【摘要】 目的 构建急性缺血性脑卒中行机械取栓治疗患者住院期间预测下肢深静脉血栓形成(DVT)列线图模型。方法 选取2017年1月1日至2024年1月1日该院行机械取栓治疗的急性前循环大血管闭塞患者901例作为研究对象。根据术后是否发生下肢DVT分为DVT组(n=112)和非DVT组(n=789)。观察指标包括患者的临床相关资料、围手术期相关指标及相关的实验室指标。采用多因素logistic回归分析相关影响因素，进而建立列线图模型。采用受试者工作特征(ROC)曲线和曲线下面积(AUC)分析模型的预测效能。通过临床决策曲线分析(DCA)曲线评估预测模型的临床效益。结果 两组患者在年龄、入院时美国国立卫生研究院卒中量表(NIHSS)评分、糖尿病病史、吸烟病史，DVT病史等方面比较，差异有统计学意义(P<0.05)。两组患者在发病到股动脉穿刺时间、发病到入院时间、股动脉穿刺到血管再通时间、术后合并肺部感染方面比较，差异有统计学意义(P<0.05)。两组患者在D-二聚体、入院时静脉血糖、PLT方面比较，差异有统计学意义(P<0.05)。多因素logistic回归分析结果显示，入院时NIHSS评分、糖尿病病史、年龄、D-二聚体、发病到股动脉穿刺时间、术后合并肺部感染为机械取栓治疗的急性缺血性脑卒中患者住院期间下肢DVT的独立影响因素(P<0.05)。ROC曲线、Bootstrap法验证结果均验证显示列线图预测能力较强；DCA曲线显示，当发生阈值为0.12～0.96时，模型的临床获益性及适用性最佳。结论 构建的列线图模型能较好地预测患者的临床结局，临床适用性较为广泛。更多还原

【Abstract】 Objective To construct a nomogram model for predicting lower extremity deep vein thrombosis(DVT) during hospitalization in the patients undergoing mechanical thrombectomy due to acute ischemic stroke.Methods A total of 901 patients with acute anterior circulation large vessel occlusion undergoing mechanical thrombectomy in the hospital from January 1,2017 to January 1,2024 were selected as the study subjects and divided into the lower extremity DVT group(n=112) and non-DVT group(n=789) according to whether DVT occurred after surgery.The observation indicators included the clinically relevant data, perioperative related indicators and related laboratory indicators.The multivariate logistic regression was used to analyze the relevant influencing factors, and then the nomogram model was established.The receiver operating characteristic(ROC) curve and area under the curve(AUC) were used to analyze the predictive efficiency of the model.The clinical benefit of the predictive model was assessed by the clinical decision curve analysis(DCA) curve.Results There were statistically significant differences in the age, NIHSS score at admission, history of diabetes mellitus, history of smoking and history of DVT between the two groups(P<0.05).There were statistically significant differences in the time from onset to femoral artery puncture, time from onset to admission, time from femoral artery puncture to revascularization and postoperative complicating pulmonary infection between the two groups(P<0.05).There were statistically significant differences in D-dimer, venous blood glucose and PLT at admission between the two groups(P<0.05).The multivariate logistic regression analysis results showed that the NIHSS score at admission, diabetes history, age, D-dimer, time from onset to femoral artery puncture and postoperative complicating pulmonary infection were the independent influencing factors for lower extremity DVT during hospitalization in the patients with acute ischemic stroke treated with mechanical thrombectomy(P<0.05).The ROC curve and Bootstrap method verification results all showed that the nomogram predictive ability was strong.The DCA curve showed that when the threshold value was 0.12-0.96,the clinical benefit and applicability of the model were the best.Conclusion The constructed nomogram model can better predict the clinical outcome of the patients, and has a wide range of clinical applicability.更多还原