Patients with lower limb paralysis following acute ischemic stroke (AIS) are at a markedly increased risk of deep vein thrombosis (DVT), which may lead to pulmonary embolism and substantially higher mortality and disability. This review comprehensively reviews studies from the past decade on the epidemiology, pathophysiology, and prevention of DVT in AIS patients with lower limb paralysis. The pathogenesis of DVT in this population is multifactorial, involving venous stasis due to immobility, stroke-induced hypercoagulability, endothelial dysfunction, neutrophil extracellular trap-mediated immunothrombosis, and autonomic dysregulation. Effective prevention requires individualized risk stratification, integrating clinical assessment, biomarkers, and imaging tools. Current prophylactic strategies include pharmacological anticoagulation (primarily low-molecular-weight heparin), mechanical interventions (such as intermittent pneumatic compression), and early mobilization and rehabilitation. While combined approaches have demonstrated significant benefits, challenges remain regarding the timing of anticoagulation, balancing bleeding risks, extended thromboprophylaxis, and novel immunothrombosis targets. Future research should focus on personalized prevention protocols, the application of artificial intelligence-based predictive models, and innovative therapies targeting endothelial injury and immune-mediated thrombosis, aiming to improve thromboprophylaxis and overall outcomes in this high-risk population.

Background/Objectives: The accurate prediction of postprocedural mortality is critical for clinical decision-making; however, research on mortality risk models for patients undergoing mechanical thrombectomy remains limited. This study aimed to develop and validate machine learning models for predicting 90-day post-mechanical thrombectomy mortality. Methods: A retrospective-prospective cohort study involving 699 retrospective patients (January 2019–December 2022) and 274 prospective patients (January 2023–June 2024) from a single institution in Sichuan was conducted. The primary outcome was all-cause mortality within 90 days, ascertained via telephone follow-up. Predictors were identified using univariate analysis and LASSO regression. Eight predictive models were developed and evaluated using existing machine learning methods via 10-fold cross-validation. Model performance was assessed through discrimination, calibration, decision curve analysis, and interpretability via Shapley additive explanations. Results: The final dataset included 593 patients in the modeling set and 247 in the validation set. The 90-day mortality rates were 25.6% and 32.0%, respectively. Key predictors included age, hyperlipidemia, atrial fibrillation, pre-stroke statin use, antiplatelet/anticoagulant therapy within 48 h of onset, dysphagia, D-dimer levels, and activities of daily living scores. Logistic regression demonstrated superior performance in the modeling cohort (AUC = 0.87), whereas the multilayer perceptron model exhibited the greatest efficacy in the validation cohort (AUC = 0.77). Conclusions: Machine learning algorithms can accurately predict 90-day mortality among patients undergoing mechanical thrombectomy. The multilayer perceptron model demonstrated robust validation performance and offers a potential tool for personalized risk assessment and optimization of clinical decision-making.