Nomogram predicted risk of peripherally inserted central catheter related thrombosis

Abstract The use of peripherally inserted central catheters (PICCs) is increasing rapidly worldwide. A number of patient-related, clinical-related and device-related characteristics might be risk factors for PICC-related thrombosis. We retrospectively reviewed a database of 320 consecutive patients...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Nan Hao, Xin Xie, Zhangjian Zhou, Jieqiong Li, Li Kang, Huili Wu, Pingli Guo, Chengxue Dang, Hao Zhang
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2017
Materias:
R
Q
Acceso en línea:https://doaj.org/article/16509049ed994d7c8905a33da037a294
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:Abstract The use of peripherally inserted central catheters (PICCs) is increasing rapidly worldwide. A number of patient-related, clinical-related and device-related characteristics might be risk factors for PICC-related thrombosis. We retrospectively reviewed a database of 320 consecutive patients who underwent PICC insertion between December 2014 and December 2015 at the First Affiliated Hospital of Xi’an Jiaotong University to explore the potential associations between risk factors and PICC-associated thrombosis. A novel nomogram for predicting risk was developed based on the data. The nomogram prediction model included ten risk factors that were derived from different relevant estimates. The nomogram prediction model showed good discriminatory power (Harrell’s C-index, 0.709) and a high degree of similarity to actual thrombosis occurring after calibration. Furthermore, principal component analysis was performed to identify the factors that most influence PICC-related thrombosis. Our novel nomogram thrombosis risk prediction model was accurate in predicting PICC-related thrombosis. Karnofsky performance scores, D-dimer and blood platelet levels and previous chemotherapy were principal components. Our findings might help clinicians predict thrombosis risk in individual patients, select proper therapeutic strategies and optimize the timing of anticoagulation therapy.