中国血液净化

中国血液净化 ›› 2024, Vol. 23 ›› Issue (03): 227-231.doi: 10.3969/j.issn.1671-4091.2024.03.016

• 护理研究 • 上一篇    下一篇

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   

  1. 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 出版日期:2024-03-12 发布日期:2024-03-12
  • 通讯作者: 胡明亮 E-mail:2604577148@ qq.com
  • 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   

  1. Department of Nephrology, People’s Hospital  of Longhua District, Guangdong, Shenzhen 518110, China
  • Received:2023-08-31 Revised:2023-11-19 Online:2024-03-12 Published:2024-03-12
  • Contact: 518110 深圳，1深圳市龙华区人民医院肾内科 E-mail:2604577148@ qq.com

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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。

关键词: 维持性血液透析, 循证, 导管相关感染, 预测模型, 护理对策

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.

Key words: Maintenance hemodialysis, Evidence-based, Catheter-associated infection, Prediction model, Nursing countermeasure

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