Chronic Respiratory DiseasesAIRCASTNCT07419555
Belgian Lung Function Study
An observational study of Chronic Respiratory Diseases, run by KU Leuven at 5 sites in Belgium.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Chronic Respiratory Diseases
- How many people
- 5,000 participants
- Where
- 5 sitesBelgium
- Study dates
- 25 Mar 2026to 1 Mar 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Chronic respiratory diseases
Who may be able to join
- 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
Who cannot join
- Patients who have had a lung transplantation
- Patients not being able to give consent to participate
Trial locations and contacts
5 sites in 1 country. Contact the study team or a site near you to ask about taking part.
5 sites in 1 country
Belgium5 sites, 5 recruiting
- Recruiting
UZ Antwerpen
Edegem, 2650
Therese Lapperre, 0032 3 821 5089, therese.lapperre@uza.be
Therese Lapperre
- Recruiting
Ziekenhuis Oost-Limburg
Genk, 3600
David Ruttens, 0032 89 80 52 00, david.ruttens@zol.be
David Ruttens
- Recruiting
UZ Gent
Ghent
Eric Derom, 0032 9 332 23 42, Eric.Derom@uzgent.be
Eric Derom
- Recruiting
UZ Leuven
Leuven, 3000
Marieke Wuyts, 016 34 31 59, marieke.wuyts@kuleuvne.be
Wim Janssens
- Recruiting
AZ Delta
Roeselare, 8800
Ingel Demedts, 0032 51 23 41 16, ingel.demedts@azdelta.be
Ingel Demedts
About this study
Official title: Belgian Lung Function Study: Personalised Longitudinal Lung Function Analysis as a Marker of Disease Progression
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.
Read the detailed description
The objective of this study is to explore the clinical value of models predicting longitudinal lung function patterns in individuals with chronic respiratory diseases across Belgium.
- The investigators will assess the accuracy of individualised lung function prediction models in a multicentre lung function dataset with prospective clinical and lung function follow-up.
- The investigators will evaluate important health outcomes, step-up in care, patient-reported outcomes in individuals identified with an expected and unexpected observed trajectory as compared to the predicted population and individualised trajectory.
The hypothesis is that patients with an unexpected decline in lung function will have worse health outcomes, such as a higher mortality rate and more hospitalisations, compared to patients with an expected lung function pattern. The investigators hypothesise to observe better health outcomes and lower mortality rates in patients with an unexpectedly positive lung function evolution compared to patients with an expected negative lung function pattern.
Individuals will be recruited from 4 Belgian Hospitals (UZ Leuven, UZ Antwerpen, AZ Delta, ZOL Genk). Based on the annual rate of pulmonary function testing in these hospitals, a sample size of 1.000 participants per centre is anticipated within one year of inclusions, resulting in a total sample size of 4.000 patients.
All available historical lung function data of included individuals will be retrieved from the individuals medical file. Additionally, the individual will be prospectively followed for 2 years where all lung function data will be collected.
