Quantifying COPD as a risk factor for cardiac disease in a primary prevention cohort
Bibliographic record
Abstract
BACKGROUND: Despite COPD being a risk factor for cardiovascular disease (CVD) and knowing that risk stratification for CVD primary prevention is important, little is known about the real-world risk of CVD among people with COPD with no history of CVD. This knowledge would inform CVD management for people with COPD. The current study aimed to examine the risk of major adverse cardiovascular events (MACE) (including acute myocardial infarction, stroke or cardiovascular death) in a large, complete real-world population with COPD without previous CVD. METHODS: We conducted a retrospective population cohort study using health administrative, medication, laboratory, electronic medical record and other data from Ontario, Canada. People without a history of CVD with and without physician-diagnosed COPD were followed between 2008 and 2016, and cardiac risk factors and comorbidities compared. Sequential cause-specific hazard models adjusting for these factors determined the risk of MACE in people with COPD. RESULTS: Among ∼5.8 million individuals in Ontario aged ≥40 years without CVD, 152 125 had COPD. After adjustment for cardiovascular risk factors, comorbidities and other variables, the rate of MACE was 25% higher in persons with COPD compared with those without COPD (hazard ratio 1.25, 95% CI 1.23-1.27). CONCLUSIONS: In a large real-world population without CVD, people with physician-diagnosed COPD were 25% more likely to have a major CVD event, after adjustment for CVD risk and other factors. This rate is comparable to the rate in people with diabetes and calls for more aggressive CVD primary prevention in the COPD population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".