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| ID | Type | Description | Link |
|---|---|---|---|
| 2025-A01544-45 | Other Identifier | ID-RCB |
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Emerging respiratory diseases represent a global threat. Viruses such as influenza and coronaviruses have been the main drivers of pandemics over the past century. More broadly, the impact of these respiratory infections is not limited to pandemic risks. Indeed, some of them also trigger seasonal epidemics with a significant medical and economic burden. Consequently, it is essential to strengthen global surveillance, and diagnostic capacities for the pathogens responsible for respiratory infections.
The diagnosis of respiratory infections is even more important in cases of severe infection, as it helps guide and adapt patient management according to the responsible pathogen.
A promising and well-recognized approach is the analysis of exhaled breath, 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, and can be disrupted by the presence of a pathogen.
A parallel clinical study, VORTEX-1, aims to establish the performance of breath analysis for the diagnosis of respiratory infections in the context of the general population, or patient triage in emergency wards. This study targets patients with non-severe respiratory infections, mostly caused by viral pathogens.
Thanks to a specific technique, the VORTEX-1 study will make it possible to test a direct on-site sampling and analysis process, painless and with real-time chemical detection.
This methodology, highly suited to triage situations, remains difficult to apply in the case of respiratory infections requiring hospitalization. Indeed, hospitalized patients are usually admitted to different units depending on their clinical status, risk factors, or bed availability. This diversity of settings makes it impossible to implement a process that depends on an instrument which cannot be available or moved in real time across all units. To address this challenge, the investigators will use an alternative method.
In the VORTEX-2 trial, samples of exhaled gases will be collected directly at the patient's bedside using a single-use device for breath collection. The samples will then be transferred to a laboratory for analysis. This approach is more suitable for severe respiratory infections.
To be as comprehensive as possible in the study of the volatilome in the context of respiratory infections, it is important to include hospitalized patients and to develop a system that can also be implemented in routine clinical practice.
The link between the two studies (VORTEX-1 and VORTEX-2) will be established through a "control" group, consisting of healthy subjects (without respiratory infections or severe/chronic diseases), whose breath will be collected using both approaches.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Bacterial respiratory infection | Experimental | To classify patients, an adjudication committee will base its decision on a range of clinical and biological factors:
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| Viral respiratory infection | Experimental | To classify patients, an adjudication committee will base its decision on a range of clinical and biological factors:
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| Undetermined respiratory infection | Experimental | All patients who matched the inclusion criteria and could not be classified into groups 1 or 2 by an adjudication committee. |
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| Healthy patients |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Volatile Organic compounds (VOC) analysis in Exhaled breath using GC-MS | Device | The collection and analysis of exhaled air for VOC detection is a non-invasive, painless procedure carried out offline. It can be summarised as follows:
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| Measure | Description | Time Frame |
|---|---|---|
| Description of the breath composition based on the area under each peak of exhaled air according to three levels of classification | Levels of classification :
| day one |
| Measure | Description | Time Frame |
|---|---|---|
| Comparison of the overall properties of the test on exhaled air with the clinical classification of a severe respiratory infection used in practice. The test will therefore aim to differentiate patients with a respiratory infection who are hospitalized w | Overall properties of the exhaled air test in comparison with the clinical classification of severe respiratory infection as used in practice.The test will aim to differentiate between hospitalised patients with a respiratory infection and a NEWS-2 score of over 4, and a 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Dr Alexandre GAYMARD | Contact | +4 72 07 10 53 | alexandre.gaymard@chu-lyon.fr |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Emergency department (Hôpital de la Croix-Rousse, Hospices Civils de Lyon) | Lyon | 69004 | France |
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This is a cross-sectional diagnostic study using case-control sampling, where the number of cases and non-cases is fixed in advance, and a gold standard comparison is used. 0
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| Placebo Comparator |
The group of healthy volunteers, serving as the control group, will be composed of adult subjects with no respiratory symptoms or known acute or chronic pathology at the time of inclusion. They will be sampled using the same methods as the other groups in order to enable a reliable comparison of the metabolic signatures obtained. |
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| Day one |
| Differentiation of the three groups of participants-those with a viral respiratory infection, those with a bacterial respiratory infection, and healthy subjects-will be evaluated using the area under the curve (AUC), in order to assess the performance | Evaluate the performance (same as secondary outcome 1) 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. | Day one |
| Evaluation of viral load in respiratory samples will be performed by retesting all nasopharyngeal samples positive for an influenza virus, SARS-CoV-2, or Legionella. This criterion will correspond to the breath composition associated with viral load. | The study will be based on the overall properties of the breath test compared with the clinical classification of severe respiratory infection used in practice. The test will therefore aim to differentiate between six groups. These performances will be evaluated using the AUC. The AUCs calculated from the predictions of each model developed will be compared to an expected AUC of 0.7 by bootstrap. | Day one |
| 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. | Day one |
| 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. | Day one |
| Description of the breath composition based on the AUC of each peak | Analyse and describe the composition of patients' exhaled air based on innate and adaptive immune responses in patients with and without respiratory infections. Understand any classification errors due to immune responses. | Day one |
| Description of the breath composition based on the AUC of each peak | Analyse and describe the chemical analysis of exhaled air between patients with and without an impaired type I interferon response. | Day one |
| Description of the breath composition based on the AUC of each peak | Analyse and describe the composition of patients' exhaled air based on the composition of the active respiratory microbiota (nasopharyngeal or pulmonary) in patients with and without respiratory infections. Understand any classification errors due to the composition of the active respiratory microbiota. | Day one |
| Description of the breath composition based on the AUC of each peak | Analyse and describe the composition of patients' exhaled air based on the value of the NEW2 score at inclusion. | Day one |
| Description of the breath composition based on the AUC of each peak | Analyse and describe the composition of patients' exhaled air based on three clinical progression groups: worsening, stagnation or improvement based on changes in the clinical score (NEW2). | Day one |
| Description of the breath composition based on the AUC of each peak | Analyse and describe the composition of healthy volunters' exhaled air based on online analysis process (VORTEX-1 clinical study) and the offline analysis process (VORTEX-2 clinical study). | Day one |
| Infectious Disease Service of Hôpital de la Croix-Rousse (Hospices Civils de Lyon) | Lyon | 69004 | France |
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| Intensive Care Unit of Hôpital de la Croix-Rousse (Hospices Civils de Lyon) | Lyon | 69004 | France |
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| Intensive Care Unit of Hôpital Lyon Sud (Hospices Civils de Lyon) | Lyon | 69004 | France |
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| Internal Medecine Unit of Hôpital de la Croix-Rousse (Hospices Civils de Lyon) | Lyon | 69004 | France |
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| National reference center for respiratory viruses (Hôpital de la Croix-Rousse, Hospices Civils de Lyon) | Lyon | 69004 | France |
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| Pneumology Unit of Hôpital de la Croix-Rousse (Hospices Civils de Lyon) | Lyon | 69004 | France |
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| ID | Term |
|---|---|
| D012141 | Respiratory Tract Infections |
| D000086382 | COVID-19 |
| D007877 | Legionnaires' Disease |
| D004194 | Disease |
| ID | Term |
|---|---|
| D007239 | Infections |
| D012140 | Respiratory Tract Diseases |
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D014777 | Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D007876 | Legionellosis |
| D016905 | Gram-Negative Bacterial Infections |
| D001424 | Bacterial Infections |
| D001423 | Bacterial Infections and Mycoses |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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