- Conditions
- Chronic Respiratory Diseases
- Keywords
- lung function, trajectory, predictions
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of lung function predictions (FEV1)
Proportion of correct and incorrect FEV1 predictions compared to the observed measure
Measured: at 1 and 2-year follow-up
Secondary outcomes (6)
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
Measured: at 1 and 2-year follow-up
Accuracy of lung function predictions
Proportion of correct and incorrect lung function predictions (FVC, TLC, RV/TLC, DLCO) compared to the observed measure
Measured: at 1 and 2-year follow-up
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)
Measured: at 1 and 2-year follow-up
Identifying the minimal needed to make predictions
Minimal number of tests/length of follow-up required for optimal predictions
Measured: after 2 years
Performance of ML-based predictions compared to linear regression analysis
Comparison of the ML-based predictions for individual and population lung function changes with predictions based on linear regression on individual historical data
Measured: at 1 and 2-year follow-up
Overall description of population
Sociodemographic information, health status, comorbidities, frailty, disease labels, interventions and prognosis of individuals with a chronic respiratory disease
Measured: baseline, 1 and 2-year follow-up
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Feb 2026
- Study start25 Mar 2026
- Primary completion1 Mar 2029 (estimated)
- Study completion1 Mar 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Chronic Respiratory DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- KU Leuven
- Sponsor type
- University, hospital or other
- Collaborators
- AZ Delta, University Hospital, Antwerp, Ziekenhuis Oost-Limburg, University Hospital, Ghent, AstraZeneca
- Responsible party
- Principal investigator, Wim Janssens, KU Leuven
- Investigators
- Wim Janssens, Principal investigator, UZ/KU Leuven
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07419555
- Sponsor's protocol ID
- S70923
- Other IDs
- C2M25045
- Acronym
- AIRCAST
- Status last verified
- Oct 2026
- First submitted
- 12 Feb 2026
- Last updated
- 20 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 20 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Belgian Lung Function Study: Personalised Longitudinal Lung Function Analysis as a Marker of Disease Progression
- ClinicalTrials.gov ID
- NCT07419555
- Acronym / sponsor ID
- AIRCAST / S70923
- Conditions
- Chronic Respiratory Diseases
- Keywords
- lung function, trajectory, predictions
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- KU LeuvenUniversity, hospital or other
- Collaborators
- AZ Delta, University Hospital, Antwerp, Ziekenhuis Oost-Limburg, University Hospital, Ghent, AstraZeneca
- Study start
- 25 Mar 2026 (actual)
- Primary completion
- 1 Mar 2029 (estimated)
- Study completion
- 1 Mar 2029 (estimated)
- First posted
- 19 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
The objective of this study is to explore the clinical value of models predicting longitudinal lung function patterns in individuals with chronic respiratory diseases across Belgium.
- The investigators will assess the accuracy of individualised lung function prediction models in a multicentre lung function dataset with prospective clinical and lung function follow-up.
- The investigators will evaluate important health outcomes, step-up in care, patient-reported outcomes in individuals identified with an expected and unexpected observed trajectory as compared to the predicted population and individualised trajectory.
The hypothesis is that patients with an unexpected decline in lung function will have worse health outcomes, such as a higher mortality rate and more hospitalisations, compared to patients with an expected lung function pattern. The investigators hypothesise to observe better health outcomes and lower mortality rates in patients with an unexpectedly positive lung function evolution compared to patients with an expected negative lung function pattern.
Individuals will be recruited from 4 Belgian Hospitals (UZ Leuven, UZ Antwerpen, AZ Delta, ZOL Genk). Based on the annual rate of pulmonary function testing in these hospitals, a sample size of 1.000 participants per centre is anticipated within one year of inclusions, resulting in a total sample size of 4.000 patients.
All available historical lung function data of included individuals will be retrieved from the individuals medical file. Additionally, the individual will be prospectively followed for 2 years where all lung function data will be collected.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Chronic respiratory diseases
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (2 listed)
- Patients who have had a lung transplantation
- Patients not being able to give consent to participate
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy of lung function predictions (FEV1)
Time frame: at 1 and 2-year follow-up
Proportion of correct and incorrect FEV1 predictions compared to the observed measure
6 secondary outcomes
Differences in clinical outcomes between correct and incorrect lung function predictions (FEV1)
Time frame: at 1 and 2-year follow-up
Accuracy of lung function predictions
Time frame: at 1 and 2-year follow-up
Differences in clinical outcomes between correct and incorrect lung function predictions
Time frame: at 1 and 2-year follow-up
Identifying the minimal needed to make predictions
Time frame: after 2 years
Performance of ML-based predictions compared to linear regression analysis
Time frame: at 1 and 2-year follow-up
Overall description of population
Time frame: baseline, 1 and 2-year follow-up
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Oct 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Wim Janssens principal investigator
UZ/KU Leuven
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Chronic Respiratory Diseases, run by KU Leuven at 5 sites in Belgium.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 20 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in Belgium. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Marieke Wuyts (016 34 31 59, marieke.wuyts@kuleuven.be), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of lung function predictions (FEV1) (time frame: at 1 and 2-year follow-up). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Oct 2026.