Predicting New-onset Exertional and Resting Hypoxemia in Fibrotic Interstitial Lung Disease
Bibliographic record
Abstract
Abstract Rationale Hypoxemia in fibrotic interstitial lung disease (ILD) indicates disease progression and is of prognostic significance. The onset of hypoxemia signifies disease progression and predicts mortality in fibrotic ILD. Accurately predicting new-onset exertional and resting hypoxemia prompts appropriate patient discussion and timely consideration of home oxygen. Objectives We derived and externally validated a risk prediction tool for both new-onset exertional and new-onset resting hypoxemia. Methods This study used ILD registries from Canada for the derivation cohort and from Australia and the United States for the validation cohort. New-onset exertional and resting hypoxemia were defined as nadir oxyhemoglobin saturation < 88% during 6-minute-walk tests, resting oxyhemoglobin saturation < 88%, or the initiation of ambulatory or continuous oxygen. Candidate predictors included patient demographics, ILD subtypes, and pulmonary function. Time-varying Cox regression was used to identify the top-performing prediction model according to Akaike information criterion and clinical usability. Model performance was assessed using Harrell’s C-index and goodness-of-fit (GoF) likelihood ratio test. A categorized risk prediction tool was developed. Results The best-performing prediction model for both new-onset exertional and new-onset resting hypoxemia included age, body mass index, a diagnosis of idiopathic pulmonary fibrosis, and percent predicted forced vital capacity and diffusing capacity of carbon monoxide. The risk prediction tool exhibited good performance for exertional hypoxemia (C-index, 0.70; GoF, P = 0.85) and resting hypoxemia (C-index, 0.77; GoF, P = 0.27) in the derivation cohort, with similar performance in the validation cohort except calibration for resting hypoxemia (GoF, P = 0.001). Conclusions This clinically applicable risk prediction tool predicted new-onset exertional and resting hypoxemia at 6 months in the derivation cohort and a diverse validation cohort. Suboptimal GoF in the validation cohort likely reflected overestimation of hypoxemia risk and indicated that the model is not flawed because of underestimation of hypoxemia.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".