Ratios of neutrophil-to-lymphocyte and platelet-to-lymphocyte predict all-cause mortality in inpatients with coronavirus disease 2019 (COVID-19): a retrospective cohort study in a single medical centre

Biomarker of severity in hospitalised patients with COVID-19: a retrospective study

BACKGROUND

The novel COVID-19 was rapidly spreading and was highly contagious. COVID-19 caused over 6 million deaths worldwide, with high mortality rates, particularly in severe cases.

OBJECTIVE

This study aimed to investigate whether serum albumin-neutrophil count to lymphocyte count ratio (NLR) score (ANS) could be used as a prognostic indicator of COVID-19 severity.

DESIGN

A retrospective study.

PARTICIPANTS

Based on the WHO diagnostic criteria, patients were classified as either non-severe (n=270) or severe (n=100).

PRIMARY AND SECONDARY OUTCOME MEASURES

NLR, serum albumin level and ANS.

MAIN RESULTS

The NLR of patients with severe disease was significantly higher than that of those with non-severe disease. Serum albumin levels were significantly lower in patients with severe disease than in those with non-severe disease. The cut-off values representing the maximum potential effectiveness of serum albumin and NLR were 33.5 g/L and 8.25, respectively, according to the Youden index. In patients with severe COVID-19, we observed that the serum albumin level, NLR and ANS were independent prognostic indicators of severe COVID-19 using logistic analysis. The relative risk of severe COVID-19 was 7.65 (95% CI 3.72 to 15.75, p<0.05) in the ANS 2 group compared with that in ANS 0.

CONCLUSIONS

ANS could be used as a prognostic indicator of COVID-19 severity.

Neutrophil Activity and Extracellular Matrix Degradation: Drivers of Lung Tissue Destruction in Fatal COVID-19 Cases and Implications for Long COVID

Pulmonary fibrosis, severe alveolitis, and the inability to restore alveolar epithelial architecture are primary causes of respiratory failure in fatal COVID-19 cases. However, the factors contributing to abnormal fibrosis in critically ill COVID-19 patients remain unclear. This study analyzed the histopathology of lung specimens from eight COVID-19 and six non-COVID-19 postmortems. We assessed the distribution and changes in extracellular matrix (ECM) proteins, including elastin and collagen, in lung alveoli through morphometric analyses. Our findings reveal the significant degradation of elastin fibers along the thin alveolar walls of the lung parenchyma, a process that precedes the onset of interstitial collagen deposition and widespread intra-alveolar fibrosis. Lungs with collapsed alveoli and organized fibrotic regions showed extensive fragmentation of elastin fibers, accompanied by alveolar epithelial cell death. Immunoblotting of lung autopsy tissue extracts confirmed elastin degradation. Importantly, we found that the loss of elastin was strongly correlated with the induction of neutrophil elastase (NE), a potent protease that degrades ECM. This study affirms the critical role of neutrophils and neutrophil enzymes in the pathogenesis of COVID-19. Consistently, we observed increased staining for peptidyl arginine deiminase, a marker for neutrophil extracellular trap release, and myeloperoxidase, an enzyme-generating reactive oxygen radical, indicating active neutrophil involvement in lung pathology. These findings place neutrophils and elastin degradation at the center of impaired alveolar function and argue that elastolysis and alveolitis trigger abnormal ECM repair and fibrosis in fatal COVID-19 cases. Importantly, this study has implications for severe COVID-19 complications, including long COVID and other chronic inflammatory and fibrotic disorders.

IL-6 serum level, ARDS, and AKI as risk factors for the COVID-19 infection's mortality in children

INTRODUCTION

Dysregulated immune responses are developed in Coronavirus disease-2019 (COVID-19) and Interleukin-6 (IL-6) levels are reflecting the severity of the clinical presentation. This study aimed to analyze IL-6 serum level, Acute Respiratory Distress Syndrome (ARDS), and Acute Kidney Injury (AKI) as risk factors for mortality in children with COVID-19.

METHODS

This prospective cohort study was conducted on children with COVID-19 infection confirmed by Real Time Polymerase Chain Reaction (RT-PCR) who were admitted to infection center at Dr. Wahidin Sudirohusodo Hospital from September 2021 to September 2022. Subjects were selected using the consecutive sampling method.

RESULTS

A total of 2,060 COVID-19 RT-PCR tests were performed, and 1,065 children were confirmed positive. There were 291 cases that met the inclusion criteria, with 28.52 percent non-survives and 71.48% survives. The risk factors for mortality were IL-6, ARDS, AKI, Prothrombin Time / Activated Partial Thromboplastin Time (PT/aPTT), oxygen saturation, Absolut lymphocyte count (ALC), leukocytes, Length of Stay (LOS), and nutritional status (p<0.05). IL-6 levels increased in all patients (23.48-252.58 pq/ml). COVID-19 patients with AKI, ARDS, low oxygen saturation and thrombocytopenia had the highest levels of IL-6 (p 0.05). The IL-6 cut-off point was >80.97 pg/ml with 93% sensitivity and 90% specificity. Area Under Curve was 0.981 (95% CI), 0.960-1.000). A multivariate analysis showed IL-6 levels with OR 18.570 (95% CI 5.320-64.803), ARDS with Odds Ratio (OR) 10.177, (95% Confidence Interval (CI) 1.310-9.040), and AKI with OR 3.220 (95% CI 1.070-10.362). A combination of increased IL-6, ARDS, and AKI can predict a mortality probability as high as 98.3%.

CONCLUSION

IL-6, ARDS, and AKI are risk factors for mortality in children with COVID-19. IL-6 level was the highest mortality risk factor.

Pathological Effects of SARS-CoV-2 Associated with Hematological Abnormalities

The SARS coronavirus 2 (SARS-CoV-2) is the causative agent of the 2019 coronavirus disease (COVID-19) pandemic that has claimed the lives of 6.9 million people and infected over 765 million. It has become a major worldwide health problem and is also known to cause abnormalities in various systems, including the hematologic system. COVID-19 infection primarily affects the lower respiratory tract and can lead to a cascade of events, including a cytokine storm, intravascular thrombosis, and subsequent complications such as arterial and venous thromboses. COVID-19 can cause thrombocytopenia, lymphopenia, and neutrophilia, which are associated with worse outcomes. Prophylactic anticoagulation is essential to prevent complications and death rates associated with the virus's effect on the coagulation system. It is crucial to recognize these complications early and promptly start therapeutic anticoagulation to improve patient outcomes. While rare, COVID-19-induced disseminated intravascular coagulation (DIC) exhibits some similarities to DIC induced by sepsis. Lactate dehydrogenase (LDH), D-dimer, ferritin, and C-reactive protein (CRP) biomarkers often increase in serious COVID-19 cases and poor prognosis. Understanding the pathophysiology of the disease and identifying risk factors for adverse outcomes is critical for effective management of COVID-19.

