[Relationship between the lung ultrasound and the clinical severity of the patient with seasonal flu]. / Lien entre l'aspect échographique du poumon et la sévérité́ clinique du patient atteint d'une grippe saisonnière.

INTRODUCTION: The seasonal flu is a very important reason for consultation every winter. Symptoms can quickly progress to severe pneumonia. Currently, few tools exist to assess the clinical severity of patients. The aim of this study is to demonstrate the role of lung ultrasound as a marker of severity in patients with influenza. METHODS: 79 patients who arrived at the emergency department with flu-like symptoms were included. A pulmonary ultrasound looking for an interstitial syndrome or consolidation was performed. The qSOFA, the SOFA, the saturation, the PaO2/FiO2 ratio, the oxygen needs, the destination of the patient made it possible to establish the seriousness of the pathology of the patient. Ultrasound was then compared to these different tools. RESULTS: The more the ultrasound became pathological, the more we observed a proportion of qSOFA (p = 0.001) and pathological SOFA (p = 0.009). Most patients with acute respiratory distress syndrome have pathological ultrasound (p < 0.001). The average admission saturation is 89.2 % in the "pathological ultrasound" group compared to 95.8 % in the "normal ultrasound" group (p < 0.001). Patients who required invasive therapies had pathological ultrasound (p < 0.001). Of the 28 patients with pathological ultrasound, 24 required hospitalization (p < 0.001). CONCLUSION: Lung ultrasound is a major asset for assessing the severity of the patient with seasonal flu. In addition, ultrasound allows better monitoring of the patient by being able to influence the destination of the latter towards a return home or monitoring in intensive care.

INTRODUCTION: La grippe saisonnière représente chaque hiver un motif de consultation très important. La symptomatologie peut rapidement évoluer vers une pneumonie sévère. Actuellement, peu d'outils existent pour évaluer la sévérité clinique des patients. Le but de cette étude est de démontrer le rôle de l'échographie pulmonaire comme marqueur de sévérité́ chez les patients atteints d'une grippe. Méthodes : L'étude a comporté 79 patients arrivés aux urgences pour grippe. Une échographie pulmonaire a été réalisée à la recherche d'un syndrome interstitiel ou d'une consolidation. Le qSOFA, le SOFA, la saturation, le rapport PaO2/FiO2, les besoins en oxygène, la destination du patient ont permis d'établir la gravité de la pathologie du patient. L'échographie a alors été comparée à ces différents outils. Résultats : Plus l'échographie devient pathologique, plus on observe une proportion de qSOFA (p = 0,001) et de SOFA pathologiques (p = 0,009). La majorité́ des patients ayant un syndrome de détresse respiratoire aiguë ont une échographie pathologique (p < 0,001). La moyenne des saturations d'admission est de 89,2 % dans le groupe «échographie pathologique¼ contre 95,8 % dans le groupe «échographie normale¼ (p < 0,001). Les patients ayant eu recours à des thérapies invasives ont une échographie pathologique (p < 0,001). Sur les 28 patients ayant une échographie pathologique, 24 ont nécessité́ une hospitalisation (p < 0,001). CONCLUSION: L'échographie pulmonaire est un atout majeur pour l'évaluation de la sévérité du patient atteint d'une grippe saisonnière. De plus, l'échographie permet une meilleure surveillance du patient en pouvant influencer la destination de celui-ci vers un retour à̀ domicile ou une surveillance aux soins intensifs.

Influenza B-induced longitudinally extensive transverse myelitis and bithalamic acute disseminated encephalomyelitis.

In the COVID-era, other viral pathogens, like influenza B, gain less attention in scientific reporting. However, influenza still is endemic, and rarely affects central nervous system (CNS). Here, we report the case of a 35-year-old male who presented with fever since 1 week, and developed acute ascending flaccid paralysis and urinary retention. The clinical presentation of paraparesis in combination with the inflammation proven by the lumbar puncture, and the MRI full spine, fulfilled the diagnostic criteria of longitudinally extensive transverse myelitis (LETM). In this case, it is most likely based on a post-viral Influenza type B. Additionally, the brain MRI showed a necrotizing encephalopathy bilaterally in the thalamus. Both locations of inflammatory disease were part of one auto-immune-mediated, monophasic CNS disorder: influenza-induced ADEM which is very unique, fortunately with favorable outcome.

