Actively Recruiting
COVID-19: Early Detection of Worsening by Voice and Respiratory Pattern Characteristics
Led by Direction Centrale du Service de Santé des Armées · Updated on 2023-06-15
1188
Participants Needed
1
Research Sites
159 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
In some clinical forms of COVID-19, an uncontrolled hyper-inflammatory reaction known as a "cytokine storm" appears abruptly, around day 7, and is associated with rapid respiratory deterioration, requiring hospitalization in an intensive care unit (ICU). At present, although risk factors for this severe form have been described, there are no validated criteria for determining which individual patients will develop this aggravation. The study of respiratory sounds (amplitude, frequency, ...) has made it possible in other respiratory pathologies (e.g., chronic obstructive pulmonary disease) to predict exacerbations several days in advance. Having a predictive respiratory pattern for worsening in COVID-19 would make it possible to anticipate the need for intensive care hospital beds, by means of a tool easily available on a mobile phone.
CONDITIONS
Official Title
COVID-19: Early Detection of Worsening by Voice and Respiratory Pattern Characteristics
Who Can Participate
Eligibility Criteria
You may qualify if you...
- French speaking
- Affiliated to a social security system
- Owning a mobile phone capable of accessing the COVOICE application
You will not qualify if you...
- Incapable adults
- People deprived of their liberty
- People under administrative or judicial supervision
- Patients intubated or hospitalized in intensive care unit
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Hôpital d'Instruction des Armées Bégin
Saint-Mandé, France, 94160
Actively Recruiting
Research Team
H
Hubety NIELLY, MD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
3
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