Role of hematological parameters in the diagnosis of influenza virus infection in patients with respiratory tract infection symptoms

AMG487 alleviates influenza A (H1N1) virus-induced pulmonary inflammation through decreasing IFN-γ-producing lymphocytes and IFN-γ concentrations

BACKGROUND AND PURPOSE

Severe influenza virus-infected patients have high systemic levels of Th1 cytokines (including IFN-γ). Intrapulmonary IFN-γ increases pulmonary IFN-γ-producing T lymphocytes through the CXCR3 pathway. Virus-infected mice lacking IP-10/CXCR3 demonstrate lower pulmonary neutrophilic inflammation. AMG487, an IP-10/CXCR3 antagonist, ameliorates virus-induced lung injury in vivo through decreasing viral loads. This study examined whether AMG487 could treat H1N1 virus-induced mouse illness through reducing viral loads or decreasing the number of lymphocytes or neutrophils.

EXPERIMENTAL APPROACH

Here, we studied the above-mentioned effects and underlying mechanisms in vivo.

KEY RESULTS

H1N1 virus infection caused bad overall condition and pulmonary inflammation characterized by the infiltration of lymphocytes and neutrophils. From Day-5 to Day-10 post-virus infection, bad overall condition, pulmonary lymphocytes, and IFN-γ concentrations increased, while pulmonary H1N1 viral titres and neutrophils decreased. Both anti-IFN-γ and AMG487 alleviated virus infection-induced bad overall condition and pulmonary lymphocytic inflammation. Pulmonary neutrophilic inflammation was mitigated by AMG487 on Day-5 post-infection, but was not mitigated by AMG487 on Day-10 post-infection. H1N1 virus induced increases of IFN-γ, IP-10, and IFN-γ-producing lymphocytes and activation of the Jak2-Stat1 pathways in mouse lungs, which were inhibited by AMG487. Anti-IFN-γ decreased IFN-γ and IFN-γ-producing lymphocytes on Day-5 post-infection. AMG487 but not anti-IFN-γ decreased viral titres in mouse lung homogenates or BALF. Higher virus load did not increase pulmonary inflammation and IFN-γ concentrations when mice were treated with AMG487.

CONCLUSION AND IMPLICATIONS

AMG487 may ameliorate H1N1 virus-induced pulmonary inflammation through decreasing IFN-γ-producing lymphocytes rather than reducing viral loads or neutrophils.

Altered counts and mitochondrial mass of peripheral blood leucocytes in patients with chronic hepatitis B virus infection

Hepatitis B virus (HBV) damages liver cells through abnormal immune responses. Mitochondrial metabolism is necessary for effector functions of white blood cells (WBCs). The aim was to investigate the altered counts and mitochondrial mass (MM) of WBCs by two novel indicators of mitochondrial mass, MM and percentage of low mitochondrial membrane potential, MMPlow%, due to chronic HBV infection. The counts of lymphocytes, neutrophils and monocytes in the HBV infection group were in decline, especially for lymphocyte (p = 0.034) and monocyte counts (p = 0.003). The degraded MM (p = 0.003) and MMPlow% (p = 0.002) of lymphocytes and MM (p = 0.005) of monocytes suggested mitochondrial dysfunction of WBCs. HBV DNA within WBCs showed an extensive effect on mitochondria metabolic potential of lymphocytes, neutrophils and monocytes indicated by MM; hepatitis B e antigen was associated with instant mitochondrial energy supply indicated by MMPlow% of neutrophils; hepatitis B surface antigen, antiviral therapy by nucleos(t)ide analogues and prolonged infection were also vital factors contributing to WBC alterations. Moreover, degraded neutrophils and monocytes could be used to monitor immune responses reflecting chronic liver fibrosis and inflammatory damage. In conclusion, MM combined with cell counts of WBCs could profoundly reflect WBC alterations for monitoring chronic HBV infection. Moreover, HBV DNA within WBCs may be a vital factor in injuring mitochondria metabolic potential.

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study

BACKGROUND

Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings.

AIM

This study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI.

METHODS

This retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011-2012 and 2019-2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance.

RESULTS

Patients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results.

CONCLUSION

This study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.

Prognostic value of the neutrophil-to-lymphocyte ratio and C-reactive-protein-to-prealbumin ratio in hospitalized older patients with coronavirus disease 2019

The neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein-to-prealbumin ratio (CPAR) are novel markers of inflammation. The CPAR is an indicator of inflammation and malnutrition. We evaluated NLR and CPAR in combination as indicators of disease severity and prognosis in hospitalized older patients with coronavirus disease 2019 (COVID-19). A total of 222 hospitalized patients with COVID-19 (aged > 60 years) were divided into non-severe and severe groups. The severe group was subdivided into the surviving and deceased subgroups. We retrospectively assessed the predictive power of NLR and CPAR in combination (NLR + CPAR) to determine the prognosis of hospitalized older patients with COVID-19. The NLR and CPAR were significantly higher in the severe group than in the non-severe group (P < .001). Furthermore, the NLR and CPAR were higher in the deceased subgroup than in the surviving subgroup (P < .001). Pearson correlation analysis showed a highly significant positive correlation between NLR and CPAR (P < .001, r = 0.530). NLR + CPAR showed an area under the curve of 0.827 and sensitivity of 83.9% in the severe group; the area under the curve was larger (0.925) and sensitivity was higher (87.1%) in the deceased subgroup. The receiver operating characteristic curve of NLR + CPAR was significantly different from the receiver operating characteristic curves of either biomarker alone (P < .001). Kaplan-Meier analysis showed that patients in the severe group with elevated NLR + CPAR had a significantly lower 90-day survival rate than patients who lacked this finding (odds ratio 7.87, P < .001). NLR + CPAR may enable early diagnosis and assessment of disease severity in hospitalized older patients with COVID-19. This may also enable the identification of high-risk older patients with COVID-19 at the time of admission.

Risk factors for hospitalization and pneumonia development of pediatric patients with seasonal influenza during February-April 2023

Objectives

In China influenza remains a low activity for continuous 3 years due to COVID-19 controls. We here sought to study the clinical characteristics and risk factors of the influenza infection among children after the mandatory COVID-19 restrictions were lifted.