CT findings of 144 in-hospital patients with influenza pneumonia: A retrospective analysis.

BACKGROUND/PURPOSE: Patients with influenza infection during their period of admission may have worse computed tomography (CT) manifestation according to the clinical status. This study aimed to evaluate the CT findings of in-hospital patients due to clinically significant influenza pneumonia with correlation of clinical presentations. METHODS: In this retrospective, single center case series, 144 patients were included. All in-hospital patients were confirmed influenza infection and underwent CT scan. These patients were divided into three groups according to the clinical status of the most significant management: (1) without endotracheal tube and mechanical ventilator (ETTMV) or extracorporeal membrane oxygenation (ECMO); (2) with ETTMV; (3) with ETTMV and ECMO. Pulmonary opacities were scored according to extent. Spearman rank correlation analysis was used to evaluate the correlation between clinical parameters and CT scores. RESULTS: The predominant CT manifestation of influenza infection was mixed ground-glass opacity (GGO) and consolidation with both lung involvement. The CT scores were all reach significant difference among all three groups (8.73 ± 6.29 vs 12.49 ± 6.69 vs 18.94 ± 4.57, p < 0.05). The chest CT score was correlated with age, mortality, and intensive care unit (ICU) days (all p values were less than 0.05). In addition, the CT score was correlated with peak lactate dehydrogenase (LDH) level and peak C-reactive protein (CRP) level (all p values were less than 0.05). Concomitant bacterial infection had higher CT score than primary influenza pneumonia (13.02 ± 7.27 vs 8.95 ± 5.99, p < 0.05). CONCLUSION: Thin-section chest CT scores correlated with clinical and laboratory parameters in in-hospital patients with influenza pneumonia.

[Chinese expert consensus on the imaging diagnosis of severe pneumonia of influenza A (H1N1) flu].

In March 2009, influenza A(H1N1) flu broke out and spread rapidly worldwide, and it has been circulating in local areas with various scales since then. Particularly, the outbreak and prevalence have occurred in China during 2023 extensively. At present, there is an absence of unified consensus on imaging diagnosis of severe influenza A (H1N1) flu pneumonia, which is not conducive to the standardized imaging diagnosis and clinical practice. Chinese experts including the Infection and Inflammatory Radiology Committee of the Chinese Research Hospital Association jointly formulate this consensus based on numerous references related to influenza A (H1N1) flu, meanwhile combining the methodological requirements of evidence-based medicine for guideline and standard formulation. This consensus aims to form a consensus on the diagnostic evidence, recommended imaging methods, diagnostic standard and differential diagnosis of severe influenza A(H1N1) flu pneumonia, and it is ought to provide clear diagnostic information and basis for relevant professional physicians and guide the clinical diagnosis and treatment of severe pneumonia caused by influenza A(H1N1) flu.

Comparison of Ultrasound and CT Imaging for the Diagnosis of Coronavirus Disease and Influenza A Pneumonia.

OBJECTIVE: The outbreak of coronavirus disease (COVID-19) coincided with the season of influenza A pneumonia, a common respiratory infectious disease. Therefore, this study compared ultrasonography and computed tomography (CT) for the diagnosis of the two diseases. METHODS: Patients with COVID-19 or influenza A infection hospitalized at our hospital were included. The patients were examined by ultrasonography every day. The CT examination results within 1 day before and after the day of the highest ultrasonography score were selected as the controls. The similarities and differences between the ultrasonography and CT results in the two groups were compared. RESULTS: There was no difference between the ultrasonography and CT scores (P = .307) for COVID-19, while there was a difference between ultrasonography and CT scores for influenza A pneumonia (P = .024). The ultrasonography score for COVID-19 was higher than that for influenza A pneumonia (P = .000), but there was no difference between the CT scores (P = .830). For both diseases, there was no difference in ultrasonography and CT scores between the left and right lungs; there were differences between the CT scores of the upper and middle lobes, as well as between the upper and lower lobes of the lungs; however, there was no difference between the lower and middle lobes of the lungs. CONCLUSION: Ultrasonography is equivalent to the gold standard CT for diagnosing and monitoring the progression of COVID-19. Because of its convenience, ultrasonography has important application value. Furthermore, the diagnostic value of ultrasonography for COVID-19 is higher than that for influenza A pneumonia.

Viral Pneumonias.

