Association of preoperative spirometry with cardiopulmonary fitness and postoperative outcomes in surgical patients: A multicentre prospective cohort study
Bibliographic record
Abstract
Background Preoperative spirometry and cardiopulmonary exercise testing (CPET) may stratify risk for respiratory complications. This secondary analysis of the Measurement of Exercise Tolerance before Surgery (METS) study examined whether CPET performance (i.e., cardiopulmonary fitness) confounds associations of spirometry with outcomes. Methods The analysis included 1200 participants having major non-cardiac surgery at 25 hospitals in Canada, Australia, New Zealand and UK. Forced expiratory volume in 1 s (FEV 1 ), and ratio of FEV 1 to forced vital capacity (FVC) were measured during preoperative spirometry, and peak oxygen consumption and ventilatory efficiency during preoperative CPET. Outcomes were respiratory morbidity (Postoperative Morbidity Survey) and pulmonary complications (pneumonia or respiratory failure). We used multivariable logistic regression models to estimate associations of FEV 1 with outcomes after adjustment for risk factors and either peak oxygen consumption or ventilatory efficiency. Findings 128 participants (11%) developed respiratory morbidity, and 48 (4%) developed pulmonary complications. There was no strong evidence that FEV 1 predicted respiratory morbidity after adjustment for peak oxygen consumption ( p = 0·80) or ventilatory efficiency ( p = 0·76), or FEV 1 predicted pulmonary complications after adjustment for ventilatory efficiency ( p = 0·37). Peak oxygen consumption (odds ratio 0·66 per 5 mL/kg/min increase; 95% CI, 0·54–0·82) was associated with respiratory morbidity. Ventilatory efficiency was associated with respiratory morbidity ( p = 0·04) and pulmonary complications ( p = 0·02). Peak oxygen consumption also confounded the association between FEV 1 and respiratory morbidity. Interpretation After accounting for fitness and clinical factors, FEV 1 was not strongly predictive of respiratory complications. Prior associations between FEV 1 and respiratory morbidity may be explained by confounding by peak oxygen consumption. Funding Canadian Institutes of Health Research, Heart and Stroke Foundation of Canada, Ontario Ministry of Health and Long-Term Care, Ontario Ministry of Research, Innovation and Science, UK National Institute of Academic Anaesthesia, UK Clinical Research Collaboration, Australian and New Zealand College of Anaesthetists, and Monash University.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".