Emerging respiratory diseases are a global health threat. Viruses such as influenza and coronaviruses have been the main cause of pandemics over the last century. In general, the impact of these respiratory infections is not limited to pandemic risks. Indeed, some pathogens also induce seasonal epidemics with a significant medical and economic burden. It is therefore essential to strengthen global surveillance, warning systems and early diagnosis capabilities for pathogens responsible for respiratory infections. One promising and recognized approach is the analysis of exhaled air, which contains a complex mixture of volatile organic compounds (VOCs), also known as the "volatilome". The volatilome is influenced by the patient's metabolism, immune system and microbiome. It can be disturbed by the presence of a pathogen. A possible approach to study the human volatilome is called the "on-line" method. Among the technologies capable of performing online analysis, analyzers using TOF (time-of-flight) technology separate ions according to velocity differences after acceleration by a fixed potential, and then measure all mass/charge ratios simultaneously. The data obtained takes the form of a mass spectrum composed of a multitude of peaks representing the abundance of each detected chemical species contained in the exhalation. The sensitivity and measurement speed of instruments using PTR-TOF-MS (Proton Transfer Reaction - Time of Flight - Mass spectrometer) technology enable real-time monitoring of the exhalation process, making it possible to analyze exhaled air as a function of time. PTR-TOF-MS instruments are usually compact in design, enabling them to be deployed in environments such as hospital emergency wards or mass screening centers. The aim of the VORTEX-1 study is to include patients presenting with signssymptoms of respiratory infections, irrespective of microbiological etiology, to approximate a routine clinical context, thus including infections of various viral or bacterial origins. In addition, a so-called "control" group will also be sampled, made up ofcomposed by healthy subjects (with nowithout respiratory infections or serious or chronic pathologies at the time of sampling). In parallel with the study of the chemical composition of the exhaled air of these patients, and to further our understanding of the factors influencing the volatilome, a combined exploratory analysis of the respiratory microbiota, the host response at the time of infection, and the pathogen(s) responsible for the infectious episode is required. To date, no such analysis exists in the scientific literature, probably due to the technical and logistical complexity of integrating data from multiple sources and the lack of a multidisciplinary consortium with the necessary expertise.In parallel with the study of the chemical composition of the exhaled air of these patients, and in order to go further in understanding the factors influencing the volatilome, a combined exploratory analysis of the respiratory microbiota, the host response at the time of infection and the pathogen(s) responsible for the infectious episode is required. To date, no such analysis exists in the scientific literature, probably due to the technical and logistical complexity of integrating data from multiple sources, but also to the absence of a multidisciplinary consortium capable of bringing together all the necessary expertise within the same project. The detection of specific VOCs could considerably improve and facilitate the diagnosis of these respiratory diseasesinfections. This research could revolutionize the diagnosis of respiratory infections by offering a rapid, non-invasive and easily scalable alternative to conventional diagnostic methods such as PCR tests, which require nasopharyngeal sampling. In parallel with the study of the chemical composition of the exhaled air of these patients, and to further our understanding of the factors influencing the volatilome, a combined exploratory analysis of the respiratory microbiota, the host response at the time of infection, and the pathogen(s) responsible for the infectious episode is required. To date, no such analysis exists in the scientific literature, probably due to the technical and logistical complexity of integrating data from multiple sources and the lack of a multidisciplinary consortium with the necessary expertise. The detection of specific VOCs could considerably improve and facilitate the diagnosis of these respiratory infections. by offering a rapid, non-invasive and easily scalable alternative to conventional diagnostic methods such as PCR tests.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
1,665
The collection and analysis of exhaled air for VOC detection is a non-invasive, painless procedure that will be carried out online and summarised as follows: . The patient's exhaled air is collected directly in the analyzer using disposable mouthpieces. 2\. Real-time chemical analysis of exhaled air over a few seconds (breath duration) using PTR-TOF-MS. 3\. Processing raw data to establish the chemical composition of VOCs. 4. Statistical analysis of all generated data to identify specific VOC profiles. Results will be compared to standard diagnostic procedure and linked to immune, metabolic and microbiome exploration.
Emergency department (Hôpital de la Croix-Rousse, Hospices Civils de Lyon) Lyon (France) 69004
Lyon, France
Maison de Santé Pluriprofessionnelle Universitaire (Hôpital de la Croix-Rousse, Hospices Civils de Lyon)
Lyon, France
National reference center for respiratory viruses (Hôpital de la Croix-Rousse, Hospices Civils de Lyon)
Lyon, France
Comparison and differentiation between patients presenting symptoms of acute respiratory infection (ARI) and a control group of healthy subjects
The test will aim to differentiate patients with one of the symptoms of acute respiratory infection from the control group of healthy patients. The overall performance of the test will be evaluated on several models, based on the area under the ROC curve (AUC) calculated from model predictions. Predicted AUCs will be compared to an expected AUC of 0.7 by bootstrapping.
Time frame: DAY 1
Validation of descriptive analyses of exhaled air test based on aggregated diagnostic parameters compared with those of the current biological reference standard for respiratory infection.
Evaluate the performance (same as main objective) of the chemical analysis of exhaled air in differentiating between three groups of participants: those with a respiratory infection caused by a virus, those with a respiratory infection caused by bacteria, and healthy subjects.
Time frame: DAY 1
Description of the breath composition based on the area under each peak of exhaled air according to six levels of classification
Evaluate the performance of chemical analysis of exhaled air in differentiating between the following six groups of patients: * respiratory infection with Legionella spp. * respiratory infection with another bacterium * respiratory infection with SARS-CoV-2 * respiratory infection with influenza * respiratory infection with another virus * uninfected patients
Time frame: DAY 1
Description of the breath composition based on the AUC of each peak
Analyse and describe the composition of patients' exhaled air according to viral (influenza and SARS-CoV-2) or bacterial (Legionella) load, in order to identify biomarkers whose excretion correlates with viral and bacterial load.
Time frame: DAY 1
Description of the breath composition based on the AUC of each peak
Analyse and describe the composition of patients' exhaled air based on the presence of co-infection, in order to define biomarkers whose excretion is associated with these co-infections.
Time frame: DAY 1
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