Viral pneumonia is usually community acquired and caused by influenza, parainfluenza, respiratory syncytial virus, human metapneumovirus, and adenovirus. Many of these infections are airway centric and chest imaging demonstrates bronchiolitis and bronchopneumonia, With the exception of adenovirus infections, the presence of lobar consolidation usually suggests bacterial coinfection. Community-acquired viral pathogens can cause more severe pneumonia in immunocompromised hosts, who are also susceptible to CMV and varicella infection. These latter 2 pathogens are less likely to manifest the striking airway-centric pattern. Airway-centric pattern is distinctly uncommon in Hantavirus pulmonary syndrome, a rare environmentally acquired infection with high mortality.

Classification of COVID-19 and Influenza Patients Using Deep Learning.

Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019-22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.

Automated lung vessel segmentation reveals blood vessel volume redistribution in viral pneumonia.

PURPOSE: It is known from histology studies that lung vessels are affected in viral pneumonia. However, their diagnostic potential as a chest CT imaging parameter has only rarely been exploited. The purpose of this study is to develop a robust method for automated lung vessel segmentation and morphology analysis and apply it to a large chest CT dataset. METHODS: In total, 509 non-enhanced chest CTs (NECTs) and 563 CT pulmonary angiograms (CTPAs) were included. Sub-groups were patients with healthy lungs (group_NORM, n = 634) and those RT-PCR-positive for Influenza A/B (group_INF, n = 159) and SARS-CoV-2 (group_COV, n = 279). A lung vessel segmentation algorithm (LVSA) based on traditional image processing was developed, validated with a point-of-interest approach, and applied to a large clinical dataset. Total blood vessel volume in lung (TBV) and the blood vessel volume percentage (BV%) of three blood vessel size types were calculated and compared between groups: small (BV5%, cross-sectional area < 5 mm2), medium (BV5-10%, 5-10 mm2) and large (BV10%, >10 mm2). RESULTS: Sensitivity of the LVSA was 84.6% (95 %CI: 73.9-95.3) for NECTs and 92.8% (95 %CI: 90.8-94.7) for CTPAs. In viral pneumonia, besides an increased TBV, the main finding was a significantly decreased BV5% in group_COV (n = 14%) and group_INF (n = 15%) compared to group_NORM (n = 18%) [p < 0.001]. At the same time, BV10% was increased (group_COV n = 15% and group_INF n = 14% vs. group_NORM n = 11%; p < 0.001). CONCLUSION: In COVID-19 and Influenza, the blood vessel volume is redistributed from small to large vessels in the lung. Automated LSVA allows researchers and clinicians to derive imaging parameters for large amounts of CTs. This can enhance the understanding of vascular changes, particularly in infectious lung diseases.

Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept.

BACKGROUND: Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task. METHODS: In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest. RESULTS: The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%). CONCLUSION: In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.

Comparative study of hematological and radiological feature of severe/critically ill patients with COVID-19, influenza A H7N9, and H1N1 pneumonia.

OBJECTIVES: This study aimed to explore clinical indexes for management of severe/critically ill patients with COVID-19, influenza A H7N9, and H1N1 pneumonia by comparing hematological and radiological characteristics. METHODS: Severe/critically ill patients with COVID-19, H7N9, and H1N1 pneumonia were retrospectively enrolled. The demographic data, clinical manifestations, hematological parameters, and radiological characteristics were compared. RESULTS: In this study, 16 cases of COVID-19, 10 cases of H7N9, and 13 cases of H1N1 who met severe/critically ill criteria were included. Compared with COVID-19, H7N9 and H1N1 groups had more chronic diseases (80% and 92.3% vs. 25%, p < 0.05), higher APACHE Ⅱ scores (16.00 ± 8.63 and 15.08 ± 6.24, vs. 5.50 ± 2.58, p < 0.05), higher mortality rates (40% and 46.2% vs. 0%, p < 0.05), significant lymphocytopenia (0.59 ± 0.31 × 109 /L and 0.56 ± 0.35 × 109 /L vs. 0.97 ± 0.33 × 109 /L, p < 0.05), and elevated neutrophil-to-lymphocyte ratio (NLR; 14.67 ± 6.10 and 14.64 ± 10.36 vs. 6.29 ± 3.72, p < 0.05). Compared with the H7N9 group, ground-glass opacity (GGO) on chest CT was common in the COVID-19 group (p = 0.028), while pleural effusion was rare (p = 0.001). CONCLUSIONS: The NLR can be used as a clinical parameter for the predication of risk stratification and outcome in COVID-19 and influenza A pneumonia. Manifestations of pleural effusion or GGO in chest CT may be helpful for the identification of different viral pneumonia.