Methods

We included 1,006 pediatric patients with influenza A virus (IAV) infection, enrolled in one tertiary hospital in Zhenjiang, Jiangsu Province, China, during February to April 2023. Patients were divided into the outpatient (n = 798) and inpatient (n = 208) groups, and their baseline characteristics were compared between two groups to conclude the risk factors for pediatric hospitalization. Separately, pediatric inpatients (n = 208) were further divided into the pneumonia and non-pneumonia groups with comparison of their clinical characteristics, including their laboratory test results and representative radiological features, to derive the key determinants for pneumonia development after hospitalization.

Results

Compared to outpatients, IAV-infected pediatric inpatients exhibited younger age, higher female: male ratio, more co-infection of influenza B virus (IBV) and hematological abnormality. Multivariate regression analysis determined the independent risk factors of hospitalization to be the clinical symptom of abdominal pain (OR = 2.63, [95% CI, 1.05-6.57], p = 0.039), co-infection of IBV (OR = 44.33, [95% CI, 25.10-78.30], p = 0.001), elevated levels of lymphocytes (OR = 2.24, [95% CI,1.65-3.05], p = 0.001) and c-reactive proteins (CRPs) (OR = 1.06, [95% CI, 1.03-1.08], p = 0.001) upon hospital admission. Furthermore, the cough symptom (OR = 17.39, [95% CI, 3.51-86.13], p = 0.001) and hospitalization length (OR = 1.36, [95% CI, 1.12-1.67], p = 0.002) were determined to be risk factors of pneumonia acquirement for pediatric inpatients.

Conclusion

While the abdominal pain, viral co-infection and some hematological abnormality mainly contribute to hospitalization of pediatric patients with IAV infection, the length of hospital stay and clinical sign of coughing upon hospital admission constitute the key determinants for nosocomial pneumonia development.

The CD19+ B cell as a marker for the febrile children infected with influenza A and Omicron variant

H3N2 and Omicron are common pathogens of respiratory infections in children. This study aimed to explore dynamic changes of lymphocyte subsets and the diagnostic value of CD19+ B cell in children infected with influenza A and Omicron. One hundred and sixty-five in-patients with H3N2, 175 in-patients with Omicron variant, and 50 age-matched healthy children from Children's Hospital of Soochow University were included in this study. The participants underwent 13 respiratory pathogens by DNA polymerase chain reaction (PCR), sputum culture, severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) DNA PCR, routine blood, and lymphocyte subset assays within 24 h of admission. The neutrophils, neutrophil-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio in the H3N2 and Omicron groups were significantly higher than in the control groups (p < 0.05). However, the lymphocytes and eosinophils in the H3N2 and Omicron groups were lower than the control groups (p < 0.05). The CD3+ T cell, CD3+ CD4+ T cell, CD3+ CD8+ T cell, CD3- CD19+ B cell, and natural killer cell were lower in the H3N2 and Omicron groups than in the control group (p < 0.05). The CD3- CD19+ cell in the Omicron group was higher than that in the H3N2 group but lower than that in the control group (p < 0.05). In addition, CD3- CD19+ cell had good diagnostic value for H3N2 (area under the receiver operating characteristic curve = 0.902, p < 0.05). The children with H3N2 were more likely to have lower lymphocytes than children with Omicron. Additionally, B-cell count had good diagnostic value for H3N2.

Preliminary Study of sCD14 and sCD163 as Predictors of Disease Severity and ICU Admission in COVID-19: Relation to Hematological Parameters, Blood Morphological Changes and Inflammatory Biomarkers

Background and Objectives

Research supports the role of monocyte/macrophage activation in COVID-19 immunopathology. This study aimed to evaluate sCD14 and sCD163 - the monocyte activation markers - and to investigate their relation to hematological parameters and blood morphology in COVID-19 infection.

Methods

This is a case-control study that included 70 COVID-19 patients. Patients were subdivided into two groups: 23 severely diseased ICU-admitted patients and another group of 47 non-ICU-admitted patients. sCD163 and sCD14 levels were determined using ELISA.

Results

sCD163 and sCD14 showed significantly higher levels in sera of patients compared to the control group, with significantly higher levels of sCD163 in ICU-admitted patients than non-ICU admitted patients. Receiver operating characteristic curve analysis demonstrated the usefulness of sCD163 with a cut-off value of 734 ng/mL as a potential marker to discriminate between ICU and non-ICU admitted COVID-19 patients (sensitivity of 81.16%; specificity of 96.67% and positive predictive value of 98% with area under the curve of 0.930). sCD163 levels showed a positive correlation with total white blood cells, absolute neutrophilic count, Neutrophil/Lymphocyte ratio, and a negative correlation with platelet count. sCD14 levels positively correlated with D-dimer values associated with a shift to the left and neutrophilic toxic granulations in blood morphology.

Conclusion

High sCD163 and sCD14 levels, hematological parameters, and blood morphology reflect monocyte activation in COVID-19 infection. sCD163 is a potential marker of disease severity. These findings support further study of therapeutics targeting macrophage activity in COVID-19 patients with high sCD163 levels.

Can we predict critical care mortality with non-conventional inflammatory markers in SARS-CoV-2 infected patients?

BACKGROUND

Severe COVID-19 disease is associated with multiple organ involvement,then failure and often fatal outcomes.In addition,inflammatory mechanisms and cytokine storms,documented in many COVID-19 patients,are responsible for the progression of the disease and high mortality rates.Inflammatory parameters,such as procalcitonin(PCT) and C-reactive protein(CRP), are widely used in clinical practice.

OBJECTIVE

To evaluate the predictive power of non-conventional inflammatory markers regarding mortality risk.

METHODS

In our prospective study 52 patients were followed for 5 days after admission to an intensive care unit immediately with severe SARS-CoV-2 infection.We compared leukocyte-,platelet antisedimentation rate (LAR, PAR),neutrophil lymphocyte ratio(NLR), CRP, PCT levels.

RESULTS

In non-surviving(NSU) patients LAR remained largely constant from D1 to D4 with a statistically significant drop(p <  0.05) only seen on D5.The NSU group showed statistically significant(p <  0.05) elevated LAR medians on D4 and D5, compared to the SU group.NLR values were continually higher in the non-survivor group.The difference between the SU and NSU groups were statistically significant on every examined day.PAR, CRP and PCT levels didn't show any significant differences between the SU and NSU groups.

