Risk stratification as a guide to goal-oriented management of patients with fibrotic interstitial lung disease: a registry-based analysis
Bibliographic record
Abstract
Introduction Idiopathic pulmonary fibrosis (IPF) has high morbidity and mortality with limited treatment options. Goal-oriented management approaches, such as the “treatable traits” concept, have yet to be implemented in IPF. This study aims to identify specific treatment goals in IPF for potential interventions by analysing outcomes from two national registries. Methods We used data from the INSIGHTS-IPF registry, comprising 1232 IPF patients, enrolled from 2014 to 2020 as derivation cohort. Baseline and 6-month follow-up data were examined to assess clinical progression and predict 1-year mortality. Variables included forced vital capacity (FVC), diffusing capacity of the lung for carbon monoxide ( D LCO ), 6-min walk distance (6MWD), body mass index (BMI) and comorbidities. For validation we used data from 490 IPF patients enrolled in the Canadian Registry for Pulmonary Fibrosis (CARE-PF) and the full 2576 fibrotic interstitial lung disease (ILD) cohort of the CARE-PF registry. Results Multivariable analysis identified FVC, D LCO , 6MWD and BMI as independent predictors of 1-year survival. We established three risk groups based on these variables: low risk (<15% 1-year mortality), intermediate risk (15–30%) and high risk (>30%). Potential treatment goals were defined based on FVC, D LCO , 6MWD and BMI, which are readily available and may be responsive to interventions. Our risk model showed equivalent accuracy in the validation cohort, both for IPF alone and the overall population. Conclusion This study provides a novel risk model for IPF patients, which may also apply to a broader spectrum of fibrotic ILD. It is based on potentially actionable variables which deserve further evaluation as measurable treatment goals in interventional clinical trials.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".