Epigenetics and NCD Prevention in Kazakhstan: Personalized Approaches and Biological Age Prediction
NCT06953180
Body Weight, Cardiovascular Diseases
Almaty, Kazakhstan
View Trial DetailsNCT Number: NCT07419555
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.
Interested in participating?
Request Info18 year and older
All sexes
Observational
UZ Antwerpen, Edegem, Belgium
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 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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: at 1 and 2-year follow-up
Proportion of correct and incorrect FEV1 predictions compared to the observed measure
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
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
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)
Time frame: after 2 years
Minimal number of tests/length of follow-up required for optimal predictions
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
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
Contact information is provided by the study sponsor or research team.
KU Leuven
Other
Belgian Lung Function Study: Personalised Longitudinal Lung Function Analysis as a Marker of Disease Progression
Acronym: AIRCAST
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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