CONCLUSIONS

In conclusion, this study suggests that LAR and NLR are especially worthy of further investigation as prognostic markers.LAR might be of particular relevance as it is not routinely obtained in current clinical practice.It would seem beneficial to include LAR in data sets to train prognostic artificial intelligence.

Tea consumption and risk of lower respiratory tract infections: a two-sample mendelian randomization study

BACKGROUND

Observational studies have reported the association between tea consumption and the risk of lower respiratory tract infections (LRTIs). However, a consensus has yet to be reached, and whether the observed association is driven by confounding factors or reverse causality remains unclear.

METHOD

A two-sample Mendelian randomization (MR) analysis was conducted to determine whether genetically predicted tea intake is causally associated with the risk of common LRTI subtypes. Genome-wide association study (GWAS) from UK Biobank was used to identify single-nucleotide polymorphisms (SNPs) associated with an extra cup of tea intake each day. The summary statistics for acute bronchitis, acute bronchiolitis, bronchiectasis, pneumonia, and influenza and pneumonia were derived from the FinnGen project.

RESULTS

We found that genetically predicted an extra daily cup of tea intake was causally associated with the decreased risk of bronchiectasis [odds ratio (OR) = 0.61, 95% confidence interval (CI) = 0.47-0.78, P < 0.001], pneumonia (OR = 0.90, 95% CI = 0.85-0.96, P = 0.002), influenza and pneumonia (OR = 0.91, 95% CI = 0.85-0.97, P = 0.002), but not with acute bronchitis (OR = 0.91, 95% CI = 0.82-1.01, P = 0.067) and acute bronchiolitis (OR = 0.79, 95% CI = 0.60-1.05, P = 0.100). Sensitivity analyses showed that no heterogeneity and pleiotropy could bias the results.

CONCLUSIONS

Our findings provided new evidence that genetically predicted an extra daily cup of tea intake may causally associated with a decreased risk of bronchiectasis, pneumonia, and influenza and pneumonia.

Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning

The tracking of pathogen burden and host responses with minimally invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e., cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing of the models. We show here that lung viral load, neutrophil counts, cytokines (such as gamma interferon [IFN-γ] and interleukin 6 [IL-6]), and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models showed that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path toward improved tracking and monitoring of influenza virus infections and possibly other respiratory infections based on minimally invasively obtained hematological parameters. IMPORTANCE During the course of respiratory infections such as influenza, we do have a very limited view of immunological indicators to objectively and quantitatively evaluate the outcome of a host. Methods for monitoring immunological markers in a host's lungs are invasive and expensive, and some of them are not feasible to perform. Using machine learning algorithms, we show for the first time that minimally invasively acquired hematological parameters can be used to infer lung viral burden, leukocytes, and cytokines following influenza virus infection in mice. The potential of the framework proposed here consists of a new qualitative vision of the disease processes in the lung compartment as a noninvasive tool.

Longitudinal transcriptional analysis of peripheral blood leukocytes in COVID-19 convalescent donors

BACKGROUND

SARS-CoV2 can induce a strong host immune response. Many studies have evaluated antibody response following SARS-CoV2 infections. This study investigated the immune response and T cell receptor diversity in people who had recovered from SARS-CoV2 infection (COVID-19).

METHODS

Using the nCounter platform, we compared transcriptomic profiles of 162 COVID-19 convalescent donors (CCD) and 40 healthy donors (HD). 69 of the 162 CCDs had two or more time points sampled.

RESULTS

After eliminating the effects of demographic factors, we found extensive differential gene expression up to 241 days into the convalescent period. The differentially expressed genes were involved in several pathways, including virus-host interaction, interleukin and JAK-STAT signaling, T-cell co-stimulation, and immune exhaustion. A subset of 21 CCD samples was found to be highly "perturbed," characterized by overexpression of PLAU, IL1B, NFKB1, PLEK, LCP2, IRF3, MTOR, IL18BP, RACK1, TGFB1, and others. In addition, one of the clusters, P1 (n = 8) CCD samples, showed enhanced TCR diversity in 7 VJ pairs (TRAV9.1_TCRVA_014.1, TRBV6.8_TCRVB_016.1, TRAV7_TCRVA_008.1, TRGV9_ENST00000444775.1, TRAV18_TCRVA_026.1, TRGV4_ENST00000390345.1, TRAV11_TCRVA_017.1). Multiplexed cytokine analysis revealed anomalies in SCF, SCGF-b, and MCP-1 expression in this subset.

CONCLUSIONS

Persistent alterations in inflammatory pathways and T-cell activation/exhaustion markers for months after active infection may help shed light on the pathophysiology of a prolonged post-viral syndrome observed following recovery from COVID-19 infection. Future studies may inform the ability to identify druggable targets involving these pathways to mitigate the long-term effects of COVID-19 infection.

TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT04360278 Registered April 24, 2020.

Could serum thrombocyte/lymphocyte (TLR), neutrophil/lymphocyte (NLR) and neutrophil/albumin (NAR) ratios be indicators of hospitalization and mortality in COVID-19?

Background and Objectives

Neutrophil / lymphocyte (NLR) and thrombocyte / lymphocyte ratios (TLR) are also a guiding factors in the prognostic evaluation of infectious diseases. Another parameter to determine inflammation and prognosis is albumin. This study was aimed to determine whether TLR, NLR and neutrophil / albumin ratios (NAR) are effective in predicting the severity and course of Corona Virus Disease-2019 (COVID-19).

Materials and Methods

In this retrospective and cross-sectional study, a total of 1597 patients who were admitted to our hospital between 15.03.2020-1.06.2020, diagnosed with COVID-19 were evaluated.

Results

In the estimation of the decision for hospitalization, TLR, NLR and NAR AUROC values were 0.596, 0.634, 0.602 for cutoff values 123.7, 2.3 and 839.5, respectively. In predicting mortality, TLR, NLR and NAR AURO sample size can be specified C values were 0.674, 0.821, 0.787 for cutoff values 168.1, 5.2 and 1303.4, respectively (p <0.001 for all).

Conclusion

In our study, it was determined that TLR, NLR and NAR are independent predictors in making the decision of hospitalization and in determining the prognosis in patients who are decided to be hospitalized.

