The Role of Biomarkers in Influenza and COVID-19 Community-Acquired Pneumonia in Adults.

Pneumonia is a growing problem worldwide and remains an important cause of morbidity, hospitalizations, intensive care unit admission and mortality. Viruses are the causative agents in almost a fourth of cases of community-acquired pneumonia (CAP) in adults, with an important representation of influenza virus and SARS-CoV-2 pneumonia. Moreover, mixed viral and bacterial pneumonia is common and a risk factor for severity of disease. It is critical for clinicians the early identification of the pathogen causing infection to avoid inappropriate antibiotics, as well as to predict clinical outcomes. It has been extensively reported that biomarkers could be useful for these purposes. This review describe current evidence and provide recommendations about the use of biomarkers in influenza and SARS-CoV-2 pneumonia, focusing mainly on procalcitonin (PCT) and C-reactive protein (CRP). Evidence was based on a qualitative analysis of the available scientific literature (meta-analyses, randomized controlled trials, observational studies and clinical guidelines). Both PCT and CRP levels provide valuable information about the prognosis of influenza and SARS-CoV-2 pneumonia. Additionally, PCT levels, considered along with other clinical, radiological and laboratory data, are useful for early diagnosis of mixed viral and bacterial CAP, allowing the proper management of the disease and adequate antibiotics prescription. The authors propose a practical PCT algorithm for clinical decision-making to guide antibiotic initiation in cases of influenza and SARS-CoV-2 pneumonia. Further well-design studies are needed to validate PCT algorithm among these patients and to confirm whether other biomarkers are indeed useful as diagnostic or prognostic tools in viral pneumonia.

Distinguishing COVID-19 From Influenza Pneumonia in the Early Stage Through CT Imaging and Clinical Features.

Background: Both coronavirus disease 2019 (COVID-19) and influenza pneumonia are highly contagious and present with similar symptoms. We aimed to identify differences in CT imaging and clinical features between COVID-19 and influenza pneumonia in the early stage and to identify the most valuable features in the differential diagnosis. Methods: Seventy-three patients with COVID-19 confirmed by real-time reverse transcription-polymerase chain reaction (RT-PCR) and 48 patients with influenza pneumonia confirmed by direct/indirect immunofluorescence antibody staining or RT-PCR were retrospectively reviewed. Clinical data including course of disease, age, sex, body temperature, clinical symptoms, total white blood cell (WBC) count, lymphocyte count, lymphocyte ratio, neutrophil count, neutrophil ratio, and C-reactive protein, as well as 22 qualitative and 25 numerical imaging features from non-contrast-enhanced chest CT images were obtained and compared between the COVID-19 and influenza pneumonia groups. Correlation tests between feature metrics and diagnosis outcomes were assessed. The diagnostic performance of each feature in differentiating COVID-19 from influenza pneumonia was also evaluated. Results: Seventy-three COVID-19 patients including 41 male and 32 female with mean age of 41.9 ± 14.1 and 48 influenza pneumonia patients including 30 male and 18 female with mean age of 40.4 ± 27.3 were reviewed. Temperature, WBC count, crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction were significantly independent associated with COVID-19. The AUC of clinical-based model on the combination of temperature and WBC count is 0.880 (95% CI: 0.819-0.940). The AUC of radiological-based model on the combination of crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction is 0.957 (95% CI: 0.924-0.989). The AUC of combined model based on the combination of clinical and radiological is 0.991 (95% CI: 0.980-0.999). Conclusion: COVID-19 can be distinguished from influenza pneumonia based on CT imaging and clinical features, with the highest AUC of 0.991, of which crazy-paving pattern and WBC count play most important role in the differential diagnosis.

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.

Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia.

BACKGROUND: Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP). METHODS: A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5-63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0-78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves. RESULTS: Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <-500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5-10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance. CONCLUSIONS: Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.

Clinical characteristics of COVID-19 and its comparison with influenza pneumonia.

OBJECTIVES: To recognise clinical features of COVID-19 pneumonia and its differences from influenza pneumonia. METHODS: 246 patients were enrolled into COVID-19 cohort and 120 patients into influenza cohort. All data were collected and analysed retrospectively. The variables under focus included demographic, epidemiological, clinical, laboratory and imaging characteristics of COVID-19 pneumonia and comparison were made with influenza pneumonia. RESULTS: The COVID-19 cohort included 53.25% female and 46.75% male. Their main symptom was fever; while 28.05% of patients had only initially fever; 21.54% of them remained feverless. After excluding prior kidney diseases, some patients showed abnormal urinalysis (32.11%), elevated blood creatinine (15.04%) and blood urea nitrogen (19.11%). Typical CT features included ground glass opacity, consolidation and band opacity, which could present as characteristic 'bat wing sign'. Our data showed that male, aged 65 or above, smoking, with comorbidities including diabetes, cardiovascular and kidney diseases, would experience more severe COVID-19 pneumonia. In comparison, COVID-19 cohort showed significantly higher incidence of clustering; the influenza cohort showed higher rate of fever. Both cohorts showed reduced lymphocyte numbers; however, 6 influenza patients showed lymphocytes increased, which was statistical significant compared with COVID-19 cohort. Also, influenza cohort displayed higher white blood cell counts and PCT values. CONCLUSION: There is no significant gender difference in the incidence of COVID-19 pneumonia. It predominantly affects the lung as well as the kidney. Age, smoking and comorbidities could contribute to disease severity. Although COVID-19 is more infectious, the rate of secondary bacterial infection is lower than influenza.