Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou 215006, China.
Comput Math Methods Med. 2022 Jan 25;2022:3452348. doi: 10.1155/2022/3452348. eCollection 2022.
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.
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 ( = 60) and a test set ( = 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 -test was used for comparison between groups, and the test was used for comparison between counting data groups.
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).
The model based on the XGBoost algorithm can predict the incidence of DVT in patients after TKA with good performance.
基于 XGBoost 算法，建立全膝关节置换术（TKA）后深静脉血栓形成（DVT）风险的预测模型，并比较预测性能。
回顾性选取 2019 年 1 月至 2020 年 12 月 100 例 TKA 患者作为研究对象，随机分为训练集（n=60）和测试集（n=40）。使用训练集数据构建 XGBoost 算法预测模型，并筛选 TKA 患者术后 DVT 的预测因素。使用测试集数据评估模型的预测效果。组间比较采用独立样本 t 检验，计数资料组间比较采用 χ 2 检验。
前 5 项综合损伤（35 分）、受伤至手术时间（28 分）、年龄（24 分）、合并冠心病（21 分）、术后 1 天 D-二聚体（16 分）。在训练集中，XGBoost 算法模型的曲线下面积为 0.832（95%CI：0.748-0.916）。
基于 XGBoost 算法的模型可以较好地预测 TKA 后 DVT 的发生。
