Abstract:

Objective To construct and verify a risk nomogram prediction model of lower extremity deep venous thrombosis（DVT） in patients with acute stroke. Methods A prospective study design was adopted. 602 patients with acute stroke hospitalized in a tertiary general hospital in Nanning，Guangxi province from January 2020 to April 2021 were selected as the research subjects，of which 415 patients from January 2020 to December 2020 were allocated into the modeling group and 187 patients from January 2021 to April 2021 in the validation group for external validation. Univariate and multivariate logistic regression were used to analyze the risk factors of lower limb DVT in acute stroke，to establish the risk prediction model and draw the nomogram model. Receiver operating characteristic（ROC） and Hosmer lemeshow were used to verify the predictive effect of the model. Results 35 of 415 cases in the modeling group had DVT，with an incidence of 8.4%;19 of 187 cases in the validation group had DVT，with an incidence of 10.2%. The results of univariate analysis in the modeling group showed that age，diagnosis，bed time，state of consciousness，degree of hemiplegia，smoking history，atrial fibrillation history，thrombosis history，use of dehydration drugs，and central venous catheterization，fibrinogen and D-dimer quantification were the influencing factors of DVT in patients with acute stroke. Multivariate logistic regression analysis showed that age，state of consciousness，degree of hemiplegia，the use of dehydrating drugs were independent risk factors for DVT of acute stroke（OR values were 1.901，1.702，1.940 and 3.231，all P<0.05）. The nomogram model was constructed with the above 4 factors;the area under the ROC curve of nomogram model is 0.850;the maximum value of Jordan index is 0.758;the sensitivity is 83%;the specificity is 82%;the best critical value is 0.071. Hosmer-Lemeshow test χ²=2.143，P=0.951;when the area under the ROC curve of the external verification group is 0.893 and the maximum value of Jordan index is 0.746，the sensitivity is 90%，the specificity is 85%，and the best critical value is 0.084. Conclusion The nomogram model can predict the risk DVT of acute stroke，and help to formulate personalized intervention measures.

Key words: Stroke, Venous Thrombosis, Risk Prediction Model, Nomogram, Nursing Care

摘要： 目的 构建并应用急性期脑卒中患者下肢深静脉血栓（deep vein thrombosis，DVT）风险列线图预测模型。方法 采用前瞻性研究设计，便利选取2020年1月—2021年4月在南宁市某三级甲等综合医院住院的602例急性期脑卒中患者作为研究对象。其中2020年1月—12月的415例作为建模组，2021年1月—4月的187例作为验证组对模型进行外部验证。采用单因素和多因素Logistic回归分析急性期脑卒中患者下肢DVT危险因素，建立风险预测模型并绘制列线图。采用受试者操作特征曲线（receiver operating characteristic，ROC）和Hosmer-Lemeshow检验验证模型预测效果。结果 建模组415例中有35例发生DVT，发生率为8.4%;验证组187例中有19例发生DVT，发生率为10.2%。建模组中单因素分析结果显示，年龄、诊断、卧床时间、意识状态、偏瘫程度，是否有吸烟史、房颤史、血栓史，是否使用脱水药物、是否留置中心静脉导管、血浆纤维蛋白原、D-二聚体定量是急性期脑卒中患者发生DVT的影响因素。多因素Logistic回归分析结果显示，年龄、意识状态、偏瘫程度、是否使用脱水药物是急性期脑卒中患者发生DVT的独立影响因素（OR值分别为1.901、1.702、1.940、3.231，均P<0.05），以上述4个因素为自变量构建列线图，模型ROC曲线下面积为0.850，约登指数最大值为0.758时，灵敏度为83%，特异度为82%，最佳临界值为0.071。Hosmer-Lemeshow拟合优度检验 χ²=2.143，P=0.951;外部验证组ROC曲线下面积为0.893，约登指数最大值为0.746时，灵敏度为90%，特异度为85%，最佳临界值为0.084。结论 构建的列线图可个性化预测急性期脑卒中患者DVT发生风险，有助于护理人员制订相应的干预措施。

关键词: 卒中, 静脉血栓形成, 风险预测模型, 列线图, 护理