View clinical trials related to Respiratory Tract Infections.
Filter by:Respiratory infections such as colds, flu and pneumonia affect millions of people around the world every year. Most cases are mild, but some people become very unwell. Influenza ('flu') is one of the most common causes of lung infection. Seasonal flu affects between 10% and 46% of the population each year and causes around 12 deaths in every 100,000 people infected. In addition, both influenza and coronaviruses have caused pandemics in recent years, leading to severe disease in many people. Although flu vaccines are available, these need to change every year to overcome rapid changes in the virus and are not completely protective. This study aims to find and develop predictive tests to better understand how and when flu-like illness progresses to more severe disease. This may help to decide which people need to be admitted to hospital, and how their treatment needs to be increased or decreased during infection. The aim is to recruit 100 patients admitted to hospital due to a respiratory infection. It is voluntary to take part and participants can choose to withdraw at any time. The study will involve some blood and nose samples. This will be done on Day 0, Day 2 and Discharge from hospital, and an out-patient follow-up visit on Day 28. The data will be used to develop novel diagnostic tools to assist in rational treatment decisions that will benefit both individual patients and resource allocation. It will also establish research preparedness for upcoming pandemics.
This is a prospective observational study using a mobile study platform (app) that is designed for use on Android phones. Study participants will provide baseline demographic and medical information and report symptoms of respiratory infection on a weekly basis using the app. Participants will also report use of prevention techniques on the weekly survey. Mobility data will be collected passively using the sensors on the participant's smartphone, if the participant has granted the proper device permissions. The overall goals of the study are to track spread of coronavirus-like illness (CLI), influenza-like illness (ILI) and non-specific respiratory illness (NSRI) on a near-real time basis and identify specific behaviors associated with an increased or decreased risk of developing these conditions.
This study examined the etiology of acute respiratory viral infections (ARVI) during the 2015-2016 season, evaluated the statistics of the incidence of influenza and ARVI in this period (epidemiology: severity of the disease and bacterial exacerbations; demographics of patients; duration and timing of treatment; safety; quality of treatment), and evaluated the effectiveness of complex therapy with an emphasis on the using of interferon inducers in hospitalized children aged 3 to 11 years.
Currently, there are few studies that have been established that consist of a variety of established and coherent approaches that sought to profile the determinants of recovery, nor used interrogative procedures to understand lasting physical impairment. In this context, measurements obtained from an assessment of cardio-respiratory responses to physiological stress could provide an important insight regarding the integrity of the pulmonary-vascular interface and characterisation of any impairment or abnormal cardio-respiratory function [4]. Indeed, current approaches are being developed to support patients using previous knowledge from other acute respiratory infections (e.g. Acute Respiratory Distress Syndrome; ARDS and Middle Eastern Respiratory Syndrome; MERS), approaches that do not consider the novel challenges presented by COVID-19. The knowledge obtained from the proposed research plan will inform the development of COVID-19 specific rehabilitation and clinical management guidelines which can be implemented globally to increase patient wellbeing, physical capacity, and functional status which will be directly related national and international health and wellbeing, economical and societal impacts.
To analyze the efficacy of daily consumption of a combination of garlic and onion extracts on the incidence of respiratory infection symptoms in healthy elderly volunteers living in a residence. The duration of symptoms and related medication will also be studied.
This research study is studying Lenvatinib in combination with Pembrolizumab in people with human papillomavirus (HPV)-associated recurrent respiratory papillomatosis (RRP). The names of the study drugs involved in this study are: - Pembrolizumab - Lenvatinib
Pneumonia is one of the most common infections in the emergency department (ED). Nevertheless, the current diagnostic tools are often slow and inaccurate. Currently, a chest x-ray is the first choice for diagnostic imaging for pneumonia in the ED, but is inaccurate with low sensitivity and specificity, resulting in both over-and underdiagnosing of pneumonia. Alternatively, computer thermography (CT) and high-resolution CT (HR-CT) offers high diagnostic accuracy but involves significantly increased radiation to the patient, and increased costs and examination time. Lately, two alternatives to chest x-ray have emerged: - The first is lung ultrasound (LUS) which has shown higher sensitivity and specificity for pneumonia than a chest x-ray when performed by experts. However, the diagnostic accuracy of lung ultrasound performed by novice operators in the ED still needs investigation. - The second alternative to chest x-ray is ultra-low-dose CT (ULD-CT). A ULD-CT is a CT scan where the radiation dose is significantly reduced, while still maintaining acceptable image quality. In effect merging the high diagnostic accuracy of chest CT with the low radiation doses of chest X-ray. The aim of this study is to investigate the diagnostic accuracy of LUS by novice operators in the ED and the diagnostic accuracy of ULD-CT thorax, in patients suspected of having pneumonia.
Project is designed as a comprehensive population-based epidemiological study in Upper-Silesian Conurbation (Poland) aiming at: 1. analysis of available data on incidence and mortality due to COVID-19 and 2. estimation of the occurrence of viral infection SARS-CoV-2 as revealed by the results of serological test (ELISA: IgM, IgG), with assessment of risk factors. The project's objectives are: to assess incidence and mortality due COVID-19 according to sex, age and coexisting diseases; to determine the level of potential "underdiagnosis" of the magnitude of COVID-19 mortality using vital statistics data for Upper-Silesian Conurbation; to assess the prevalence of SARS-CoV-2 based on the level of seropositivity in Upper-Silesian Conurbation; to identify host-related and environmental risk factors if the infection. Analysis of existing data will include monthly records on incidence and mortality over the period 01.01.2020-31.12.2020 and comparison of the findings with the monthly records of 2018 and 2019, for the same population. Cross-sectional epidemiological study will be located in three towne (Katowice, Sosnowiec, Gliwice). In each town a representative age-stratified sample of 2000 subjects will undergo questionnaire assessment and serological examination performed by serological test. The project corresponds with analogous population-based studies on COVID-19 in a number of countries and responds to the WHO recommendation in that field.
This study evaluates the use of Kagocel for the prevention of acute respiratory viral infections (ARVI) and influenza during the epidemic rise in the incidence of diseases in Russia in 2018 (epidemiology: the number of cases during the period of Kagocel administration and follow-up, bacterial exacerbations, the number of repeated episodes (reinfection), demographics of patients, safety, adherence to treatment) in students at risk due to stress, lack of sleep and fatigue.
This study will reach out to patients who have undergone diagnostic testing for the following respiratory illnesses from January 1st, 2018 to July 9th, 2023: COVID-19, Influenza, Rhinovirus, and Respiratory Syncytial Virus. This study aims to develop a forecasting model to predict infection onset prior to symptom onset using wearable device data and known symptom onset and test dates.