新疆某骨科医院手术患者静脉血栓栓塞发生的高危因素探讨及预警模式研究

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Abstract Objective To investigate the high risk factors of venous embolization surgery thrombosis(VTE)in orthopedics patients at Xinjiang，to establishment of early warning mechanism based on statistical data，to provide reference for the prevention.Methods From January 2013 to December 2016，we collected data of 356 cases using a case cohort study model.According to the result of 2 month follow-up，the patients were divided into case group(VTE occur)and control group(no VTE occur).General information，clinical data，treatment data were collected.And we analyzed the risk factors using the data.An early warning model was established based on the results of high-risk factors and the weights of OR values.Results Compared with the Han，Uygur and Kazak was a risk factor for the occurrence for VTE occurrence(OR＝1.844，3.569).Compared with the urban，rural residents was a risk factor for VTE occurrence(OR＝6.083).Compared with the Lower education cases，higher education was a protection factor.Infusion and blood transfusion were risk factors of VTE(OR＝1.642，3.833).The application of hemostatic drugs were high risk factors for the occurrence of VTE surgery(OR＝3.884).Thrombolytic drugs use were protect factor for VTE occurrence〓(OR＝0.266).Case group’s age，weight and BMI were significantly higher than control group(P＜0.05).High risk of VTE for surgical patients in the department of orthopedics were residence in rural areas，intraoperative blood transfusion，hemostatic drugs used，BMI greater than 20，older than 50 years old，weight greater than 58 kg，intraoperative infusion.Protective factors include patients with high school education and preventive drug.The early warning model was validated by the ROC curve，and patients with the score higher than 16.5 points was in high risk.Conclusion The risk warning mechanism established in this study can be applied clinically to reduce the incidence of VTE and improve the prognosis in Xinjiang aera.

Key words： surgery venous thromboembolism high risk factors warning model

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