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

Retrospective analysis of biochemical markers in COVID-19 intensive care unit patients

Background

The aim of the study was to evaluate the presence and effects of hematological and biological parameters in the diagnosis of the disease by performing blood tests on COVID-19 patients admitted to the intensive care unit (ICU).

Results

Biochemical parameters from the blood samples of 279 patients who were confirmed to have COVID-19 and met the criteria for admission to the ICU were compared between discharged and deceased patients. Multiple logistic regression analysis was performed in terms of mortality and probability of being discharged. The predictive value of serum C-reactive protein (CRP), procalcitonin (PCT), lymphocyte, neutrophil, leucocyte, and platelet (PLT) levels was evaluated by measuring the area under the receiver operating characteristic curve (AUROC).

Comparisons made according to deceased and survival patients results revealed that while no statistically significant difference was observed between test groups lymphocyte and platelet-lymphocyte ratio values, statistically significant difference was found between the test groups regarding platelet, leukocyte, neutrophil, PCT, neutrophil-lymphocyte ratio (NLR), and thrombocyte count × neutrophil count/lymphocyte count (SII) values.

Conclusions

This study showed that biochemical parameters examined are important in determining the prognosis of the disease and may be useful in determining the direction of the treatment process and predicting the risk of discharge or death after the initial evaluation of the patients in the ICU.

Platelets in Viral Infections - Brave Soldiers or Trojan Horses

Viral infections are often associated with platelet activation and haemostatic complications. In line, low platelet counts represent a hallmark for poor prognosis in many infectious diseases. The underlying cause of platelet dysfunction in viral infections is multifaceted and complex. While some viruses directly interact with platelets and/or megakaryocytes to modulate their function, also immune and inflammatory responses directly and indirectly favour platelet activation. Platelet activation results in increased platelet consumption and degradation, which contributes to thrombocytopenia in these patients. The role of platelets is often bi-phasic. Initial platelet hyper-activation is followed by a state of platelet exhaustion and/or hypo-responsiveness, which together with low platelet counts promotes bleeding events. Thereby infectious diseases not only increase the thrombotic but also the bleeding risk or both, which represents a most dreaded clinical complication. Treatment options in these patients are limited and new therapeutic strategies are urgently needed to prevent adverse outcome. This review summarizes the current literature on platelet-virus interactions and their impact on viral pathologies and discusses potential intervention strategies. As pandemics and concomitant haemostatic dysregulations will remain a recurrent threat, understanding the role of platelets in viral infections represents a timely and pivotal challenge.

Neutrophil-to-Lymphocyte Ratio (NLR) Is a Promising Predictor of Mortality and Admission to Intensive Care Unit of COVID-19 Patients

The neutrophil-to-lymphocyte ratio (NLR) is an inflammatory marker predicting the prognosis of several diseases. We aimed to assess its role as a predictor of mortality or admission to the intensive care unit in COVID-19 patients. We retrospectively evaluated a cohort of 411 patients with COVID-19 infection. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and C-reactive protein (CRP) of patients with COVID-19 were compared. The median age of our sample was 72 years (interquartile range: 70−75); 237 were males. Hypertension, diabetes and ischemic heart disease were the most common comorbidities. The study population was subdivided into three groups according to NLR tertiles. Third-tertile patients were older, showing significantly higher levels of inflammatory markers; 133 patients (32%) died during hospitalization, 81 of whom belonged to the third tertile; 79 patients (19%) were admitted to ICU. NLR showed the largest area under the curve (0.772), with the highest specificity (71.9%) and sensitivity (72.9%), whereas CRP showed lower sensitivity (60.2%) but slightly higher specificity (72.3%). Comparisons between NLR and CRP ROC curves were significantly different (p = 0.0173). Cox regression models showed that the association between NLR and death was not weakened after adjustment for confounders. Comparisons of ROC curves showed no significant differences between NLR, PLR, and CRP. Cox regression analysis showed that NLR predicted the risk of admission to ICU independently of demographic characteristics and comorbidities (HR: 3.9597, p < 0.0001). These findings provide evidence that NLR is an independent predictor of mortality and a worse outcome in COVID-19 patients and may help identify high-risk individuals with COVID-19 infection at admission.

Clinical characteristics and related risk factors of disease severity in 101 COVID-19 patients hospitalized in Wuhan, China

Coronavirus disease 2019 (COVID-19) broke out in December 2019. Due its high morbility and mortality, it is necessary to summarize the clinical characteristics of COVID-19 patients to provide more theoretical basis for future treatment. In the current study, we conducted a retrospective analysis of the clinical characteristics of COVID-19 patients and explored the risk factors for the severity of illness. A total of 101 COVID-19 patients hospitalized in Leishenshan Hospital (Wuhan, China) was classified into three sub-types: moderate (n = 47), severe (n = 36), and critical (n = 18); their clinical data were collected from the Electronic Medical Record. We showed that among the 101 COVID-19 patients, the median age was 62 years (IQR 51-74); 50 (49.5%) patients were accompanied by hypertension, while 25 (24.8%) and 22 (21.8%) patients suffered from diabetes and heart diseases, respectively, with complications. All patients were from Wuhan who had a definite history of exposure to the epidemic area. Multivariate logistic regression analysis revealed that older age, diabetes, chronic liver disease, percentage of neutrophils (N%) > 75%, CRP > 4 mg/L, D-dimer > 0.55 mg/L, IL-2R > 710 U/mL, IL-8 > 62 pg/mL, and IL-10 > 9.1 pg/mL were independent variables associated with severe COVID-19. In conclusion, we have identified the independent risk factors for the severity of COVID-19 pneumonia, including older age, diabetes, chronic liver disease, higher levels of N%, CRP, D-dimer, IL-2R, IL-8, and IL-10, providing evidence for more accurate risk prediction.

Longitudinal investigation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in older patients in the province of Palermo (Southern Italy) during the early wave of the pandemic

INTRODUCTION

Clinical presentation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in old adults from Southern Italy is little known. This study aims to investigate the mortality risk related to risk factors, therapy and clinical course and to suggest prognostic indicators based on day-to-day follow-up of clinical and laboratory findings.

