Nomogram for Predicting Deep Venous Thrombosis in Lower Extremity Fractures. (23rd June 2021)

• Record Type: Journal Article
• Authors: Lin, Ze; Mi, Bobin; Liu, Xuehan; Panayi, Adriana C.; Xiong, Yuan; Xue, Hang; Zhou, Wu; Cao, Faqi; Liu, Jing; Hu, Liangcong; Hu, Yiqiang; Chen, Lang; Yan, Chenchen; Xie, Xudong; Guo, Junfei; Hou, Zhiyong; Sun, Yun; Zhang, Yingze; Hu, Yu; Liu, Guohui
• Other Names: Scribante Andrea Academic Editor.
• Is Part Of: BioMed research international. Volume 2021(2021)
• Publication Date: 2021-06-23
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2021/9930524
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Deep venous thrombosis (DVT) is a common complication in patients with lower extremity fractures, causing delays in recovery short-term and possible impacts on quality of life long-term. Early prediction and prevention of thrombosis can effectively reduce patient pain while improving outcomes. Although research on the risk factors for thrombosis is prevalent, there is a stark lack of clinical predictive models for DVT occurrence specifically in patients with lower limb fractures. In this study, we aim to propose a new thrombus prediction model for lower extremity fracture patients. Data from 3300 patients with lower limb fractures were collected from Wuhan Union Hospital and Hebei Third Hospital, China. Patients who met our inclusion criteria were divided into a thrombosis and a nonthrombosis group. A multivariate logistic regression analysis was carried out to identify predictors with obvious effects, and the corresponding formulas were used to establish the model. Model performance was evaluated using a discrimination and correction curve. 2662 patients were included in the regression analysis, with 1666 in the thrombosis group and 996 in the nonthrombosis group. Predictive factors included age, Body Mass Index (BMI), fracture-fixation types, energy of impact at the time of injury, blood transfusion during hospitalization, and use of anticoagulant drugs. The discriminative ability of the model was verified using the C-statistic (0.676). For the convenience of clinical use, a score table and nomogram were compiled. Data from two centers were used to establish a novel thrombus prediction model specific for patients with lower limb fractures, with verified predictive ability.