Longitudinal assessment of lung clearance index to monitor disease progression in children and adults with cystic fibrosis
Bibliographic record
Abstract
Background Lung clearance index (LCI) is a valuable research tool in cystic fibrosis (CF) but clinical application has been limited by technical challenges and uncertainty about how to interpret longitudinal change. In order to help inform clinical practice, this study aimed to assess feasibility, repeatability and longitudinal LCI change in children and adults with CF with predominantly mild baseline disease. Methods Prospective, 3-year, multicentre, observational study of repeated LCI measurement at time of clinical review in patients with CF >5 years, delivered using a rapid wash-in system. Results 112 patients completed at least one LCI assessment and 98 (90%) were still under follow-up at study end. The median (IQR) age was 14.7 (8.6–22.2) years and the mean (SD) FEV 1 z-score was −1.2 (1.3). Of 81 subjects with normal FEV 1 (>−2 z-scores), 63% had raised LCI (indicating worse lung function). For repeat stable measurements within 6 months, the mean (limits of agreement) change in LCI was 0.9% (−18.8% to 20.7%). A latent class growth model analysis identified four discrete clusters with high accuracy, differentiated by baseline LCI and FEV 1 . Baseline LCI was the strongest factor associated with longitudinal change. The median total test time was under 19 min. Conclusions Most patients with CF with well-preserved lung function show stable LCI over time. Cluster behaviours can be identified and baseline LCI is a risk factor for future progression. These results support the use of LCI in clinical practice in identifying patients at risk of lung function decline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".