Zhang Mengsu, Zhang Jie, Jin Guangxin, Zhang Fangqin, Dai Mengjun, Xu Shengxi, Zhang Xuebin, Pu Jun
Department of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, 200127, China.
Department of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Cardiovasc Intervent Radiol. 2025 Aug 25. doi: 10.1007/s00270-025-04067-x.
To develop and validate a model for predicting totally implantable venous access device (TIVAD)-related infections.
MATERIALS & METHODS: Patients who underwent placement of TIVADs between 2014 and 2023 were included and divided into a training set (n = 5465) and a validation set (n = 2341). The clinical characteristics of 7806 patients with TIVADs were analysed. Model 1 was developed using both least absolute shrinkage and selection operator (LASSO) regression and bidirectional stepwise logistic regression, whereas Model 2 was developed using only bidirectional stepwise logistic regression. Two models were compared using the Akaike information criterion (AIC), decision curve analysis (DCA), clinical impact curves (CICs), area under the curve (AUC) of receiver operating characteristic curves (ROCs) and calibration curves, and the better model was chosen as the final model.
Model 1 included body mass index (BMI), sex, outpatient or inpatient status, tunnel length, catheter-related thrombosis (CRT), primary catheter dislodgement and age. Model 2 included sex, catheter diameter, CRT, age, outpatient or inpatient status, catheter length, primary catheter dislodgement, BMI and tumour type. Both models demonstrated clinical usefulness according to DCA and CICs. Model 2 was chosen as the final model because of its superior AIC value and calibration performance.
We developed and validated a model for predicting TIVAD-related infections. We recommend a 5F catheter and prophylactic antibiotics for patients with a Model 2 infection risk score above 0.026. We also suggest appropriately increasing the tunnel length, using a smaller catheter, and strictly adhering to heparin flush protocols to prevent thrombosis.
开发并验证一种用于预测完全植入式静脉通路装置（TIVAD）相关感染的模型。
纳入2014年至2023年间接受TIVAD植入的患者，并分为训练集（n = 5465）和验证集（n = 2341）。分析了7806例TIVAD患者的临床特征。模型1采用最小绝对收缩和选择算子（LASSO）回归及双向逐步逻辑回归开发，而模型2仅采用双向逐步逻辑回归开发。使用赤池信息准则（AIC）、决策曲线分析（DCA）、临床影响曲线（CIC）、受试者操作特征曲线（ROC）的曲线下面积（AUC）和校准曲线对两个模型进行比较，选择更好的模型作为最终模型。
模型1包括体重指数（BMI）、性别、门诊或住院状态、隧道长度、导管相关血栓形成（CRT）、原发性导管移位和年龄。模型2包括性别、导管直径、CRT、年龄、门诊或住院状态、导管长度、原发性导管移位、BMI和肿瘤类型。根据DCA和CIC，两个模型均显示出临床实用性。由于其优越的AIC值和校准性能，模型2被选为最终模型。
我们开发并验证了一种用于预测TIVAD相关感染的模型。对于模型2感染风险评分高于0.026的患者，我们推荐使用5F导管和预防性抗生素。我们还建议适当增加隧道长度，使用较小的导管，并严格遵守肝素封管方案以预防血栓形成。
