摘要
目的:探究长期血液透析病人继发脑出血的危险因素,建立相关列线图模型。方法:选取204例行长期血液透析的终末期尿毒症病人为研究对象,根据是否继发脑出血分为非脑出血组和脑出血组,分析继发脑出血的独立预测因素,并构建风险预警模型。结果:长期血液透析病人脑出血的发生率为29.9%。多因素Logistic回归分析结果显示,透析龄、平均动脉压(MAP)、总胆固醇(TC)、血清蛋白(ALB)、高敏C反应蛋白(hs⁃CRP)、血小板计数(PLT)是继发脑出血的独立危险因素(P<0.05)。基于6个危险因素构建相关列线图模型,且列线图模型具有良好的预测效能,工作者特征(ROC)下面积为0.888。结论:长期血液透析病人脑出血的发生率较高,透析龄、MAP、TC、ALB、hs⁃CRP、PLT是脑出血的独立预测因子,可利用建立的列线图模型预测长期血液透析病人脑出血的发生风险。
Objective:To explore the risk factors of secondary intracerebral hemorrhage in long⁃term hemodialysis patients,and establish the Nomograph model.Methods:A total of 204 end⁃stage uremia patients who underwent long⁃term hemodialysis were selected as the research objects,and according to whether they had secondary intracerebral hemorrhage or not,they were divided into two groups:the intracerebral hemorrhage group and non⁃intracerebral hemorrhage group.Independent predictors of secondary intracerebral hemorrhage were analyzed,and the Nomograph model was constructed.Results:The incidence of intracerebral hemorrhage in long⁃term hemodialysis patients was 29.9%.Multivariate regression analysis showed that dialysis age,mean arterial pressure(MAP),total cholesterol(TC),serum albumin(ALB),high sensitivity C⁃reactive protein(hs⁃CRP)and platelet count(PLT)were independent risk factors for secondary intracerebral hemorrhage(P<0.05).The rosette model was constructed based on six risk factors,and the Nomogram model had good predictive efficiency,and the consistency index of ROC was 0.888.Conclusion:The incidence of intracerebral hemorrhage in long⁃term hemodialysis patients was high.Dialysis age,MAP,TC,ALB,Hs⁃CRP and PLT are independent predictors of cerebral hemorrhage.The Nomogram model can be used to predict the risk of intracerebral hemorrhage in long⁃term hemodialysis patients.
作者
李丹妮
谭晶鑫
李墨奇
何文昌
LI Danni;TAN Jingxin;LI Moqi;HE Wenchang(Second Affiliated Hospital of Army Medical University,Chongqing 400037 China)
出处
《护理研究》
北大核心
2022年第13期2269-2274,共6页
Chinese Nursing Research
关键词
血液透析
脑出血
影响因素
风险模型
护理
调查研究
hemodialysis
cerebral hemorrhage
influencing factors
risk model
nursing
investigation
