AUTHOR=Liu Binliang , Xie Junying , Sun Xiaoying , Wang Yanfeng , Yuan Zhong , Liu Xiyu , Huang Zhou , Wang Jiani , Mo Hongnan , Yi Zongbi , Guan Xiuwen , Li Lixi , Wang Wenna , Li Hong , Ma Fei , Zeng Yixin 

TITLE=Development and Validation of a New Clinical Prediction Model of Catheter-Related Thrombosis Based on Vascular Ultrasound Diagnosis in Cancer Patients

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 7 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2020.571227

DOI=10.3389/fcvm.2020.571227

ISSN=2297-055X

ABSTRACT=Background: The central venous catheter brings convenience for drug delivery and improves comfort for cancer patients, it also causes serious complications. The most common one is catheter-related thrombosis (CRT). 
Objectives:This study aimed to evaluate the incidence and risk factors of CRT in cancer patients and to develop an effective prediction model for CRT in cancer patients. 
Methods: The development of our prediction model was based on the data of a retrospective cohort (n=3131) from National Cancer Center. The validation of our prediction model was done in a prospective cohort from National Cancer Center (n=685) and a retrospective cohort from Hunan Cancer Hospital (n=61). The predictive accuracy and the discriminative ability were determined by the receiver operating characteristic curves and calibration plots. 
Results: Multivariate analysis demonstrated that sex, cancer type, catheter type, position of the catheter tip, chemotherapy status, and antiplatelet/anticoagulation status at baseline were independent risk factors for CRT. The area under receiver operating characteristic (ROC) curve of our prediction model was 0.741 (CI: 0.715-0.766) in the primary cohort; 0.754 (CI: 0.704-0.803) and 0.658 (CI: 0.470-0.845) in validation cohorts respectively. Good calibration and clinical impact were also shown in primary and validation cohorts.
Conclusions: Our model is a novel prediction tool for CRT risk which helps to assign cancer patients into high-risk or low-risk groups accurately. Our model will be valuable for clinicians in the decision making of thromboprophylaxis.