骨科大手术相关深静脉血栓风险预测模型的研究进展

Research progress on risk prediction models of deep venous thrombosis in major orthopedic surgery

深静脉血栓(DVT)在骨科大手术患者中具有高发病率、高致死率的特点,严重影响了患者的预后,增加了患者的经济负担。有效、精确地预测患者发生DVT的风险,及时、准确地为患者采取预防措施,是DVT风险管理的保障。目前,骨科患者常用的DVT风险预测模型包括Caprini、Autar、Wells-DVT和RAPT血栓评估模型,对患者DVT的发生有一定的预测作用且各具优缺点。随着大数据时代的到来,人工智能快速发展,基于机器学习算法建立的DVT风险预测模型表现出更好的预测性能,是未来DVT风险预测模型发展的趋势。

Deep vein thrombosis(DVT)is characterized by high morbidity and mortality in patients undergoing major orthopedic surgery,which seriously affects the prognosis of patients and increases the economic burden of patients.Effective and accurate prediction of patients'risk of DVT and timely and accurate preventive measures for patients are the guarantee of DVT risk management.Currently,the commonly used DVT risk prediction models for orthopaedic patients include Caprini,Autar,Wells-DVT,and RAPT thrombosis assessment models,which have certain predictive effects on the occurrence of DVT in patients,and each has its own advantages and disadvantages.With the advent of the era of big data and the rapid development of artificial intelligence,the DVT risk prediction model based on machine learning algorithm shows better prediction performance,which is the development trend of the future DVT risk prediction model.