Guo Zi-Qing, Zhao Meng-Han, Zhang Bing, Qi Qi, Ma Yao-Yao, Liu Jin-Ping, Mao Yi-Ping
College of Nursing, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Department of Healthcare-associated Infection Management, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
J Vasc Access. 2025 Jan 16:11297298241308147. doi: 10.1177/11297298241308147.
To develop and validate a nomogram model for predicting central venous catheter-related infections (CRI) in patients with maintenance hemodialysis (MHD).
MHD patients with central venous catheters (CVCs) visiting the outpatient hemodialysis (HD) center of Xuzhou Medical University Affiliated Hospital from January 2020 to December 2023 were retrospectively selected through a HD monitoring system. Patient data were collected, and the patients were divided into training and validation sets in a 7:3 ratio. The training set was used to establish the model, which was verified using the validation set. Multiple logistic regression analysis was performed to identify risk factors for central venous CRI and develop a nomogram prediction model.
A total of 300 MHD patients were enrolled. Multivariate analysis showed that catheter duration, catheter site, catheter reinsertion, history of catheter infection, diabetes, and albumin <35 g/L were risk factors for central venous CRI. The area under the receiver operating characteristic (ROC) curve (AUC) for the training set was 0.902 (95% confidence interval (CI) = 0.862-0.941), with a sensitivity of 85.7%, specificity of 80%, and a Youden index of 65.7%, and that for the validation set was 0.826 (95% CI = 0.726-0.905), with a sensitivity of 80.5%, specificity of 77.9%, and a Youden index of 58.4%. The model demonstrated good discrimination and calibration (Hosmer-Lemeshow goodness-of-fit test statistics: training set: = 4.709, = 0.788; validation set: = 7.171, = 0.518).
This study identified six risk factors associated with central venous CRI in MHD patients. This predictive model demonstrates good prognostic performance and can be used by clinicians to screen for high-risk patients with central venous CRI, thereby enabling the early implementation of risk management strategies.
建立并验证用于预测维持性血液透析(MHD)患者中心静脉导管相关感染(CRI)的列线图模型。
通过血液透析监测系统回顾性选取2020年1月至2023年12月期间在徐州医科大学附属医院门诊血液透析(HD)中心就诊的留置中心静脉导管(CVC)的MHD患者。收集患者数据,并按7:3的比例将患者分为训练集和验证集。训练集用于建立模型,并用验证集进行验证。进行多因素逻辑回归分析以确定中心静脉CRI的危险因素并建立列线图预测模型。
共纳入300例MHD患者。多因素分析显示,导管留置时间、导管部位、导管重新插入、导管感染史、糖尿病和白蛋白<35 g/L是中心静脉CRI的危险因素。训练集的受试者工作特征(ROC)曲线下面积(AUC)为0.902(95%置信区间(CI)=0.862 - 0.941),灵敏度为85.7%,特异度为80%,约登指数为65.7%;验证集的AUC为0.826(95% CI = 0.726 - 0.905),灵敏度为80.5%,特异度为77.9%,约登指数为58.4%。该模型显示出良好的区分度和校准度(Hosmer-Lemeshow拟合优度检验统计量:训练集:=4.709,=0.788;验证集:=7.171,=0.518)。
本研究确定了与MHD患者中心静脉CRI相关的六个危险因素。该预测模型具有良好的预后性能,临床医生可用于筛查中心静脉CRI的高危患者,从而能够早期实施风险管理策略。