ArticlePloS one2024
Risk factors and predictive model construction for lower extremity arterial disease in diabetic patients.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis.Scientific reports · 2025Pooled it
- Assessment of the performances of blood tests for the antemortem diagnosis of aspergillosis in wild or captive aquatic birds.Veterinary research · 2026Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo understand the prevalence and associated risk factors of lower extremity arterial disease (LEAD) in Chinese diabetic patients and to construct a risk prediction model.
methodsData from the Diabetes Complications Warning Dataset of the China National Population Health Science Data Center were used. Logistic regression analysis was employed to identify related factors, and machine learning algorithms were used to construct the risk prediction model.
resultsThe study population consisted of 3,000 patients, with 476 (15.9%) having LEAD. Multivariate regression analysis indicated that male gender, atherosclerosis, carotid artery stenosis, fatty liver, hematologic diseases, endocrine disorders, and elevated glycosylated serum proteins were independent risk factors for LEAD. The risk prediction models constructed using Logistic regression and MLP algorithms achieved moderate discrimination performance, with AUCs of 0.73 and 0.72, respectively.
conclusionOur study identified the risk factors for LEAD in Chinese diabetic patients, and the constructed risk prediction model can aid in the diagnosis of LEAD.
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