Epidemiology of respiratory syncytial virus in hospitalized children with community-acquired pneumonia in Guangzhou: a 10-year study.

Background: Respiratory syncytial virus (RSV) is one of the most common virus causing community-acquired pneumonia (CAP) in children. To guide the prevention, diagnosis and treatment of RSV, we aimed to analyze the epidemiology of RSV in hospitalized children with CAP. Methods: A total of 9,837 hospitalized children (≤14 years old) with CAP from January 2010 to December 2019 were reviewed. Using the real-time polymerase chain reaction (RT-PCR), the oropharyngeal swab specimens were collected and tested for RSV, influenza virus A (INFA), influenza virus B (INFB), parainfluenza virus (PIV), enterovirus (EV), coronavirus (CoV), human metapneumovirus (HMPV), human bocavirus (HBoV), human rhinovirus (HRV), and adenovirus (ADV) for each patient. Results: The detection rate of RSV was 15.3% (1,507/9,837). From 2010 to 2019, the RSV detection rate showed a wavy change (χ2=166.982, P<0.001), with the highest detection rate in 2011 (158/636, 24.8%). RSV can be detected throughout the year, with the highest detection rate in February (123/482, 25.5%). Children younger than 0.5 years old had the highest detection rate (410/1,671, 24.5%). The detection rate of RSV in male children (1,024/6,226, 16.4%) was higher than that in female children (483/3,611, 13.4%) (P<0.001). A proportion of 17.7% (266/1,507) of RSV positive cases were also co-infected with other viruses, and INFA (41/266, 15.4%) was the most common coinfection virus. After adjusting for potential confounders, the RSV-positive children were associated with increased risk of severe pneumonia [odds ratio (OR) 1.26, 95% confidence interval (CI): 1.04 to 1.53, P=0.019]. Moreover, children with severe pneumonia had significantly lower cycle threshold (CT) values of RSV than those without severe pneumonia (28.88±3.89 vs. 30.42±3.33, P<0.01). Patients with coinfection (38/266, 14.3%) had a higher risk of severe pneumonia than those without coinfection (142/1,241, 11.4%), but the difference was not statistically significant (OR 1.39, 95% CI: 0.94 to 2.05, P=0.101). Conclusions: The detection rate of RSV in CAP hospitalized children changed by years, months, ages, and sexes. CAP hospitalized children with RSV are more likely to develop severe pneumonia than those without RSV. Policy makers and doctors should make timely adjustments to prevention measures, medical resources and treatment options based on these epidemiological characteristics.

The Clinical Utility of Relative Bradycardia for Identifying Cases of Coronavirus Disease 2019 Pneumonia: A Retrospective Pneumonia Cohort Study.

Objective Both coronavirus disease 2019 (COVID-19) pneumonia and relative bradycardia are common conditions among clinicians; however, the association between these has not been well studied. The present study assessed whether or not relative bradycardia on admission was more predominant in patients with COVID-19 pneumonia than in those with other infectious pneumonia. Methods For this single-center, retrospective cohort study, we collected data through electronic medical records and examined the occurrence of relative bradycardia on admission. We used logistic regression analyses to compare outcomes with and without relative bradycardia on admission. The primary outcome was COVID-19 pneumonia. The secondary outcome was hypoxemia during the hospital stay. We performed multivariable regression with adjusting for the effects of age, sex, healthcare-associated pneumonia, body mass index, Charlson comorbidity index, and bilateral infiltration on computed tomography (CT) as confounding factors. Patients Adult patients with new-onset hospitalized infectious pneumonia confirmed by CT between January 1, 2020, and July 31, 2021. Results This study included 395 participants. On admission, 87 (22.0%) participants exhibited relative bradycardia, and 302 (76.5%) participants had COVID-19. Relative bradycardia on admission was not significantly associated with COVID-19 pneumonia [adjusted odds ratio (OR) 1.32; 95% confidence interval (CI) 0.49-3.54, p=0.588] but was associated with hypoxemia (adjusted OR 4.74; 95%CI 2.64-8.52, p<0.001). Conclusion The study results showed that relative bradycardia on admission was not associated with COVID-19 in cases of infectious pneumonia. However, relative bradycardia may be associated with the incidence of hypoxemia in pneumonia.

