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Belgian Lung Function Study

ConditionChronic Respiratory Diseases
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorKU Leuven

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

This trial does not accept healthy volunteers

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 population

Chronic respiratory diseases

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

Not listed

Trial groups

No trial groups listed

Trial outcomes

Primary outcomes

1

Accuracy of lung function predictions (FEV1)

Proportion of correct and incorrect FEV1 predictions compared to the observed measure

Time frame
at 1 and 2-year follow-up

Secondary outcomes

1

Differences in clinical outcomes between correct and incorrect lung function predictions (FEV1)

Differences between patients with correct and incorrect individual lung function predictions for FEV1 on clinical endpoints (such as mortality, hospitalisations, frailty, health status and step-up in care

Time frame
at 1 and 2-year follow-up
2

Accuracy of lung function predictions

Proportion of correct and incorrect lung function predictions (FVC, TLC, RV/TLC, DLCO) compared to the observed measure

Time frame
at 1 and 2-year follow-up
3

Differences in clinical outcomes between correct and incorrect lung function predictions

Differences between patients with correct and incorrect individual lung function predictions for FVC, TLC, RV/TLC, DLCO on clinical endpoints (such as mortality, hospitalisations, frailty, health status and step-up in care)

Time frame
at 1 and 2-year follow-up
4

Identifying the minimal needed to make predictions

Minimal number of tests/length of follow-up required for optimal predictions

Time frame
after 2 years

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

KU Leuven

Lead sponsor

AZ Delta

Collaborator

University Hospital, Antwerp

Collaborator

Ziekenhuis Oost-Limburg

Collaborator

UZ Gent, Belgium

Collaborator

Source ClinicalTrials.gov
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