Hematological parameters and their predictive value for assessing disease severity in laboratory-confirmed COVID-19 patients: a retrospective study

BACKGROUND

The coronavirus disease 19 (COVID-19) infection has spread globally and caused a substantial amount of mortality and morbidity. Early detection of severe infections will improve care and reduce deaths. The use of hematological parameters in predicting COVID-19 disease severity, patient outcomes, and early risk stratification is limited. Therefore, the study was aimed at determining hematological parameters and their predictive value for assessing disease severity in laboratory-confirmed COVID-19 patients in Northwest Ethiopia.

METHODS

A retrospective cross-sectional study was conducted at the University of Gondar comprehensive specialized hospital and Tibebe Ghion comprehensive specialized referral hospital on 253 patients diagnosed with COVID-19 and admitted between March 2021 and February 2022. Data were extracted, and entered into Epi-data 4.2.0.0, and analyzed using SPSS version 25 software. Hematological parameters were provided as the median and interquartile range (IQR). Categorical variables were represented by their frequency, and the χ2 test was applied to compare observed results with expected results. The receiver-operating curve (ROC) was used to establish the predictive value of hematological parameters for COVID-19 severity. A p-value < 0.05 was considered statistically significant.

RESULTS

On a total of 253 patients, there were 43.87% severe cases, with a mortality rate of 26.9%. The ROC analysis showed the optimal cutoff values for hematological parameters were ANC (3370), lymphocyte (680), NLR (9.34), PLR (290.77), platelets (332,000), and WBCs (4390.65). The area under the curve (AUC) values for NLR (0.679) and ANC (0.631) were high, with the highest sensitivity and specificity, and could potentially be used to predict COVID-19 severity.

CONCLUSION

This study proved that high NLR and high ANC have prognostic value for assessing disease severity in COVID-19. Thus, assessing and considering these hematological parameters when triaging COVID-19 patients may prevent complications and improve the patient's outcome.

Haemogram indices are as reliable as CURB-65 to assess 30-day mortality in Covid-19 pneumonia

Background Mortality due to Covid-19 and severe community-acquired pneumonia (CAP) remains high, despite progress in critical care management. We compared the precision of CURB-65 score with monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) in prediction of mortality among patients with Covid-19 and CAP presenting to the emergency department. Methods We retrospectively analysed two cohorts of patients admitted to the emergency department of Canakkale University Hospital, namely (i) Covid-19 patients with severe acute respiratory symptoms presenting between 23 March 2020 and 31 October 2020, and (ii) all patients with CAP either from bacterial or viral infection within the 36 months preceding the Covid-19 pandemic. Mortality was defined as in-hospital death or death occurring within 30 days after discharge. Results The first study group consisted of 324 Covid-19 patients and the second group of 257 CAP patients. The non-survivor Covid-19 group had significantly higher MLR, NLR and PLR values. In univariate analysis, in Covid-19 patients, a 1-unit increase in NLR and PLR was associated with increased mortality, and in multivariate analysis for Covid-19 patients, age and NLR remained significant in the final step of the model. According to this model, we found that in the Covid-19 group an increase in 1-unit in NLR would result in an increase by 5% and 7% in the probability of mortality, respectively. According to pairwise analysis, NLR and PLR are as reliable as CURB-65 in predicting mortality in Covid-19. Conclusions Our study indicates that NLR and PLR may serve as reliable predictive factors as CURB-65 in Covid-19 pneumonia, which could easily be used to triage and manage severe patients in the emergency department.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Clinical Significance and Diagnostic Utility of NLR, LMR, PLR and SII in the Course of COVID-19: A Literature Review

Nowadays, society is increasingly struggling with infectious diseases that are characterized by severe course and even death. Recently, the whole world has faced the greatest epidemiological threat, which is COVID-19 caused by SARS CoV-2 virus. SARS CoV-2 infection is often accompanied by severe inflammation, which can lead to the development of different complications. Consequently, clinicians need easily interpreted and effective markers of inflammation that can predict the efficacy of the treatment and patient prognosis. Inflammation is associated with changes in many biochemical and hematological parameters, including leukocyte counts and their populations. In COVID-19, changes in leukocytes count populations such as neutrophils, lymphocytes or monocytes are observed. The numerous research confirm that indicators like neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelets-to-lymphocyte ratio (PLR) and systemic inflammatory index (SII) may prove effective in assessment patient prognosis and choosing optimal therapy. Therefore, in this review, we would like to summarize the latest knowledge about the diagnostic utility of systemic inflammatory ratios - NLR, LMR, PLR and SII in patients with COVID-19. We focused on the papers evaluating the diagnostic utility of inflammatory ratios using ROC curve published in the recent 3 years. Identification of biomarkers associated with inflammation would help the selection of patients with severe course of COVID-19 and high risk of death.

Biogenesis aberration: One of the mechanisms of thrombocytopenia in COVID-19

Background: The pathogenesis of COVID-19, including thrombocytopenia, has not been fully clarified. The lungs are a major organ of platelet production and thrombocytopenia induced by severe COVID-19 was proposed. Methods: the change of platelet level was analysed with clinical parameters in 95 hospitalized COVID-19 patients in Wuhan Third Hospital. The production of platelets in the lungs was explored in an ARDS rat model. Results: The level of platelets was negatively correlated with disease severity and was recovered with disease improvement. The non-survivors were accompanied by lower levels of platelet. The odds ratio (OR) of the valley level of the platelet count (PLTlow) was greater than 1, suggesting that PLTlow could be a death exposure factor. The platelet/lymphocyte ratio (PLR) was positively associated with severity of COVID-19, and the platelet/lymphocyte ratio threshold of 248.5 was best correlated with death risk (sensitivity 0.641 and specificity 0.815). To demonstrate the possible biogenesis aberration of platelet in lungs, an LPS-induced ARDS rat model was applied. Lower level of platelet in peripheral and less production of platelet from lungs in ARDS were demonstrated. Though megakaryocyte (MK) number in ARDS lungs is higher than controls, the immature platelet fraction (IPF) in postpulmonary blood is still at the same level as prepulmonary in ARDS rat, indicating that ARDS rats generated fewer platelets in lungs. Conclusion: Our data suggested that COVID-19-induced severe lung inflammation may impair platelet production in the lung. Thrombocytopenia may be mainly caused by platelet consumption for multiorgan thrombosis; however, biogenesis aberration of platelet in the lung induced by diffuse interstitial pulmonary damage cannot be ruled out.

