Hemoptysis and the Risk for Lung Transplant or Death without Transplant in Individuals with Cystic Fibrosis in the United States
Bibliographic record
Abstract
Abstract Rationale Hemoptysis is a common and important complication in persons with cystic fibrosis (PwCF). Despite this, there is limited literature on the impact of hemoptysis on contemporary cystic fibrosis (CF) outcomes. Objectives Evaluate whether hemoptysis increases the risk of lung transplant or death without a transplant in PwCF. Methods We reviewed a dataset of PwCF ages 12 years or older from the CFFPR (CF Foundation Patient Registry) that included 29,587 individuals. We identified hemoptysis as our predictor of interest and categorized PwCF as either no hemoptysis, any hemoptysis (submassive and/or massive), or massive hemoptysis. We subsequently evaluated whether hemoptysis, as defined above, was associated with death without transplant or receipt of lung transplant via logistic regression. We adjusted for age, sex, body mass index, forced expiratory volume in one second (FEV1), number of exacerbations, supplemental oxygen use, CF-related diabetes, and Pseudomonas aeruginosa colonization status. Subgroup analyses were performed in advanced lung disease, defined as PwCF with an FEV1 <40% predicted. Results PwCF with any form of hemoptysis were more likely to progress to lung transplant or die without transplant than PwCF who did not have hemoptysis (odds ratio [OR], 1.3 [95% confidence interval (CI), 1.1–1.7]). The effect size of these associations was larger when hemoptysis events were classified as “massive” (massive hemoptysis OR, 2.2 [95% CI, 1.2–3.8]) or in PwCF with advanced lung disease (massive hemoptysis in advanced lung disease OR, 3.2 [95% CI 1.3–8.2]). Conclusions Hemoptysis is associated with an increased risk of lung transplant and death without a transplant in PwCF, especially among those with massive hemoptysis or advanced lung disease. Our results suggest that hemoptysis functions as a useful predictor of serious outcomes in PwCF and may be important to incorporate into risk prediction models and/or transplant decisions in CF.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".