Comparative Analysis of Early-Stage Clinical Features Between COVID-19 and Influenza A H1N1 Virus Pneumonia

The blood routine test holds screening values for influenza A in 2023: a retrospective study

Background

Influenza A is the most common viral pathogen isolated from pediatric clinics during influenza seasons. Some young patients with influenza manifest rapid progression with high fever and severe sequelae, such as pneumonia and meningitis. Therefore, early diagnosis and prompt treatment are highly important. Specific diagnostic tests currently include antigen detection, antibody detection, nucleic acid test and virus isolation. Rapid antigen testing is the most commonly adopted method in the outpatient setting, but false negative results are frequently observed, which causes delayed treatment and severe outcome. Routine blood test is the most commonly used detection for the outpatients. Incorporating specific blood cell counts into rapid antigen test may overcome some technical issues and enable accurate early diagnosis.

Methods

We enrolled 537 children with influenza-like symptoms like fever or respiratory symptoms from pediatric outpatients and 110 children without infectious diseases for control. Routine blood tests detected by a routine analyzer and influenza A virus antigen detection were performed in the patients. Significant blood routine parameters between groups were examined by statistical tests. Parameters in routine blood test were assessed by the receiver operating characteristic curve to find the screening indicators of influenza A. Multivariate logistic regression were used to establish the optimal combinations of blood routine parameters in our screening model.

Results

Two subgroups were set according to age: ≤6 years old group and >6 years old group. In each group, patients were further divided into three subgroups: the influenza A-positive-result group (A+ group) (n=259), influenza A-negative-result group (A- group) (n=277) and healthy control group (H group) (n=110). Most routine blood parameters showed significant differences among the three subgroups in each age group. Notably, lymphocyte (LYM) number, platelet (PLT) number, lymphocyte-to-monocyte ratio (LMR) and LYM multiplied by PLT (LYM*PLT) exhibited extremely significant differences. Using A- group as a reference based on the area under the curve (AUC), both age groups had a similar trend. For A- group, the optimal cutoff value of LYM*PLT was 221.6, the AUC, the sensitivity and specificity were 0.6830, 55.71% and 76.92% in the ≤6 years old group. Meanwhile, the cutoff value of LYM*PLT was 196.7, and the AUC, the sensitivity and specificity were 0.6448, 53.97% and 70.81%, respectively in the >6 years old group. Screening model based on multivariate logistic regression model revealed that LYM*PLT was the optimal parameter combinations in ≤6 years old group (AUC =0.7202), while LYM and PLT were the optimal parameter combinations in >6 years old group (AUC =0.6760).

Conclusions

Several blood routine parameters in children with influenza A demonstrate differential levels in both age subgroups. The LYM*PLT exhibits the potential screening value of influenza infection.

Can Haematological Parameters Discriminate COVID-19 from Influenza?

Symptoms of COVID-19 are similar to the influenza virus, but because treatments and prognoses are different, it is important to accurately and rapidly differentiate these diseases. The aim of this study was to evaluate whether the analysis of complete blood count (CBC), including cellular population (CPD) data of leukocytes and automated flow cytometry analysis, could discriminate these pathologies. In total, 350 patients with COVID-19 and 102 patients with influenza were included between September 2021 and April 2022 in the tertiary hospital of Suresnes (France). Platelets were lower in patients with influenza than in patients with COVID-19, whereas the CD16pos monocyte count and the ratio of the CD16pos monocytes/total monocyte count were higher. Significant differences were observed for 9/56 CPD of COVID-19 and flu patients. A logistic regression model with 17 parameters, including among them 11 CPD, the haemoglobin level, the haematocrit, the red cell distribution width, and B-lymphocyte and CD16pos monocyte levels, discriminates COVID-19 patients from flu patients. The sensitivity and efficiency of the model were 96.2 and 86.6%, respectively, with an area under the curve of 0.862. Classical parameters of CBC are very similar among the three infections, but CPD, CD16pos monocytes, and B-lymphocyte levels can discriminate patients with COVID-19.

Bibliometric analysis of publication trends and topics of influenza-related encephalopathy from 2000 to 2022

BACKGROUND

Influenza-related encephalopathy is a rapidly progressive encephalopathy that usually presents during the early phase of influenza infection and primarily manifests as central nervous system dysfunction. This study aimed to analyze the current research status and hotspots of influenza-related encephalopathy since 2000 through bibliometrics analysis.