MATERIAL AND METHODS

It was designed as a retrospective longitudinal cohort study of adult SARS-CoV-2 patients admitted at Partinico COVID Hospital in Palermo, Southern Italy. Patients were recruited between 4 March and 25 April and followed up until 31 May 2020, day-to-day until death or hospital discharge. Clinical data, laboratory tests and treatment data were extracted from medical records and epidemiologic information was obtained by clinical history and the medical interview.

RESULTS

Forty-seven patients (median age = 75 IQR: 59.50-86.00) were followed up during a 87 days observation period, accounting for a total of 1,035 person days. At the end of follow-up, 28 (60%) patients were discharged and 19 (40%) died, so that the estimated incidence density rate was 0.018 deaths per day (18 SARS-CoV-2-related deaths per 1,000 patient days). Diabetes (HR = 8.13, 95% CI: 1.91-34.67), chronic kidney failure (HR = 5.86, 95% CI: 1.36-25.21), dementia (HR = 7.84, 95% CI: 1.80-34.20), and neutrophil/lymphocyte ratio > 7 (HR = 10.37, 95% CI: 2.24-48.14) were found as significant prognostic factors.

CONCLUSIONS

The joint evaluation of dementia, diabetes, chronic kidney failure and neutrophil/lymphocyte ratio showed an optimal prognostic value already in the first week of follow-up. The day-to-day follow-up provides essential information for clinical monitoring and treatment of the disease in a hospital setting and improves the disease's home management, especially for older patients with frailty.

C-reactive protein and Neutrophil-Lymphocyte Ratio as predictors of clinical severity of COVID-19

COVID-19 is an infectious disease that can manifest quite differently. In this study we examined the relationship between the value of serum CRP(C-reactive protein) andneutrophil-lymphocyte ratio (NLR) as predictor factors for the development of a severe clinical manifestation in COVID19 patients. Materials and methods: We followed 95 COVID-19 positive patients who were hospitalized at the University Clinic for Eye Diseases - COVID Center. We analyzed the initial laboratory parameters of white blood cells and CRP on admission of the patients and the results of laboratory analyses performed before they left the Clinic, or the last parameters before the lethal outcome in those patients who died. Several models of logistic regression were tested to analyze the predictive value of these markers of inflammation for lethal outcome in patients hospitalized for COVID-19. Results: Bivariate analysis demonstrated that the length of hospital stay was significantly shorter in patients with lethal outcome (p=0.001). The NLR was significantly higher in patients with lethal outcome at both times (p=0.005; and p=0.017). Leukocyte’s count (p=0.046, and p<0.001) and CRP (p=0.013,and p=0.005) were also significantly higher in patients with lethal outcome at both times. The increase on the NLR scale both at hospitalization and at discharge (or the last analysis before death) leads to increase in the odds of lethal outcome (T1:40.4% increased odds; T2:36% increased odds). Conclusion: CRP and NLR are laboratory parameters that can predict the severity of the clinical manifestation in patients with COVID-19.

Neutrophil-to-lymphocyte Ratio and Platelet-to-lymphocyte Ratio as Markers for Predicting the Severity in COVID-19 Patients: A Prospective Observational Study

Background: Coronavirus disease-2019 (COVID-19) pandemic has shown unpredictable course in individual patients. Few patients develop severe disease with progression after admission to a healthcare facility. Multiple parameters have been investigated to identify a marker to predict disease progression. Neutrophil-to-lymphocyte ratio (NLR) or platelet-to-lymphocyte (PLR) ratio has shown some promise. The current investigation explores the role of NLR and PLR to predict the disease progression.

Materials and methods: After obtaining ethics committee approval, 608 patients were screened for inclusion in the prospective observational study, and 201 patients were included in the final analysis. The NLR and PLR were derived from routinely obtained complete blood count analysis. The patients were followed to determine the development of severity of the disease during the course. The NLR and PLR were analyzed in both univariate and multivariable models to assess the association and prediction.

Results: In nonsevere (NS) group, the mean age of patients was 50.9 ± 16.3 years, and 66 (61.2%) were male, while in severe group (S), the mean age of patients was 53.7 ± 16.4 years, and 65 (69.89%) were male. NLR at day 1 and day 3 was significantly lower in survivors as compared to nonsurvivors, while the relation of PLR in both the groups was not statistically significant. The NLR is better in predicting the severity of disease as well as mortality than PLR.

Conclusion: The NLR calculated at the time of admission has high predictive value for disease deterioration and adverse clinical outcome.

How to cite this article: Singh Y, Singh A, Rudravaram S, Soni KD, Aggarwal R, Patel N, et al. Neutrophil-to-lymphocyte Ratio and Platelet-to-lymphocyte Ratio as Markers for Predicting the Severity in COVID-19 Patients: A Prospective Observational Study. Indian J Crit Care Med 2021;25(8):847–852.

Prognostic value of neutrophil-to-lymphocyte ratio in COVID-19 compared with Influenza and respiratory syncytial virus infection

A high neutrophil to lymphocyte ratio (NLR) is considered an unfavorable prognostic factor in various diseases, including COVID-19. The prognostic value of NLR in other respiratory viral infections, such as Influenza, has not hitherto been extensively studied. We aimed to compare the prognostic value of NLR in COVID-19, Influenza and Respiratory Syncytial Virus infection (RSV). A retrospective cohort of COVID-19, Influenza and RSV patients admitted to the Tel Aviv Medical Center from January 2010 to October 2020 was analyzed. Laboratory, demographic, and clinical parameters were collected. Two way analyses of variance (ANOVA) was used to compare the association between NLR values and poor outcomes among the three groups. ROC curve analyses for each virus was applied to test the discrimination ability of NLR. 722 COVID-19, 2213 influenza and 482 RSV patients were included. Above the age of 50, NLR at admission was significantly lower among COVID-19 patients (P < 0.001). NLR was associated with poor clinical outcome only in the COVID-19 group. ROC curve analysis was performed; the area under curve of poor outcomes for COVID-19 was 0.68, compared with 0.57 and 0.58 for Influenza and RSV respectively. In the COVID-19 group, multivariate logistic regression identified a high NLR (defined as a value above 6.82) to be a prognostic factor for poor clinical outcome, after adjusting for age, sex and Charlson comorbidity score (odds ratio of 2.9, P < 0.001). NLR at admission is lower and has more prognostic value in COVID-19 patients, when compared to Influenza and RSV.