Fatal case of simply influenza A (H3N2)-associated encephalitis in immunocompetent patient.

Influenza virus-associated encephalopathy/encephalitis is a rare entity in adults that can lead to severe neurological sequelae and even death. The clinical presentation can be quite diverse. This absence of a typical presentation along with the difficulty detecting the virus in the cerebrospinal fluid represents a diagnostic challenge. We present the case of a 79-year-old male with sudden onset of decreased consciousness and signs of right hemisphere damage. The presence of influenza A (H3N2) virus in respiratory sample along with compatible findings in cranial magnetic resonance led to the diagnosis. The patient died without responding to treatment with antivirals and immunomodulators and the anatomopathological study did not detect infectious agent. Early diagnostic suspicion is essential to establish adequate treatment and improve the prognosis.

Febrile infants without respiratory symptoms or sick contacts: are chest radiographs or RSV/influenza testing indicated?

BACKGROUND: Serious bacterial infection rates in febrile infants < 60 days are about 8-11%. Less than 1% of febrile infants with no respiratory symptoms will have pneumonia however, chest radiography (CXR) rates remain between 30 and 60%. Rapid Respiratory Syncytial Virus (RSV) and influenza (flu) testing is common, however, there is not enough data to determine if febrile infants without any respiratory symptoms should be tested. The goal of this study is to determine the rate of positive CXR and RSV/flu results in febrile infants with no respiratory symptoms and no sick contacts. METHODS: Well-appearing febrile infants between 7 and 60 days of age who presented to the pediatric emergency department (PED) from September 1st, 2015 through October 30th, 2017 were enrolled. Demographic data, respiratory symptoms, CXR findings and RSV/flu results were collected. SAS statistical software was used for analysis. RESULTS: 129 infants met enrollment criteria. Of the 129 infants, 58 (45.0%) had no respiratory symptoms and no sick contacts. Of these 58, 36 (62.1%) received a CXR and none of them had any abnormal findings, 48 (82.8%) had RSV/flu testing, no patients tested positive for RSV and only one patient tested positive for flu. Costs of CXR and RSV/flu testing for this cohort was $19,788. CONCLUSION: The absence of positive CXRs in this patient population reinforces the current recommendations that CXR is not indicated. The low incidence of RSV/flu indicate that routine testing may not be necessary in this population especially outside of the flu season. Reduced testing could decrease overall costs to the healthcare system as well as radiation exposure to this population.

Ultrasound and Influenza: The Spectrum of Lung and Cardiac Ultrasound Findings in Patients with Suspected Influenza A and B.

In patients with influenza, cardiac and lung ultrasound may help determine the severity of illness and predict clinical outcomes. To determine the ultrasound characteristics of influenza and define the spectrum of lung and cardiac findings in patients with suspected influenza A or B, we conducted a prospective observational study in patients presenting to the emergency department at a tertiary care academic institution. An ultrasound protocol consisting of cardiac, lung and inferior vena cava scans was performed within 6 h of admission. We compared the ultrasound findings in cases with positive and negative influenza polymerase chain reaction, while controlling for comorbidities. We enrolled 117 patients, 41.9% of whom (49/117) tested positive for influenza. In those with influenza, ultrasound confirmed preserved left ventricular and right ventricular (RV) function in 81.3% of patients. The most common cardiac pathology was RV dilation (10.4%), followed by left ventricular systolic dysfunction (8.3%). Patients with negative influenza polymerase chain reaction with RV dysfunction demonstrated higher hospital admission than those those with normal RV function (45.1%, 23/51, vs. 17.9%, 5/28; p = 0.016). B-lines were prevalent in both influenza and non-influenza groups (40.8% and 69.1%, respectively; p = 0.013). Lung consolidation was identified in only 8.25% of patients with influenza. In conclusion, in patients with influenza we were unable to define distinct ultrasound features specific to influenza A or B, suggesting that ultrasound may not be beneficial in diagnosing influenza nor in evaluating its severity.

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.

