结直肠癌术后静脉血栓栓塞症风险预测模型研究进展OA

Venous thromboembolism(VTE)constitutes a severe postoperative complication of colorectal cancer(CRC),significantly increases patient mortality and healthcare burden.In China,the incidence of post-CRC VTE reaches 11.2%within one month,while adherence to guideline-compliant prophylaxis rates remains low at 10.3%.This highlights the critical need for precise risk assessment tools.While widely utilized generic models such as Caprini may lack CRC specificity,and the Khorana score shows moderate discrimination(C-statistic 0.7),emerging CRC-specific models(e.g.,CRC-VTE score,AUC 0.72)and machine learning approaches(e.g.,XGBoost,AUC up to 0.908)demonstrate better performance.Nonetheless,the majority of these models are derived from single-center retrospective data,which limits their generalizability.Consequently,there is a pressing necessity to develop and validate the dynamic population-tailored prediction models to optimize perioperative VTE prevention and reduce related morbidity and mortality.This review systemati-cally examines the risk factors associated with VTE formation following CRC surgery,synthesizes research progress and clinical applications of predictive models,aims to provide evidence-based support for optimizing perioperative VTE prevention and treatment strategies in CRC.