Exercise Ventilatory Inefficiency Adds to Lung Function in Predicting Mortality in COPD
Bibliographic record
Abstract
Severity of resting functional impairment only partially predicts the increased risk of death in chronic obstructive pulmonary disease (COPD). Increased ventilation during exercise is associated with markers of disease progression and poor prognosis, including emphysema extension and pulmonary vascular impairment. Whether excess exercise ventilation would add to resting lung function in predicting mortality in COPD, however, is currently unknown. After an incremental cardiopulmonary exercise test, 288 patients (forced expiratory volume in one second ranging from 18% to 148% predicted) were followed for a median (interquartile range) of 57 (47) months. Increases in the lowest (nadir) ventilation to CO2 output (VCO2) ratio determined excess exercise ventilation. Seventy-seven patients (26.7%) died during follow-up: 30/77 (38.9%) deaths were due to respiratory causes. Deceased patients were older, leaner, had a greater co-morbidity burden (Charlson Index) and reported more daily life dyspnea. Moreover, they had poorer lung function and exercise tolerance (p < 0.05). A logistic regression analysis revealed that ventilation/VCO2 nadir was the only exercise variable that added to age, body mass index, Charlson Index and resting inspiratory capacity (IC)/total lung capacity (TLC) ratio to predict all-cause and respiratory mortality (p < 0.001). Kaplan-Meier analyses showed that survival time was particularly reduced when ventilation/VCO2 nadir > 34 was associated with IC/TLC ≤ 0.34 or IC/TLC ≤ 0.31 for all-cause and respiratory mortality, respectively (p < 0.001). Excess exercise ventilation is an independent prognostic marker across the spectrum of COPD severity. Physiological abnormalities beyond traditional airway dysfunction and lung mechanics are relevant in determining the course of the disease.
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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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".