Using oscillometry and machine learning to identify asthma and COPD patterns
Official title: Feasibility Study of Forced Oscillometry in the Prediction of Chronic Respiratory Diseases Using Machine Learning Approaches
Run by Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau · NCT07447596 · Data updated 2026-11-12
This project will test whether machine learning applied to oscillometry and spirometry data from adults with asthma or COPD can recognize different respiratory patterns.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 99 Years |
| Sex | All |
| Enrollment | 50 (estimated) |
| Sponsor | Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau Academic / other |
| Locations | 1 site (Barcelona) |
| Status | Recruiting |
| Started | October 15, 2025 |
| Main results expected | July 15, 2026 |
| Study ends | September 1, 2026 |
| Registry record updated | March 3, 2026 |
| Trial ID | NCT07447596 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults aged 18–90 with a confirmed diagnosis of asthma, COPD, or interstitial lung disease and available spirometry/oscillometry data recorded at the participating hospital are ideal candidates.
✕Probably not a fit
Patients without spirometry or oscillometry data, children under 18, those with an acute respiratory infection at the time of testing, or those lacking a confirmed diagnosis are unlikely to benefit from this analysis.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This is a single-center retrospective analysis using clinical records, spirometry, and impulse oscillometry measurements collected at Hospital de la Santa Creu i Sant Pau. Investigators will apply and compare various machine learning algorithms to detect characteristic respiratory patterns associated with asthma, COPD, and other chronic lung diseases. The dataset includes adults with confirmed diagnoses and available spirometry; cases with acute respiratory infection are excluded. Performance of the models will be measured against established clinical diagnoses and pulmonary function metrics.
Why it matters
Potential benefit: If successful, the approach could help clinicians more accurately distinguish respiratory disease patterns from routine lung tests and support earlier or more tailored treatment decisions.
How similar studies have gone: Similar approaches combining oscillometry and machine learning are emerging and have shown promising early results, but they remain relatively novel and not widely validated.
Who can join: the full rules
Show full inclusion / exclusion criteria
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
Where it's running
-
Hospital de la Santa Creu i Sant Pau
Barcelona, Spain Recruiting
Study contacts
- Principal investigator: Astrid Crespo-Lessmann, PhD — Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau
- Study coordinator: Astrid Crespo, PhD
- Email: acrespo@santpau.cat
- Phone: +34-935565972
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.