摘要
目的构建中老年糖尿病患者抑郁风险预测模型,并对模型进行验证。方法基于中国健康与退休纵向调查(CHARLS)中第5轮(2020年)调查的数据,提取出糖尿病患者900例,在剔除了数据缺失和无效问卷的参与者后,有769例患者被纳入分析,以7∶3的比例将患者分为训练集和验证集,通过单因素分析及Logistic回归分析筛选出糖尿病患者抑郁的最佳预测变量,构建列线图模型。采用受试者工作特征曲线下面积(AUC)评价模型的预测能力。使用Bootstrap法、校准曲线、Hosmer-Lemeshow法检验模型的准确性,并利用决策曲线(DCA)和临床影响曲线(CIC)评价该模型的临床有效性。结果769例糖尿病患者中366例患者(47.59%)有抑郁症状。Logistic回归分析显示,居住地、疼痛、如厕困难、洗澡困难、睡眠时长、运动、生活满意度、子女满意度是糖尿病患者抑郁的预测因素。基于这些变量构建列线图模型,其AUC为0.775,最佳截断值为0.557时,模型灵敏度为59.1%,特异度为84.8%,表明模型具有良好的区分能力。Hosmer-Lemeshow检验结果显示χ^(2)=15.821(P=0.105),表明模型预测值与实际观察值具有较高的一致性。验证集的AUC为0.778,Hosmer-Lemeshow检验结果显示χ^(2)=8.557(P=0.575)。DCA和CIC的结果表明,该模型具有较高的临床应用价值。结论本研究构建的糖尿病患者抑郁风险预测模型预测性能良好,可帮助临床尽早识别糖尿病患者中的抑郁高风险人群,为实施针对性干预提供理论依据。
Objective To construct and validate a depression risk prediction model for middle-aged and elderly patients with diabetes.Methods Data were extracted from the fifth wave(2020)of the China Health and Retirement Longitudinal Study(CHARLS).A total of 900 diabetic patients were identified,and after excluding those with missing data or invalid questionnaires,769 patients were included in the analysis.Patients were randomly divided into a training set and a validation set in a 7∶3 ratio.Univariate analysis and logistic regression analysis were performed to screen the optimal predictors of depression in diabetic patients,and a nomogram model was developed.The predictive performance of the model was assessed by the area under the receiver operating characteristic curve(AUC).Model calibration and accuracy were evaluated using bootstrap resampling,calibration plots,and the Hosmer-Lemeshow test.The clinical utility was further assessed by decision curve analysis(DCA)and clinical impact curves(CIC).Results Among the 769 patients,366(47.59%)had depression.Logistic regression analysis showed that place of residence,pain,difficulty in toileting,difficulty in bathing,sleep duration,physical exercise,life satisfaction,and children's satisfaction were independent predictors of depression in diabetic patients.A nomogram was constructed based on these variables,yielding an AUC of 0.775.At the optimal cutoff value of 0.557,the model demonstrated a sensitivity of 59.1%and a specificity of 84.8%,indicating good discriminative ability.The Hosmer-Lemeshow test showed(χ^(2)=15.821,P=0.105),suggesting good agreement between predicted and observed outcomes.In the validation set,the AUC was 0.778,with Hosmer-Lemeshow(χ^(2)=8.557,P=0.575).DCA and CIC indicated favorable clinical applicability of the model.Conclusions The depression risk prediction model constructed in this study demonstrated good predictive performance.It can assist clinicians in early identification of high-risk individuals with diabetes and provide a theoretical basis for targeted interventions.
作者
杨蕾
郝亚平
唐与晓
迟俊涛
赵凌燕
顾桂芹
王亮
Yang Lei;Hao Yaping;Tang Yuxiao;Chi Juntao;Zhao Lingyan;Gu Guiqin;Wang Liang(Department of Endocrinology,Yantai Yuhuangding Hospital,Yantai 264000,China;Department of Nursing,Yantai Yuhuangding Hospital,Yantai 264000,China)
出处
《中华现代护理杂志》
2025年第29期3976-3983,共8页
Chinese Journal of Modern Nursing
关键词
糖尿病
中老年人
抑郁症
预测模型
中国健康与退休纵向调查
Diabetes mellitus
Middle-aged and elderly
Depression
Prediction model
China Health and Retirement Longitudinal Study
