Survival models to support shared decision-making about advance care planning for people with advanced stage cystic fibrosis
Bibliographic record
Abstract
BACKGROUND: For people with advanced stage cystic fibrosis (CF), tailored survival estimates could facilitate preparation for decision-making in the event of acutely deteriorating respiratory function. METHODS: We used the US CF Foundation national database (2008-2013) to identify adult people with incident advanced stage CF (forced expiratory volume in 1 s (FEV1) ≤45% predicted). Using the lasso method for variable selection, we divided the dataset into training and validation samples (2:1), and developed two multivariable Cox proportional hazards models to calculate probabilities of survival from baseline (T0 model), and from 1 year after (T12 model). We also performed Kaplan-Meier survival analyses. RESULTS: colonisation; cirrhosis; depression; dialysis; current smoking; unclassifiable mutation class and cumulative CF exacerbations predicted increased mortality. Baseline transplant evaluation status of 'accepted, on waiting list' predicted decreased mortality. For the T12 model, interim decrease in FEV1 >10%, and pulmonary exacerbations additionally increased predicted mortality. Lung transplantation was associated with lower mortality. Of the 4752, 93.5%, 86.4%, 79.7% and 73.9% survived to 1, 2, 3 and 4 years, respectively, without considering any confounding variables. The models had moderate predictive ability indicated by the area under the time-dependent receiver operating characteristic curve (0.787, 95% CI 0.769 to 0.794 for T0 model; and 0.779, 95% CI 0.767 to 0.797 for T12 model). CONCLUSION: We have developed models predicting survival in people with incident advanced stage CF, which can be reapplied over time to support shared decision-making about end-of-life treatment choices and lung transplantation. These estimates must be updated as data become available regarding long-term outcomes for people treated with CF transmembrane conductance regulator modulators.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".