Global epidemiology of lower extremity artery disease in the 21st century (2000–21): a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: The epidemiology of lower extremity artery disease (LEAD) is evolving. This meta-analysis of aggregate data aimed to (i) determine the global prevalence of LEAD and by regions in the 21st century and (ii) update the associated risk factors in this period. METHODS AND RESULTS: A systematic literature review was performed through PubMed, Cochrane, Scopus, Science Direct, and Google Scholar databases, restricted to general population studies between January 2000 and September 2021, with LEAD defined by a low (</≤0.90) ankle-brachial index. The Newcastle-Ottawa Scale was used to evaluate the quality of the articles before data extraction. Due to high heterogeneity, the random effect model was applied to this meta-analysis. Among 1418 references, 38 studies (127 961 participants) were retained. The global prevalence in adults, mostly ≥40 years, was estimated at 9.7% [95% confidence interval (CI): 7.1-12.4], higher in women (10.2%) than in men (8.8%), increasing sharply with age. The highest prevalence was found in South-Central Asia (14.5%) and the lowest in North America (5.6%). Significant associations were found between LEAD and current [odds ratio (OR) = 1.9, 95% CI: 1.4-2.5] and past smoking (OR = 1.6, 95% CI: 1.3-1.9) and between LEAD and diabetes (OR = 2.3, 95% CI: 2.0-2.8). Hypertension was significantly associated with LEAD (OR = 2.3, 95% CI: 1.9-2.8) and in particular in South America (OR = 4.0). Obesity (OR = 1.5, 95% CI: 1.2-1.8) and hypercholesterolaemia ≥200 mg/dL (OR = 1.9, 95% CI: 1.3-2.8) were also significantly associated with LEAD. CONCLUSION: This meta-analysis highlights a currently high prevalence of LEAD worldwide, with substantial differences in global regions and between sexes. The strongest associations were found with metabolic risk factors.
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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".