摘要
目的 构建老年2型糖尿病住院患者低血糖风险预测模型,为医护人员早期识别低血糖高风险人群及精准干预提供参考.方法 通过文献回顾、2轮专家函询及课题组讨论,制订老年2型糖尿病患者低血糖风险因素筛查表,选取2023年6-10月内分泌科收治的585例老年2型糖尿病患者作为研究对象,进行资料收集.采用Lasso回归和多因素logistic回归分析筛选低血糖的风险因素,建立列线图预测模型,分别在全样本集以及采用Bootstrap法(500次)进行内部验证.结果 193例患者(32.99％)发生低血糖.Lasso回归筛选出24个变量,其中11个变量(性别、舒张压、家庭人均月收入、饮酒、近1年发生低血糖次数、识别低血糖症状、高血压、高血脂、多重用药、随机C肽、糖化血红蛋白)构建列线图.全样本集及Bootstrap法验证的ROC曲线下面积分别为0.830和0.8 21,Brier分数分别为0.156和0.159,灵敏度为0.702和0.747,特异度为0.839和0.809.结论 构建的老年2型糖尿病住院患者低血糖风险预警模型具有较好的区分度和校准度,可为临床医护人员筛查低血糖的高危人群提供参考.
Abstract
Objective To construct a hypoglycemia risk prediction model for hospitalized elderly patients with type 2 diabetes,and to provide healthcare professionals with a reference for early identification of high-risk individuals and targeted interventions.Methods A hypoglycemia risk factor screening checklist was developed through literature review,two rounds of expert consultation,and discussion within the research team.A total of 585 elderly patients with type 2 diabetes,admitted to the endocrinology department from June to October 2023,were included as study subjects,and data were collected accordingly.Lasso regression and multivariate logistic regression analyses were used to screen for hypoglycemia risk factors,and a nomogram prediction model was established.The model's performance was internally validated using both the full sample set and a 500-iteration Bootstrap ap-proach.Results Among the 585 patients,193(32.99％)experienced hypoglycemia.Lasso regression identified 24 variables,of which 11(gender,diastolic blood pressure,monthly per capita household income,alcohol consumption,frequency of hypoglyce-mia in the past year,awareness of hypoglycemia symptoms,hypertension,hyperlipidemia,polypharmacy,random C-peptide level,and glycated hemoglobin)were used to construct the nomogram.The area under the ROC curve(AUC)was 0.830 in the full sample data set and 0.821 in the Bootstrap validation,with Brier scores of 0.156 and 0.159,sensitivity of 0.702 and 0.747,and specificity of 0.839 and 0.809,respectively.Conclusion The hypoglycemia risk prediction model for hospitalized elderly pa-tients with type 2 diabetes demonstrates good discrimination and calibration,providing a reference for clinical healthcare profes-sionals in identifying high-risk patients for hypoglycemia.
维普
万方数据