Cardiopulmonary Exercise Testing Provides Prognostic Information in Advanced Cystic Fibrosis Lung Disease
Bibliographic record
Abstract
Abstract Rationale Cardiopulmonary exercise testing (CPET) provides prognostic information in cystic fibrosis (CF); however, its prognostic value for patients with advanced CF lung disease is unknown. Objectives To determine the prognostic value of CPET on the risk of death or lung transplant (LTX) within 2 years. Methods We retrospectively collected data from 20 CF centers in Asia, Australia, Europe, and North America on patients with a forced expiratory volume in 1 second (FEV1) ⩽ 40% predicted who performed a cycle ergometer CPET between January 2008 and December 2017. Time to death/LTX was analyzed using mixed Cox proportional hazards regression. Conditional inference trees were modeled to identify subgroups with increased risk of death/LTX. Results In total, 174 patients (FEV1, 30.9% ± 5.8% predicted) were included. Forty-four patients (25.5%) died or underwent LTX. Cox regression analysis adjusted for age, sex, and FEV1 revealed percentage predicted peak oxygen uptake (V˙o2peak) and peak work rate (Wpeak) as significant predictors of death/LTX: adjusted hazard ratios per each additional 10% predicted were 0.60 (95% confidence interval, 0.43–0.90; P = 0.008) and 0.60 (0.48–0.82; P < 0.001). Tree-structured regression models, including a set of 11 prognostic factors for survival, identified Wpeak to be most strongly associated with 2-year risk of death/LTX. Probability of death/LTX was 45.2% for those with a Wpeak ⩽ 49.2% predicted versus 10.9% for those with a Wpeak > 49.2% predicted (P < 0.001). Conclusions CPET provides prognostic information in advanced CF lung disease, and Wpeak appears to be a promising marker for LTX referral and candidate selection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".