Asthma COPD
Conditions
Brief summary
Unicentric retrospective study designed to analyses the performance of various machine learning approaches to predict patterns of chronic respiratory diseases such as asthma, based mainly on clinical information and respiratory spirometry/oscillometry.
Detailed description
Impulse oscillometry is a technique that allows evaluation of pulmonary mechanics through the application of sound waves of different frequencies, collecting the oscillations produced in the patient in response. The use of mathematical algorithms in the interpretation of oscillometry improves the evaluation of pulmonary function. The aim of the present study is to evaluate machine learning approaches to recognize respiratory patterns of different diseases.
Interventions
OTHER1
Compare oscillometry results with spirometryClick to apply
Sponsors
Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 99 Years
Healthy volunteers
Yes
Inclusion criteria
* 18 - 90 years * Spirometry available * Confirmed clinical diagnosis of COPD, asthma, interstitial lung disease according to national or international guidelines
Exclusion criteria
* Acute respiratory infection
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Oscillometric breathing pattern | 1 year | Analyze results obtained |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Respiratory pattern spirometry | 1 year | Forced expiratory volume in 1 second |
Countries
Spain
Contacts
CONTACTAstrid Crespo, PhD
CONTACTAstrid Crespo-Lessmann, PhD
PRINCIPAL_INVESTIGATORAstrid Crespo-Lessmann, PhD
Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau
Outcome results
None listed