ArticleRisk management and healthcare policy2026
Risk Factors and Development of a Risk Prediction Model for PICC-Related Thrombosis in Radiotherapy Inpatients: A Single-Center Retrospective Cohort Study.
Article in Risk management and healthcare policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: 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. Methods: 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. Results: 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 ( Conclusion: 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.
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