基于生物电阻抗矢量分析的维持性血液透析患者贫血和营养状况的评估方法研究

Evaluation method for anemia and nutritional status in hemodialysis patients based on bioelectrical impedance vector analysis

目的 探讨基于生物电阻抗矢量分析(BIVA)和机器学习算法建立的预测模型对维持性血液透析(MHD)患者贫血和营养状况的预测价值。方法 收集人体成分分析仪(BCM)测得的MHD患者生物电信号数据和白蛋白(Alb)、血红蛋白(Hb)、低密度脂蛋白胆固醇(LDL-C)、总胆固醇(TC)等血生化指标数据,基于BIVA和3种机器学习算法(随机森林、支持向量机和Adaboost算法)分别建立3个预测模型,比较3个模型对Alb、LDL-C、Hb、TC指标的预测效能。结果 个体相关性分析结果显示,生物电学指标与营养指标(Alb、LDL-C、Hb、TC)显著相关(P<0.05或P<0.01);3个模型中,基于随机森林算法的模型性能最佳,预测Alb、LDL-C、Hb、TC的准确率分别为0.880、0.879、0.904、0.937。结论 基于BIVA和机器学习算法(随机森林算法)建立的预测模型在MHD患者贫血和营养状况评估中具有较高价值,可辅助临床决策。

Objective To explore the predictive value of a predictive model based on bioelectrical impedance vector analysis(BIVA)and machine learning algorithm for anemia and nutritional status in maintenance hemodialysis(MHD)patients.Methods The bioelectrical signial data of MHD patients measured by body composition monitor(BCM)and albumin(Alb),hemoglobin(Hb),low density lipoprotein cholesterol(LDL-C),total cholesterol(TC)and other blood biochemical indexes data were collected.Three prediction models were established based on BIVA and three machine learning algorithms(random forest,support vector machine and Adaboost algorithm)respectively,and the prediction efficiency of the three models on Alb,LDL-C,Hb and TC indexes was compared.Results The results of individual correlation analysis showed that the bioelectrical indexes were significantly correlated with the nutritional indexes(Alb,LDL-C,Hb,TC)(P<0.05 or P<0.01).Among the three models,the model based on random forest algorithm had the best performance,and the accuracies of predicting Alb,LDL-C,Hb and TC were 0.880,0.879,0.904 and 0.937,respectively.Conclusion Predictive models based on BIVA and machine learning algorithms(random forest algorithms)have high value in the assessment of anemia and nutritional status in MHD patients and can assist clinical decision making.