Bronchiolitis
Conditions
Keywords
wheezing, bronchiolitis, respiratory sound algorithm
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.
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).
Interventions
None listed
Sponsors
Assistance Publique - Hôpitaux de Paris
Study design
Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
1 Months to 24 Months
Healthy volunteers
No
Inclusion criteria
* infant 1 to 24 months old * respiratory complaint in the emergency room
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| positive and negative predictive values of the algorithm for wheezing diagnosis | 8 months |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| sensibility and specificity of the algorithm in subgroups | 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) |
Countries
France
Outcome results
None listed