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.

The neutrophil to lymphocyte ratio is an independent predictor for severe COVID-19 : Evidence from a multicenter case-control study and meta-analyses

Purpose

The aim of this study was to determine whether the neutrophil to lymphocyte ratio (NLR) can predict severe Coronavirus disease 2019 (COVID-19).

Patients and methods

A multicenter case-control study was conducted to investigate whether the NLR can help predict the severity of COVID-19. Patients confirmed to have COVID-19 between 16 January 2020 and 15 March 2020 were enrolled. Furthermore, meta-analyses were conducted based on both previous studies and our case-control study.

Results

In the case-control study, 213 patients (severe: 81) were included. The results suggested that the NLR was an independent risk factor (odds ratio [OR], 1.155, 95% confidence interval [95% CI]: 1.043–1.279, P = 0.006) and a great predictor (the area under the ROC curve was 0.728, 95% CI: 0.656–0.800) for severe COVID-19. In total, 18 datasets from 16 studies combined with our case-control study (severe: 1211; non-severe: 5838) were included in the meta-analyses and the results showed that the NLR of the severe COVID-19 group was significantly higher than that of the non-severe group (SMD = 1.10, 95% CI: 0.90–1.31, P < 0.001). Based on the 2 × 2 data from 6 studies, the SROC of NLR for predicting severe COVID-19 was 0.802, with a sensitivity of 0.67 (95% CI: 0.61–0.72) and a specificity of 0.75 (95% CI: 0.73–0.78).

Conclusion

Based on a multicenter case-control study and a meta-analysis, we found that the initial NLR was a great predictor of severe COVID-19.

Supplementary Information

The online version of this article (10.1007/s00508-021-01917-9) contains supplementary material, which is available to authorized users.

Comparison of clinical and biochemical features of hospitalized COVID-19 and influenza pneumonia patients

Both severe acute respiratory syndrome coronavirus 2 and influenza viruses cause similar clinical presentations. It is essential to assess severely ill patients presenting with a viral syndrome for diagnostic and prognostic purposes. We aimed to compare clinical and biochemical features between pneumonia patients with coronavirus disease 2019 (COVID‐19) and H1N1. Sixty patients diagnosed with COVID‐19 pneumonia and 61 patients diagnosed with influenza pneumonia were hospitalized between October 2020–January 2021 and October 2017–December 2019, respectively. All the clinical data and laboratory results, chest computed tomography scans, intensive care unit admission, invasive mechanical ventilation, and outcomes were retrospectively evaluated. The median age was 65 (range 32–96) years for patients with a COVID‐19 diagnosis and 58 (range 18–83) years for patients with influenza (p = 0.002). The comorbidity index was significantly higher in patients with COVID‐19 (p = 0.010). Diabetes mellitus and hypertension were statistically significantly more common in patients with COVID‐19 (p = 0.019, p = 0.008, respectively). The distribution of severe disease and mortality was not significantly different among patients with COVID‐19 than influenza patients (p = 0.096, p = 0.049).). In comparison with inflammation markers; C‐reactive protein (CRP) levels were significantly higher in influenza patients than patients with COVID‐19 (p = 0.033). The presence of sputum was predictive for influenza (odds ratio [OR] 0.342 [95% confidence interval [CI], 2.1.130–0.899]). CRP and platelet were also predictive for COVID‐19 (OR 4.764 [95% CI, 1.003–1.012] and OR 0.991 [95% CI 0.984–0.998], respectively. We conclude that sputum symptoms by itself are much more detected in influenza patients. Besides that, lower CRP and higher PLT count would be discriminative for COVID‐19.

It is essential to distinguish two respiratory viral infections COVID‐19 and influenzae. We aimed to compare clinical and biochemical features between pneumonia patients with two diseases.While sputum symptoms by itself are much more detected in influenza patients, lower CRP and higher PLT count would be discriminative for COVID‐19.

The diagnostic and prognostic role of neutrophil-to-lymphocyte ratio in COVID-19: a systematic review and meta-analysis

Background: The world urgently requires surrogate markers to diagnose COVID-19 and predict its progression. The severity is not easily predicted via currently used biomarkers. Critical COVID-19 patients need to be screened for hyperinflammation to improve mortality but expensive cytokine measurement is not routinely conducted in most laboratories. The neutrophil-to-lymphocyte ratio (NLR) is a novel biomarker in patients with various diseases. We evaluated the diagnostic and prognostic accuracy of the NLR in COVID-19 patients.Methods: We searched for relevant articles in seven databases. The quantitative analysis was conducted if at least two studies were evaluating the NLR role in COVID-19.Results: We included 8,120 individuals, including 7,482 COVID-19 patients, from 32 articles. Patients with COVID-19 had significantly higher levels of NLR compared to negative individuals. Advanced COVID-19 stages had significantly higher levels of NLR than earlier stages.Expert Opinion: We found significantly higher levels of NLR in advanced stages compared to earlier stages of COVID-19 with good accuracy to diagnose and predict the disease outcome, especially mortality prediction. A close evaluation of critical SARS-CoV-2 patients and efficient early management are essential measures to decrease mortality. NLR could help in assessing the resource allocation in severe COVID-19 patients even in restricted settings.

Comparison of clinical features on admission between coronavirus disease 2019 and influenza a among children: a retrospective study in China

BACKGROUND

Coronavirus disease 2019 (COVID-19) share similar symptoms with influenza A (IA), but it is more worthwhile to understand the disparities of the two infections regarding their clinical characteristics on admission.

METHODS

A total of 71 age-matched pediatric IA and COVID-19 patient pairs were formed and their clinical data on admission were compared.

RESULTS

Fever, cough, nasal congestion and nausea/vomiting were the most common symptoms on admission for both infections but occurred less often in COVID-19. The IA patients were more likely to have lower-than-normal levels of lymphocyte count and percentage and to have higher-than-normal levels of activated partial thromboplastin time, prothrombin time, serum C-reactive protein, and serum procalcitonin, while the COVID-19 patients had higher odds of having lower-than-normal levels of neutrophil count and percentage.

