Belgian Lung Function Study: Personalised Longitudinal Lung Function Analysis as a Marker of Disease Progression
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- KU Leuven
- 入组人数
- 4,000
- 试验地点
- 4
- 主要终点
- Accuracy of lung function predictions (FEV1)
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •Patients who have had a lung transplantation
- •Patients not being able to give consent to participate
结局指标
主要结局
Accuracy of lung function predictions (FEV1)
时间窗: at 1 and 2-year follow-up
Proportion of correct and incorrect FEV1 predictions compared to the observed measure
次要结局
- Overall description of population(baseline, 1 and 2-year follow-up)
- Differences in clinical outcomes between correct and incorrect lung function predictions (FEV1)(at 1 and 2-year follow-up)
- Accuracy of lung function predictions(at 1 and 2-year follow-up)
- Differences in clinical outcomes between correct and incorrect lung function predictions(at 1 and 2-year follow-up)
- Identifying the minimal needed to make predictions(after 2 years)
- Performance of ML-based predictions compared to linear regression analysis(at 1 and 2-year follow-up)