Risk factor analysis and nomogram model establishment for predicting lower limb deep vein thrombosis in patients with high-energy injuries undergoing ankle surgery
10.3760/cma.j.cn341190-20221114-00902
- VernacularTitle:高能量损伤踝关节术后患者发生下肢深静脉血栓风险及列线图模型建立
- Author:
Feng WANG
1
;
Benqian ZHAO
Author Information
1. 温州市中西医结合医院创伤骨科，温州 325000
- Keywords:
Ankle joint;
Fractures, bone;
Thrombosis;
Lower extremity;
Risk factors;
Factor analysis, statistical;
Regression analysis;
Nomograms
- From:
Chinese Journal of Primary Medicine and Pharmacy
2024;31(5):641-645
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the risk of lower limb deep vein thrombosis (DVT) in patients with high-energy injuries undergoing ankle surgery and establish a nomogram model.Methods:A retrospective analysis was conducted on the clinical data of 96 patients with high-energy injuries who underwent ankle surgery at the Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine between January 2014 and January 2021. In this case-control study, patients were divided into a case group ( n = 35 patients with lower limb DVT) and a control group ( n = 61 patients without lower limb DVT). The clinical data of patients with high-energy injuries undergoing ankle surgery were collected. Univariate analysis and logistic regression analysis were conducted to identify independent risk factors for DVT of the lower limbs. Based on the results of the logistic regression analysis, a nomogram model was further established. Results:Age (95% CI: 1.312-7.421), smoking history (95% CI: 1.564-7.160), diabetes history (95% CI: 1.868-14.229), and elevated total cholesterol (95% CI: 1.775-5.135) were identified as risk factors for lower limb DVT in patients with high-energy injuries undergoing ankle surgery (all P < 0.05). The nomogram risk prediction model demonstrated an increased risk factor score, indicating an elevated risk of developing lower limb DVT. The receiver operating characteristic curve predicts that the area under the model's curve was 0.873 ( P < 0.05). Repeated sampling was conducted using the Bootstrap method and 1 000 internal verifications were performed. The model demonstrated good goodness of fit ( χ2 = 5.36, P = 0.327). The GiViTI calibration curve, with a confidence interval of 80%-90%, did not cross the 45° angle bisector in the graph ( P > 0.05). When the threshold probability in the decision curve analysis was at 30.8%, the net benefit for patients was maximized. Conclusion:Advanced age, smoking, history of diabetes, and elevated total cholesterol are independent risk factors for the development of lower limb DVT among patients with high-energy ankle injuries undergoing ankle surgery. As the risk factor scores increase based on the nomogram model, the risk of developing lower limb DVT increases.
