and CRP-to-lymphocyte ratio (CRP/Ly) values were found in severe patients (p < 0.001). In addition, they have statistically significant prognostic potential (p < 0.001). The area under the curve (AUC) for CRP/Ly, dNLR, NLPR, NLR, and NPR were 0.693, 0.619, 0.619, 0.616, and 0.603, respectively. The sensitivity and specificity were 65.7% and 65.6% for CRP/Ly, 51.6% and 70.8 for dNLR, 61.6% and 57.3% for NLPR, 40.6% and 80.4% for NLR, and 48.8% and 69.1% for NPR.

Conclusion

The results of the study suggest that NLR, dNLR, CRP/Ly, NPR, and NLPR can be considered as potentially useful markers for stratifying patients with a severe form of the disease. HDRs derived from routine blood tests results should be included in common laboratory practice since they are readily available, easy to calculate, and inexpensive.

The turning point of COVID-19 severity is associated with a unique circulating neutrophil gene signature

COVID-19 has a broad spectrum of clinical manifestations associated with the host immune response heterogeneity. Despite the advances in COVID-19 research, it is still crucial to seek a panel of molecular markers that enable accurate stratification of COVID-19 patients. Here, we performed a study that combined analysis of blood transcriptome, demographic data, clinical aspects and laboratory findings from 66 participants classified into different degrees of COVID-19 severity and healthy subjects. We identified a perturbation in blood-leukocyte transcriptional profile associated with COVID-19 aggravation, which was mainly related to processes that disfavoured lymphocyte activation and favoured neutrophil activation. This transcriptional profile stratified patients according to COVID-19 severity. Hence, it enabled identification of a turning point in transcriptional dynamics that distinguished disease outcomes and non-hospitalized from hospitalized moderate patients. Central genes of this unique neutrophil signature were S100A9, ANXA3, CEACAM6, VNN1, OLFM4, IL1R2, TCN1 and CD177. Our study indicates the molecular changes that are linked with the differing clinical aspects presented by humans when suffering from COVID-19, which involve neutrophil activation.

A two-gene marker for the two-tiered innate immune response in COVID-19 patients

For coronavirus disease 2019 (COVID-19), a pandemic disease characterized by strong immune dysregulation in severe patients, convenient and efficient monitoring of the host immune response is critical. Human hosts respond to viral and bacterial infections in different ways, the former is characterized by the activation of interferon stimulated genes (ISGs) such as IFI27, while the latter is characterized by the activation of anti-bacterial associated genes (ABGs) such as S100A12. This two-tiered innate immune response has not been examined in COVID-19. In this study, the activation patterns of this two-tiered innate immune response represented by IFI27 and S100A12 were explored based on 1421 samples from 17 transcriptome datasets derived from the blood of COVID-19 patients and relevant controls. It was found that IFI27 activation occurred in most of the symptomatic patients and displayed no correlation with disease severity, while S100A12 activation was more restricted to patients under severe and critical conditions with a stepwise activation pattern. In addition, most of the S100A12 activation was accompanied by IFI27 activation. Furthermore, the activation of IFI27 was most pronounced within the first week of symptom onset, but generally waned after 2-3 weeks. On the other hand, the activation of S100A12 displayed no apparent correlation with disease duration and could last for several months in certain patients. These features of the two-tiered innate immune response can further our understanding on the disease mechanism of COVID-19 and may have implications to the clinical triage. Development of a convenient two-gene protocol for the routine serial monitoring of this two-tiered immune response will be a valuable addition to the existing laboratory tests.

Clinical Utility of Circulating Pneumoproteins as Diagnostic and Prognostic Biomarkers in COVID-19: A Systematic Review and Meta-analysis

INTRODUCTION

This study explored circulating pneumoproteins in the diagnosis, severity, and prognosis of COVID-19 by meta-analysis.

METHODS

We searched five databases and other sources until December 16, 2021. Standardized mean difference (SMD) and 95% confidence interval (CI) were the overall outcomes. RevMan 5.3, Stata 16, and Meta-DiSc 1.4 were utilized for pooled analysis.

RESULTS

A total of 2432 subjects from 26 studies were included. Patients with COVID-19 had higher circulating KL-6, SP-D, and SP-A levels (SMD 1.34, 95% CI [0.60, 2.08]; SMD 1.74, 95% CI [0.64, 2.84]; SMD 3.42, 95% CI [1.31, 5.53], respectively) than healthy individuals. Circulating SP-D levels were not significantly different in survivors and non-survivors (SMD - 0.19, 95% CI [- 0.78, 0.40]). Circulating KL-6, SP-D, and RAGE levels in patients with mild to moderate COVID-19 were significantly lower (SMD - 0.93, 95% CI [- 1.22, - 0.65]; SMD - 1.32, 95% CI [- 2.34, - 0.29]; SMD - 1.17, 95% CI [- 2.06, - 0.28], respectively) than in patients with severe COVID-19. Subgroup analysis suggested that country and total number may be related to the heterogeneity when analyzing SP-D in patients with mild to moderate vs. severe COVID-19. The meta-analysis of diagnostic accuracy including KL-6 for severity, KL-6 for mortality, and SP-D for severity demonstrated that they all had limited diagnostic value.

