This study aims to develop a predictive model based on clinical and laboratory indicators to assess the risk of deep vein thrombosis (DVT) in patients with knee fractures, thereby facilitating early identification of high-risk individuals and guiding personalised interventions. Patients with knee fractures were randomly assigned to either a training or validation cohort in a 7:3 ratio. Discrepancy analysis was conducted to compare the variables between the two cohorts. A multiple logistic regression model was employed to identify the independent predictors of lower limb DVT in patients with knee fractures. Receiver operating characteristic (ROC) curves were subsequently utilized to evaluate the diagnostic precision of risk markers, with predictive efficacy assessed through area under the curve calculations. Among 923 hospitalised patients meeting the study criteria, we identified six independent factors—age, PTA, FIB, TT, D-dimer, and BMI—for nomogram construction from 21 variables. The area under the ROC curve for predicting lower extremity DVT was 0.75 (95% confidence interval CI: 0.71–0.79) in the training set and 0.76 in the validation set. Decision curve analysis confirmed the nomogram’s clinical applicability. The nomogram developed in this study shows intense discrimination, calibration, and clinical utility, suggesting promise for predicting lower extremity DVT and functioning as a quantitative instrument in clinical settings.
Jin et al. (Fri,) studied this question.