Platelet-to-lymphocyte ratio and mean platelet volume-to-platelet count ratio for predicting mortality in critical COVID-19 patients

Introduction: Defining the markers that can be used in clinical practice for predicting the mortality of critical patients will be cautionary for taking necessary measures in high-risk cases. Although there are a large number of studies conducted during the pandemic, no mortality marker to predict the prognosis of intensive care unit (ICU) patients with COVID-19 has yet been defined. Platelet indices can be easily evaluated with a complete blood count (CBC) analysis, one of the most accessible tests worldwide. This study aimed to evaluate the role of platelet indices such as mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT), platelet-to-lymphocyte ratio (PLR), and MPV-to-platelet count ratio (MPV/PLT) in predicting the mortality of ICU patients with COVID-19. Material and Method: This single-center, retrospective, cross-sectional study included 201 critical COVID-19 patients over 18 years of age who were hospitalized in ICU between August 2020 and February 2021. Patients were divided into two groups as survivors and non-survivors. The relationship of MPV, PDW, PCT, PLR, and MPV/PLT parameters evaluated at ICU admission with mortality was investigated. Results: There was no significant difference between the survivor and non-survivor groups in terms of platelet count, MPV, PCT, and PDW. The comparison of the platelet ratios revealed higher PLR and MPV/PLT ratio in the non-survivor group than in the survivor group (p

Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio Can Predict the Severity in COVID-19 Patients from Ethiopia: A Retrospective Study

Background

Coronaviruses are a broad family of pathogens that can cause mild to severe respiratory illnesses. Due to a strong inflammatory response and a weak immunological response, viral pneumonia inflammation, like Coronavirus Disease 2019 (COVID-19), displays an unbalanced immune response. Therefore, circulating biomarkers of inflammation and the immune system can serve as reliable predictors of a patient's prognosis for COVID-19. Hematological ratios are reliable markers of inflammation that are frequently utilized in pneumonia, primarily in viral infections with low cost in developing countries.

Purpose

To examine the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) in predicting the severity of COVID-19 patients.

Methods

An institutional-based retrospective study was done on 105 hospitalized COVID-19 patients at the University of Gondar comprehensive specialized referral hospital, Northwest Ethiopia. The laboratory evaluations that were gathered, evaluated, and reported on included the total leucocyte count (TLC), absolute neutrophil count (ANC), absolute lymphocyte count (ALC), absolute monocyte count (AMC), NLR, LMR, and PLR. The Kruskal-Wallis test and Wilcoxon matched-pairs signed test were used to see whether there were any differences between the continuous variables. Receiver operating curve (ROC) analysis was used to determine the appropriate cut-off values for NLR, PLR, and LMR. P-value <0.05 was considered a statistically significant association.

Results

ANC, NLR, and PLR were highest in the critical group (p = 0.001), while this group had the least ALC and LMR (p = 0.001). We calculated the optimal cut-off values of the hematological ratios; NLR (8.4), LMR (1.4), and PLR (18.0). NLR had the highest specificity and sensitivity, at 83.8% and 80.4%, respectively.

Conclusion

Our research showed that NLR and PLR were good indicators of severity in COVID-19. However, our findings indicate that MLR is not a reliable predictor.

COVID-19, obesity, and immune response 2 years after the pandemic: A timeline of scientific advances

In the 2 years since the COVID-19 pandemic was officially declared, science has made considerable strides in understanding the disease's pathophysiology, pharmacological treatments, immune response, and vaccination, but there is still much room for further advances, especially in comprehending its relationship with obesity. Science has not yet described the mechanisms that explain how obesity is directly associated with a poor prognosis. This paper gathers all published studies over the past 2 years that have described immune response, obesity, and COVID-19, a historical and chronological record for researchers and the general public alike. In summary, these studies describe how the cytokine/adipokine levels and inflammatory markers, such as the C-reactive protein, are associated with a higher body mass index in COVID-19-positive patients, suggesting that the inflammatory background and immune dysregulation in individuals with obesity may be expressed in the results and that adiposity may influence the immune response. The timeline presented here is a compilation of the results of 2 years of scientific inquiry, describing how the science has progressed, the principal findings, and the challenges ahead regarding SARS-CoV-2, COVID-19, and emerging variants, especially in patients with obesity.

Evaluation of diaphragm thickness to predict intubation requirement and mortality in critical COVID-19 patients

OBJECTIVES

To investigate the value of measuring the diaphragm thickness (DT) on thorax computed tomography (CT) at intensive care unit (ICU) admission for predicting intubation requirement and mortality among COVID-19 patients.

METHODS

This study was carried out in Ankara City Hospital, Ankara, Turkey, from September 2020 to January 2021, with 94 critical COVID-19 patients. The patients' demographic characteristics, laboratory parameters, DT measurements, mechanical ventilation (MV) requirements, and mortality statuses were retrospectively screened. The relationships between DT on initial CT, MV requirement, and mortality were investigated.

RESULTS

Diaphragm thickness was lower in patients who required intubation after ICU admission than in non-intubated patients (p=0.006); it was also lower in non-survivors (p=0.009). The threshold values for MV need was 3.35 mm (p=0.004) and 3.275 mm for mortality (p=0.006), according to the receiver operating characteristic analysis used to assess the predictive potential of DT. The non-survivor group had a greater neutrophil-to-lymphocyte ratio (p=0.026). Absolute neutrophil count (p=0.017), neutrophil-to-lymphocyte ratio (p=0.010), and interleukin-6 levels (p=0.027) were higher among patients requiring MV than among non-intubated patients.

CONCLUSION

Mortality and MV requirements can be predicted from DT measurements. Diaphragm thickness can facilitate the identification of high-risk patients on CT evaluation at ICU admission.

Neutrophil-to-Lymphocyte Ratio and Cut-off Values as Predictor of Severity and Mortality in COVID-19 Patients in Millennium COVID-19 Care Center, Addis Ababa, Ethiopia

Background

Early identification of patients at high risk of poor clinical outcomes is the key to success in saving the lives of patients with coronavirus disease 2019 (COVID-19). Neutrophil to Lymphocyte Ratio (NLR) is an easily available and cheap surrogate inflammatory marker, its baseline NLR role in African COVID-19 patients remains to be investigated. The objective of the study aimed to evaluate the role of NLR as a predictor of severity and mortality of COVID-19 patients admitted at the Millennium COVID 19 care center in Addis Ababa, Ethiopia.

Methods

A cross-sectional study was conducted on patients with COVID-19 admitted to the Millennium COVID-19 care center from August 1 to October 30, 2021. Receiver Operating Characteristic curve analysis was used to calculate the area under the curve to assess the predictive capacity of NLR on mortality and severity. Multivariable logistic regression analysis was done to identify the association between independent variables and disease outcomes with an Adjusted Odds Ratio (AOR), P-value, and 95% CI for AOR were used for testing significance.

Results

The NLR of 9.47 was identified as the optimal cut-off value for predicting mortality with a sensitivity of 88.7% and a specificity of 95.4% (Area Under the Curve (AUC):0.95, 95% CI 0.92-98; P<0.001) and the NLR of 5.86 was an effective threshold value in predicting the severity of disease with a sensitivity of 92.2% and a specificity of 75% (AUC:0.85, 95% CI 0.800-0.905; P<0.001). In multivariable logistic regression analysis, after adjusting for confounding factors, NLR of more than 9.47 and 5.86 was significantly associated with all-cause of in-hospital mortality (AOR=4.73, 95% CI, 1.19-33.68; P<0.02), and severity of disease (AOR=12.98, 95% CI 3.85-43.80; P=0.001), respectively.

Conclusion

NLR greater than 9.47 and 5.86 effectively predict mortality and severity of the disease, respectively. It provides an objective input for early decision-making in inpatient management especially in resources limited area.

