Wheezing Diagnosis Using a Smartphone

Bronchiolitis Clinical Trial

— WheezSmart  

Official title:

Wheezing Diagnosis Using a Smartphone in Infants Referred for Bronchiolitis

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT02897960
• Other study ID #: ID RCB : 2015-A01499-40
• Status: Completed
• Start date: October 2016
• Est. completion date: June 2017

Study information

• Verified date: August 2016
• Source: Assistance Publique - Hôpitaux de Paris
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Description:

Infants (1 to 24 months old) are recruited in two emergency departments (Robert Debré; Antoine Béclère hospitals of Assistance publique - Hôpitaux de Paris) based on a respiratory complaint. Six characteristics are recorded (age, sex, SpO2, presence or absence of wheezing, other respiratory sound, initial diagnosis). Two recordings of respiratory sounds are obtained almost simultaneously: one with a Smartphone at the mouth (5 cm) and one with an electronic stetoscope (Littmann). Two expert pediatricians listen the recordings giving thee groups: with wheezing (agreement), without wheezing (agreement) and non agreement diagnosis. The recordings made with the Smartphone are subjected to the wheezing recognition algorithm as previously described. The sensitivity, specificity, PPV, NPP are then evaluated. The algorithm will further be improved if necessary using the true negative and true positive recordings (those with expert agreement).

Recruitment information / eligibility

• Status: Completed
• Enrollment: 600
• Est. completion date: June 2017
• Est. primary completion date: April 2017
• Accepts healthy volunteers: No
• Gender: All
• Age group: 1 Month to 24 Months

Eligibility

Inclusion Criteria:

• infant 1 to 24 months old

• respiratory complaint in the emergency room

Exclusion Criteria:

Related Conditions & MeSH terms

• Bronchiolitis
• Respiratory Sounds

Locations

Country	Name	City	State
France	Hôpital Robert Debré; service de Physiologie	Paris

Sponsors (1)

Lead Sponsor	Collaborator
Assistance Publique - Hôpitaux de Paris

Country where clinical trial is conducted

France, 

References & 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	positive and negative predictive values of the algorithm for wheezing diagnosis		8 months
Secondary	sensibility and specificity of the algorithm in subgroups	The sensibility and specificity of the algorithm will be assessed for recordings with other respiratory sounds (crackles for instance) The agreement (kappa value) between the emergency room sound diagnosis and both the expert and algorithm diagnosis (diagnostic ability of the physician in the emergency room)	8 months