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RESEARCH PRODUCT
Development and Validation of a New Clinical Prediction Model of Catheter-Related Thrombosis Based on Vascular Ultrasound Diagnosis in Cancer Patients
Lixi LiJunying XieXiyu LiuYixin ZengYixin ZengZhou HuangHong LiWenna WangBinliang LiuZhong YuanXiaoying SunFei MaZongbi YiJiani WangHongnan MoXiuwen GuanYanfeng Wangsubject
0301 basic medicinemedicine.medical_specialtylcsh:Diseases of the circulatory (Cardiovascular) systemMultivariate analysis030204 cardiovascular system & hematologyCardiovascular Medicinecathetersnomogram03 medical and health sciences0302 clinical medicineInternal medicinemedicinecancerRisk factorProspective cohort studythrombosisOriginal ResearchReceiver operating characteristicbusiness.industryCancerRetrospective cohort studyNomogrammedicine.disease030104 developmental biologyrisk factorlcsh:RC666-701CohortCardiology and Cardiovascular Medicinebusinessdescription
Background: Central venous catheters are convenient for drug delivery and improved comfort for cancer patients, but they also cause serious complications. The most common complication is catheter-related thrombosis (CRT). Objectives: This study aimed to evaluate the incidence and risk factors for CRT in cancer patients and develop an effective prediction model for CRT in cancer patients. Methods: The development of our prediction model was based on a retrospective cohort (n = 3,131) from the National Cancer Center. Our prediction model was confirmed in a prospective cohort from the National Cancer Center (n = 685) and a retrospective cohort from the Hunan Cancer Hospital (n = 61). The predictive accuracy and discriminative ability were determined by receiver operating characteristic (ROC) 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 the ROC curve of our prediction model was 0.741 (CI: 0.715-0.766) in the primary cohort and 0.754 (CI: 0.704-0.803) and 0.658 (CI: 0.470-0.845) in validation cohorts 1 and 2, respectively. The model also showed good calibration and clinical impact in the primary and validation cohorts. Conclusions: Our model is a novel prediction tool for CRT risk that accurately assigns cancer patients into high- and low-risk groups. Our model will be valuable for clinicians when making decisions regarding thromboprophylaxis.
year | journal | country | edition | language |
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2020-10-01 | Frontiers in Cardiovascular Medicine |