Case report: COVID-19 infection in a pregnant 33-year-old kidney transplant recipient

Patients facing severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infections with comorbidities, especially patients whose immune system is weakened have higher chances to face severe outcomes. One of the main reasons behind the suppression of the immune system is iatrogenic, in patients who have autoimmune diseases and/or had an organ transplant. Although there are studies that are examining immunocompromised and/or transplanted patients with COVID-19 infection, furthermore there is a limited number of studies available which are dealing with COVID-19 in pregnant women; however, it is unique and is worth reporting when these factors are coexisting. In this study, we present the case of a 33-year-old Caucasian pregnant woman, who had a kidney transplant in 2009 and contracted the SARS-CoV-2 virus on the 26th gestational week, in 2021. After her infection, superimposed preeclampsia was diagnosed and due to the worsening flowmetric parameters, she gave birth to a premature male newborn with cesarean section. Our kidney transplant patient’s case highlights how COVID-19 disease can lead to preeclampsia and artificial termination of gestation.

Platelet-to-Lymphocyte Ratio (PLR) Is Not a Predicting Marker of Severity but of Mortality in COVID-19 Patients Admitted to the Emergency Department: A Retrospective Multicenter Study

(1) Introduction: In the present study, we investigate the prognostic value of platelet-to-lymphocyte ratio (PLR) as a marker of severity and mortality in COVID-19 infection. (2) Methods: Between 1 March and 30 April 2020, we conducted a multicenter, retrospective cohort study of patients with moderate to severe coronavirus 19 (COVID-19), all of whom were hospitalized after being admitted to the emergency department (ED). (3) Results: A total of 1035 patients were included in our study. Neither lymphocytes, platelets or PLR were associated with disease severity. Lymphocyte count was significantly lower and PLR values were significantly higher in the group of patients who died, and both were associated with mortality in the univariate analysis (OR: 0.524, 95% CI: (0.336−0.815), p = 0.004) and (OR: 1.001, 95% CI: (1.000−1.001), p = 0.042), respectively. However, the only biological parameter significantly associated with mortality in the multivariate analysis was platelet count (OR: 0.996, 95% CI: (0.996−1.000), p = 0.027). The best PLR value for predicting mortality in COVID-19 was 356.6 (OR: 3.793, 95% CI: (1.946−7.394), p < 0.001). (4) Conclusion: A high PLR value is however associated with excess mortality.

The Neutrophil-to-Lymphocyte Ratio and the Platelet-to-Lymphocyte Ratio as Predictors of Mortality in Older Adults Hospitalized with COVID-19 in Peru

Background

The prognostic value of the neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) in patients with COVID-19 is rarely described in older adults. We aimed to estimate the prognostic value of NLR and PLR, determining the mortality of adults over 60 years of age hospitalized for COVID-19 in three hospitals in Peru from March to May 2020.

Methods

We performed a secondary analysis of data from a retrospective cohort carried out in Lambayeque, Peru, from March 18 to May 13, 2020. Older adults hospitalized for COVID-19 were included. The outcome variable was in-hospital mortality by all causes, while the exposure variable was the NLR and PLR (categorized in tertiles and numerically, performing a logarithmic transformation). We included sociodemographic variables, comorbidities, vital functions, laboratory markers, and treatment received during hospital stay. We evaluated the association between NLR and PLR using the hazard ratio (HR) in a Cox regression model. We estimated HR with their respective 95% confidence intervals (95% CI). We estimated cumulative/dynamic time-dependent ROC curves and reported area under the curve ROC (AUC-ROC) for 15-, 30-, and 60-day mortality with their respective simultaneous confidence intervals (confidence bands (CB)). Also, we estimated an optimal cut-off point based on the maximally selected rank statistics.

Results

A total of 262 hospitalized older adults were analyzed, 71.8% (n = 188) of whom were male with a median age of 70 years (interquartile range: 65-78). The mean NLR and PLR were 16.8 (95% CI: 14.9-18.7; SD: 15.5) and 50.3 (95% CI: 44.6-55.9; SD: 46.3), respectively. The mortality rate was 68.7% (95% CI: 62.7-74.3). The adjusted Cox regression analysis showed that the high NLR (adjusted HR (aHR) = 2.12; 95% CI: 1.43-3.14) and PLR (aHR = 1.90; 95% CI: 1.30-2.79) tertiles were associated with a higher risk of mortality. The maximum AUC-ROC values at 60 days of follow-up for NLR and PLR were 0.713 (95%CB: 0.627-0.800) and 0.697 (95%CB: 0.583-0.754), respectively.

Conclusions

The NLR and PLR are predictors of higher risk of mortality, and these results suggest that both could be reliable and practical markers for the identification of older adults at high risk of mortality by COVID-19. NLR and PLR have prognostic value, with an AUC greater than 0.5; however, by themselves, they are weak prognostic markers. It is important to carry out future studies incorporating these two markers into preexisting models or designing new ones considering them.

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.

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.

Visualizing the Knowledge Base and Research Hotspot of Public Health Emergency Management: A Science Mapping Analysis-Based Study

Public health emergency management has been one of the main challenges of social sustainable development since the beginning of the 21st century. Research on public health emergency management is becoming a common focus of scholars. In recent years, the literature associated with public health emergency management has grown rapidly, but few studies have used a bibliometric analysis and visualization approach to conduct deep mining and explore the characteristics of the public health emergency management research field. To better understand the present status and development of public health emergency management research, and to explore the knowledge base and research hotspots, the bibliometric method and science mapping technology were adopted to visually evaluate the knowledge structure and research trends in the field of public health emergency management studies. From 2000 to 2020, a total of 3723 papers related to public health emergency management research were collected from the Web of Science Core Collection as research data. The five main research directions formed are child prevention, mortality from public health events, public health emergency preparedness, public health emergency management, and coronavirus disease 2019 (COVID-19). The current research hotspots and frontiers are climate change, COVID-19 and related coronaviruses. Further research is needed to focus on the COVID-19 and related coronaviruses. This study intends to contribute inclusive support to related academia and industry in the aspects of public health emergency management and public safety research, as well as research hotspots and future research directions.

Significance of hemogram‐derived ratios for predicting in‐hospital mortality in COVID‐19: A multicenter study

Background

To address the problem of resource limitation, biomarkers having a potential for mortality prediction are urgently required. This study was designed to evaluate whether hemogram‐derived ratios could predict in‐hospital deaths in COVID‐19 patients.

Materials and Methods

This multicenter retrospective study included hospitalized COVID‐19 patients from four COVID‐19 dedicated hospitals in Sylhet, Bangladesh. Data on clinical characteristics, laboratory parameters, and survival outcomes were analyzed. Logistic regression models were fitted to identify the predictors of in‐hospital death.

