【摘要】 目的 探究老年维持性血液透析(maintenance hemodialysis, MHD)患者社会衰弱现状及影响因素并构建列线图预测模型。方法 采用一般资料调查表、社会衰弱量表、血液透析自我管理量表、多伦多述情障碍量表、家庭关怀度指数问卷、社会支持评定量表对340名老年MHD患者进行调查，筛选独立影响因素，构建并评估列线图预测模型。结果 老年MHD患者社会衰弱患病率为28.82%。二分类logistic回归分析结果显示，近一年跌倒史(OR=3.117, 95%CI:1.249～7.778)、自理能力(OR=4.058, 95%CI:2.002～8.228)、述情障碍(OR=1.073, 95%CI:1.009～1.141)、家庭关怀度(OR=0.744,95%CI:0.612～0.903)及社会支持(OR=0.886, 95%CI:0.801～0.981)是老年MHD患者发生社会衰弱的影响因素(均P<0.05)。所构建模型的受试者工作特征曲线下面积为0.921(95%CI:0.893～0.950)，约登指数最大值为0.688，最佳截断值为0.330，灵敏度为83.7%，特异度为85.1%，说明模型区分度较高。Hosmer-Lemeshow检验结果为χ~2=10.465,P=0.234，说明模型拟合度较好。内部验证校准曲线示模型校准度较好。决策曲线分析示模型的临床实用性较高。结论 本研究构建的列线图预测模型是一种实用、方便的工具，有助于预测老年MHD患者社会衰弱的发生风险。更多还原

【Abstract】 Objective To investigate the status and influencing factors of social frailty in elderly patients on Maintenance Hemodialysis(MHD) and to construct a nomogram prediction model. Methods A survey was conducted among 340 elderly MHD patients using a general information questionnaire, social frailty scale, hemodialysis self-management scale, Toronto Alexithymia Scale, Family Care Index, and Social Support Rating Scale. Independent influencing factors were identified, and a nomogram prediction model was constructed and evaluated. Results The prevalence of social frailty among elderly MHD patients was 28.82%. Binary logistic regression analysis revealed that a history of falls in the past year(OR=3.117, 95% CI:1.249-7.778), self-care ability(OR=4.058, 95% CI: 2.002-8.228), alexithymia(OR=1.073, 95% CI: 1.009-1.141), family care level(OR =0.744, 95% CI: 0.612-0.903), and social support(OR =0.886, 95% CI: 0.801-0.981) were significant influencing factors for social frailty in elderly MHD patients(all P<0.05). The area under the receiver operating characteristic curve for the constructed model was 0.921(95% CI: 0.893-0.950), with the maximum Youden index being 0.688, an optimal cutoff value of 0.330, sensitivity of 83.7%, and specificity of 85.1%, indicating high discrimination of the model. The Hosmer-Leeshawn test yielded χ~2=10.465, P=0.234, suggesting good model fit. Internal validation calibration curves indicated satisfactory calibration of the model. Decision curve analysis demonstrated high clinical utility of the model. Conclusion The nomogram prediction model developed in this study is a practical and convenient tool that aids in predicting the risk of social frailty in elderly MHD patients.更多还原