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数据挖掘在预防导尿管相关尿路感染中的应用

Application of Data Mining in Prevention of Catheter-Related Urinary Tract Infection

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【作者】 盛秋张灿徐薇张林

【Author】 SHENG Qiu;ZHANG Can;XU Wei;ZHANG Lin;Ward 208,Northern Jiangsu People’s Hospital;Department of Nursing, Northern Jiangsu People’s Hospital;Hepatobiliary Pancreatic Center, Northern Jiangsu People’s Hospital;

【通讯作者】 张灿;

【机构】 苏北人民医院208病区苏北人民医院护理部苏北人民医院肝胆胰中心

【摘要】 目的 探讨导尿管相关尿路感染因素中潜在的、有价值的关联规则,以制订相应的预防措施,降低其发病率。方法 便利抽样选取某医院171例导尿管相关尿路感染病例,采用数据挖掘中的关联规则Apriori算法对其进行信息分析,建立有价值的强关联规则,并分析产生导尿管相关尿路感染的原因。结果 通过对导尿管相关尿路感染病例的信息分析,最终筛选出10条具有实际临床意义的关联规则。导尿管相关尿路感染中高风险因素主要包括:女性、年龄>65岁、意识障碍、长期卧床、使用型号大于16F的乳胶尿管、留置尿管时间>7 d。结论 通过关联规则分析发现了导尿管相关尿路感染患者之间的关联特点,提示护理人员应重点关注年龄>65岁、有意识障碍、留置尿管时间>7 d、使用16F及以上型号导尿管的女性患者,并制订相应的预防措施,从而降低导尿管相关尿路感染的发生率。

【Abstract】 Objective To explore the potential and valuable association rules among the factors of catheter-related urinary tract infection(CAUTI),so as to formulate targeted prevention measures and reduce its incidence.Methods Convenient sampling was used to select 171 cases of CAUTI from a hospital.The Apriori algorithm of association rules in data mining was used to analyze relevant information, establish valuable strong association rules, and then analyze the causes of CAUTI.Results A total of 10 association rules with practical clinical significance were finally screened out through the information analysis of CAUTI cases.The moderate and high risk factors of CAUTI included female gender, age over 65 years, disturbance of consciousness, long-term stay in bed, use of latex catheters with size larger than 16F,and catheterization retention for more than 7 days.Conclusions Association characteristics among CAUTI patients are revealed using association rule analysis.Nursing staff should pay more attention to female patients aged over 65 years, with disturbance of consciousness and catheterization retention for more than 7 days, and using catheters with size larger than 16F.Targeted prevention measures should be formulated to reduce the incidence of CAUTI.【

【基金】 江苏省卫生健康委员会科研项目(H2018032)
  • 【分类号】R472
  • 【下载频次】124
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