Construction of Prediction Model of Deep Vein Thrombosis Risk after Total Knee Arthroplasty Based on XGBoost Algorithm

• Published in: Computational and mathematical methods in medicine Vol. 2022; pp. 1 - 6
• Main Authors: Chen, Yuhuan, Jiang, Yingqing
• Format: Journal Article
• Language: English
• Published: United States Hindawi 25.01.2022
• Subjects: Aged; Algorithms; Arthroplasty, Replacement, Knee - adverse effects; Computational Biology; Decision Trees; Female; Fibrin Fibrinogen Degradation Products - metabolism; Heart Disease Risk Factors; Hemoglobins - metabolism; Humans; Machine Learning; Male; Middle Aged; Models, Cardiovascular; Postoperative Complications - blood; Postoperative Complications - etiology; Predictive Value of Tests; Retrospective Studies; Venous Thrombosis - blood; Venous Thrombosis - etiology
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2022/3452348

Objective. Based on the XGBoost algorithm, the prediction model of the risk of deep vein thrombosis (DVT) in patients after total knee arthroplasty (TKA) was established, and the prediction performance was compared. Methods. A total of 100 patients with TKA from January 2019 to December 2020 were retrospectively selected as the study subjects and randomly divided into a training set (n=60) and a test set (n=40). The training set data was used to construct the XGBoost algorithm prediction model and to screen the predictive factors of postoperative DVT in TKA patients. The prediction effect of the model was evaluated by using the test set data. An independent sample T-test was used for comparison between groups, and the χ2 test was used for comparison between counting data groups. Results. The top five items were combined with multiple injuries (35 points), time from injury to operation (28 points), age (24 points), combined with coronary heart disease (21 points), and D-dimer 1 day after operation (16 points). In the training set, the area under the curve of the XGBoost algorithm model was 0.832 (95% CI: 0.748-0.916). Conclusion. The model based on the XGBoost algorithm can predict the incidence of DVT in patients after TKA with good performance.