Belgian Lung Function Study

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.

Inclusion Criteria: * 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 * Planned routine follow-up within standard clinical care in one of the participating hospitals Exclusion Criteria: * Patients who have had a lung transplantation * Patients not being able to give consent to participate