Design of Neuraminidase-Targeted Imaging and Therapeutic Agents for the Diagnosis and Treatment of Influenza Virus Infections.

The last step in influenza virus replication involves the assembly of viral components on the infected cell's plasma membrane followed by budding of intact virus from the host cell surface. Because viral neuraminidase and hemagglutinin are both inserted into the host cell's membrane during this process, influenza virus-infected cells are distinguished from uninfected cells by the presence of viral neuraminidase and hemagglutinin on their cell surfaces. In an effort to exploit this difference in cell surface markers for development of diagnostic and therapeutic agents, we have modified an influenza neuraminidase inhibitor, zanamivir, for targeting of attached imaging and therapeutic agents selectively to influenza viruses and virus-infected cells. We have designed here a zanamivir-conjugated rhodamine dye that allows visual monitoring of binding, internalization, and intracellular trafficking of the fluorescence-labeled neuraminidase in virus-infected cells. We also synthesize a zanamivir-99mTc radioimaging conjugate that permits whole body imaging of the virus's biodistribution and abundance in infected mice. Finally, we create both a zanamivir-targeted cytotoxic drug (i.e., zanamivir-tubulysin B) and a viral neuraminidase-targeted CAR T cell and demonstrate that they are both able to kill viral neuraminidase-expressing cells without damaging healthy cells. Taken together, these data suggest that the influenza virus neuraminidase inhibitor, zanamivir, can be exploited to improve the diagnosis, imaging, and treatment of influenza virus infections.

Differentiation of Chest CT Findings Between Influenza Pneumonia and COVID-19: Interobserver Agreement Between Radiologists.

OBJECTIVES: To investigate the chest CT and clinical characteristics of COVID-19 pneumonia and H1N1 influenza, and explore the radiologist diagnosis differences between COVID-19 and influenza. MATERIALS AND METHODS: This cross-sectional study included a total of 43 COVID-19-confirmed patients (24 men and 19 women, 49.90 ± 18.70 years) and 41 influenza-confirmed patients (17 men and 24 women, 61.53 ± 19.50 years). Afterwards, the chest CT findings were recorded and 3 radiologists recorded their diagnoses of COVID-19 or of H1N1 influenza based on the CT findings. RESULTS: The most frequent clinical symptom in patients with COVID-19 and H1N1 pneumonia were dyspnea (96.6%) and cough (62.5%), respectively. The CT findings showed that the COVID-19 group was characterized by GGO (88.1%), while the influenza group had features such as GGO (68.4%) and consolidation (66.7%). Compared to the influenza group, the COVID-19 group was more likely to have GGO (88.1% vs. 68.4%, p = 0.032), subpleural sparing (69.0% vs. 7.7%, p <0.001) and subpleural band (50.0% vs. 20.5%, p = 0.006), but less likely to have pleural effusion (4.8% vs. 33.3%, p = 0.001). The agreement rate between the 3 radiologists was 65.8%. CONCLUSION: Considering similarities of respiratory infections especially H1N1 and COVID-19, it is essential to introduce some clinical and para clinical modalities to help differentiating them. In our study we extracted some lung CT scan findings from patients suspected to COVID-19 as a newly diagnosed infection comparing with influenza pneumonia patients.

Comparison of Chest CT Findings of COVID-19, Influenza, and Organizing Pneumonia: A Multireader Study.

