Lin Zhencan, Sun Hao, Li Deng, Cai Zhiqing, Huang Zhencheng, Liu Fangzhou, Chen Meiyi, Wang Yimin, Xu Jie, Ma Ruofan
Department of Orthopedics, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong China, 510120, China.
Department of Orthopedics, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong China, 518000, China.
Thromb J. 2023 Oct 12;21(1):106. doi: 10.1186/s12959-023-00538-8.
Deep venous thrombosis (DVT) prediction after total hip and knee arthroplasty remains challenging. Early diagnosis and treatment of DVT are crucial. This research aimed to develop a nomogram for early DVT prediction.
A total of 317 patients undergoing primary total hip and knee arthroplasty in Sun Yat-sen Memorial Hospital were enrolled between May 2020 and September 2022. Data from May 2020 to February 2022 were used as the development datasets to build the nomogram model (n = 238). Using multivariate logistic regression, independent variables and a nomogram for predicting the occurrence of DVT were identified. Datasets used to validate the model for internal validation ranged from March 2022 to September 2022 (n = 79). The nomogram's capacity for prediction was also compared with the Caprini score.
For both the development and validation datasets, DVT was found in a total of 38 (15.97%) and 9 patients (11.39%) on post-operative day 7 (pod7), respectively. 59.6% patients were symptomatic DVT (leg swelling). The multivariate analysis revealed that surgical site (Knee vs. Hip), leg swelling and thrombin-antithrombin complex (TAT) were associated with DVT. The previously indicated variables were used to build the nomogram, and for the development and validation datasets, respectively. In development and validation datasets, the area under the receiver operating characteristic curve was 0.836 and 0.957, respectively. In both datasets, the predictive value of the Nomogram is greater than the Caprini score.
A proposed nomogram incorporating surgical site (Knee vs. Hip), leg swelling, and thrombin antithrombin complex (TAT) may facilitate the identification of patients who are more prone to develop DVT on pod7.
全髋关节和膝关节置换术后深静脉血栓形成（DVT）的预测仍然具有挑战性。DVT的早期诊断和治疗至关重要。本研究旨在开发一种用于早期DVT预测的列线图。
2020年5月至2022年9月期间，中山大学孙逸仙纪念医院共有317例行初次全髋关节和膝关节置换术的患者入组。2020年5月至2022年2月的数据用作开发数据集以构建列线图模型（n = 238）。使用多因素逻辑回归，确定预测DVT发生的自变量和列线图。用于内部验证模型的数据集为2022年3月至2022年9月（n = 79）。还将列线图的预测能力与Caprini评分进行了比较。
在开发和验证数据集中，术后第7天（pod7）分别共有38例（15.97%）和9例（11.39%）患者发生DVT。59.6%的患者为有症状的DVT（腿部肿胀）。多因素分析显示，手术部位（膝关节与髋关节）、腿部肿胀和凝血酶 - 抗凝血酶复合物（TAT）与DVT相关。分别使用上述指出的变量构建列线图，用于开发和验证数据集。在开发和验证数据集中，受试者工作特征曲线下面积分别为0.836和0.957。在两个数据集中，列线图的预测价值均大于Caprini评分。
一种纳入手术部位（膝关节与髋关节）、腿部肿胀和凝血酶抗凝血酶复合物（TAT）的列线图可能有助于识别在pod7更易发生DVT的患者。