Results

Out of 442 patients, 55 (12.44%) suffered in‐hospital death. The proportion of male was higher in nonsurvivor group (61.8%). The mean age was higher in nonsurvivors (69 ± 13 vs. 59 ± 14 years, p < 0.001). Compared to survivors, nonsurvivors exhibited higher frequency of comorbidities, such as chronic kidney disease (34.5% vs. 15.2%, p ≤ 0.001), chronic obstructive pulmonary disease (23.6% vs. 10.6%, p = 0.011), ischemic heart disease (41.8% vs. 19.4%, p < 0.001), and diabetes mellitus (76.4% vs. 61.8%, p = 0.05). Leukocytosis and lymphocytopenia were more prevalent in nonsurvivors (p < 0.05). Neutrophil‐to‐lymphocyte ratio (NLR), derived NLR (d‐NLR), and neutrophil‐to‐platelet ratio (NPR) were significantly higher in nonsurvivors (p < 0.05). After adjusting for potential covariates, NLR (odds ratio [OR] 1.05; 95% confidence interval [CI] 1.009‐1.08), d‐NLR (OR 1.08; 95% CI 1.006‐1.14), and NPR (OR 1.20; 95% CI 1.09‐1.32) have been found to be significant predictors of mortality in hospitalized COVID‐19 patients. The optimal cut‐off points for NLR, d‐NLR, and NPR for prediction of in‐hospital mortality for COVID‐19 patients were 7.57, 5.52 and 3.87, respectively.

Conclusion

Initial assessment of NLR, d‐NLR, and NPR values at hospital admission is of good prognostic value for predicting mortality of patients with COVID‐19.

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

Background and aims

The albumin-to-globulin ratio (AGR) has been used to predict severity and mortality in infectious diseases. The aim of this study is to evaluate the prognostic value of the AGR in COVID-19 patients.

Methods

A systematic review and meta-analysis were conducted. We included observational studies assessing the association between the AGR values upon hospital admission and severity or all-cause mortality in COVID-19 patients. In the meta-analyses we used random effect models. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). The effect measures were expressed as mean difference (MD) and their 95% confidence intervals (CI). We performed Egger's test and funnel plots to assess the publication bias.

Results

The included studies had a total of 11356 patients corresponding to 31 cohort studies. Severe COVID-19 patients had lower AGR values than non-severe COVID-19 patients (mean difference (MD), −0.27; 95% IC, −0.32 to −0.22; p < 0.001; I² = 88%). Non-survivor patients with COVID-19 had lower AGR values than survivor patients (MD, −0.29; 95% IC, −0.35 to −0.24; p < 0.001; I² = 79%). In the sensitivity analysis, we only included studies with low risk of bias, which decreased the heterogeneity for both outcomes (severity, I² = 20%; mortality, I² = 5%).

Conclusions

Low AGR values upon hospital admission were found in COVID-19 patients with a worse prognosis.

Does Neutrophil-to-lymphocyte Ratio at Admission Predict Severity and Mortality in COVID-19 Patients? A Systematic Review and Meta-analysis

Background

Coronavirus disease-2019 (COVID-2019) pandemic continues to be a significant public health problem. Severe COVID-19 cases have a poor prognosis and extremely high mortality. Prognostic factor evidence can help healthcare providers understand the likely prognosis and identify subgroups likely to develop severe disease with increased mortality risk so that timely treatments can be initiated. This meta-analysis has been performed to evaluate the neutrophil-to-lymphocyte ratio (NLR) at admission as a prognostic factor to predict severe coronavirus disease and mortality.

Materials and methods

A literature search was conducted through April 30, 2021, to retrieve all published studies, including gray literature and preprints, investigating the association between NLR and severity or mortality in COVID-19 patients. Screening of studies and data extraction have been done by two authors independently. The methodological quality of the included studies was assessed by the Quality in Prognosis Studies (QUIPS) tool.

Results

Twenty-four studies involving 4,080 patients reported the prognostic value of NLR for severe COVID-19. The pooled sensitivity (SEN), specificity (SPE), and area under the curve were 0.75 (95% CI 0.69–0.80), 0.74 (95% CI 0.70–0.78), and 0.81 (95% CI 0.77–0.84). Fifteen studies involving 4,071 patients reported the prognostic value of NLR for mortality in COVID-19. The pooled sensitivity (SEN), specificity (SPE), and area under curve were 0.80 (95% CI 0.72–0.86), 0.78 (95% CI 0.69–0.85), and 0.86 (95% CI 0.83–0.89).

Conclusion

The prognostic value of NLR at admission for severity and mortality in patients with COVID-19 is good. Evaluating the NLR at admission can assist treating clinicians to identify early the cases likely to worsen. This would help to conduct early triage, identify potentially high-risk cases, and start optimal monitoring and management, thus reducing the overall mortality of COVID-19.

Trial registry

This meta-analysis was prospectively registered on PROSPERO database (Registration Number: CRD42021247801).

How to cite this article

Sarkar PG, Pant P, Kumar J, Kumar A. Does Neutrophil-to-lymphocyte Ratio at Admission Predict Severity and Mortality in COVID-19 Patients? A Systematic Review and Meta-analysis. Indian J Crit Care Med 2022;26(3):361–375.

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.

Role of platelet-to-lymphocyte count ratio (PLR), as a prognostic indicator in COVID-19: A systematic review and meta-analysis

Prognostic predictors are of paramount interest for prompt intervention and optimal utilization of the healthcare system in the ongoing context of the COVID-19 pandemic. The platelet-to-lymphocyte count ratio (PLR), has emerged as a potential tool for risk stratification of critically ill patients with sepsis. The current systematic review explores the utility of PLR as a prognostic predictor of COVID-19 patients. We screened the electronic databases until May 15, 2021 after enrolling in PROSPERO (CRD42021220269). Studies evaluating the association between PLR on admission and outcomes in terms of mortality and severity among COVID-19 patients were included. We retrieved 32 studies, with a total of 2768 and 3262 COVID-19 patients for mortality and disease severity outcomes. Deceased and critically ill patients had higher PLR levels on admission in comparison to survivors and non-severe patients (mean differences [MD] = 66.10; 95% confidence interval [CI]: 47.75-84.44; p < 0.00001 and MD = 86.74; 95% CI: 67.7-105.7; p < 0.00001, respectively). A higher level of PLR on admission in COVID-19 patients is associated with increased morbidity and mortality. However, the evidence is of low quality and further studies regarding the cut-off value of PLR are the need of the hour.

Analysis of Factors Affecting Post-Stroke Fatigue: An Observational, Cross-Sectional, Retrospective Chart Review Study

Post-stroke fatigue (PSF) is one of the most common emotional and mood disorders in stroke survivors. Several studies have suggested associations between PSF and various factors. However, they describe conflicting results. Therefore, this study aimed to evaluate the factors affecting PSF. We retrospectively reviewed the medical records of 178 hospitalized stroke patients. The collected data were compared between the PSF and control groups. To evaluate the association between factors and PSF, regression analysis was conducted. A total of 96 patients (53.9%) were assigned to the PSF group, and 82 patients were assigned to the control group. Age, neurological deficits, cognitive dysfunction, degree of depression, hs-CRP, and ESR differed significantly between the two groups. For both types of stroke, multiple linear regression analyses showed that degree of depression and degree of inflammation were significantly associated with PSF. Through subgroup analysis, multiple linear regression analyses showed that the degree of depression in ischemic and hemorrhagic stroke and the platelet-to-lymphocyte ratio in hemorrhagic stroke had a significant association with PSF. In conclusion, post-stroke depression and degree of inflammation could be clinically significant predictors of PSF in all types of stroke patients. However, larger, prospective studies are required to obtain more concrete results.