CONCLUSION

Therefore, circulating pneumoproteins (KL-6, SP-D, and RAGEs) reflect the diagnosis, severity, and prognosis of COVID-19, and follow-up studies are still needed.

The Cost-Effectiveness of Requesting a Complete Blood Count (CBC) in the Management of COVID-19 in Saudi Arabia

Background: Since the beginning of the COVID-19 pandemic, studies have attempted to determine the value of the different laboratory investigations. This study aims to assess the cost-effectiveness of requesting a complete blood count (CBC) for COVID-19 patients, as opposed to ordering a COVID-19 antibody titer in Saudi Arabia. Methods: The prices of a CBC, COVID-19 PCR, and antibody titer were obtained from 40 healthcare establishments in Saudi Arabia, between January and February 2022. Results: Requesting a CBC is significantly cheaper than requesting a COVID-19 antibody titer, which was available in almost all of the establishments, as compared to COVID-19 PCR and antibody titer testing. The investigation prices did not differ significantly between hospitals and private laboratories, nor across cities in Saudi Arabia. Conclusions: CBC, which provides valuable information on the patient’s condition and prognosis, is a cost-effective and widely available tool for managing COVID-19. The price and availability of CBC warrant it to be included in the COVID-19 management protocols.

Distribution of Pathogens and Predictive Values of Biomarkers of Inflammatory Response at ICU Admission on Outcomes of Critically Ill COVID-19 Patients with Bacterial Superinfections-Observations from National COVID-19 Hospital in Croatia

BACKGROUND

Superinfections contribute to mortality and length of stay in critically ill COVID-19 patients. The aim of this study was to determine the incidence and pathogen distribution of bacterial and fungal superinfections of the lower respiratory tract (LRTI), urinary tract (UTI) and bloodstream (BSI) and to determine the predictive value of biomarkers of inflammatory response on their ICU survival rates.

METHODS

A retrospective observational study that included critically ill COVID-19 patients treated during an 11-month period in a Croatian national COVID-19 hospital was performed. Clinical and diagnostic data were analyzed according to the origin of superinfection, and multivariate regression analysis was performed to determine the predictive values of biomarkers of inflammation on their survival rates.

RESULTS

55.3% critically ill COVID-19 patients developed bacterial or fungal superinfections, and LRTI were most common, followed by BSI and UTI. Multidrug-resistant pathogens were the most common causes of LRTI and BSI, while Enterococcus faecalis was the most common pathogen causing UTI. Serum ferritin and neutrophil count were associated with decreased chances of survival in patients with LRTI, and patients with multidrug-resistant isolates had significantly higher mortality rates, coupled with longer ICU stays.

CONCLUSION

The incidence of superinfections in critically ill COVID-19 patients was 55.3%, and multidrug-resistant pathogens were dominant. Elevated ferritin levels and neutrophilia at ICU admission were associated with increased ICU mortality in patients with positive LRTI.

The GNB3 c.825C>T (rs5443) polymorphism and protection against fatal outcome of corona virus disease 2019 (COVID-19)

Background and aims: Albeit several factors which influence the outcome of corona virus disease (COVID-19) are already known, genetic markers which may predict the outcome of the disease in hospitalized patients are still very sparse. Thus, in this study, we aimed to analyze whether the single-nucleotide polymorphism (SNP) rs5443 in the gene GNB3, which was associated with higher T cell responses in previous studies, might be a suitable biomarker to predict T cell responses and the outcome of COVID-19 in a comprehensive German cohort.

Methods: We analyzed the influence of demographics, pre-existing disorders, laboratory parameters at the time of hospitalization, and GNB3 rs5443 genotype in a comprehensive cohort (N = 1570) on the outcome of COVID-19. In a sub cohort, we analyzed SARS-CoV-2-specific T cell responses and associated GNB3 rs5443 genotypes. We investigated the influence of all factors on COVID-19 fatality in multivariable analysis.

Results: We found a younger patient age, normotension or absence of diabetes mellitus or cardiovascular diseases, normal blood cell counts, and low inflammatory markers at hospital admission were protective factors against fatal course of disease. In addition, the rs5443 TT genotype was significantly associated with protection against COVID-19 fatality (OR: 0.60, 95% CI: 0.40–0.92, p = 0.02). We also observed significantly increased SARS-CoV-2-specific T cell responses in rs5443 TT genotype carriers (p = 0.01). Although we observed a significant association of the factors described previously in univariate analysis, only a younger age of the patients, normal blood cell counts, and the GNB3 rs5443 TT genotype remained independent predictors against COVID-19 fatality in multivariable analysis.

Conclusion: Immutable predictors for COVID-19 fatality are relatively rare. In this study we could show that the TT genotype of the SNP rs5443 in the gene GNB3 is associated with protection against COVID-19 fatality. It was as well correlated to higher SARS-CoV-2-specific T cell responses, which could result in a milder course of disease in those patients. Based on those observations we hereby provide a further prognostic biomarker, which might be used in routine diagnostics as a predictive factor for COVID-19 mortality already upon hospitalization.

