Peripheral Arterial Disease in Systemic Lupus Erythematosus: Prevalence and Risk Factors
Bibliographic record
Abstract
OBJECTIVE: To analyze the prevalence of peripheral arterial disease (PAD) and cardiovascular (CV) risk factors in a cohort of patients with systemic lupus erythematosus (SLE) and to identify variables potentially related to PAD. METHODS: The study included 216 patients with SLE from the Lupus-Cruces prospective observational cohort. The ankle brachial index (ABI) was determined in each patient, with values < 0.9 considered diagnostic of PAD. Demographic and clinical variables, presence of traditional risk factors and CV events, cardiovascular risk calculated by Systematic Coronary Risk Evaluation (SCORE), and treatments received by each patient were analyzed. RESULTS: Ninety-two percent of patients were women. The mean age (SD) was 49 years (15), with a mean followup (SD) of 12 years (9). The prevalence of low ABI was 21%. CV risk factors were frequent: smoking, 30% of patients; high blood pressure, 32.7%; diabetes mellitus, 3.2%; hypercholesterolemia, 34.1%; and metabolic syndrome, 9.7%. The following variables were associated with low ABI in the univariate analysis: age (p < 0.001), hypertension (p = 0.002), diabetes (p = 0.018), hypercholesterolemia (p = 0.018), CV events (p < 0.001), SCORE (p = 0.004), cumulative dose of cyclophosphamide (p = 0.03), and fibrinogen levels (p = 0.002). In the multivariate analysis, the only independent variable in the final model was age (OR 1.04, 95% CI 1.02-1.07, p < 0.001), with a tendency for the presence of any vascular risk factor (diabetes, hypertension, hypercholesterolemia, or current smoking; OR 2.3, 95% CI 0.99-5.1, p = 0.053). CONCLUSION: The prevalence of low ABI in patients with SLE is higher than expected. While the association with CV risk factors and vascular disease in other territories was strong, we could not identify SLE-specific variables independently associated with PAD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".