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Wheezing Diagnosis Using a Smartphone (WheezSmart)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT02897960
Recruitment Status : Completed
First Posted : September 13, 2016
Last Update Posted : August 16, 2017
Information provided by (Responsible Party):
Assistance Publique - Hôpitaux de Paris

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.

Condition or disease

Detailed 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).

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Study Type : Observational
Actual Enrollment : 600 participants
Observational Model: Case-Control
Time Perspective: Cross-Sectional
Official Title: Wheezing Diagnosis Using a Smartphone in Infants Referred for Bronchiolitis
Actual Study Start Date : October 2016
Actual Primary Completion Date : April 2017
Actual Study Completion Date : June 2017

Primary Outcome Measures :
  1. positive and negative predictive values of the algorithm for wheezing diagnosis [ Time Frame: 8 months ]

Secondary Outcome Measures :
  1. sensibility and specificity of the algorithm in subgroups [ Time Frame: 8 months ]
    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)

Information from the National Library of Medicine

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Ages Eligible for Study:   1 Month to 24 Months   (Child)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Infant selected in the emergency department: respiratory complaint

Inclusion Criteria:

  • infant 1 to 24 months old
  • respiratory complaint in the emergency room

Exclusion Criteria:

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT02897960

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Hôpital Robert Debré; service de Physiologie
Paris, France, 75019
Sponsors and Collaborators
Assistance Publique - Hôpitaux de Paris

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Responsible Party: Assistance Publique - Hôpitaux de Paris Identifier: NCT02897960    
Other Study ID Numbers: ID RCB : 2015-A01499-40
First Posted: September 13, 2016    Key Record Dates
Last Update Posted: August 16, 2017
Last Verified: August 2016
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No
Keywords provided by Assistance Publique - Hôpitaux de Paris:
respiratory sound algorithm
Additional relevant MeSH terms:
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Respiratory Sounds
Bronchial Diseases
Respiratory Tract Diseases
Lung Diseases, Obstructive
Lung Diseases
Respiratory Tract Infections
Signs and Symptoms, Respiratory
Signs and Symptoms