METHODS

The Web of Science Core Collection (WOSCC) was used to extract global papers on influenza-related encephalopathy from 2000 to 2022. Meanwhile, the VOSviewer and CiteSpace software were used for data processing and result visualization.

RESULTS

A total of 561 published articles were included in the study. Japan was the country that published the most articles, with 205 articles, followed by the United States and China. Okayama University and Tokyo Medical University published the most articles, followed by Nagoya University, Tokyo University, and Juntendo University. Based on the analysis of keywords, four clusters with different research directions were identified: "Prevalence of H1N1 virus and the occurrence of neurological complications in different age groups," "mechanism of brain and central nervous system response after influenza virus infection," "various acute encephalopathy" and "diagnostic indicators of influenza-related encephalopathy."

CONCLUSIONS

The research progress, hotspots, and frontiers on influenza-related encephalopathy after 2000 were described through the visualization of bibliometrics. The findings will lay the groundwork for future studies and provide a reference for influenza-related encephalopathy. Research on influenza-related encephalopathy is basically at a stable stage, and the number of research results is related to outbreaks of the influenza virus.

Eosinophils as potential biomarkers in respiratory viral infections

Eosinophils are bone marrow-derived granulocytes that, under homeostatic conditions, account for as much as 1-3% of peripheral blood leukocytes. During inflammation, eosinophils can rapidly expand and infiltrate inflamed tissues, guided by cytokines and alarmins (such as IL-33), adhesion molecules and chemokines. Eosinophils play a prominent role in allergic asthma and parasitic infections. Nonetheless, they participate in the immune response against respiratory viruses such as respiratory syncytial virus and influenza. Notably, respiratory viruses are associated with asthma exacerbation. Eosinophils release several molecules endowed with antiviral activity, including cationic proteins, RNases and reactive oxygen and nitrogen species. On the other hand, eosinophils release several cytokines involved in homeostasis maintenance and Th2-related inflammation. In the context of SARS-CoV-2 infection, emerging evidence indicates that eosinophils can represent possible blood-based biomarkers for diagnosis, prognosis, and severity prediction of disease. In particular, eosinopenia seems to be an indicator of severity among patients with COVID-19, whereas an increased eosinophil count is associated with a better prognosis, including a lower incidence of complications and mortality. In the present review, we provide an overview of the role and plasticity of eosinophils focusing on various respiratory viral infections and in the context of viral and allergic disease comorbidities. We will discuss the potential utility of eosinophils as prognostic/predictive immune biomarkers in emerging respiratory viral diseases, particularly COVID-19. Finally, we will revisit some of the relevant methods and tools that have contributed to the advances in the dissection of various eosinophil subsets in different pathological settings for future biomarker definition.

Fast COVID-19 versus H1N1 screening using Optimized Parallel Inception

COVID-19 and swine-origin influenza A (H1N1) are both pandemics that sparked significant concern worldwide. Since these two diseases have common symptoms, a fast COVID-19 versus H1N1 screening helps better manage patients at healthcare facilities. We present a novel deep model, called Optimized Parallel Inception, for fast screening of COVID-19 and H1N1 patients. We also present a Semi-supervised Generative Adversarial Network (SGAN) to address the problem related to the smaller size of the COVID-19 and H1N1 research data. To evaluate the proposed models, we have merged two separate COVID-19 and H1N1 data from different sources to build a new dataset. The created dataset includes 4,383 positive COVID-19 cases, 989 positive H1N1 cases, and 1,059 negative cases. We applied SGAN on this dataset to remove issues related to unequal class densities. The experimental results show that the proposed model's screening accuracy is 99.2% and 99.6% for COVID-19 and H1N1, respectively. According to our analysis, the most significant symptoms and underlying chronic diseases for COVID-19 versus H1N1 screening are dry cough, breathing problems, diabetes, and gastrointestinal.

Radiological Findings in SARS-CoV-2 Viral Pneumonia Compared to Other Viral Pneumonias: A Single-Centre Study

BACKGROUND

Thorax computed tomography (CT) imaging is widely used as a diagnostic method in the diagnosis of coronavirus disease 2019 (COVID-19)-related pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients have gained importance, particularly during the pandemic.

