中华骨质疏松和骨矿盐疾病杂志2024，Vol.17Issue(6)：580-586,7.DOI:10.3969/j.issn.1674-2591.2024.06.006
基于巢式病例对照研究构建椎体压缩骨折患者术前深静脉血栓形成的风险预测模型
Construction of a risk prediction model for preoperative deep vein thrombosis in patients with vertebral compression fractures based on nested case-control studies
刘录 1司海朋 1李春林 1任丽 1朱玉娇 1贺茜1
作者信息
- 1. 266000 山东青岛,山东大学齐鲁医院 (青岛) 脊柱外科
- 折叠
摘要
Abstract
Objective To analyze the risk factors for preoperative deep vein thrombosis(DVT)in patients with vertebral compression fractures and to construct a DVT diagnostic model.Methods Patients with osteoporotic vertebral compression fracture admitted to the Department of Spine Surgery,Qilu Hospital of Shandong University(Qingdao)from January 2020 to October 2023 were selected as the research subjects.The nested case-control study was conducted with admission and diagnosis of osteoporotic vertebral compression fracture as the starting point and discharge as the end point.The extreme gradient lifting algorithm XGBoost was used to train the DVT diagnosis model,and the accuracy and area un-der receiver operating characteristic(ROC)curve(AUC)were used to evaluate the model performance.Results Based on XGBoost training DVT diagnostic model and visualization of the top 20 important features,it could be seen that the two indicators of prothrombin time parathyroid hormone are more important for the diagnosis of DVT.The diagnostic accuracy of 90.48%could be achieved by evaluating the model on the test set.The AUC of the DVT diagnostic model calculated by ROC curve was 0.955 8,and the difference was statistically significant(P＜0.05).Conclusion The DVT diagnostic model based on XGBoost training has good performance and generalization ability for screening high-risk DVT patients be-fore surgery.By visualizing the top 20 risk factors that can cause deep vein thrombosis in patients with vertebral compres-sion fracture before surgery,this study facilitates clinical identification of high-risk DVT patients in time and gives corre-sponding intervention measures to avoid prolonged surgery waiting time.关键词
椎体压缩性骨折/深静脉血栓形成/机器学习算法/巢式病例对照研究Key words
compression fracture of vertabral body/deep vein thrombosis/machine learning algorithm/nes-ted case-control study分类
医药卫生引用本文复制引用
刘录,司海朋,李春林,任丽,朱玉娇,贺茜..基于巢式病例对照研究构建椎体压缩骨折患者术前深静脉血栓形成的风险预测模型[J].中华骨质疏松和骨矿盐疾病杂志,2024,17(6):580-586,7.