Novel Systemic Inflammation Markers to Predict COVID-19 Prognosis

Coronavirus disease 2019 (COVID-19) has resulted in a global pandemic, challenging both the medical and scientific community for the development of novel vaccines and a greater understanding of the effects of the SARS-CoV-2 virus. COVID-19 has been associated with a pronounced and out-of-control inflammatory response. Studies have sought to understand the effects of inflammatory response markers to prognosticate the disease. Herein, we aimed to review the evidence of 11 groups of systemic inflammatory markers for risk-stratifying patients and prognosticating outcomes related to COVID-19. Numerous studies have demonstrated the effectiveness of neutrophil to lymphocyte ratio (NLR) in prognosticating patient outcomes, including but not limited to severe disease, hospitalization, intensive care unit (ICU) admission, intubation, and death. A few markers outperformed NLR in predicting outcomes, including 1) systemic immune-inflammation index (SII), 2) prognostic nutritional index (PNI), 3) C-reactive protein (CRP) to albumin ratio (CAR) and high-sensitivity CAR (hsCAR), and 4) CRP to prealbumin ratio (CPAR) and high-sensitivity CPAR (hsCPAR). However, there are a limited number of studies comparing NLR with these markers, and such conclusions require larger validation studies. Overall, the evidence suggests that most of the studied markers are able to predict COVID-19 prognosis, however NLR seems to be the most robust marker.

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.

Epidemiology, clinical features, and outcomes of coronavirus disease among children in Al-Madinah, Saudi Arabia: A retrospective study

Background and Objective

Coronavirus disease (COVID-19) is milder with favorable outcomes in children than in adults. However, detailed data regarding COVID-19 in children from Saudi Arabia are scarce. This study aimed to describe COVID-19 among children in Al-Madinah, Saudi Arabia.

Methods

This retrospective observational study included children <14 years old hospitalized with COVID-19 between May 1, 2020 and July 31, 2020. Clinical data, COVID-19 disease severity, and outcomes were collected. The total number of presenting symptoms and signs were computed by counting those recorded upon presentation. The Kruskal-Wallis non-parametric test was used to compare the number of symptoms and signs across all levels of COVID-19 severity.

Result

Overall, 106 patients met the inclusion criteria; their ages ranged from 2 weeks to 13 years. Most patients were ≤12 months of age (43.4%). Bronchial asthma was the most common comorbidity (9.4%). Among 99 symptomatic patients, fever was the most common symptom (84.8%); seven patients (7%) were diagnosed with febrile seizure. Most COVID-19 cases were mild (84%); one patient (0.94%) was in critical condition and one patient (0.94%) met the Multisystem Inflammatory Syndrome in children criteria. The mean number of symptoms and signs in children with severe or critical COVID-19 was significantly higher than that in children with mild cases or non-severe pneumonia (P < .001). One patient died owing to COVID-19 (0.94%).

Conclusions

COVID-19 mortality in children is rare; however, while most children exhibit mild disease with favorable outcomes, children with chronic lung disease may be at higher risk for severe disease.

Neurologic Manifestations of COVID 19 in Children: Prospective Study in a Single Center

Background:

The data related to the neurologic manifestations of coronavirus disease 2019 (COVID-19) in children are limited. The frequency of the neurologic manifestations and the risk factors in the development of these symptoms are not clear.

Objectives:

We aimed to determine the exact frequency of the neurological symptoms in pediatric patients with confirmed COVID-19 and to identify the risk factors for the development of neurological manifestations.

Materials and Methods:

We included pediatric Covid-19 patients admitted to the Children's Hospital of Ankara City Hospital between March 22 and June 1, 2020. Neurological findings were questioned by interviewing the patients and their families and detailed neurologic examinations were performed within protection measures.

Results:

A total of 312 pediatric patients with the diagnosis of COVID-19 were enrolled in the study. Sixty-six participants (21.15%) showed neurologic symptoms during COVID-19. Headache was the most common neurologic symptom and present in 14% (n: 44) of the cases. The other neurologic symptoms were myalgia (n: 30, 9.6%), anosmia/hyposmia (n: 6, 1.9%), ageusia (n: 2, 0.6%), and vertigo (n: 1, 0.3%). Neutrophil-to-lymphocyte ratio (NLR) (P = 0.002) and platelet-to-lymphocyte ratio (PLR) (P = 0.001) were significantly elevated in patients with neurological symptoms when compared to the patients without the symptoms.

Conclusions:

Physicians should be alert to the neurologic involvement of COVID-19 disease in children. NLR and PLR ratios could have a predictive value for the development of neurological manifestations.

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

Background

Coronavirus disease 2019 (COVID-19) is highly contagious and continues to spread rapidly. However, there are no simple and timely laboratory techniques to determine the severity of COVID-19. In this meta-analysis, we assessed the potential of the neutrophil-lymphocyte ratio (NLR) as an indicator of severe versus nonsevere COVID-19 cases.

Methods

A search for studies on the NLR in severe and nonsevere COVID-19 cases published from January 1, 2020, to July 1, 2021, was conducted on the PubMed, EMBASE, and Cochrane Library databases. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and area under the curve (AUC) analyses were done on Stata 14.0 and Meta-disc 1.4 to assess the performance of the NLR.

Results

Thirty studies, including 5570 patients, were analyzed. Of these, 1603 and 3967 patients had severe and nonsevere COVID-19, respectively. The overall sensitivity and specificity were 0.82 (95% confidence interval (CI), 0.77-0.87) and 0.77 (95% CI, 0.70-0.83), respectively; positive and negative correlation ratios were 3.6 (95% CI, 2.7-4.7) and 0.23 (95% CI, 0.17-0.30), respectively; DOR was 16 (95% CI, 10-24), and the AUC was 0.87 (95% CI, 0.84-0.90).

Conclusion

The NLR could accurately determine the severity of COVID-19 and can be used to identify patients with severe disease to guide clinical decision-making.