AIMS

We aimed to investigate whether there is a difference between CT images from patients with COVID-19-associated pneumonia compared to CT images of patients with pneumonia due to other viral agents and which finding may be more effective in diagnosis. Study Design. The study included 249 adult patients with pneumonia identified by thorax CT examination and with a positive COVID-19 RT-PCR test compared to 94 patients diagnosed with non-COVID-19 pneumonia (viral PCR positive but no bacterial or fungal agents detected in other cultures) between 2015 and 2019. CT images were retrospectively analyzed using the PACS system. CT findings were evaluated by two radiologists with 5 and 20 years of experience, in a blinded fashion, and the outcome was decided by consensus.

METHODS

Demographic data (age, gender, and known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, and pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups-non-COVID-19 and COVID-19 pneumonia-and compared statistically with chi-squared tests and multiple regression analysis of independent variables.

RESULTS

RSNA and CORADS classifications of CT scan images were able to successfully differentiate between positive and negative COVID-19 pneumonia patients. Statistically significant differences were found between the two patient groups in various categories including the percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation, and mediastinal/hilar lymphadenopathy (p < 0.01). Multiple linear regression analysis of independent variables found a significant effect in reverse halo sign (β = 0.097, p < 0.05) and pleural effusion (β = 10.631, p < 0.05) on COVID-19 pneumonia patients.

CONCLUSION

The presence of reverse halo and absence of pleural effusion was found to be characteristic of COVID-19 pneumonia and therefore a reliable diagnostic tool to differentiate it from non-COVID-19 pneumonia.

Comparison of temporal evolution of computed tomography imaging features in COVID-19 and influenza infections in a multicenter cohort study

Purpose

To compare temporal evolution of imaging features of coronavirus disease 2019 (COVID-19) and influenza in computed tomography and evaluate their predictive value for distinction.

Methods

In this retrospective, multicenter study 179 CT examinations of 52 COVID-19 and 44 influenza critically ill patients were included. Lung involvement, main pattern (ground glass opacity, crazy paving, consolidation) and additional lung and chest findings were evaluated by two independent observers. Additional findings and clinical data were compared patient-wise. A decision tree analysis was performed to identify imaging features with predictive value in distinguishing both entities.

Results

In contrast to influenza patients, lung involvement remains high in COVID-19 patients > 14 days after the diagnosis. The predominant pattern in COVID-19 evolves from ground glass at the beginning to consolidation in later disease. In influenza there is more consolidation at the beginning and overall less ground glass opacity (p = 0.002). Decision tree analysis yielded the following: Earlier in disease course, pleural effusion is a typical feature of influenza (p = 0.007) whereas ground glass opacities indicate COVID-19 (p = 0.04). In later disease, particularly more lung involvement (p < 0.001), but also less pleural (p = 0.005) and pericardial (p = 0.003) effusion favor COVID-19 over influenza. Regardless of time point, less lung involvement (p < 0.001), tree-in-bud (p = 0.002) and pericardial effusion (p = 0.01) make influenza more likely than COVID-19.

Conclusions

This study identified differences in temporal evolution of imaging features between COVID-19 and influenza. These findings may help to distinguish both diseases in critically ill patients when laboratory findings are delayed or inconclusive.

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Decision tree analysis helps to distinguish COVID-19 and Influenza.

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Pleural effusion is a typical feature of influenza in early disease.

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Ground glass opacities indicate COVID-19 in early disease.

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Lung involvement remains high in COVID-19 patients > 14 days after the diagnosis.

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Pleural and pericardial effusion favor influenza over COVID-19 in later disease.

Comparison of Hospitalized Coronavirus Disease 2019 and Influenza Patients Requiring Supplemental Oxygen in a Cohort Study: Clinical Impact and Resource Consumption

BACKGROUND

To compare clinical characteristics, outcomes, and resource consumption of patients with coronavirus disease 2019 (COVID-19) and seasonal influenza requiring supplemental oxygen.

METHODS

Retrospective cohort study conducted at a tertiary-care hospital. Patients admitted because of seasonal influenza between 2017 and 2019, or with COVID-19 between March and May 2020 requiring supplemental oxygen were compared. Primary outcome: 30-day mortality. Secondary outcomes: 90-day mortality and hospitalization costs. Attempted sample size to detect an 11% difference in mortality was 187 patients per group.

