Diabetic Foot Complications and Their Risk Factors from a Large Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Foot complications are considered to be a serious consequence of diabetes mellitus, posing a major medical and economical threat. Identifying the extent of this problem and its risk factors will enable health providers to set up better prevention programs. Saudi National Diabetes Registry (SNDR), being a large database source, would be the best tool to evaluate this problem. METHODS: This is a cross-sectional study of a cohort of 62,681 patients aged ≥ 25 years from SNDR database, selected for studying foot complications associated with diabetes and related risk factors. RESULTS: The overall prevalence of diabetic foot complications was 3.3% with 95% confidence interval (95% CI) of (3.16%-3.44%), whilst the prevalences of foot ulcer, gangrene, and amputations were 2.05% (1.94%-2.16%), 0.19% (0.16%-0.22%), and 1.06% (0.98%-1.14%), respectively. The prevalence of foot complications increased with age and diabetes duration predominantly amongst the male patients. Diabetic foot is more commonly seen among type 2 patients, although it is more prevalent among type 1 diabetic patients. The Univariate analysis showed Charcot joints, peripheral vascular disease (PVD), neuropathy, diabetes duration ≥ 10 years, insulin use, retinopathy, nephropathy, age ≥ 45 years, cerebral vascular disease (CVD), poor glycemic control, coronary artery disease (CAD), male gender, smoking, and hypertension to be significant risk factors with odds ratio and 95% CI at 42.53 (18.16-99.62), 14.47 (8.99-23.31), 12.06 (10.54-13.80), 7.22 (6.10-8.55), 4.69 (4.28-5.14), 4.45 (4.05-4.89), 2.88 (2.43-3.40), 2.81 (2.31-3.43), 2.24 (1.98-2.45), 2.02 (1.84-2.22), 1.54 (1.29-1.83), and 1.51 (1.38-1.65), respectively. CONCLUSIONS: Risk factors for diabetic foot complications are highly prevalent; they have put these complications at a higher rate and warrant primary and secondary prevention programs to minimize morbidity and mortality in addition to economic impact of the complications. Other measurements, such as decompression of lower extremity nerves, should be considered among diabetic patients.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".