Prognostic nutritional index as indicator of immune nutritional status of patients with COVID-19

Purpose: This study aimed to investigate the effect of the nutritional status, as assessed by the prognostic nutritional index (PNI) on the disease prognosis of patients with COVID-19. Methods: This retrospective study included 282 patients with COVID-19. The PNI score of all patients, 147 of whom were male, with a mean age of 56.4±15.3 years, was calculated. According to the PNI score, the patients with normal and mild malnutrition constituted group-1 (n=159) and the patients with moderate-to-severe and serious malnutrition constituted group-2 (n=123). Results: The PNI score was correlated with age (r=-0.146, p=0.014); oxygen saturation (r=0.190, p=0.001); heart rate (r=-0.117, p=0.05); hospitalization duration (r=-0.266, p<0.001); white blood cells (r=0.156, p=0.009); hemoglobin (r=0.307, p<0.001); C-reactive protein (CRP) (r=-0.346, p<0.001); creatinine (r=-0.184, p=0.002); D-dimer (r=-0.304, p<0.001); ferritin (r=-0.283, p<0.001); procalcitonin (r=-0.287, p<0.001); the confusion, urea, respiratory rate, blood pressure, and age ≥65 years score (r=-0.217, p<0.001); and the quick sequential organ failure assessment score (r=-0.261, p<0.001) in patients with COVID-19. Mortality was significantly higher in Group 2 (p<0.001). Survival was significantly higher if PNI score was >41.2 (p<0.001, sensitivity: 78.7% and specificity: 84.2%). In multivariate regression analysis, among various other parameters, only PNI score and oxygen saturation had a significant effect on the disease course (p=0.02 and p=0.045, respectively). Conclusion: PNI, calculated from the serum albumin concentration and total lymphocyte count, is a simple and objective indicator that assesses the immune nutritional status of patients with COVID-19. The presence of malnutrition has a high predictive value in predicting the severity of COVID-19. Our data suggest that the PNI might be useful for risk stratification of patients with COVID-19 in clinical practice.

The diagnostic value of neutrophil to lymphocyte ratio in determining the severity of COVID-19

Background: Changes in hematological parameters play a role in the pathogenesis of coronavirus disease 2019 (COVID-19). We aimed to investigate the significance of neutrophil-lymphocyte ratio (NLR) and hematologic parameters in determining the severity of COVID-19. Methods: This retrospective cross-sectional study was conducted on adult patients diagnosed with COVID-19 in two pandemic hospitals between 01, April, and 01, July 2020. Using the COVID-19 diagnostic criteria of the world health organization (WHO), the patients were divided into two groups as severe and non-severe. Demographic and clinical characteristics, white blood cell (WBC), neutrophil, lymphocyte and platelet counts, and NLR of all patients were examined at the first admission. Multivariate analyzes were performed to determine the independent predictive data and ROC analysis to test the diagnostic accuracy of the hematological parameters. Results: Of the 381 patients included in the study, 42 (11%) had severe COVID-19 infection. While the mean NLR was 7.61±7.48 in patients with severe COVID-19, the mean NLR of non-severe patients was 2.97±2.37 (95% CI: 2.294 to 6.984, p<0.001). Long duration of hospital stay, elevated NLR ratio, female gender were predictive variables of severe COVID-19 cases (OR =0.833, 95% CI: 0.744 to 0.934, p=0.002; OR=0.195, 95% CI: 0.057 to 0.6731, p=0.010; OR=0.664, 95% CI: 0.501 to 0.881, p=0.005, respectively). In ROC analysis, NLR ratio had 2.625 optimum cut-off value, 60% specificity (95% CI: 54.7 to 65.4), 86% sensitivity (95% CI: 71.5 to 94.6), positive likelihood ratio (PLR) of 4.2 (95% CI: 2.0 to 8.9) and negative likelihood ratio (NLR) of 0.46 (95% CI: 0.4 to 0.6) for severe COVID-19 cases. Conclusion: The results of this study revealed that there might be a relationship between elevated NLR and severity in COVID-19 cases. IMC J Med Sci 2022; 16(1): 001 *Correspondence: Dr. Umut Gulacti, Adiyaman University Training and Research Hospital, Emergency Medicine, Adiyaman, Turkey. E-mail: umutgulacti@gmail.com

Clinical value of blood markers to assess the severity of coronavirus disease 2019

BACKGROUND

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is threatening the world with the symptoms of seasonal influenza. This study was conducted to investigate the patient characteristics and clinical value of blood markers to assess the severity of coronavirus disease 2019 (COVID-19).

METHODS

187 patients, diagnosed with COVID-19 (non-severe and severe cases) and admitted to hospital between January 27th and March 8th of 2020, were enrolled in the present study.

RESULTS

A higher proportion of clinical symptoms, including cough, expectoration, myalgia, and fatigue were observed in the non-severe group. The level of white blood cell count, neutrophils, CRP, IL-6 and IL-8 were significantly increased, while the platelet count was remarkedly decreased in the severe group. The risk model based on lymphocyte, IL-6, IL-8, CRP and platelet counts had the highest area under the receiver operator characteristic curve (AUROC). The baseline of IL-6, IL-8 and CRP was positively correlated with other parameters except in the cases of lymphocyte, hemoglobin and platelet counts. The baseline of the platelet count was negatively correlated with other parameters except in the lymphocyte and hemoglobin counts. Additionally, there was no connection between the severity of COVID-19 and cultures of blood, sputum or catheter secretion.

CONCLUSIONS

The present study suggested that high leucocyte and low platelets counts were independent predictive markers of the severity of COVID-19.

Biomarkers of severe COVID-19 pneumonia on admission using data-mining powered by common laboratory blood tests-datasets

In the epidemiological COVID-19 research, artificial intelligence is a unique approach to make predictions about disease severity to manage COVID-19 patients. A limitation of artificial intelligence is, however, the high risk of bias. We investigated the skill of data mining and machine learning, two advanced forms of artificial intelligence, to predict severe COVID-19 pneumonia based on routine laboratory tests. A sample of 4009 COVID-19 patients was divided into Severe (PaO₂< 60 mmHg, 489 cases) and Non-Severe (PaO₂ ≥ 60 mmHg, 3520 cases) groups according to blood hypoxemia on admission and their laboratory datasets analyzed by the R software and WEKA workbench. After curation, data were processed for the selection of the most influential features including hemogram, pCO₂, blood acid-base balance, prothrombin time, inflammation biomarkers, and glucose. The best fit of variables was successfully confirmed by either the Multilayer Perceptron, a feedforward neural network algorithm that performed machine recognition of severe COVID-19 with 96.5% precision, or by the C4.5 software, a supervised learning algorithm based on an objective-predefined variable (severity) that generated a decision tree with 89.4% precision. Finally, a complex bivariate Pearson's correlation matrix combined with advanced hierarchical clustering (dendrograms) were conducted for knowledge discovery. The hidden structure of the datasets revealed shift patterns related to the development of COVID-19-induced pneumonia that involved the lymphocyte-to-C-reactive protein and leukocyte-to-C-protein ratios, neutrophil %, pH and pCO₂. The data mining approaches to the hematological fluctuations associated with severe COVID-19 pneumonia could not only anticipate adverse clinical outcomes, but also reveal putative therapeutic targets.

We Know the Prognosis but can We Change It?

Bandopadhyay S. We Know the Prognosis but can We Change It? Indian J Crit Care Med 2021;25(8):835-836.

Differences in the neutrophil/lymphocyte ratio and the platelet/lymphocyte ratio in pregnant women with and without COVID-19

OBJECTIVE

To determine the differences in the neutrophil/lymphocyte ratio (NLR) and the platelet/lymphocyte ratio (PLR) in pregnant women with and without COVID-19.

