Chronic Obstructive Pulmonary Disease as a Risk Factor for Cardiovascular Disease. A View from the SUMMIT
Bibliographic record
Abstract
Although the importance of CVDs in COPD has been well established, COPD as a risk factor for CVDs is less well known. Not surprisingly, most major CVD consensus guidelines do not list COPD as an important risk factor for CVDs (5). Although some CVD risk prediction tools, such as QRISK (https://qrisk.org), include selected chronic inflammatory conditions such as rheumatoid arthritis, COPD is not included (6). Although previous studies have shown that reduced lung function is associated with a future risk of CV events, including myocardial infarction (MI), heart failure, and sudden death, even among lifetime nonsmokers, how (and why) this occurs is largely unknown (7). As described in this issue of the Journal, Kunisaki and colleagues (pp. 51–57) performed a secondary analysis of the SUMMIT (Study to Understand Mortality and Morbidity) trial to fill in some critical gaps in our knowledge regarding the relationship between COPD and CVDs (8). They showed that CVD events occurred mostly during periods of acute exacerbations (AECOPDs), with the highest risk occurring within the first 30 days after an AECOPD (relative risk, 3.8) and the risk returning to baseline levels at 1 year after the AECOPD. The risk was particularly notable when the presentation of the AECOPD event—a composite of the severity of the underlying COPD and the trigger—was severe enough to warrant a hospitalization. Remarkably, with these serious AECOPDs, the relative risk of a CV event was 10-fold higher in the first 30 days after hospitalization.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".