Socio-demographic and Clinical Profile of Children with Asthma attending Chest Clinic at B. P. Koirala Institute of Health Sciences, Nepal

Introduction: Asthma in children is one of the most common chronic diseases and little information available on factors associated with this disease in our part of the world. Objective: The present study is an attempt to find out the socio-demographic and clinical profile of children with asthma. Methodology: This was a retrospective analysis of data of asthmatic children below 14 years attending pediatric chest clinic from July 2014 till March 2016. Results: Of the 200 children, there were 142 (71%) males. The median age of presentation was 3 years and 139 (69.5%) from the age group 1-5years One third had poorly controlled asthma. Comorbidity was present in 59(29.5%) and allergic rhinitis (7%) was the most common. 90.5% had onset of wheezing before 5 years of age. Family history of asthma and/or atopy and smoking was present in 24% and 31%, respectively. 22% had exposure to pet animals. Upper respiratory tract infection (URTI) (37%) was the most common trigger for exacerbation. Cough (99%) and fast breathing (98%) were the most common symptoms. Conclusion: The majorities were males of young age with rhinitis as most common co-morbidity and many of them had a history of parental smoking at home. One third of them had poorly controlled asthma which shows the need for proper management of asthma including its comorbidity in younger children and changing certain habits like parental smoking at home.

Effectiveness of Chest CT in Children: CT Findings in Relation to the Clinical Question

Purpose To estimate the effectiveness and efficiency of chest CT in children based on the suspected diagnosis in relation to the number of positive, negative, and inconclusive CT results. Materials and Methods In this monocentric retrospective study at a university hospital with a division of pediatric radiology, 2019 chest CT examinations (973 patients; median age: 10.5 years; range: 2 days to 17.9 years) were analyzed with regards to clinical data, including the referring department, primary questions or suspected diagnosis, and CT findings. It was identified if the clinical question was answered, whether the suspected diagnosis was confirmed or ruled out, and if additional findings (clinically significant or minor) were detected. Results The largest clinical subgroup was the hematooncological subgroup (n = 987), with frequent questions for inflammation/pneumonia (66 % in this subgroup). Overall, CT provided conclusive results in 97.6 % of all scans. In 1380 scans (70 %), the suspected diagnosis was confirmed. In 406/2019 cases (20 %), the CT scan was negative also in terms of an additional finding. In 8 of 9 clinical categories, the proportion of positive results was over 50 %. There were predominantly negative results (110/179; 61 %) in pre-stem cell transplant evaluation. In the subgroup of trauma management, 81/144 exams (57 %) showed positive results, including combined injuries (n = 23). 222/396 (56 %) of all additional findings were estimated to be clinically significant. Conclusion In a specialized center, the effectiveness of pediatric chest CT was excellent when counting the conclusive results. However, to improve efficiency, the clinical evaluation before imaging appears crucial to prevent unnecessary CT examinations. Key Points: Citation Format

Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

Background Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare the objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images. Methods This retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1–18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). A phantom scan was performed including the dose range of the clinical images used in our study, and the noise power spectrum (NPS) was calculated. Quantitative and qualitative parameters were compared using repeated-measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests. Results DLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8%, and 53.1% respectively. The subjective assessment of overall image quality and the noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods. From NPS analysis, DLR methods showed a pattern of reducing the magnitude of noise while maintaining the texture. Conclusion Compared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.