探讨糖尿病肾病(DN)患者血液透析(HD)过程中发生低血糖的影响因素，建立低血糖预测模型及评分表。

选取2019年2月至2021年7月于我院血液透析中心进行维持性HD的DN患者242例，根据患者HD过程中是否发生低血糖分为未发生低血糖组(n＝112)和发生低血糖组(n＝130)，通过问卷调查收集患者临床资料，采用单因素Logistic分析及Lasso-Logistic回归分析筛选相关变量，以是否发生低血糖为因变量建立Logistic回归模型与评分表，采用受试者工作特征曲线(ROC)和校准曲线验证模型的区分度和准确度。

两组DN患者年龄、身体质量指数(BMI)、用药依从性、血糖平均值(MBG)、血糖变异系数(CVBG)、每日运动时间、合理控制饮食、抑郁状态及照顾能力比较，差异均具有统计学意义(P<0.05)。单因素Logistic回归分析结果显示，年龄、BMI、MBG、CVBG、用药依从性、每日运动时间、合理控制饮食、抑郁状态及照顾能力均为DN患者HD过程中发生低血糖的影响因素(P<0.05)。Lasso-Logistic回归分析结果亦显示，MBG、CVBG、用药依从性、合理控制饮食、抑郁状态及照顾能力是患者发生低血糖的影响因素(P<0.05)。构建的Logistic风险预测模型的ROC曲线AUC 0.826，95%CI：0.785~0.897，校准图显示该模型预测值与实际观测值较为一致。根据建立的低血糖预测评分表，0~9分对应的低血糖发生概率为7.3%~100.0%。最大约登指数为0.49时评分表切点为5分，该分值下评分表的灵敏度为83.61%、特异度为85.83%、准确率为84.71%。

基于Lasso-Logistic回归模型开发的低血糖评分量表联合MBG、CVBG、用药依从性、合理控制饮食、抑郁状态、照顾能力对DN患者HD过程中低血糖做出可靠预测，为患者临床治疗过程中血糖控制提供依据，具有一定的临床应用价值。

To investigate the influencing factors of hypoglycemia during hemodialysis (HD) in patients with diabetic nephropathy (DN), and establish a predictive model and scoring system for hypoglycemia.

242 DN patients who underwent maintenance hemodialysis (HD) in the hemodialysis center of our hospital from February 2019 to July 2021 were selected. According to whether hypoglycemia occurred during HD, they were divided into non-hypoglycemia group (n= 112) and hypoglycemia group (n= 130). The clinical data of patients were collected through questionnaire survey, and the relevant variables were screened by single factor logistic analysis and Lasso-Logistic regression analysis. The logistic regression model and scoring table were established with hypoglycemia as the dependent variable. The discrimination and accuracy of the model were verified by receiver operating characteristic (ROC) curve and calibration curve.

There were significant differences in age, body mass index (BMI), medication compliance, mean blood glucose (MBG), coefficient of variation of blood glucose (CVBG), daily exercise time, reasonable diet control, depression, and care ability between the two groups (P< 0.05). Univariate logistic regression analysis showed that age, BMI, MBG, CVBG, medication compliance, daily exercise time, reasonable diet control, depression, and care ability were the influencing factors of hypoglycemia in patients with DN during HD (P< 0.05). Lasso-Logistic regression analysis showed that MBG, CVBG, medication compliance, reasonable diet control, depression, and care ability were also the influencing factors of hypoglycemia in maintenance HD patients with DN (P< 0.05). The AUC of ROC curve of the constructed logistic risk prediction model was 0.826, and the 95%CI was 0.785-0.897, and the calibration chart showed that the predicted value of the model was consistent with the actual value observed. According to the prediction score table constructed, the occurrence probability of hypoglycemia corresponding to 0-9 points was 7.3%-100.0%. When the maximum Yoden index was 0.49, the cut-off point of the scoring table was 5 points. Under this score, the sensitivity, specificity, and accuracy of the scoring table were 83.61%, 85.83%, and 84.71%, respectively.

The hypoglycemia score scale developed based on Lasso-Logistic regression model combined with MBG, CVBG, medication compliance, reasonable diet control, depression and, care ability could reliably predict hypoglycemia in patients with DN during HD, which may provide basis for blood glucose control during clinical treatment, and have certain clinical application value.