About this trial
Currently, it remains unclear how to manage serial lung function measurements in a clinical setting. The investigators aimed to tackle this problem by developing a machine learning (ML) model that can accurately predict population and individual lung function trajectories. These predictions would enable the investigators to identify positive or negative deviations, thereby revealing unexpected disease patterns.
A prospective validation is needed that includes data on mortality, hospitalisations, emergency-room visits and patient-reported outcomes. Within this study, the goal is to validate the ML model with the data collected from this observational study.
Eligibility criteria
Qualifiers
Above 18 years old
Diagnosed with a chronic respiratory disease and followed up in one of the participating Belgian hospitals
Performed a complete lung function test (spirometry, body plethysmography and diffusion capacity) at baseline
Have at least 3 historical spirometry measurements over a minimal time window of 2 years prior to inclusion
Disqualifiers
Patients who have had a lung transplantation
Patients not being able to give consent to participate
Trial design
Treatments tested in this trial
- Not listed
Trial groups
Sponsors and collaborators
KU Leuven
Lead sponsor
AZ Delta
Collaborator
University Hospital, Antwerp
Collaborator
Ziekenhuis Oost-Limburg
Collaborator