The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.
Front Endocrinol (Lausanne). 2024 Oct 22;15:1477570. doi: 10.3389/fendo.2024.1477570. eCollection 2024.
To evaluate the prevalence and risk factors of painful diabetic peripheral neuropathy (PDPN) in patients with type 2 diabetic peripheral neuropathy (DPN) in Hunan Province, and establish and verify the prediction model.
This was a retrospective study involving 4908 patients, all patients were randomly divided into the training dataset(3436 cases)and the validation dataset (1472 cases) in a ratio of 7:3. Electroneurogram, clinical signs,and symptoms were used to evaluate neuropathy. Least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal factors, and multifactorial logistic regression analysis was used to build a clinical prediction model. Calibration plots, decision curve analysis (DCA), and subject work characteristic curves (ROC) were used to assess the predictive effects.
The prevalence of PDPN was 33.2%, and the multivariate logistic regression model showed that peripheral artery disease, duration of diabetes, smoking, and HBA1c were independent risk factors for PDPN in patients with type 2 diabetes. ROC analysis results showed that the AUC of the established prediction model was 0.872 in the training dataset and 0.843 in the validation dataset. The calibration curve and decision curve show that the model has good consistency and significant net benefit.
33.2% of DPN patients had PDPN in Hunan Province, China. Peripheral artery disease, duration of diabetes, smoking, and HBA1c are risk factors for PDPN in patients with type 2 diabetes. The prediction model is based on the above factors, which can well predict the probability of PDPN.
评估湖南省 2 型糖尿病周围神经病变（DPN）患者伴发痛性糖尿病周围神经病变（PDPN）的患病率及相关危险因素，并建立及验证预测模型。
这是一项回顾性研究，共纳入 4908 例患者，所有患者均按照 7∶3 的比例随机分为训练数据集（3436 例）和验证数据集（1472 例）。采用神经电生理、临床体征和症状对神经病变进行评估。使用最小绝对收缩和选择算子（LASSO）回归选择最佳因素，并进行多因素逻辑回归分析以建立临床预测模型。使用校准图、决策曲线分析（DCA）和受试者工作特征曲线（ROC）评估预测效果。
PDPN 的患病率为 33.2%，多因素逻辑回归模型显示，周围动脉疾病、糖尿病病程、吸烟和糖化血红蛋白（HbA1c）是 2 型糖尿病患者发生 PDPN 的独立危险因素。ROC 分析结果显示，所建立的预测模型在训练数据集和验证数据集中的 AUC 分别为 0.872 和 0.843。校准曲线和决策曲线显示该模型具有良好的一致性和显著的净获益。
中国湖南省 33.2%的 DPN 患者患有 PDPN。周围动脉疾病、糖尿病病程、吸烟和 HbA1c 是 2 型糖尿病患者发生 PDPN 的危险因素。该预测模型基于上述因素，可较好地预测 PDPN 的发生概率。
