Clinical characteristics and predictors of reduced survival for adult-diagnosed cystic fibrosis patients – a population based study
Bibliographic record
Abstract
Background: Approximately 5-10% of cystic fibrosis (CF) diagnoses are made during adulthood. These patients are a minority, and there is a paucity of literature describing their characteristics and prognosis. The objectives of this study are to describe the clinical characteristics, estimate survival, and identify clinical predictors of reduced survival at the time of diagnosis in adult-diagnosed CF patients. Methods: There were 362 newly diagnosed adult CF (≥18 years) patients from 1990 to 2014 in the Canadian CF Patient Registry. Clinical characteristics were described, the Kaplan-Meier method was employed for 10- and 15-year lung transplant-free survival estimates and multivariable Cox regression analysis was conducted to identify significant predictors of reduced survival at baseline. Adjusted survival curves were used to illustrate the impact of the significant predictors on lung transplant-free survival. Results: The median follow-up time observed was 7.7 years (range: 0.0-23.6) and included 33 deaths and 15 transplants for a total of 48 events in 3,106 patient years (15.5 events per 1,000 PYs). The median age at diagnosis was 34.3 years (range: 18.0-73.8), with the majority presenting with pulmonary and/or gastrointestinal symptoms (70%) and a nearly equal distribution of males and females. During the study period, 15% were diagnosed with CF-related diabetes (CFRD), 35% with pancreatic insufficiency and 50% were culture positive for P. aeruginosa. The most common genotype identified was ∆F508 heterozygous (38%). Lung transplant-free survival was 88% at 10 years and 86% by 15 years. Age at diagnosis (HR: 1.32 per 5-year increase, 95% CI: 1.13-1.54), CFRD (HR: 7.86, 95% CI: 2.09-29.55) and lower lung function (HR: 0.76 per 5% increase, 95% CI: 0.69-0.83) at baseline were significant predictors of reduced survival. In terms of clinical utility, low lung function (FEV1 % predicted < 60%) and CFRD were predictors that impacted lung transplant-free survival substantially. Conclusions: Adult-diagnosed CF patients have a milder phenotype of disease and a better prognosis than previously reported. Older age at diagnosis, lower lung function, and CFRD were important predictors of reduced survival. Adult CF clinicians and other CF caregivers can use this information to educate patients about their prognosis and to guide treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".