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广西医科大学第一附属医院内分泌科,南宁 530021
臧黎荣,Email:cc25vip@126.com,研究方向:内分泌代谢性疾病。
罗佐杰,Email:zluo888@163.com。
收稿:2026-01-06,
修回:2026-03-12,
录用:2026-03-16,
纸质出版:2026-04-03
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臧黎荣, 罗佐杰. 2型糖尿病周围神经病变临床预测模型的建立和验证[J/OL]. 重庆医科大学学报, 2026,1-8.
Zang Lirong,Luo Zuojie.Establishment and validation of a clinical prediction model for type 2 diabetic peripheral neuropathy[J].Journal of Chongqing Medical University,
臧黎荣, 罗佐杰. 2型糖尿病周围神经病变临床预测模型的建立和验证[J/OL]. 重庆医科大学学报, 2026,1-8. DOI:
Zang Lirong,Luo Zuojie.Establishment and validation of a clinical prediction model for type 2 diabetic peripheral neuropathy[J].Journal of Chongqing Medical University, DOI:10.13406/j.cnki.cyxb..
目的
2
分析2型糖尿病(type 2 diabetes mellitus,T2DM)并发糖尿病周围神经病变(diabetic peripheral neuropathy,DPN)的危险因素并构建临床预测模型。
方法
2
收集广西医科大学第一附属医院内分泌科2017年1月至2021年12月住院的950例T2DM患者资料,以2020年12月为临界,2020年12月及以前收治的患者为训练集(
n
=684),该时段之后收治的患者为验证集(
n
=266)。采用二元logistic回归分析法在训练集中分析T2DM并发DPN的危险因素,基于危险因素建立预测模型并进行模型的内外部验证。
结果
2
本研究中,T2DM并发DPN的发生率为52.32 %(497例/950例)。训练集危险因素分析显示:年龄增长、T2DM病程延长、糖尿病视网膜病变(diabetic retinopathy,DR)、外周动脉疾病(peripheral arterial disease,PAD)及胱抑素C(cystatin c,CysC)、甘油三酯(triglycerides,TG)、空腹血糖(fasting blood glucose,FBG)、糖化血红蛋白(hemoglobin A1c,HbA1c)水平升高是T2DM并发DPN的独立危险因素(
P
<
0.05),基于此构建预测模型,受试者工作特征曲线(receiver operating characteristic curve,ROC)曲线分析显示:曲线下面积(area under the curve,AUC)为0.825(95%置信区间(confidence interval,CI)=0.795~0.855),临界值为0.526,敏感度、特异度分别为72.8%、78.4%;Hosmer-Lemeshow拟合优度检验
χ
2
=8.398,
P
=0.396;决策曲线(decision curve analysis,DCA)曲线显示阈值概率在0.1~0.99时,模型有实用价值。在验证集进行模型验证,ROC曲线分析显示:AUC为0.822(95%CI=0.773~0.871),临界值为0.518,敏感度74.8%、特异度74.8%;Hosmer-Lemeshow拟合优度检验
χ
2
=3.152,
P
=0.925;DCA曲线显示阈值概率在0.08~0.85时,模型有实用价值。
结论
2
年龄增长、T2DM病程延长、DR、PAD及CysC、TG、FBG、HbA1c水平升高是T2DM并发DPN的独立危险因素。基于上述危险因素构建的预测模型具有良好的区分度和准确度,建立的DPN预测模型网页计算器有较高的临床实用性,可用于指导治疗决策。
Objective
2
To analyze the risk factors of type 2 diabetes mellitus(T2DM) with diabetic peripheral neuropathy (DPN) and to construct a clinical prediction model.
Methods
2
The data of 950 patients with T2DM admitted to Department of Endocrinology of the First Affiliated Hospital of Guangxi Medical University from January 2017 to December 2021 were collected. Taking December 2020 as the dividing line,patients admitted in and before December 2020 were use
d as the training set(
n
=684),and patients admitted after December 2020 were taken as the validation set(
n
=266). Binary logistic regression analysis was used to analyze the risk factors of T2DM complicated with DPN in the training set. A prediction model was established based on the risk factors and verified internally and externally.
Results
2
In this study,the probability of DPN in T2DM patients was 52.32%(497/950). In the training set,the risk factor analysis showed that increasing age,prolonged course of T2DM,diabetic retinopathy(DR),peripheral arterial disease(PAD),and increased levels of cystatin C(CysC),triglycerides(TG),fasting blood glucose(FBG) and hemoglobin A1c(HbA1c) were independent risk factors of T2DM with DPN(all
P
<
0.05). The prediction model was constructed based on the above factors,and the receiver operating characteristic(ROC) curve analysis showed that the area under the curve(AUC),critical value,sensitivity,and specificity were 0.825 (95%CI=0.795-0.855),0.526,72.8%,and 78.4%,respectively. The Hosmer-Lemeshow goodness-of-fit test revealed
χ
2
=8.398(
P
=0.396). The decision curve analysis(DCA) showed that the model had practical value when the threshold probability was between 0.1 and 0.99. Model validation was performed in the validation set,and the ROC curve analysis revealed that the AUC was 0.822(95%CI=0.773-0.871),with critical value of 0.518,sensitivity of 74.8%,and specificity of 74.8%. The Hosmer-Lemeshow goodness-of-fit test showed
χ
2
=3.152(
P
=0.925). The DCA showed that when the threshold probability ranged from 0.08 to 0.85, the model exhibited practical value.
Conclusion
2
Increasing age,prolonged course of T2DM,DR,PAD,and increased levels of CysC,TG,FBG and HbA1c are independent risk factors of DPN in T2DM patients. The prediction model based on the above risk factors has good discriminatory ability and accuracy. The established DPN prediction model web calculator has high clinical practicability and can be used to guide clinical treatment decisions.
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