背景:临床研究表明,糖尿病周围神经病变(DPN)呈上升趋势,大多数患者表现为严重和进行性症状。目前,大多数可用的DPN预测模型来自一般临床信息和实验室指标.已利用几种中药(TCM)指标来构建预测模型。在这项研究中,我们利用中医临床特征建立了一种新颖的基于机器学习的多特征中西医结合DPN预测模型。
方法:收集安徽中医药大学第一附属医院内分泌科收治的1581例2型糖尿病患者的临床资料。数据(包括一般信息,在数据清理后,选择了1142例T2DM患者的实验室参数和中医特征)。在对变量进行基线描述分析后,数据分为训练集和验证集.建立了四个预测模型,并使用验证集评估了它们的性能。同时,准确性,精度,召回,使用十倍交叉验证计算ROC的F1评分和曲线下面积(AUC)以进一步评估模型的性能。使用基于机器学习的预测模型的SHAP框架对DPN预测模型的结果进行了解释性分析。
结果:在1142名T2DM患者中,681患有DPN合并症,461没有。两组在年龄方面有显著差异,疾病的原因,收缩压,HbA1c,ALT,红细胞,Cr,BUN,尿液中的红细胞,尿液中的葡萄糖,和尿液中的蛋白质(p<0.05)。伴有DPN合并症的T2DM患者表现出不同的中医症状。包括肢体麻木,四肢疼痛,乏力,渴望饮料,口干和喉咙,视力模糊,阴郁的肤色,和不平稳的脉冲,差异具有统计学意义(p<0.05)。我们的结果表明,提出的多特征中西医结合预测模型优于没有中医特征指标的常规模型。该模型表现出最佳性能(准确度=0.8109,精确度=0.8029,召回率=0.9060,F1得分=0.8511,AUC=0.9002)。SHAP分析显示,导致DPN的主要危险因素是中医症状(肢体麻木,渴望饮料,视力模糊),年龄,疾病的原因,和糖化血红蛋白.这些危险因素对DPN预测模型产生了积极影响。
结论:多特征,建立并验证了中西医结合的DPN预测模型。该模型在T2DM的诊断和治疗中提高了DPN高危人群的早期识别。同时也为糖尿病等慢性病的智能管理提供信息支持。
BACKGROUND: Clinical studies have shown that diabetic peripheral neuropathy (DPN) has been on the rise, with most patients presenting with severe and progressive symptoms. Currently, most of the available prediction models for DPN are derived from general clinical information and laboratory indicators. Several Traditional Chinese medicine (TCM) indicators have been utilised to construct prediction models. In this study, we established a novel machine learning-based multi-featured Chinese-Western medicine-integrated prediction model for DPN using clinical features of TCM.
METHODS: The clinical data of 1581 patients with Type 2 diabetes mellitus (T2DM) treated at the Department of Endocrinology of the First Affiliated Hospital of Anhui University of Chinese Medicine were collected. The data (including general information, laboratory parameters and TCM features) of 1142 patients with T2DM were selected after data cleaning. After baseline description analysis of the variables, the data were divided into training and validation sets. Four prediction models were established and their performance was evaluated using validation sets. Meanwhile, the accuracy, precision, recall, F1 score and area under the curve (AUC) of ROC were calculated using ten-fold cross-validation to further assess the performance of the models. An explanatory analysis of the results of the DPN prediction model was carried out using the SHAP framework based on machine learning-based prediction models.
RESULTS: Of the 1142 patients with T2DM, 681 had a comorbidity of DPN, while 461 did not. There was a significant difference between the two groups in terms of age, cause of disease, systolic pressure, HbA1c, ALT, RBC, Cr, BUN, red blood cells in the urine, glucose in the urine, and protein in the urine (p < 0.05). T2DM patients with a comorbidity of DPN exhibited diverse TCM symptoms, including limb numbness, limb pain, hypodynamia, thirst with desire for drinks, dry mouth and throat, blurred vision, gloomy complexion, and unsmooth pulse, with statistically significant differences (p < 0.05). Our results showed that the proposed multi-featured Chinese-Western medicine-integrated prediction model was superior to conventional models without characteristic TCM indicators. The model showed the best performance (accuracy = 0.8109, precision = 0.8029, recall = 0.9060, F1 score = 0.8511, and AUC = 0.9002). SHAP analysis revealed that the dominant risk factors that caused DPN were TCM symptoms (limb numbness, thirst with desire for drinks, blurred vision), age, cause of disease, and glycosylated haemoglobin. These risk factors were exerted positive effects on the DPN prediction models.
CONCLUSIONS: A multi-feature, Chinese-Western medicine-integrated prediction model for DPN was established and validated. The model improves early-stage identification of high-risk groups for DPN in the diagnosis and treatment of T2DM, while also providing informative support for the intelligent management of chronic conditions such as diabetes.