ArticleDiabetology & metabolic syndrome2021
A potent weighted risk model for evaluating the occurrence and severity of diabetic foot ulcers.
Article in Diabetology & metabolic syndrome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Temporal machine learning framework for diabetic foot ulcer healing trajectory prediction.Biomedical engineering online · 2026Article
- Development and validation of a nomogram model for predicting diabetic foot in type 2 diabetes.PeerJ · 2026Article
- Development and validation of a simplified pre-screening model for diabetic foot ulcer identification in diabetic patients.Frontiers in endocrinology · 2026Article
- A novel inflammatory-nutritional index: NPAR-correlates with the severity of type 2 diabetic foot ulcers.Frontiers in nutrition · 2026Article
- Evaluation of Machine Learning Model Performance in Diabetic Foot Ulcer: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- From Control to Cure: Insights into the Synergy of Glycemic and Antibiotic Management in Modulating the Severity and Outcomes of Diabetic Foot Ulcers.International journal of molecular sciences · 2025Review
- Association between anemia and the risk of diabetic foot ulcer: A meta-analysis.World journal of diabetes · 2025Article
- Risk factors for diabetic foot ulcer in diabetic patients at the Tehran diabetes clinic: a case-control study.Journal of diabetes and metabolic disorders · 2025Article
- Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning.Scientific reports · 2025Article
- The Relationship Between Plasma Fibrinogen Levels and the Severity of Diabetic Foot Ulcers in Diabetic Patients.Cureus · 2025Article
- Role of macrophage polarization in diabetic foot ulcer healing: A bibliometric study.World journal of diabetes · 2025Article
- A risk prediction model for poor joint function recovery after ankle fracture surgery based on interpretable machine learning.Frontiers in medicine · 2025Article
- Development and validation of a nomogram for predicting diabetic foot ulcer risk in patients with type 2 diabetes mellitus.Frontiers in endocrinology · 2025Article
- Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.Frontiers in endocrinology · 2025Article
- Multimodal Identification of Molecular Factors Linked to Severe Diabetic Foot Ulcers Using Artificial Intelligence.International journal of molecular sciences · 2024Article
- Diabetic foot ulcers risk prediction in patients with type 2 diabetes using classifier based on associations rule mining.Scientific reports · 2024Article
- Factors associated with severity and anatomical distribution of diabetic foot ulcer in Uganda: a multicenter cross-sectional study.BMC public health · 2023Article
- Risk assessment of amputation in patients with diabetic foot.Experimental and therapeutic medicine · 2023Article
- Role of Increased miR-222-3p Expression in Peripheral Blood and Wound Marginal Tissues of Type 2 Diabetes Mellitus Patients with Diabetic Foot Ulcer.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
- Fibrinogen function indexes are potential biomarkers for evaluating the occurrence and severity of diabetic foot.Diabetology & metabolic syndrome · 2022Article
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10 authors.
Funding
Abstract
backgroundDiabetic foot ulcer (DFU) is a serious chronic complication of diabetes. This study aimed to establish weighted risk models for determining DFU occurrence and severity in diabetic patients.
methodsThis was a multi-center hospital-based cross-sectional study. A total of 1488 diabetic patients with or without an ulcer from three tertiary hospitals were included in the study. Random forest method was used to develop weighted risk models for assessing DFU risk and severity. Receiver operating characteristic curves were used to validate the models and calculate the optimal cut-off values of the important risk factors.
resultsWe developed potent weighted risk models for evaluating DFU occurrence and severity. The top eight important risk factors for DFU onset were plasma fibrinogen, neutrophil percentage and hemoglobin levels in whole blood, stroke, estimated glomerular filtration rate, age, duration of diabetes, and serum albumin levels. The top 10 important risk factors for DFU severity were serum albumin, neutrophil percentage and hemoglobin levels in whole blood, plasma fibrinogen, hemoglobin A1c, estimated glomerular filtration rate, hypertension, serum uric acid, diabetic retinopathy, and sex. Furthermore, the area under curve values in the models using plasma fibrinogen as a single risk factor for determining DFU risk and severity were 0.86 (sensitivity 0.74, specificity 0.87) and 0.73 (sensitivity 0.76, specificity 0.58), respectively. The optimal cut-off values of plasma fibrinogen for determining DFU risk and severity were 3.88 g/L and 4.74 g/L, respectively.
conclusionsWe have established potent weighted risk models for DFU onset and severity, based on which precise prevention strategies can be formulated. Modification of important risk factors may help reduce the incidence and progression of DFUs in diabetic patients.
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