目的 分析老年维持性血液透析(maintenance hemodialysis，MHD)患者透析中进食误吸危险因素并构建预测模型。 方法 选取无锡市第二人民医院血液透析中心行MHD老年(年龄≥60岁)患者220例。通过最小绝对值选择与收缩算子(least absolute selection and shrinkage operator，LASSO)方法筛选预测因子，采用多因素Logistic回归分析建立预测模型，并用列线图展示；接受者操作特征曲线(receiver operating characteristic curve，ROC曲线)评估模型预测效果，校准曲线评价模型的校准度。 结果 卒中史(OR=9.460，95% CI：3.218～27.813，P＜0.001)、合并肺炎(OR=6.499，95% CI：1.912～22.096，P=0.003)、镇静催眠药(OR=5.978，95% CI：1.323～27.005，P=0.020)、慢性肾脏病矿物质与骨异常(chronic kidney disease-mineral and bone disorder，CKD-MBD)类药(OR=3.069，95% CI：1.074～8.773，P=0.036)、血氧饱和度(＜95%)(OR=11.545，95% CI：3.932～33.901，P＜0.001)及进食非固体食物(OR=0.253，95% CI：0.078～0.826，P=0.023)为影响老年MHD患者发生误吸的独立危险因素。ROC曲线下面积(area under curve，AUC)为0.917(95% CI：0.879～0.955)，P＜0.001。校准曲线、决策曲线评价其一致性及获益性良好。 结论 本研究构建模型预测效果较好，为临床医务人员识别老年MHD患者透析中进食发生误吸及采取相应护理措施提供参考。
Objective To explore the risk factors for food aspiration during hemodialysis sessions in patients on maintenance hemodialysis (MHD) and to develop a prediction model for the aspiration risk. Method A total of 220 MHD patients over 60 years old and treated in a tertiary hospital in Wuxi city were enrolled in this study. Least Absolute Selection and Shrinkage Operator (LASSO) regression analysis was used to select predictors. Multivariate logistic regression was applied to build the risk prediction model, which was also shown as nomogram. Receiver operating characteristic curve (ROC curve) and calibration curve were used to assess the discrimination and calibration abilities of the model. Results History of stroke (OR=9.460, 95% CI: 3.218～27.813, P<0.001), pneumonia (OR=6.499, 95% CI: 1.912～22.096, P=0.003), sedative and hypnotic drugs (OR=5.978, 95% CI: 1.323～27.005, P=0.020), drugs for chronic kidney disease-mineral and bone disorder (CKD-MBD; OR=3.069, 95% CI: 1.074～8.773, P=0.036), oxygen saturation <95% (OR=11.545, 95% CI: 3.932～33.901, P＜0.001) and non-solid food (OR=0.253, 95% CI: 0.078～0.826, P=0.023) were the independent risk factors for food aspiration in MHD patients (P＜0.05). The area under curve (AUC) of the ROC curve was 0.917 (95% CI: 0.879～0.955, P＜0.001). The consistency and benefit of the model evaluated by calibration and decision curves obtained better results. Conclusion This model has satisfactory effects that provides references for nurses to identify high-risk patients and implement preventive measures.
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