BACKGROUND. Previous studies compared CT findings of COVID-19 pneumonia with those of other infections; however, to our knowledge, no studies to date have included noninfectious organizing pneumonia (OP) for comparison. OBJECTIVE. The objectives of this study were to compare chest CT features of COVID-19, influenza, and OP using a multireader design and to assess the performance of radiologists in distinguishing between these conditions. METHODS. This retrospective study included 150 chest CT examinations in 150 patients (mean [± SD] age, 58 ± 16 years) with a diagnosis of COVID-19, influenza, or non-infectious OP (50 randomly selected abnormal CT examinations per diagnosis). Six thoracic radiologists independently assessed CT examinations for 14 individual CT findings and for Radiological Society of North America (RSNA) COVID-19 category and recorded a favored diagnosis. The CT characteristics of the three diagnoses were compared using random-effects models; the diagnostic performance of the readers was assessed. RESULTS. COVID-19 pneumonia was significantly different (p < .05) from influenza pneumonia for seven of 14 chest CT findings, although it was different (p < .05) from OP for four of 14 findings (central or diffuse distribution was seen in 10% and 7% of COVID-19 cases, respectively, vs 20% and 21% of OP cases, respectively; unilateral distribution was seen in 1% of COVID-19 cases vs 7% of OP cases; non-tree-in-bud nodules was seen in 32% of COVID-19 cases vs 53% of OP cases; tree-in-bud nodules were seen in 6% of COVID-19 cases vs 14% of OP cases). A total of 70% of cases of COVID-19, 33% of influenza cases, and 47% of OP cases had typical findings according to RSNA COVID-19 category assessment (p < .001). The mean percentage of correct favored diagnoses compared with actual diagnoses was 44% for COVID-19, 29% for influenza, and 39% for OP. The mean diagnostic accuracy of favored diagnoses was 70% for COVID-19 pneumonia and 68% for both influenza and OP. CONCLUSION. CT findings of COVID-19 substantially overlap with those of influenza and, to a greater extent, those of OP. The diagnostic accuracy of the radiologists was low in a study sample that contained equal proportions of these three types of pneumonia. CLINICAL IMPACT. Recognized challenges in diagnosing COVID-19 by CT are furthered by the strong overlap observed between the appearances of COVID-19 and OP on CT. This challenge may be particularly evident in clinical settings in which there are substantial proportions of patients with potential causes of OP such as ongoing cancer therapy or autoimmune conditions.

A Comparative Systematic Review of COVID-19 and Influenza.

BACKGROUND: Both SARS-CoV-2 and influenza virus share similarities such as clinical features and outcome, laboratory, and radiological findings. METHODS: Literature search was done using PubMed to find MEDLINE indexed articles relevant to this study. As of 25 November 2020, the search has been conducted by combining the MeSH words "COVID-19" and "Influenza". RESULTS: Eighteen articles were finally selected in adult patients. Comorbidities such as cardiovascular diseases, diabetes, and obesity were significantly higher in COVID-19 patients, while pulmonary diseases and immunocompromised conditions were significantly more common in influenza patients. The incidence rates of fever, vomiting, ocular and otorhinolaryngological symptoms were found to be significantly higher in influenza patients when compared with COVID-19 patients. However, neurologic symptoms and diarrhea were statistically more frequent in COVID-19 patients. The level of white cell count and procalcitonin was significantly higher in influenza patients, whereas thrombopenia and elevated transaminases were significantly more common in COVID-19 patients. Ground-grass opacities, interlobular septal thickening, and a peripheral distribution were more common in COVID-19 patients than in influenza patients where consolidations and linear opacities were described instead. COVID-19 patients were significantly more often transferred to intensive care unit with a higher rate of mortality. CONCLUSIONS: This study estimated differences of COVID-19 and influenza patients which can help clinicians during the co-circulation of the two viruses.

Combining initial chest CT with clinical variables in differentiating coronavirus disease 2019 (COVID-19) pneumonia from influenza pneumonia.

Coronavirus disease 2019 (COVID-19) has spread in more than 100 countries and regions around the world, raising grave global concerns. COVID-19 has a similar pattern of infection, clinical symptoms, and chest imaging findings to influenza pneumonia. In this retrospective study, we analysed clinical and chest CT data of 24 patients with COVID-19 and 79 patients with influenza pneumonia. Univariate analysis demonstrated that the temperature, systolic pressure, cough and sputum production could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the clinical features are 0.783 and 0.747, and the AUC value is 0.819. Univariate analysis demonstrates that nine CT features, central-peripheral distribution, superior-inferior distribution, anterior-posterior distribution, patches of GGO, GGO nodule, vascular enlargement in GGO, air bronchogram, bronchiectasis within focus, interlobular septal thickening, could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the CT features are 0.750 and 0.962, and the AUC value is 0.927. Finally, a multivariate logistic regression model combined the variables from the clinical variables and CT features models was made. The combined model contained six features: systolic blood pressure, sputum production, vascular enlargement in the GGO, GGO nodule, central-peripheral distribution and bronchiectasis within focus. The diagnostic sensitivity and specificity for the combined features are 0.87 and 0.96, and the AUC value is 0.961. In conclusion, some CT features or clinical variables can differentiate COVID-19 from influenza pneumonia. Moreover, CT features combined with clinical variables had higher diagnostic performance.