RESULTS

COVID-19 cases were younger (median years of age, 67; interquartile range [IQR] 54-78 vs 76 [IQR 64-83]; P < .001) and more frequently overweight, whereas influenza cases had more hypertension, immunosuppression, and chronic heart, respiratory, and renal disease. Compared with influenza, COVID-19 cases had more pneumonia (98% vs 60%, <.001), higher Modified Early Warning Score (MEWS) and CURB-65 (confusion, blood urea nitrogen, respiratory rate, systolic blood pressure, and age >65 years) scores and were more likely to show worse progression on the World Health Organization ordinal scale (33% vs 4%; P < .001). The 30-day mortality rate was higher for COVID-19 than for influenza: 15% vs 5% (P = .001). The median age of nonsurviving cases was 81 (IQR 74-88) and 77.5 (IQR 65-84) (P = .385), respectively. COVID-19 was independently associated with 30-day (hazard ratio [HR], 4.6; 95% confidence interval [CI], 2-10.4) and 90-day (HR, 5.2; 95% CI, 2.4-11.4) mortality. Sensitivity and subgroup analyses, including a subgroup considering only patients with pneumonia, did not show different trends. Regarding resource consumption, COVID-19 patients had longer hospital stays and higher critical care, pharmacy, and complementary test costs.

CONCLUSIONS

Although influenza patients were older and had more comorbidities, COVID-19 cases requiring supplemental oxygen on admission had worse clinical and economic outcomes.

Triage Modeling for Differential Diagnosis Between COVID-19 and Human Influenza A Pneumonia: Classification and Regression Tree Analysis

Background: The coronavirus disease 2019 (COVID-19) pandemic has lasted much longer than an influenza season, but the main signs, symptoms, and some imaging findings are similar in COVID-19 and influenza patients. The aim of the current study was to construct an accurate and robust model for initial screening and differential diagnosis of COVID-19 and influenza A.

Methods: All patients in the study were diagnosed at Fuyang No. 2 People's Hospital, and they included 151 with COVID-19 and 155 with influenza A. The patients were randomly assigned to training set or a testing set at a 4:1 ratio. Predictor variables were selected based on importance, assessed by random forest algorithms, and analyzed to develop classification and regression tree models.

Results: In the optimal model A, the best single predictor of COVID-19 patients was a normal or high level of low-density lipoprotein cholesterol, followed by low level of creatine kinase, then the presence of <3 respiratory symptoms, then a highest temperature on the first day of admission <38°C. In the suboptimal model B, the best single predictor of COVID-19 was a low eosinophil count, then a normal monocyte ratio, then a normal hematocrit value, then a highest temperature on the first day of admission of <37°C, then a complete lack of respiratory symptoms.

Conclusions: The two models provide clinicians with a rapid triage tool. The optimal model can be used to developed countries/regions and major hospitals, and the suboptimal model can be used in underdeveloped regions and small hospitals.

Simultaneous detection of SARS-CoV-2 and pandemic (H1N1) 2009 virus with real-time isothermal platform

The recent ongoing outbreak of novel coronavirus SARS-CoV-2 (known as COVID-19) is a severe threat to human health worldwide. By press time, more than 3.3 million people have died from COVID-19, with many countries experiencing peaks in infections and hospitalizations. The main symptoms of infection with SARS-CoV-2 include fever, chills, coughing, shortness of breath or difficulty breathing, fatigue, muscle or body aches and pains. While the symptoms of the pandemic (H1N1) 2009 virus have many similarities to the signs and transmission routes of the novel coronavirus, e.g., fever, cough, sore throat, body aches, headache, chills and fatigue. And a few cases of serious illness, rapid progress, can appear viral pneumonia, combined with respiratory failure, multiple organ function damage, serious people can die. Therefore, there is an urgent need to develop a rapid and accurate field diagnostic method to effectively identify the two viruses and treat these early infections on time, thus helping to control the spread of the disease. Among molecular detection methods, RT-LAMP (real-time reverse transcription-loop-mediated isothermal amplification) has some advantages in pathogen detection due to its rapid, accurate and effective detection characteristics. Here, we combined the primers of the two viruses with the fluorescent probes on the RT-LAMP detection platform to detect the two viruses simultaneously. Firstly, RT-LAMP method was used respectively to detect the two viruses at different concentrations to determine the effectiveness and sensitivity of probe primers to the RNA samples. And then, the two virus samples were detected simultaneously in the same reaction tube to validate if testing for the two viruses together had an impact on the results compared to detecting alone. We verified the detection efficiency of three highly active BST variants during RT-LAMP assay. We expect that this assay can effectively and accurately distinguish COVID-19 from the pandemic (H1N1) 2009, so that these two diseases with similar symptoms can be appropriately differentiated and treated.