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

Background

Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection.

Methods

Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman's rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score.

Results

The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count.

Conclusion

The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

The Role of Neutrophil-to-Lymphocyte Ratio in Risk Stratification and Prognostication of COVID-19: A Systematic Review and Meta-Analysis

Several studies have proposed that the neutrophil−lymphocyte ratio (NLR) is one of the various biomarkers that can be useful in assessing COVID-19 disease-related outcomes. Our systematic review analyzes the relationship between on-admission NLR values and COVID-19 severity and mortality. Six different severity criteria were used. A search of the literature in various databases was conducted from 1 January 2020 to 1 May 2021. We calculated the pooled standardized mean difference (SMD) for the collected NLR values. A meta-regression analysis was performed, looking at the length of hospitalization and other probable confounders, such as age, gender, and comorbidities. A total of sixty-four studies were considered, which included a total of 15,683 patients. The meta-analysis showed an SMD of 3.12 (95% CI: 2.64−3.59) in NLR values between severe and non-severe patients. A difference of 3.93 (95% CI: 2.35−5.50) was found between survivors and non-survivors of the disease. Upon summary receiver operating characteristics analysis, NLR showed 80.2% (95% CI: 74.0−85.2%) sensitivity and 75.8% (95% CI: 71.3−79.9%) specificity for the prediction of severity and 78.8% (95% CI: 73.5−83.2%) sensitivity and 73.0% (95% CI: 68.4−77.1%) specificity for mortality, and was not influenced by age, gender, or co-morbid conditions. Conclusion: On admission, NLR predicts both severity and mortality in COVID-19 patients, and an NLR > 6.5 is associated with significantly greater the odds of mortality.

Association of Prognostic Nutritional Index with Severity and Mortality of Hospitalized Patients with COVID-19: A Systematic Review and Meta-Analysis

The associations of prognostic nutritional index (PNI) with disease severity and mortality in patients with coronavirus disease 2019 (COVID-19) remain unclear. Electronic databases, including MEDLINE, EMBASE, Google scholar, and Cochrane Library, were searched from inception to 10 May 2022. The associations of PNI with risk of mortality (primary outcome) and disease severity (secondary outcome) were investigated. Merged results from meta-analysis of 13 retrospective studies (4204 patients) published between 2020 and 2022 revealed a lower PNI among patients in the mortality group [mean difference (MD): −8.65, p < 0.001] or severity group (MD: −5.19, p < 0.001) compared to those in the non-mortality or non-severity groups. A per-point increase in PNI was associated with a reduced risk of mortality [odds ratio (OR) = 0.84, 95% CI: 0.79 to 0.9, p < 0.001, I2 = 67.3%, seven studies] and disease severity (OR = 0.84, 95% CI: 0.77 to 0.92, p < 0.001, I2 = 83%, five studies). The pooled diagnostic analysis of mortality yielded a sensitivity of 0.76, specificity of 0.71, and area under curve (AUC) of 0.79. Regarding the prediction of disease severity, the sensitivity, specificity, and AUC were 0.8, 0.61, and 0.65, respectively. In conclusion, this study demonstrated a negative association between PNI and prognosis of COVID-19. Further large-scale trials are warranted to support our findings.

Predictive Value of Systemic Immune-Inflammation index and Neutrophil-to-Lymphocyte Ratio in Patients with Severe COVID-19

Objective: It was initially reported that a novel coronavirus (COVID-19) had been identified in Wuhan, China, in December 2019.To date, COVID-19 is still threatening all humanity and has affected the public healthcare system and the world economic situation. Neutrophil-to-lymphocyte ratio (NLR) has also been demonstrated that associated with severity of COVID-19, but little is known about systemic immune-inflammation index (SII) relation with COVID-19. Methods: One hundred and twenty-five patients with diagnosed COVID-19 including non-severe cases (n = 77) and severe cases (n = 48) were enrolled in this study. Each patient of clinical characteristic information, blood routine parameters, and the haemogram-derived ratios were collected, calculated, and retrospectively analyzed. Receiver operating characteristics (ROC) was performed to investigate whether these parameters could be used to the predictive value of patients with severe COVID-19. Results: White blood cell count (WBC), neutrophil count (NEU), red cell volume distribution width (RDW), NLR, Platelet to lymphocyte ratio (PLR), neutrophil-to-platelet ratio (NPR), and SII were significantly higher in the severe groups than in the non-severe group (p < 0.01).Conversely, the severe group had a markedly decreased lymphocyte count, basophil (Baso#) count, red blood cell count (RBC), Hemoglobin (HGB), hematocrit (HCT), and lymphocyte-to-monocyte ratio (LMR) (P < 0.01).ROC curve analysis showed the AUC, optimal cut-off value, sensitivity, specificity of NLR and SII to early predict severe-patients with COVID-19 were 0.867, 7.25, 70.83%, 92.21% and 0.860, 887.20, 81.25%, 81.82%, respectively. Conclusion The results suggest that the SII and NLR is a potential new diagnosed biomarker in severe-patients with COVID-19.