Atrio-Ventricular Block by Legionella Disease.

Although Legionnaires' disease mainly affects the lungs, it can also present with other systemic involvement, including rare cardiac manifestations. Recognised presentations are endocarditis, myocarditis, pericarditis, and pericardial effusion. A 72-year-old British man presented with a six-day history of dry cough and a four-day history of fever during the peak of the COVID-19 pandemic. His electrocardiogram showed Mobitz type II atrio-ventricular block. Although all the cultures were negative, the chest X-ray demonstrated COVID-19 infection-like features. With high clinical suspicions and chest X-ray features, the polymerase chain reaction of the COVID tests was repeated three times and all were negative. He had a positive urinary Legionella antigen, and his bradycardia and heart block improved after treatment with amoxicillin/clavulanic acid, and clarithromycin. As the electrocardiogram showed Mobitz type II, a permanent pacemaker was implanted. The follow-up pacemaker check showed that he still required active pacing.

Features Discriminating COVID-19 From Community-Acquired Pneumonia in Pediatric Patients.

Purpose: To discuss the different characteristics of clinical, laboratory and chest computed tomography (CT) between coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) in pediatric patients. Methods: We retrospectively retrieved data of inpatients with COVID-19 from January 21st to March 14th, 2020, and CAP from November 1st, 2019 to December 31st, 2019 in Wuhan Children's Hospital. We divided CAP into mycoplasma pneumonia and other viral pneumonia. We analyzed clinical and radiological features from those patients, and compared the differences among COVID-19, mycoplasma pneumonia and other viral pneumonia. Results: Eighty COVID-19 inpatients from January 21st to March 14th, 2020, as well as 95 inpatients with mycoplasma pneumonia and 50 inpatients with other viral pneumonia from November 1st, 2019 to December 31st, 2019 were included in our study. All patients were confirmed with RT-PCR. The clinical symptoms were similar in the three groups. Except fever and cough, diarrhea (6/80, 7.5%), tachypnea (2/80, 2.5%), and fatigue (6/80, 7.5%) were less common in COVID-19 patients. Compared to mycoplasma pneumonia and other viral pneumonia inpatients, COVID-19 patients present remarkably increased alanine aminotransferase (69/80, 86.3%). The typical CT feature of COVID-19 is ground-glass opacity, and it was more common in COVID-19 patients (32/80, 40%). Conclusion: The COVID-19 shared similar onsets with CAP. Even though the ground-glass opacity and elevated level of ALT were frequent in COVID-19, the better way for treatment and management of this disease is quickly and accurately identifying the pathogen.

Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays.

The COVID-19 outbreak continues to threaten the health and life of people worldwide. It is an immediate priority to develop and test a computer-aided detection (CAD) scheme based on deep learning (DL) to automatically localize and differentiate COVID-19 from community-acquired pneumonia (CAP) on chest X-rays. Therefore, this study aims to develop and test an efficient and accurate deep learning scheme that assists radiologists in automatically recognizing and localizing COVID-19. A retrospective chest X-ray image dataset was collected from open image data and the Xiangya Hospital, which was divided into a training group and a testing group. The proposed CAD framework is composed of two steps with DLs: the Discrimination-DL and the Localization-DL. The first DL was developed to extract lung features from chest X-ray radiographs for COVID-19 discrimination and trained using 3548 chest X-ray radiographs. The second DL was trained with 406-pixel patches and applied to the recognized X-ray radiographs to localize and assign them into the left lung, right lung or bipulmonary. X-ray radiographs of CAP and healthy controls were enrolled to evaluate the robustness of the model. Compared to the radiologists' discrimination and localization results, the accuracy of COVID-19 discrimination using the Discrimination-DL yielded 98.71%, while the accuracy of localization using the Localization-DL was 93.03%. This work represents the feasibility of using a novel deep learning-based CAD scheme to efficiently and accurately distinguish COVID-19 from CAP and detect localization with high accuracy and agreement with radiologists.