| 李永峰.重症医学科铜绿假单胞菌血流感染的危险因素分析及风险预测模型[J].济宁医学院学报,2024,47(5):398-401 |
| 重症医学科铜绿假单胞菌血流感染的危险因素分析及风险预测模型 |
| Risk factors analysis and risk prediction model construction of pseudomonas aeruginosa bloodstream infection in ICU |
| 投稿时间：2024-04-03 |
| DOI：10.3969/j.issn.1000-9760.2024.05.008 |
| 中文关键词: 重症医学科;铜绿假单胞菌;血流感染;危险因素;风险预测模型 |
| 英文关键词: ICU;Pseudomonas aeruginosa;Blood flow infection;Risk factors;Risk prediction model |
| 基金项目: |
| 作者 | 单位 | | 李永峰 | 泗水县人民医院重症医学科, 泗水 273200 |
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| 中文摘要: |
| 目的 研究重症医学科(intensive care unit,ICU)铜绿假单胞菌血流感染的危险因素及构建风险预测模型。方法 回顾调查铜绿假单胞菌血流感染患者资料。使用logistic回归分析进行单因素和多因素筛选出独立危险因素,构建铜绿假单胞菌血流感染风险预测评分模型。结果 合并其他疾病、入住ICU时间、机械通气、APACHEⅡ评分是ICU铜绿假单胞菌血流感染的独立危险因素。Logistic回归模型Logit(P)=-69895+1.616×合并其他疾病+2.610×入住ICU时间+1.846×机械通气+2.831×APACHEⅡ评分。ROC曲线下面积为0.712,灵敏度为88.2%,特异度为75.2%,95%CI为[0.612,0.854],最佳截断值为13.412。结论 合并其他疾病、入住ICU时间、机械通气、APACHEⅡ评分是ICU铜绿假单胞菌血流感染的独立危险因素。Logistic回归模型便于感染风险的预测。 |
| 英文摘要: |
| Objective To study the risk factors of pseudomonas aeruginosa bloodstream infection in ICU and construct a risk prediction model. Method A retrospective investigation was conducted on the data of patients with pseudomonas aeruginosa bloodstream infection.Use logistic regression analysis to screen independent risk factors for single and multiple factors,and construct a risk prediction scoring model for pseudomonas aeruginosa bloodstream infection.The combination of other diseases, length of stay in the ICU,mechanical ventilation,and APACHE Ⅱ score are independent risk factors for pseudomonas aeruginosa bloodstream infection in the ICU.Logistic regression model Logit (P)=-69895+1.616×Other comorbidities+2.610×ICU stay time+1.846×Mechanical ventilation+2.831×APACHE Ⅱ score.The area under the curve is 0.712,the sensitivity is 88.2%,the specificity is 75.2%,the 95%CI is [0.612,0.854],and the optimal cutoff value is 13.412. Conclusion The combination of other diseases, length of stay in the ICU,mechanical ventilation,and APACHE Ⅱ score are independent risk factors for pseudomonas aeruginosa bloodstream infection in the ICU.The logistic regression model facilitates the prediction of infection risk. |
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