Control of Cardiovascular Risk Factors in Patients with Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Abstract Rationale Cardiovascular disease accounts for one-third of deaths in patients with chronic obstructive pulmonary disease (COPD). Better control of cardiovascular risk factors in primary care could improve outcomes. Objectives To define the prevalence, monitoring, treatment, and control of risk factors in patients with COPD. Methods Repeated cross-sectional analysis of primary care electronic medical records for all patients with COPD in the Canadian Primary Care Sentinel Surveillance Network from 2013 to 2018 (n = 32,695 in 2018). A control group was matched 1:1 for age, sex, and rural residence (n = 32,638 in 2018). Five risk factors were defined using validated definitions including laboratory results: hypertension, dyslipidemia, diabetes, obesity, and smoking. Results All risk factors were more common in patients with COPD compared with matched control subjects, including hypertension (52.3% vs. 44.9%), dyslipidemia (62.0% vs. 57.8%), diabetes (25.0% vs. 20.2%), obesity (40.8% vs. 36.8%), and smoking (40.9% vs. 11.4%), respectively. The mean Framingham risk score was 20.6% versus 18.6%, with 53.8% of patients with COPD being high risk (⩾20%). Monitoring of risk factors within the last year in patients with COPD in 2018 was suboptimal: 71.8% hypertension, 39.4% dyslipidemia, 74.5% diabetes, 52.3% obesity. Smoking status was infrequently recorded in the electronic record. In those monitored, guideline recommended targets were achieved in 60.8%, 46.6%, 57.4%, 10.6% and 12.0% for each risk factor. Cardiovascular therapies including angiotensin-converting enzyme inhibitors (69%), statins (69%), and smoking cessation therapies (27%) were underused. Conclusions In patients with COPD, major cardiovascular risk factors are common, yet inadequately monitored, undertreated, and poorly controlled. Strategies are needed to improve comprehensive risk factor management proven to reduce cardiovascular morbidity and mortality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".