Belgian Lung Function Study (AIRCAST)

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.

Study Overview

• Status: Recruiting
• Conditions: Chronic Respiratory Diseases

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.

1. 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.
2. 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 Type: Observational
• Enrollment (Estimated): 4000

Contacts and Locations

• Study Contact: Name: Marieke Wuyts; Phone Number: 016 34 31 59; Email: marieke.wuyts@kuleuven.be

Study Locations

• Belgium
  • Edegem, Belgium, 2650
    • Not yet recruiting
    • Uz Antwerpen
    • Contact: Therese Lapperre; Phone Number: 0032 3 821 5089; Email: therese.lapperre@uza.be
    • Principal Investigator: Therese Lapperre
    • Sub-Investigator: Kevin De Soomer
  • Genk, Belgium, 3600
    • Not yet recruiting
    • Ziekenhuis Oost-Limburg
    • Contact: David Ruttens; Phone Number: 0032 89 80 52 00; Email: david.ruttens@zol.be
    • Principal Investigator: David Ruttens
    • Sub-Investigator: Maarten Criel
  • Leuven, Belgium, 3000
    • Recruiting
    • UZ Leuven
    • Principal Investigator: Wim Janssens
    • Contact: Marieke Wuyts; Phone Number: 016 34 31 59; Email: marieke.wuyts@kuleuvne.be
  • Roeselare, Belgium, 8800
    • Not yet recruiting
    • AZ Delta
    • Principal Investigator: Ingel Demedts
    • Contact: Ingel Demedts; Phone Number: 0032 51 23 41 16; Email: ingel.demedts@azdelta.be
    • Sub-Investigator: Bernard Bouckaert

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Chronic respiratory diseases

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of lung function predictions (FEV1)	Proportion of correct and incorrect FEV1 predictions compared to the observed measure	at 1 and 2-year follow-up

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	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	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)	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	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	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	baseline, 1 and 2-year follow-up

Collaborators and Investigators

• Sponsor: KU Leuven
• Collaborators: University Hospital, Antwerp; AZ Delta; Ziekenhuis Oost-Limburg
• Investigators: Principal Investigator: Wim Janssens, UZ/KU Leuven

Study record dates

Study Major Dates

• Study Start (Actual): March 25, 2026
• Primary Completion (Estimated): March 1, 2029
• Study Completion (Estimated): March 1, 2029

Study Registration Dates

• First Submitted: February 12, 2026
• First Submitted That Met QC Criteria: February 12, 2026
• First Posted (Actual): February 19, 2026

Study Record Updates

• Last Update Posted (Actual): March 30, 2026
• Last Update Submitted That Met QC Criteria: March 25, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: lung function; trajectory; predictions
• Other Study ID Numbers: S70923; C2M25045 (Other Grant/Funding Number: Internal funding KULeuven)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No