Wheezing Diagnosis Using a Smartphone
WheezSmart
1 other identifier
observational
600
1 country
1
Brief Summary
Abnormal respiratory sounds (wheezing and/or crackles) are diagnosis criteria of acute bronchiolitis. One third of these infants will develop recurrent episodes, leading to the diagnosis of infant asthma. Nowadays, no available treatment shortens the course of bronchiolitis or hastens the resolution of symptoms, thus, therapy is supportive. Our hypothesis is that the diagnosis of wheezing during bronchiolitis (\~60% of infants) will help to select infants who will benefit from anti-asthma therapy. In this setting the diagnosis of wheezing is crucial, and an objective tool for recognition of wheezing is of clinical value. The investigators developed a wheezing recognition algorithm from recorded respiratory sounds with a Smartphone placed near the mouth (Bokov P, Comput Biol Med, 2016). The objectives of the present cross sectional, observational study are 1/ to further validate our approach in a larger sample of infants (1 to 24 months) admitted to hospital for a respiratory complaint during the period of viral bronchiolitis, and 2/ to use gold standard diagnosis of wheezing by respiratory sound recording (Littmann) and subsequent analysis by two experienced pediatricians.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Oct 2016
Shorter than P25 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
September 1, 2016
CompletedFirst Posted
Study publicly available on registry
September 13, 2016
CompletedStudy Start
First participant enrolled
October 1, 2016
CompletedPrimary Completion
Last participant's last visit for primary outcome
April 1, 2017
CompletedStudy Completion
Last participant's last visit for all outcomes
June 1, 2017
CompletedFebruary 6, 2023
August 1, 2016
6 months
September 1, 2016
February 3, 2023
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
positive and negative predictive values of the algorithm for wheezing diagnosis
8 months
Secondary Outcomes (1)
sensibility and specificity of the algorithm in subgroups
8 months
Eligibility Criteria
Infant selected in the emergency department: respiratory complaint
You may qualify if:
- infant 1 to 24 months old
- respiratory complaint in the emergency room
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Hôpital Robert Debré; service de Physiologie
Paris, 75019, France
Related Publications (1)
Bokov P, Mahut B, Flaud P, Delclaux C. Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population. Comput Biol Med. 2016 Mar 1;70:40-50. doi: 10.1016/j.compbiomed.2016.01.002. Epub 2016 Jan 8.
PMID: 26802543RESULT
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- CASE CONTROL
- Time Perspective
- CROSS SECTIONAL
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
September 1, 2016
First Posted
September 13, 2016
Study Start
October 1, 2016
Primary Completion
April 1, 2017
Study Completion
June 1, 2017
Last Updated
February 6, 2023
Record last verified: 2016-08
Data Sharing
- IPD Sharing
- Will not share