METHODS

Observational, cross-sectional, retrospective, comparative, open, controlled study done from January to April 2021 at the UMAE Hospital de Gineco Obstetricia "Luis Castelazo Ayala" Instituto Mexicano del Seguro Social. Mexico City, Mexico: Patients were divided into those with a negative test for COVID-19 and those with a positive test, with the latter then being divided according to disease severity into mild, moderate, and severe groups. In all the NLR and PLR were calculated. Symptoms, vital signs, and oxygen saturation were documented.

STATISTICAL ANALYSIS

Central tendency and dispersion measures, Kruskal-Wallis test, Mann-Whitney U test, and chi-squared test were used. Sensitivity, specificity, positive predictive value, negative predictive value, and odds ratio (OR) were calculated.

RESULTS

Seventy-seven patients were included, 24 without COVID-19 and 53 with COVID-19. There were 33, 10, and 10 patients in the mild, moderate, and severe disease groups, respectively. There was no difference in NLR between the groups and the PLR was significantly higher in the severe disease group. With a 5.1 NLR cutoff point, between normal and those with severe disease, the sensitivity was 70% and specificity 63%, whilst with a 221 PLR cutoff point, the sensitivity was 90% and the specificity 83% (OR 45, 95% CI 4.40-461.7).

CONCLUSION

PLR more than NLR was useful to detect pregnant patients with COVID-19 with severe disease.

Comparison of hemoglobin level and neutrophil to lymphocyte ratio as prognostic markers in patients with COVID-19

Background

Anemia can negatively affect the outcome of many diseases, including infections and inflammatory conditions.

Aim

To compare the prognostic value of hemoglobin level and the neutrophil/lymphocyte ratio (NLR) for prediction of coronavirus disease 2019 (COVID-19) severity.

Methods

In this retrospective cohort study, clinical data from patients with laboratory-confirmed COVID-19 were collected from hospital records from 10 April 2020 to 30 July 2020.

Results

The proportions of patients with mild, moderate, and severe COVID-19 differed significantly in association with hemoglobin levels, neutrophil counts, lymphocyte counts, NLR, and total leukocyte counts. Patients with severe COVID-19 had significantly lower hemoglobin levels than those with moderate or mild COVID-19. There were statistically significant negative associations between hemoglobin and D-dimer, age, and creatinine. The optimal hemoglobin cut-off value for prediction of disease severity was 11.6 g/dL. Using this cut-off value, hemoglobin had higher negative predictive value and sensitivity than NLR (92.4% and 51.3%, respectively). The specificity of hemoglobin as a prognostic marker was 79.3%.

Conclusion

Both NLR and hemoglobin level are of prognostic value for predicting severity of COVID-19. However, hemoglobin level displayed higher sensitivity than NLR. Hemoglobin level should be assessed upon admission in all patients and closely monitored throughout the disease course.

The Link between SARS-CoV-2 Infection, Inflammation and Hypercoagulability-Impact of Hemorheologic Alterations on Cardiovascular Mortality

The link between severe forms of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection and cardiovascular diseases has been well documented by various studies that indicated a higher risk of cardiovascular complications in COVID-19 patients, in parallel with a higher risk of mortality in COVID-19 patients with underlying cardiovascular diseases. It seems that inflammation, which is a major pathophysiological substrate for both acute myocardial infarction and severe forms of COVID-19, may play a pivotal role in the interrelation between these two critical conditions, and hypercoagulability associated with SARS-CoV-2 infection could be responsible for acute cardiovascular complications. The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) proved to be independent predictors for prognosis in acute coronary syndromes and systemic inflammatory diseases; therefore, they may be used as independent prognostic markers of disease severity in COVID-19 infection. The aim of this review is to present the most recent advances in understanding the complex link between SARS-CoV-2 infection, inflammation and alteration of blood coagulability and hemorheology, leading to major cardiovascular events.

Can Hematological Ratios Predict Outcome of COVID-19 Patients? A Multicentric Study

Introduction

Coronaviruses belong to a large family that leads to respiratory infection of various severity. Hematological ratios are indicators of inflammatory response widely used in viral pneumonia with affordability in developing countries.

Purpose

Study the role of the neutrophil lymphocyte ratio (NLR), derived NLR ratio (d-NLR), platelet lymphocyte ratio (PLR), and lymphocyte monocyte ratio (LMR) in predicting the outcome of COVID-19 Egyptian patients.

Methods

A retrospective study on 496 COVID-19 Egyptian patients, managed in four tertiary centers, grouped into non-severe, severe, and critical. Patients’ laboratory assessment including total leucocyte count (TLC), absolute neutrophil count (ANC), absolute lymphocyte count (ALC), absolute monocyte count (AMC), NLR, d-NLR, LMR and, PLR were reported as well as C reactive protein (CRP), D-dimer and serum ferritin.

Results

TLC, ANC, AMC, NLR, d-NLR and, PLR were highest in the critical group (p<0.001 for all except AMC p=0.033), while this group had the least ALC and LMR (p=0.049 and <0.001, respectively). Higher CRP and d-dimer levels were reported in the critical group (p<0.001). At the same time, higher ferritin was found in the severe group more than the critical and non-severe groups (p<0.001, p=0.005, respectively). We calculated the optimal cut-off values of the hematological ratio; NLR (3.5), d-NLR (2.86), PLR (192), and LMR (3). D-NLR had the highest specificity (89.19%), while NLR had the highest sensitivity (71.38%). By univariate logistic regression, age, DM, HTN, cardiovascular diseases, COPD, NLR, d-NLR, LMR and PLR, CRP, steroid, oxygen aids, and mechanical ventilation were associated with the severity of COVID-19. Still, only age, NLR, CRP, and oxygen aid were independent predictors in multivariate logistic regression.

Conclusion

NLR is a predictor for severity in COVID-19. LMR, d-NLR, and PLR may assist in risk stratification.

Neutrophils Set Extracellular Traps to Injure Lungs in Coronavirus Disease 2019

SARS-CoV-2 induced lower respiratory tract (LRT) disease can deteriorate to acute respiratory distress syndrome (ARDS). Because the release of neutrophil extracellular traps (NETs) is implicated in ARDS pathogenesis, we investigated the presence of NETs and correlates of pathogenesis in blood and LRT samples of critically ill COVID-19 patients. Plasma NET levels peaked early after ICU admission and correlated with SARS-CoV-2 RNA load in sputum and levels of neutrophil-recruiting chemokines and inflammatory markers in plasma. Baseline plasma NET quantity correlated with disease severity, but was not associated with soluble markers of thrombosis nor with development of thrombosis. High NET levels were present in LRT samples and persisted during the course of COVID-19, consistent with the detection of NETs in bronchi and alveolar spaces in lung tissue from fatal COVID-19 patients. Thus, NETs are produced and retained in the LRT of critical COVID-19 patients and could contribute to SARS-CoV-2-induced ARDS pathology.  

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.