Epidemiology and Burden of Peripheral Artery Disease in People With Type 2 Diabetes: A Systematic Literature Review
Bibliographic record
Abstract
Type 2 diabetes (T2D) and lower-extremity peripheral artery disease (PAD) are growing global health problems associated with considerable cardiovascular (CV) and limb-related morbidity and mortality, poor quality of life and high healthcare resource use and costs. Diabetes is a well-known risk factor for PAD, and the occurrence of PAD in people with T2D further increases the risk of long-term complications. As the available evidence is primarily focused on the overall PAD population, we undertook a systematic review to describe the burden of comorbid PAD in people with T2D. The MEDLINE, Embase and Cochrane Library databases were searched for studies including people with T2D and comorbid PAD published from 2012 to November 2021, with no restriction on PAD definition, study design or country. Hand searching of conference proceedings, reference lists of included publications and relevant identified reviews and global burden of disease reports complemented the searches. We identified 86 eligible studies, mostly observational and conducted in Asia and Europe, presenting data on the epidemiology (n = 62) and on the clinical (n = 29), humanistic (n = 12) and economic burden (n = 12) of PAD in people with T2D. The most common definition of PAD relied on ankle-brachial index values ≤ 0.9 (alone or with other parameters). Incidence and prevalence varied substantially across studies; nonetheless, four large multinational randomised controlled trials found that 12.5%-22% of people with T2D had comorbid PAD. The presence of PAD in people with T2D was a major cause of lower-limb and CV complications and of all-cause and CV mortality. Overall, PAD was associated with poor quality of life, and with substantial healthcare resource use and costs. To our knowledge, this systematic review provides the most comprehensive overview of the evidence on the burden of PAD in people with T2D to date. In this population, there is an urgent unmet need for disease-modifying agents to improve outcomes.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".