Abstract:
Background: A peripherally inserted central catheter (PICC) is often necessary for patients receiving chemotherapy, but there is a risk of PICC-related venous thrombosis (VT).
Objective: To use the Caprini thrombosis risk model and color Doppler flow imaging (CDFI) for the dynamic monitoring of an eventual thrombosis in patients receiving chemotherapy.
Methods: This prospective study was carried out from 01/2018 to 05/2019 in patients who underwent PICC implantation and maintenance at the First Affiliated Hospital of Fujian Medical University. The outcome event was the occurrence of PICC-related upper extremity venous thrombosis confirmed by CDFI.
Results: A total of 201 participants were enrolled, of whom 108 (53.7%) developed VT. Three participants (1.5%) developed symptomatic VT. Univariable logistic regression analysis suggested that the Caprini score (OR=1.243, 95%CI: 1.074-1.438, P=0.003), the PICC model (OR=0.448, 95%CI: 0.223-0.901, P=0.024), and a previous history of PICC-related deep vein thrombosis (OR=9.388, 95%CI: 1.178-74.786, P=0.034) were associated with PICC-related upper extremity VT. Multivariable logistic regression analysis showed that only the Caprini score (OR=1.188, 95%CI: 1.018-1.386, P=0.029) was an independent risk factor for PICC-related venous thrombus. Receiver operating characteristic (ROC) curve analysis showed the Caprini risk assessment model had a predictive value for upper extremity VT, with an area under the curve of 0.615 (95%CI: 0.538-0.693), 74.1% sensitivity, and 44.1% specificity.
Conclusion: The Caprini score is an independent predictor of the development of PICC-related VT in cancer patients. However, the moderate sensitivity and low specificity of the Caprini risk assessment model may limit its predictive value in the clinical setting.
Reference:
Lin Y, Zeng Z, Lin R, Zheng J, Liu S, Gao X. The Caprini thrombosis risk model predicts the risk of PICC-related upper extremity venous thrombosis in cancer patients. J Vasc Surg Venous Lymphat Disord. 2020 Dec 28:S2213-333X(20)30731-9. doi: 10.1016/j.jvsv.2020.12.075. Epub ahead of print. PMID: 33383236.