CONCLUSIONS

This study suggests that influenza A is more symptomatic than COVID-19 for children and might be an overall more severe infection at the time of admission.

Comparison of COVID-19 and influenza characteristics

The emergence of coronavirus disease 2019 (COVID-19) not only poses a serious threat to the health of people worldwide but also affects the global economy. The outbreak of COVID-19 began in December 2019, at the same time as the influenza season. However, as the treatments and prognoses of COVID-19 and influenza are different, it is important to accurately differentiate these two different respiratory tract infections on the basis of their respective early-stage characteristics. We reviewed official documents and news released by the National Health Commission of the People's Republic of China, the Chinese Center for Disease Control and Prevention (China CDC), the United States CDC, and the World Health Organization (WHO), and we also searched the PubMed, Web of Science, Excerpta Medica database (Embase), China National Knowledge Infrastructure (CNKI), Wanfang, preprinted bioRxiv and medRxiv databases for documents and guidelines from earliest available date up until October 3rd, 2020. We obtained the latest information about COVID-19 and influenza and summarized and compared their biological characteristics, epidemiology, clinical manifestations, pathological mechanisms, treatments, and prognostic factors. We show that although COVID-19 and influenza are different in many ways, there are numerous similarities; thus, in addition to using nucleic acid-based polymerase chain reaction (PCR) and antibody-based approaches, clinicians and epidemiologists should distinguish between the two using their respective characteristics in early stages. We should utilize experiences from other epidemics to provide additional guidance for the treatment and prevention of COVID-19.

The role of haematological parameters in patients with COVID-19 and influenza virus infection

SARS-CoV-2, the causative agent of coronavirus disease 19 (COVID-19), was identified in Wuhan, China. Since then, the novel coronavirus started to be compared to influenza. The haematological parameters and inflammatory indexes are associated with severe illness in COVID-19 patients. In this study, the laboratory data of 120 COVID-19 patients, 100 influenza patients and 61 healthy controls were evaluated. Lower lymphocytes, eosinophils, basophils, platelets and higher delta neutrophil index (DNI), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were found in COVID-19 and influenza groups compared to healthy controls. The eosinophils, lymphocytes and PLR made the highest contribution to differentiate COVID-19 patients from healthy controls (area under the curves (AUCs): 0.819, 0.817 and 0.716, respectively; P-value is <0.0001 for all). The NLR, the optimal cut-off value was 3.58, which resulted in a sensitivity of 30.8 and a specificity of 100 (AUC: 0.677, P < 0.0001). Higher leucocytes, neutrophils, DNI, NLR, PLR and lower lymphocytes, red blood cells, haemoglobin, haematocrit levels were found in severe patients at the end of treatment. Nonsevere patients showed an upward trend for lymphocytes, eosinophils and platelets, and a downward trend for neutrophils, DNI, NLR and PLR. However, there was an increasing trend for eosinophils, platelets and PLR in severe patients. In conclusion, NLR and PLR can be used as biomarkers to distinguish COVID-19 patients from healthy people and to predict the severity of COVID-19. The increasing value of PLR during follow-up may be more useful compared to NLR to predict the disease severity.

Neutrophil-to-lymphocyte ratio, a critical predictor for assessment of disease severity in patients with COVID-19

INTRODUCTION

Monitoring of laboratory indicators is important for predicting changes in disease severity and clinical outcomes. We aimed to identify the critical predictors that can effectively assess the disease conditions of patients with COVID-19 by analyzing the clinical characteristics and laboratory findings of patients with SARS-CoV-2 infection.

METHODS

All consecutive patients (n = 294) with confirmed SARS-CoV-2 infection admitted to the General Hospital of Central Theater Command of the PLA from February 6 to February 21, 2020, were enrolled. These patients were divided into the severe group and the nonsevere group according to disease severity during hospitalization.

RESULTS

The median neutrophil-to-lymphocyte ratio (NLR) value of the severe patients was dramatically higher than that of the nonsevere patients (10.4 vs 2.6; P < .001). The NLR value equal to 5 was a boundary value worthy of reference, because more than 80% severe patients had an NLR value greater than 5 and over 80% nonsevere patients had an NLR value less than 5. The NLR value of these COVID-19 patients was positively and respectively correlated with the values of C-reactive protein (R = .5921, P < .001), lactate dehydrogenase (R = .4509, P < .001), procalcitonin (R = .5504, P < .001), fibrinogen (R = .4710, P < .001), and D-dimers (R = .4425, P < .001). However, the NLR value was merely and positively correlated with the interleukin-6 value (R = .3594, P < .05), but had no correlations with the values of interleukin-10, interleukin-4, interleukin-17, interferon-γ, and tumor necrosis factor-α (P > .05).

DISCUSSION

Neutrophil-to-lymphocyte ratio is a critical predictor for assessment of disease severity in patients with COVID-19, and it has a close relation with the laboratory indicators related to disease conditions.

Diagnostic value of peripheral hematologic markers for coronavirus disease 2019 (COVID-19): A multicenter, cross-sectional study

Background

To determine the diagnostic value of hematologic markers for coronavirus disease 2019 (COVID‐19) and explore their relationship with disease severity.

Methods

Subjects included 190 COVID‐19 patients, 190 healthy subjects, and 105 influenza pneumonia (IP) patients. COVID‐19 patients were divided into the ARDS and non‐ARDS groups. Routine blood examination, biochemistry indicator, days in hospital, body temperature, pneumonia severity index (PSI), CURB‐65, and MuLBSTA were recorded. Correlations between variables were assessed using Spearman's correlation analysis. Receiver operating characteristic (ROC) curves were used to study the accuracy of the various diagnostic tests.

Results

Compared with healthy subjects, COVID‐19 patients had lower white blood cell (WBC), lymphocyte, platelet, and hemoglobin levels; higher percentages of neutrophils and monocytes; lower percentages of lymphocytes and higher neutrophil‐to‐lymphocyte ratio (NLR), monocyte‐to‐lymphocyte ratio (MLR), and platelet‐to‐lymphocyte ratio (PLR) values (P < .05). COVID‐19 patients had higher WBC and neutrophil levels and lower percentages of lymphocytes compared to IP (P < .05). ROC curve analysis revealed that MLR had a high diagnostic value in differentiating COVID‐19 patients from healthy subjects, but not from IP patients. NLR showed significant positive correlations with PSI, CURB‐65, and MuLBSTA. Lymphocyte count was lower in the ARDS group and yielded a higher diagnostic value than the other variables.