Comparing COVID-19 and Influenza Presentation and Trajectory

Background: COVID-19 is a newly recognized illness with a predominantly respiratory presentation. It is important to characterize the differences in disease presentation and trajectory between COVID-19 patients and other patients with common respiratory illnesses. These differences can enhance knowledge of pathogenesis and help in guiding treatment. Methods: Data from electronic medical records were obtained from individuals admitted with respiratory illnesses to Rambam Health Care Campus, Haifa, Israel, between October 1st, 2014 and October 1st, 2020. Four groups of patients were defined: COVID-19 (693), influenza (1,612), severe acute respiratory infection (SARI) (2,292), and Others (4,054). The variable analyzed include demographics (7), vital signs (8), lab tests (38), and comorbidities (15) from a total of 8,651 hospitalized adult patients. Statistical analysis was performed on biomarkers measured at admission and for their disease trajectory in the first 48 h of hospitalization, and on comorobidity prevalence. Results: COVID-19 patients were overall younger in age and had higher body mass index, compared to influenza and SARI. Comorbidity burden was lower in the COVID-19 group compared to influenza and SARI. Severely- and moderately-ill COVID-19 patients older than 65 years of age suffered higher rate of in-hospital mortality compared to hospitalized influenza patients. At admission, white blood cells and neutrophils were lower among COVID-19 patients compared to influenza and SARI patients, while pulse rate and lymphoctye percentage were higher. Trajectories of variables during the first 2 days of hospitalization revealed that white blood count, neutrophils percentage and glucose in blood increased among COVID-19 patients, while decreasing among other patients. Conclusions: The intrinsic virulence of COVID-19 appeared higher than influenza. In addition, several critical functions, such as immune response, coagulation, heart and respiratory function, and metabolism were uniquely affected by COVID-19.

Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds

CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (n = 526), all imaging biomarkers significantly differed between healthy lungs and lungs with viral pneumonia (all p < 0.001), a finding that was not reproduced for CTPAs (n = 504). Standard deviation (histogram-derived) and relative high attenuation area [600-0 HU] (HU-thresholding) differed most. The strongest correlation with disease severity was found for absolute high attenuation area [600-0 HU] (r = 0.56, 95% CI = 0.46-0.64). Deep-learning segmentation-based histogram and HU threshold analysis could be deployed in chest CT evaluation for the differentiating of healthy lungs from AP lungs.

Non-Respiratory Symptoms of Patients Infected with SARS-CoV-2 (Coronavirus Disease 2019): Lessons from Their Initial Presentation at the Hospital

Background and objectives: As the COVID-19 pandemic spreads, it is becoming increasingly evident that this coronavirus is not limited to the respiratory system and that the musculoskeletal system can also be affected. The purpose of the present study was to describe non-respiratory symptoms of laboratory-confirmed COVID-19 cases. Materials and Methods: All patients with SARS-CoV-2 admitted to our hospital, between 1 August and 30 September 2020, were included in this retrospective study. Data were extracted from medical records. Epidemiological, clinical, laboratory and radiological characteristics at the initial presentation at the hospital were collected and analyzed. Results: A total of 79 COVID-19 patients were enrolled. The mean age of the patients was 44.08 years (age range, 18–87 years) and 59.5% were male. The most common symptoms were fatigue in 60 (75.9%) patients, followed by fever (73.4%), myalgia (51.9%), cough (41.8%), anosmia (38%) and arthralgia (36.7%). The muscles of the upper back and the knee joint were the most painful anatomic region and joint, respectively. The laboratory findings on admission showed that D-dimer, CRP and procalcitonin levels were increased, without significant gender differences (p > 0.05). Chest imaging demonstrated pneumonia in 20 (25.3%) patients. Conclusions: Our results indicate that from the onset of the symptoms of COVID-19 patients, musculoskeletal symptoms, such as fatigue, myalgia and arthralgia, were present in three-quarters of all patients. These findings could help elaborate updated triage and admission protocols for suspect COVID-19 patients at the hospital and Emergency Department presentation.

CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia

Background

In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia.

Methods

A total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis.

Results

The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%).

Conclusions

CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-021-00564-w.

