ArticleEndocrine connections2021
Development and validation of a risk score model for prediction of lower extremity arterial disease in Chinese with type 2 diabetes aged over 50 years.
Article in Endocrine connections, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 5 citations in OpenAlex.
- Development and validation of prediction model for stage I patients with lower extremity atherosclerotic disease in type 2 diabetes mellitus in China.Acta diabetologica · 2025Article
- Establishment of dynamic nomogram and risk score models for T2DM: a retrospective cohort study in Beijing.BMC public health · 2022Article
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4 authors at 2 institutions in 1 country.
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Abstract
backgroundLower extremity arterial disease (LEAD) is highly prevalent in people with diabetes in China, but half of cases are underdiagnosed due to diversities of clinical presentations and complexities of diagnosis approaches. The purpose of this study was to develop a risk score model for LEAD to facilitate early screening among type 2 diabetes (T2DM) patients.
methodsA total of 8313 participants with T2DM from the China DIA-LEAD study, a multicenter, cross-sectional epidemiological study, were selected as the training dataset to develop a risk score model for LEAD by logistic regression. The area under receiver operating characteristic curve (AUC) and bootstrapping were utilized for internal validation. A dataset of 287 participants consecutively enrolled from a teaching hospital between July 2017 and November 2017 was used as external validation for the risk score model.
resultsA total of 931 (11.2%) participants were diagnosed as LEAD in the training dataset. Factors including age, current smoking, duration of diabetes, blood pressure control, low density lipoprotein cholesterol, estimated glomerular filtration rate, and coexistence of cardio and/or cerebrovascular disease correlated with LEAD in logistic regression analysis and resulted in a weighed risk score model of 0-13. A score of ≥5 was found to be the optimal cut-off for discriminating moderate-high risk participants with AUC of 0.786 (95% CI: 0.778-0.795). The bootstrapping validation showed that the AUC was 0.784. Similar performance of the risk score model was observed in the validation dataset with AUC of 0.731 (95% CI: 0.651-0.811). The prevalence of LEAD was 3.4, 12.1, and 27.6% in the low risk (total score 0-4), moderate risk (total score 5-8), and high risk (total score 9-13) groups of LEAD in the training dataset, respectively, which were 4.3, 19.6, and 30.2% in the validation dataset.
conclusionThe weighed risk score model for LEAD could reliably discriminate the presence of LEAD in Chinese with T2DM aged over 50 years, which may be helpful for a precise risk assessment and early diagnosis of LEAD.
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