Associated factors and predictive modeling of deep vein thrombosis in stroke patients undergoing rehabilitation.

Background: This study aimed to identify factors associated with deep vein thrombosis (DVT) and to develop predictive models to facilitate individualized risk assessment in this population. Methods: We retrospectively analyzed 240 patients with stroke admitted to a rehabilitation department, including 63 patients with DVT and 177 without DVT. Baseline demographic, clinical, and laboratory variables were collected. Univariate analysis was performed to screen variables potentially associated with DVT, followed by multivariable logistic regression to identify variables independently associated with DVT. In parallel, six machine-learning models were developed using all baseline variables. The full cohort was evaluated across 100 stratified 8:2 train-test splits, with repeated five-fold cross-validation performed within each training subset and corresponding held-out subsets used for testing. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), and SHapley Additive exPlanations (SHAP) were used to enhance interpretability. Results: Multivariable analysis identified lower Barthel Index [odds ratio (OR) = 0.979, Conclusion: In conclusion, our findings support the use of integrated predictive approaches for DVT risk assessment and targeted screening in stroke rehabilitation settings.