Eosinophils and COVID-19: diagnosis, prognosis, and vaccination strategies

The unprecedented impact of the coronavirus disease 2019 (COVID-19) pandemic has resulted in global challenges to our health-care systems and our economic security. As such, there has been significant research into all aspects of the disease, including diagnostic biomarkers, associated risk factors, and strategies that might be used for its treatment and prevention. Toward this end, eosinopenia has been identified as one of many factors that might facilitate the diagnosis and prognosis of severe COVID-19. However, this finding is neither definitive nor pathognomonic for COVID-19. While eosinophil-associated conditions have been misdiagnosed as COVID-19 and others are among its reported complications, patients with pre-existing eosinophil-associated disorders (e.g., asthma, eosinophilic gastrointestinal disorders) do not appear to be at increased risk for severe disease; interestingly, several recent studies suggest that a diagnosis of asthma may be associated with some degree of protection. Finally, although vaccine-associated aberrant inflammatory responses, including eosinophil accumulation in the respiratory tract, were observed in preclinical immunization studies targeting the related SARS-CoV and MERS-CoV pathogens, no similar complications have been reported clinically in response to the widespread dissemination of either of the two encapsulated mRNA-based vaccines for COVID-19.

A Retrospective Study of the C-Reactive Protein to Lymphocyte Ratio and Disease Severity in 108 Patients with Early COVID-19 Pneumonia from January to March 2020 in Wuhan, China

Background

The aim of this study was to determine the effect of C-reactive protein (CRP), lymphocytes (LYM), and the ratio of CRP to LYM (CRP/LYM) on assessing the prognosis of COVID-19 severity at early stages of disease.

Material/Methods

A total of 108 hospitalized patients diagnosed with COVID-19 in Zhongnan Hospital of Wuhan University from January 17, 2020 to March 12, 2020 were enrolled. Data of demographic parameters, clinical characteristics, laboratory indicators, clinical manifestation, and outcome of disease were collected. The patients were divided into a severe group and a non-severe group according to diagnosis and classification, which followed the guidelines and management of the Chinese National Health Council COVID-19. The receiver-operating characteristic (ROC) analysis and comparison of ROC curves were used for the laboratory findings for assessment of COVID-19 severity.

Results

Of the 108 patients, 42 patients (38.9%) were male and 24 patients (22.2%) were considered severe cases, with the mean age of 51.0 years old. Males and patients with comorbidities were more likely to become severe cases. CRP increased and LYM decreased in the severe group.The results for the areas under the curve (AUC) of CRP/LYM and CRP used to assess severe COVID-19 were 0.787 (95% CI 0.698–0.860, P<0.0001) and 0.781 (95% CI 0.693–0.856, P<0.0001), respectively; both results were better than that of LYM. The associated criterion value of CRP/LYM was calculated, with an excellent sensitivity of 95.83%.

Conclusions

The effect of CRP/LYM and CRP on the assessment for severe COVID-19 may be superior to LYM alone. CRP/LYM is a highly sensitive indicator to assess the severity of COVID-19 in the early stage of disease.

Artificial Intelligence Empowers Radiologists to Differentiate Pneumonia Induced by COVID-19 versus Influenza Viruses

Background

Given the current pandemic, differentiation between pneumonia induced by COVID-19 or influenza viruses is of utmost clinical significance in the patients' management. For this purpose, this study was conducted to develop sensitive artificial intelligence (AI) models to assist radiologists to decisively differentiate pneumonia due to COVID-19 versus influenza viruses.

Methods

Cross sectional chest CT images (N=12744) from well-evaluated cases of pneumonias induced by COVID-19 or H1N1 Influenza viruses, and normal individuals were collected. We examined the computer tomographic (CT) chest images from 137 individuals. Various pre-trained convolutional neural network models, such as ResNet-50, InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19 were fine-tuned on our datasets. The datasets were used for training (60%), validation (20%), and testing (20%) of the final models. Also, the predictive power and means of precision and recall were determined for each model.

Results

Fine-tuned ResNet-50 model differentiated the pneumonia due to COVID-19 or H1N1 influenza virus with accuracies of 96.7% and 92%, respectively This model outperformed all others, i.e., InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19.

Conclusion

Fine-tuned and pre-trained image classifying models of AI enable radiologists to reliably differentiate the pneumonia induced by COVID-19 versus H1N1 influenza virus. For this purpose, ResNet-50 followed by InceptionV3 models proved more promising than other AI models. Also in the supplements, we share the source codes and our fine-tuned models for use by researchers and clinicians globally toward the critical task of image differentiation of patients infected with COVID-19 versus H1N1 Influenza viruses.