Risk Factors and Development of a Risk Prediction Model for PICC-Related Thrombosis in Radiotherapy Inpatients: A...

This study aimed to identify independent risk factors associated with peripherally inserted central catheter-related venous thrombosis (PICC-RVT) in patients undergoing radiotherapy and to develop a risk prediction model to support early identification and inform targeted preventive strategies.

A single-center retrospective cohort study was conducted involving 335 patients with malignant tumors who underwent PICC placement in the Department of Radiotherapy Oncology between August 2023 and September 2024. Participants were randomly assigned to a modeling group (n = 250) and a validation group (n = 85) in a 3:1 ratio. Univariate analyses were conducted to identify potential risk factors, followed by multivariate logistic regression to determine independent predictors. A risk prediction model was subsequently constructed, and its performance was assessed using receiver operating characteristic (ROC) curve analysis.

Of the 335 patients who underwent ultrasound examination, 70 were found to have thrombosis, yielding a PICC-related thrombosis incidence of 20.89% (70/335). Of these, 5 cases (7.14%) were symptomatic and 65 (92.86%) were asymptomatic. Univariate analysis indicated significant associations between thrombosis and both disease diagnosis (p = 0.007) and total cumulative radiation dose (p = 0.049). Multivariate logistic regression identified total cumulative radiation dose (odds ratio = 1.020, 95% confidence interval: 0.962-0.999) and a history of thrombosis (odds ratio = 6.838, 95% confidence interval: 1.084-43.157) as independent risk factors. The area under the ROC curve was 0.589 in the modeling group and 0.591 in the validation group. Sensitivity values were 74.5% and 83.3%, respectively; however, specificity remained limited (43.9% in the modeling group and 39.4% in the validation group).

The predictive model identified total cumulative radiation dose and a prior history of thrombosis as independent risk factors for PICC-RVT in patients receiving radiotherapy. However, the model's specificity was low, restricting its utility to preliminary screening. Future studies with larger sample sizes and multicenter validation are needed to optimize the model and improve its predictive performance.