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.

Infographics or video; which one is more effective in asthmatic patients' health? a randomized clinical trial.

Objective: Asthma disease is a complex medical condition for which the control of symptoms depends on sufficient patient knowledge, self-care, and adherence to medication protocols. Researchers conducted this study to evaluate the impact of infographics and video on asthma patients' adherence to medication. Methods: A randomized clinical trial in which 80 asthmatics were enrolled and allocated to two study groups (infographic and video groups). Researchers used questionnaires to gather demographic data. A nurse assessed Morisky adherence to medication. Researchers performed data analysis using repeated measurements and Least Significant Difference (LSD) in SPSS software version 23. Results: There was no significant difference between the two study tracks (P > 0.05) in the demographic data and adherence to medication in the pretest. The differences between the two intervention groups throughout the one-month follow-up were statistically significant (P < 0.05). There were significant differences between the two intervention groups in pretest and post-test, and pretest and follow-up (P < 0.05). However, there were no significant differences between the two intervention groups in post-test and follow-up (P > 0.05). Conclusions: According to the study findings, both the infographic and video formats may have led to an increase in adherence to medication protocols among asthma patients; but it seems that the infographic format is preferred for long- term use because it does not require usage of a facility. However, education format of asthmatic people is dependent on conditions and patient preferences.

Evaluation of atorvastatin for the treatment of patients with asthma: a double-blind randomized clinical trial.

PURPOSE: Statins are known as cholesterol-lowering agents, but have been suggested for the treatment of asthma because of their anti-inflammatory effects. In this study, the potential therapeutic effects of atorvastatin were investigated in asthmatic patients. METHODS: A total of 62 patients with persistent mild to moderate asthma who presented at asthma clinics of Arak University of Medical Sciences were recruited in a double-blind randomized clinical trial. The asthma clinical control score was assessed based on the standardized Asthma Control Test. Lung volume, i.e., percentage of forced expiratory volume in one second (FEV1%) and percentage of forced vital capacity (FVC%), and peripheral blood eosinophils were also measured. The intervention group was treated with atorvastatin 40 mg per day for 8 weeks, while the control group received a placebo. Asthma controller treatments were not changed. At the beginning and end of the study, serum cholesterol and triglyceride levels were measured to evaluate adherence of the patients to the treatment. RESULTS: The asthma control score did not significantly differ between the intervention and control groups (P=0.06). Difference in FEV1%, FVC%, and blood eosinophil count between the intervention and control groups were not statistically significant (P>0.05). The differences in post-treatment cholesterol and low-density lipoprotein cholesterol levels were significant (P<0.05). CONCLUSIONS: Our study shows that atorvastatin is not effective in the treatment of persistent mild to moderate asthma.