Factors Influencing the Prescription of Cardiovascular Preventive Therapies in Patients with Peripheral Arterial Disease
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend that patients with peripheral arterial disease should be medically treated to reduce the occurrence of serious cardiovascular events. Despite these recommendations, studies conducted in the early 2000s reported that medical therapies for secondary cardiovascular prevention are not given systematically to patients with peripheral arterial disease (PAD). We identified factors associated with the prescription of preventive therapies in patients with symptomatic PAD. METHODS AND FINDINGS: Consecutive patients with symptomatic peripheral arterial disease (n = 362) treated between 2008 and 2010 in one tertiary care center (CHU de Quebec, Canada) were considered. Data were collected from the medical charts. The main outcome was the combined prescription of three therapies: 1) statins, 2) antiplatelets, 3) angiotensin-converting-enzyme inhibitors or angiotensin receptor blockers. The mean age was 70 years and 43% had a pre-existing coronary artery disease. Antiplatelet therapy was the most prescribed drug (83%). A total of 52% of the patients received the three combined therapies. Less than 10% of patients had a known contraindication to one class of medication. Having at least three cardiovascular risk factors (Odds Ratio (OR) = 4.51; 95% CI: 2.76-7.37) was the factor most strongly associated with the prescription of the combined therapies. Pre-existing coronary artery disease (OR = 2.28; 95% CI: 1.43-3.65) and history of peripheral vascular surgery (OR = 2.30; 95% CI: 1.37-3.86) were two factors independently associated with the prescription of the combined therapies. However, peripheral arterial disease patients with chronic critical limb ischemia were less likely to receive the combined therapies (OR = 0.53; 95% CI: 0.32-0.87) than those with claudication. The retrospective nature of this study, not allowing for an exhaustive report of the contraindication to medication prescription, is the main limitation. CONCLUSION: About half of the patients with peripheral arterial disease were not optimally managed. Patients with multiple cardiovascular risk factors were more likely to receive the combined therapies. We still need to better understand the barriers and facilitators to the application of the guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".