Li Wei, Cao Jing, Du Yu-Luo, Wen Yan-di, Luo Wei-Xiang, Liu Xue-Yan
Department of Critical Care Medicine, Shenzhen People's Hospital, Shenzhen, Guangdong, 518020, China.
Department of Nursing, Shenzhen People's Hospital, Shenzhen, Guangdong, 518020, China.
Heliyon. 2024 Apr 3;10(8):e29158. doi: 10.1016/j.heliyon.2024.e29158. eCollection 2024 Apr 30.
To establish a predictive modeling for the risk of bloodstream infection associated with peripherally inserted central catheter (PICC).
Patients receiving PICC treatment in Shenzhen People's Hospital from June 2020 to December 2020 were retrospectively enrolled and divided into the infection group and the non-infection group according to the presence and absence of PICC-related infections. Then, relevant clinical information of patients was collected and the predictors of PICC-related infection were screened by the least absolute shrinkage and selection operator regression (LASSO) model. Besides, multivariate logistic regression was used to analyze the influencing factors of PICC-related infection, A nomogram was constructed based on the results of the multivariate analysis. Ultimately, a receiver operating characteristic (ROC) curve was plotted to analyze the application value of influencing factors to predict PICC-related infections.
A total of 505 patients were included, including 75 patients with PICC-related infections (14.85%). The main pathogen was gram-positive cocci. The predictors screened by LASSO included age >60 years, catheter movement, catheter maintenance cycle, insertion technique, immune function, complications, and body temperature ≥37.2 °C before PICC placement. Multivariate logistic regression analysis showed that independent risk factors of infections related to PICC included age >60 years [odds ratio (OR) = 1.722; 95% confidence interval (CI) = 1.312-3.579; = 0.006], catheter movement (OR = 1.313; 95% CI = 1.119-3.240; = 0.014), catheter maintenance cycle >7 days (OR = 2.199; 95% CI = 1.677-4.653; = 0.000), direct insertion (OR = 1.036; 95% CI = 1.019-2.743; = 0.000), poor immune function (OR = 2.322; 95% CI = 2.012-4.579; = 0.000), complications (OR = 1.611; 95% CI = 1.133-3.454; = 0.019), and body temperature ≥37.2 °C before PICC placement (OR = 1.713; 95% CI = 1.172-3.654; = 0.012). Besides, the area under the ROC curve was 0.889.
PICC-related infections are associated with factors such as age >60 years, catheter movement, catheter maintenance cycle, insertion technique, immune function, complications, and body temperature ≥37.2 °C before PICC placement. Additionally, the LASSO model is moderately predictive for predicting the occurrence of PICC-related infections.
建立与经外周静脉穿刺中心静脉置管(PICC)相关的血流感染风险预测模型。
回顾性纳入2020年6月至2020年12月在深圳市人民医院接受PICC治疗的患者,根据是否发生PICC相关感染分为感染组和非感染组。然后,收集患者的相关临床信息,并通过最小绝对收缩和选择算子回归(LASSO)模型筛选PICC相关感染的预测因素。此外,采用多因素logistic回归分析PICC相关感染的影响因素,根据多因素分析结果构建列线图。最后,绘制受试者工作特征(ROC)曲线,分析影响因素预测PICC相关感染的应用价值。
共纳入505例患者,其中75例发生PICC相关感染(14.85%)。主要病原菌为革兰氏阳性球菌。LASSO筛选出的预测因素包括年龄>60岁、导管移位、导管维护周期、置管技术、免疫功能、并发症以及PICC置管前体温≥37.2℃。多因素logistic回归分析显示,PICC相关感染的独立危险因素包括年龄>60岁[比值比(OR)=1.722;95%置信区间(CI)=1.312 - 3.579;P = 0.006]、导管移位(OR = 1.313;95%CI = 1.119 - 3.240;P = 0.014)、导管维护周期>7天(OR = 2.199;95%CI = 1.677 - 4.653;P = 0.000)、直接穿刺(OR = 1.036;95%CI = 1.019 - 2.743;P = 0.000)、免疫功能差(OR = 2.322;95%CI = 2.012 - 4.579;P = 0.000)、并发症(OR = 1.611;95%CI = 1.133 - 3.454;P = 0.019)以及PICC置管前体温≥37.2℃(OR = 1.713;95%CI = 1.172 - 3.654;P = 0.012)。此外,ROC曲线下面积为0.889。
PICC相关感染与年龄>60岁、导管移位、导管维护周期、置管技术、免疫功能、并发症以及PICC置管前体温≥37.2℃等因素有关。此外,LASSO模型对预测PICC相关感染的发生具有中等预测能力。