Conclusions

Monocyte‐to‐lymphocyte ratio showed an acceptable efficiency to separate COVID‐19 patients from healthy subjects, but failed to rule out IP patients. NLR may be a reliable marker to evaluate the disease severity of COVID‐19. Lymphocyte count may be useful to establish the early diagnosis of ARDS in the COVID‐19 patients.

Use of Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios in COVID-19

BACKGROUND

As the pandemic of coronavirus disease 2019 (COVID-19) continues, prognostic markers are now being identified. The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are easily accessible values that have been known to correlate with inflammation and prognosis in several conditions. We used the available data to identify the association of NLR and PLR with the severity of COVID-19.

METHODS

A literature search using EMBASE, MEDLINE, and Google Scholar for studies reporting the use of NLR and PLR in COVID-19 published until April 28, 2020, was performed. Random effects meta-analysis was done to estimate standard mean difference (SMD) of NLR and PLR values with 95% confidence interval (CI) between severe and non-severe COVID-19 cases.

RESULTS

A total of 20 studies with 3,508 patients were included. Nineteen studies reported NLR values, while five studies reported PLR values between severe and non-severe COVID-19 patients. Higher levels of NLR (SMD: 2.80, 95% CI: 2.12 - 3.48, P < 0.00001) and PLR (SMD: 1.82, 95% CI: 1.03 - 2.61, P < 0.00001)) were seen in patients with severe disease compared to non-severe disease.

CONCLUSIONS

NLR and PLR can be used as independent prognostic markers of disease severity in COVID-19.

The value of clinical parameters in predicting the severity of COVID-19

To study the relationship between clinical indexes and the severity of coronavirus disease 2019 (COVID-19), and to explore its role in predicting the severity of COVID-19. Clinical data of 443 patients with COVID-19 admitted to our hospital were retrospectively analyzed, which were divided into nonsevere group (n = 304) and severe group (n = 139) according to their condition. Clinical indicators were compared between different groups. The differences in sex, age, the proportion of patients with combined heart disease, leukocyte, neutrophil-to-lymphocyte ratio (NLR), neutrophil, lymphocyte, platelet, D-dimer, C-reactive protein (CRP), procalcitonin, lactate dehydrogenase, and albumin on admission between the two groups were statistically significant (P  <  .05). Multivariate logistic regression analysis showed NLR and CRP were independent risk factors for severe COVID-19. Platelets were independent protective factors for severe COVID-19. The receiver operating characteristic (ROC) curve analysis demonstrated area under the curve of NLR, platelet, CRP, and combination was 0.737, 0.634, 0.734, and 0.774, respectively. NLR, CRP, and platelets can effectively assess the severity of COVID-19, among which NLR is the best predictor of severe COVID-19, and the combination of three clinical indicators can further predict severe COVID-19.

Dynamic profile and clinical implications of hematological parameters in hospitalized patients with coronavirus disease 2019

Objectives

As people across the world suffer from coronavirus disease 2019 (COVID-19), further studies are needed to facilitate evaluating the severity and prognosis of COVID-19 patients. In the study, we aimed to dissect the dynamic profile and clinical implications of hematological findings in hospitalized patients with COVID-19.

Methods

We retrospectively analyzed the hematological findings of 72 patients with COVID-19 admitted from January 21 to February 17, 2020. The final date of follow-up was March 20, 2020. Dynamic profile of vital hematological parameters in severe and non-severe patients was presented at different time points (day 1, 5, 7, 9, 11, 13, 15 after admission), and the correlation of hematological parameters with hospitalization time was indicated.

Results

Of 72 patients with COVID-19, lymphopenia and leukopenia occurred in 39 (54.2%) and 20 (27.8%) patients with COVID-19, respectively. Fifteen (20.8%) patients were defined as severe cases and 57 (79.2%) were non-severe cases. Compared to non-severe patients, leukocyte count, neutrophil count and neutrophil-to-lymphocyte ratio (NLR) were significantly higher, whereas lymphocyte count was declined in severe patients at each time point. A growing trend in platelet count was found in non-severe patients over the follow-up period. In addition, a positive correlation of NLR with hospitalization time was detected from day 5 after admission.

Conclusions

Dynamic changes in vital hematological parameters from severe and non-severe patients had been characterized in the course of hospitalization. During hospitalization, NLR was found to have certain relevance to the hospitalization days and a role in forecasting disease prognosis for patients with COVID-19.

Role of hematological parameters in the diagnosis of influenza virus infection in patients with respiratory tract infection symptoms

Background

The differential diagnoses of patients hospitalized for respiratory infections due to influenza virus vs other pathogens are challenging. Our study investigated whether hematological parameters such as neutrophil (N), lymphocyte (L), platelet (PLT), and neutrophil‐to‐lymphocyte ratio (NLR) contributed in diagnosing influenza virus infections and in discriminating other respiratory infections.

Methods

We retrospectively analyzed the laboratory characteristics of 307 patients with respiratory infections caused by influenza/non‐influenza virus and bacteria. The diagnostic abilities of hematological indexes were evaluated in the patients compared with 100 healthy people.

Results

The hematological parameters in patients with influenza virus infection were dramatically altered compared with those in the controls. Additionally, among the systemic inflammatory markers, the sensitivity of NLR for influenza detection was higher than that of N and L. PLT was significantly lower in influenza virus‐positive infection than in influenza virus‐negative infection. Moreover, when patients with influenza virus infection were cured, PLT returned to a normal level. The red blood cell (RBC) and hemoglobin (Hb) levels of influenza virus infection were higher than those of bacterial infection. Compared with traditional N and L, NLR and platelet‐to‐neutrophil (PNR) showed greater significance between influenza virus and bacterial infection (P < .01).

Conclusion

Neutrophil‐to‐lymphocyte ratio with high sensitivity is a preferable diagnostic tool to screen influenza virus‐infected patients than N and L. PLT accounts in the differential diagnoses of respiratory infections due to influenza virus and other pathogens among patients. In addition, RBC, Hb, NLR, and PNR can significantly differentiate between influenza virus infections and bacterial infections.