Prediction model for the risk of peripheral arterial disease among patients with type 2 diabetes mellitus
DU Yuxin1, YU Huan1, LI Yaqian1, TANG Meili2, LIU Yiyuan2, YU Yanmei2, LIU Xueyan2
1. School of Nursing, Jinan University, Guangzhou, Guangdong 510632, China; 2. The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510630, China
Abstract：Objective To establish a prediction model for the risk of peripheral arterial disease (PAD) among patients with type 2 diabetes mellitus (T2DM), so as to provide a basis for risk assessment and early screening of PAD. Methods Patients with T2DM hospitalized at the First Affiliated Hospital of Jinan University from December 2018 to December 2020 were selected as study subjects. Basic information, including gender and age, laboratory indicators, including fasting blood glucose and glycated hemoglobin, and foot screening indicators, including foot calluses, abnormal foot sensations and current perception threshold (CPT), were collected through the electronic medical record system. The study subjects were randomly divided into a training set and a validation set at a ratio of 7∶3. Predictors were screened using LASSO regression and multivariable logistic regression, and a nomogram prediction model was established. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow test and decision curve analysis. Results A total of 972 patients with T2DM were collected, including 535 (55.04%) males and 437 (44.96%) females. The median age was 59.00 (interquartile range, 18.00) years. PAD was detected in 84 patients, with a detection rate of 8.64%. The training set included 681 patients, with 59 PAD cases, while the validation set included 291 patients, with 25 PAD cases. Multivariable logistic regression analysis showed that age (OR=1.053, 95%CI: 1.027-1.080), abnormal foot sensation (pain, OR=4.517, 95%CI: 1.610-12.667) and CPT (OR=1.094, 95%CI: 1.022-1.171) were predictors of PAD among patients with T2DM. In the training set, the AUC, sensitivity and specificity were 0.771, 0.780 and 0.701, with a higher net clinical benefit at risk threshold probabilities of 1%-26%. In the validation set, the AUC, sensitivity and specificity were 0.752, 0.800 and 0.628, respectively, with a higher net clinical benefit at risk threshold probabilities of 1%-43%. The Hosmer-Lemeshow test showed good model calibration in both the training and validation sets (both P>0.05). Conclusion The nomogram prediction model constructed using three predictors, including age, abnormal foot sensation and CPT, showed good discrimination, calibration and net clinical benefit, and can be used to predict the risk of PAD in patients with T2DM.
杜雨欣, 余欢, 李雅倩, 唐美丽, 刘宜苑, 余艳梅, 刘雪彦. 2型糖尿病患者外周动脉疾病风险预测模型研究[J]. 预防医学, 2026, 38(10): 1012-1016.
DU Yuxin, YU Huan, LI Yaqian, TANG Meili, LIU Yiyuan, YU Yanmei, LIU Xueyan. Prediction model for the risk of peripheral arterial disease among patients with type 2 diabetes mellitus. Preventive Medicine, 2026, 38(10): 1012-1016.
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