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NCT Number: NCT07419555

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.

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Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

UZ Antwerpen, Edegem, Belgium

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About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  1. 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

Secondary outcomes

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

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

    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

  2. Accuracy of lung function predictions

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

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

  3. Differences in clinical outcomes between correct and incorrect lung function predictions

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

    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)

  4. Identifying the minimal needed to make predictions

    Time frame: after 2 years

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

  5. Performance of ML-based predictions compared to linear regression analysis

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

    Comparison of the ML-based predictions for individual and population lung function changes with predictions based on linear regression on individual historical data

  6. Overall description of population

    Time frame: baseline, 1 and 2-year follow-up

    Sociodemographic information, health status, comorbidities, frailty, disease labels, interventions and prognosis of individuals with a chronic respiratory disease

Study contacts

Contact information is provided by the study sponsor or research team.

Marieke Wuyts

CONTACT

[email protected]

016 34 31 59

Sponsors and collaborators

Lead sponsor

KU Leuven

Other

Collaborators

  • AZ Delta
  • University Hospital, Antwerp
  • Ziekenhuis Oost-Limburg

Registry information

Official study title

Belgian Lung Function Study: Personalised Longitudinal Lung Function Analysis as a Marker of Disease Progression

Acronym: AIRCAST

Important dates

Study start
2026
Primary completion
2029
Study completion
2029
First posted
Feb 19, 2026
Registry last updated
Mar 30, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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