AUTHOR=Duan Jincai , Xiao Tianjie , Xing Tianyou , Qin Wei , Wang Zhihui , Dou Haoduan , Wu Di , Du Yuanliang 

TITLE=A nomogram clinical model for predicting preoperative lower limb venous thrombosis in knee ligament injuries: a retrospective study

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1486625

DOI=10.3389/fmed.2025.1486625

ISSN=2296-858X

ABSTRACT=PurposeTo identify independent risk factors for preoperative lower limb venous thrombosis (LLVT) in knee ligament injuries and to develop a diagnostic prediction model based on these factors.MethodsPatients with knee ligament injuries who presented to our hospital between July 2021 and December 2023 were included in this study. Logistic regression analysis was utilized to determine independent risk factors for preoperative LLVT in knee ligament injuries and to construct a diagnostic prediction model. The diagnostic performance of the model was evaluated using receiver operating characteristic curves (ROC) and calibration curves.ResultsCompared with the None-LLVT group, the LLVT group showed statistically significant differences in age, gender, damaged ligament site, injury-examination time, low density lipoprotein (LDL), glucose (G), D-dimer, and fibrinogen degradation products (FDP) (P < 0.05). Multivariate logistic regression analysis showed that gender (P = 0.006, 95% CI [1.647-19.450]), damaged ligament site (P = 0.016, 95% CI [1.385-23.060]), and D-dimer > 0.55 mg/L (P < 0.001, 95% CI [3.029-37.845]) were independent risk factors for preoperative LLVT in patients with knee ligament invasion. The ROC showed good diagnostic efficacy, with an area under the curve (AUC) of 0.888, and the calibration curves showed good agreement (mean absolute error = 0.013).ConclusionGender, damaged ligament site, and D-dimer level can be used as independent risk factors for the preoperative prediction of LLVT, and the nomogram model proposed in this study can better assist clinicians in making clinical decisions.