[1]王驿洹,何花,董大立△.基于 “治未病” 理论与XGBoost算法的膝骨关节炎患者下肢深静脉血栓风险预测模型的构建与验证[J].中国中医骨伤科杂志,2026,34(08):14-20.[doi:10.20085/j.cnki.issn1005-0205.260803]
　WANG Yihuan,HE Hua,DONG Dali.Construction and Validation of a Risk Prediction Model for Lower Extremity Deep Venous Thrombosis in Patients with Knee Osteoarthritis Based on the “Preventive Treatment of Disease” Theory and XGBoost Algorithm[J].Chinese Journal of Traditional Medical Traumatology & Orthopedics,2026,34(08):14-20.[doi:10.20085/j.cnki.issn1005-0205.260803]
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基于 “治未病” 理论与XGBoost算法的膝骨关节炎患者下肢深静脉血栓风险预测模型的构建与验证()

《中国中医骨伤科杂志》[ISSN:1005-0205/CN:42-1340/R]

卷:
第34卷
期数:
2026年08期
页码:
14-20
栏目:
临床研究
出版日期:
2026-08-10

文章信息/Info

Title:
Construction and Validation of a Risk Prediction Model for Lower Extremity Deep Venous Thrombosis in Patients with Knee Osteoarthritis Based on the “Preventive Treatment of Disease” Theory and XGBoost Algorithm
文章编号:
1005-0205(2026)08-0014-07
作者:
王驿洹1; 何花1; 董大立1△
1湖南中医药大学第二附属医院(长沙,410005)
Author(s):
WANG Yihuan1; HE Hua1; DONG Dali1△
1The Second Hospital of Hunan University of Chinese Medicine, Changsha 410005,China.
关键词:
膝骨关节炎;  下肢深静脉血栓;  治未病;  预测模型;  XGBoost算法
Keywords:
knee osteoarthritis;  lower extremity deep vein thrombosis;  preventive treatment of disease;  prediction model;  xgboost algorithm
分类号:
R684.3
DOI:
10.20085/j.cnki.issn1005-0205.260803
文献标志码:
A
摘要:
目的:基于“消未起之患,治未病之疾”理论,构建膝骨关节炎患者下肢深静脉血栓的风险预测模型,为早期预防和干预提供量化工具。方法:回顾性分析2018年3月至2023年3月收治的883例膝骨关节炎住院患者的临床资料,通过下肢深静脉彩超检查,将患者分为深静脉血栓组和无深静脉血栓组,提取血常规、凝血功能、生化指标等25项特征,利用SPSS 25.0软件进行单因素分析,筛选出有显著差异的因素,然后采用XGBoost算法构建预测模型,将883例患者按7:3 比例随机分为训练集(n=618)与验证集(n=265),采用递归特征消除法筛选关键特征,探究膝骨关节炎患者发生下肢深静脉血栓的风险因素。结果:膝骨关节炎患者下肢深静脉血栓发生率为8.61%。XGBoost模型在验证集曲线下面积(AUC)为0.804,特异性为0.877,敏感性为0.643,筛选出14个关键特征,其中血浆凝血酶原时间(PT)、血细胞比容(HCT)、中性粒细胞(NEU)百分比是核心危险因素,基于XGBoost算法的预测模型可有效识别膝骨关节炎患者下肢深静脉血栓风险。结论:该风险预测模型能有效预测膝骨关节炎患者发生下肢深静脉血栓的风险因素,为早期预防血栓提供参考。
Abstract:
Objective:Based on the theory of “eliminating impending risks and treating diseases before they manifest”(a core concept of preventive healthcare in traditional Chinese medicine),this study aims to establish a risk prediction model for lower extremity deep vein thrombosis(DVT)in patients with knee osteoarthritis(KOA),thereby providing a quantitative tool for early prevention and intervention.Methods:A retrospective analysis was conducted on the clinical data of 883 hospitalized KOA patients treated from March 2018 to March 2023.All patients were divided into the DVT group and non-DVT group based on the results of lower extremity deep vein color Doppler ultrasound examination.A total of 25 features,including routine blood test indicators,coagulation function indicators,and biochemical indicators,were extracted.SPSS 25.0 software was used for univariate analysis to screen out factors with significant differences.Subsequently,the XGBoost algorithm was adopted to construct the prediction model.The 883 patients were randomly divided into a training set(n=618)and a validation set(n=265)at a ratio of 7:3.Recursive feature elimination(RFE)was used to screen key features and explore the risk factors of lower extremity DVT in KOA patients.Results:The incidence of lower extremity DVT in KOA patients was 8.61%.For the XGBoost model,the area under the curve(AUC)in the validation set was 0.804,with a specificity of 0.877 and a sensitivity of 0.643.Fourteen key features were screened out,among which prothrombin time(PT),hematocrit(HCT),and neutrophil percentage(NEU%)were the core risk factors.The prediction model based on the XGBoost algorithm could effectively identify the DVT risk in KOA patients.Conclusion:This study can effectively predict the risk factors of DVT in KOA patients and provide a reference for the early prevention of thrombosis.

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(收稿日期:2025-10-31)

备注/Memo

备注/Memo:
基金项目:湖南省自然科学基金项目(2025JJ80903,2025JJ80920)
湖南省中医药科研计划项目(B2024078)
湖南省教育厅科学研究项目(25B0347)
△通信作者 E-mail:479459666@qq.com
更新日期/Last Update: 2026-08-15