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护理研究

维持性血液透析患者导管相关感染风险预测模型构建与应对策略

  • 熊媛媛 ,
  • 钟芳萍 ,
  • 彭爱平 ,
  • 王丽 ,
  • 胡明亮
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  • 深圳,1深圳市龙华区人民医院肾内科

收稿日期: 2023-08-31

  修回日期: 2023-11-19

  网络出版日期: 2024-03-12

基金资助

深圳市龙华区医疗卫生机构区级科研项目(2023012);深圳市科技计划项目(JCYJ20190808095615389)

Construction of a risk prediction model for catheter-related infection and treatment strategies of the infection in maintenance hemodialysis patients

  • XIONG Yuan-Yuan ,
  • ZHONG Fang-Ping ,
  • PENG Ai-Ping ,
  • WANG Li ,
  • HU Ming-Liang
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  • Department of Nephrology, People’s Hospital  of Longhua District, Guangdong, Shenzhen 518110, China

Received date: 2023-08-31

  Revised date: 2023-11-19

  Online published: 2024-03-12

摘要

目的 探讨维持性血液透析(maintenance hemodialysis,MHD)患者导管相关感染(catheter-related bloodstream infection,CRBSI)的影响因素,并构建预测模型与制订对策。 方法 选取深圳市龙华区人民医院MHD患者204例为对照组,将其分为感染组(n=63)和无感染组(n=141)。Logistic回归分析患者临床资料并构建模型;据模型、临床经验与文献报道,重新构建干预体系。另选取MHD患者198例为观察组,评价新干预体系效果。 结果 糖尿病史(OR=2.237,95% CI:1.067~5.679,P=0.003)、置管时间(OR=3.647,95% CI:1.326~7.892,P=0.024)、导管护理频率(OR=6.679,95% CI:2.534~16.476,   P=0.003)、无菌培训率(OR=2.889,95% CI:1.146~6.654,P=0.030)、血尿酸(OR=2.937,95% CI:1.627~8.883,P=0.014)是MHD患者发生CRBSI的独立影响因素。预测模型方程为Y=-20.673+1.116×血尿酸+1.012×无菌培训率+2.017×导管护理频率+1.912×置管时间+1.079×糖尿病史,采用Hosmer和Lemeshow拟合优度检验方程(χ2=10.678,P=0.236),受试者操作特征曲线显示曲线下面积为0.813,敏感性为89.47%,特异性为63.93%。观察组CRBSI发生率、不良事件发生率均低于对照组(χ2=3.606、32.780,均P<0.001)。 结论  预测模型具有良好的判别效度,基于循证的系统化护理干预体系具有良好的防控MHD患者发生CRBSI效果。

本文引用格式

熊媛媛 , 钟芳萍 , 彭爱平 , 王丽 , 胡明亮 . 维持性血液透析患者导管相关感染风险预测模型构建与应对策略[J]. 中国血液净化, 2024 , 23(03) : 227 -231 . DOI: 10.3969/j.issn.1671-4091.2024.03.016

Abstract

Objective   To investigate the influencing factors for catheter-associated infection (CRBSI), to construct a prediction model, and to build up treatment strategies of the injection in patients with maintenance hemodialysis (MHD).  Methods   A total of 204 MHD patients in our hospital were selected as the control subjects. They were divided into infected group (n=63) and non-infected group (n=141). Logistic regression was used to analyze the patients’ clinical data and to construct the model. A new intervention system was then established based on the model, clinical experience and literature reports. In addition, a total of 198 MHD patients were selected as the observation subjects to evaluate the effects of the new intervention system.  Results Diabetes history (OR=2.237, 95% CI: 1.067~5.679, P=0.003), catheter placement time (OR=3.647, 95% CI: 1.326~7.892, P=0.024), frequency of catheter care (OR=6.679, 95% CI: 2.534~16.476, P=0.003), aseptic training rate (OR=2.889, 95% CI: 1.146~6.654, P=0.030) and blood uric acid (OR=2.937, 95% CI: 1.627~8.883, P=0.014) were the independent influencing factors for CRBSI in MHD patients. The model equation was Y=-20.673+1.116×blood uric acid+1.012×aseptic training rate+2.017×catheter care frequency+1.912×catheterization time+1.079×diabetes history. Hosmer and Lemeshow's goodness of fit test equation was used (χ2=10.678, P=0.236). Receiver operating characteristic curve showed that the area under the curve was 0.813, the sensitivity was 89.47%, and the specificity was 63.93%. The incidence of CRBSI and adverse events were lower in the observation group than those in the control group (χ2=3.606 and 32.780; P <0.001).  Conclusion  The prediction model has a better discrimination validity, and the evidence-based systematic nursing intervention system has a better effect on preventing and controlling CRBSI in MHD patients.
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