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中华胸部外科电子杂志

中华胸部外科电子杂志 ›› 2017, Vol. 04 ›› Issue (03) : 186 -189. doi: 10.3877/cma.j.issn.2095-8773.2017.03.11

所属专题： 专题评论； 文献；

论著

Caprini和Rogers风险评估模型联合使用可以提高胸外科术后患者筛选静脉血栓栓塞症的准确性
田博1, 宋春凤1, 李辉1,*(), 傅毅立1, 张文谦1, 胡晓星1, 陈其瑞1, 游宾1, 陈硕1, 李彤1, 胡滨1, 侯生才1   
  1. 1. 100020　首都医科大学附属北京朝阳医院胸外科
  • 收稿日期:2017-05-10 出版日期:2017-08-28
  • 通信作者: 李辉

The combination of Caprini and Rogers risk assessment models can improve the accuracy of screening for venous thromboembolism in patients undergoing thoracic surgery

Bo Tian1, Chunfeng Song1, Hui Li1,†(), Yili Fu1, Wenqian Zhang1, Xiaoxing Hu1, Qirui Chen1, Bin You1, Shuo Chen1, Tong Li1, Bin Hu1, Shengcai Hou1   

  1. 1. Department of Thoracic Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
  • Received:2017-05-10 Published:2017-08-28
  • Corresponding author: Hui Li
  • About author:
    Corresponding author: Li Hui, Email:
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田博, 宋春凤, 李辉, 傅毅立, 张文谦, 胡晓星, 陈其瑞, 游宾, 陈硕, 李彤, 胡滨, 侯生才. Caprini和Rogers风险评估模型联合使用可以提高胸外科术后患者筛选静脉血栓栓塞症的准确性[J]. 中华胸部外科电子杂志, 2017, 04(03): 186-189.

Bo Tian, Chunfeng Song, Hui Li, Yili Fu, Wenqian Zhang, Xiaoxing Hu, Qirui Chen, Bin You, Shuo Chen, Tong Li, Bin Hu, Shengcai Hou. The combination of Caprini and Rogers risk assessment models can improve the accuracy of screening for venous thromboembolism in patients undergoing thoracic surgery[J]. Chinese Journal of Thoracic Surgery(Electronic Edition), 2017, 04(03): 186-189.

目的

验证Caprini和Rogers风险评估模型在胸外科手术后患者中筛选静脉血栓栓塞症(VTE)的有效性。

方法

采用单中心回顾性研究，以2016年7—12月首都医科大学附属北京朝阳医院胸外科行手术治疗的194例患者为样本，以Caprini和Rogers风险评估模型对所有患者进行回顾性血栓风险评分，并通过Logistic二分类回归分析得到Caprini和Rogers联合使用后的预测概率值，分别绘制受试者工作特征(ROC)曲线，计算曲线下面积(AUC)，进行两两比较，以Youden指数最大的分界点作为最佳诊断分界点。

结果

胸外科术后总的VTE发生率为13.4%。Caprini模型AUC为(0.713±0.043，P<0.001)，Rogers模型AUC为(0.577±0.062，P＝0.207)，预测概率模型AUC为(0.730±0.041，P<0.001)。Caprini模型分别与Rogers和预测概率模型AUC比较，差异均无统计学意义(P>0.05)；但预测概率模型较Rogers模型AUC显著增加，差异有统计学意义(P＝0.015)。Caprini模型在Youden指数为0.393时，敏感度为0.923，特异度为0.47；Rogers模型在Youden指数为0.135时，敏感度为0.385，特异度为0.75；预测概率模型在Youden指数为0.444时，敏感度为0.962，特异度为0.48。

结论

Caprini和Rogers风险评估模型联合使用可以提高胸外科手术后患者筛选VTE的准确性。

关键词: Caprini风险评估模型, Rogers风险评估模型, 胸外科, 静脉血栓栓塞症
Objective

To verify the efficacy of Caprini and Rogers risk assessment models of screening for venous thromboembolism (VTE) in patients undergoing thoracic surgery.

Methods

A single center retrospective study was performed in the department of thoracic surgery in Beijing Chao-Yang Hospital affiliated to Capital Medical University from July to December in 2016. The Caprini and Rogers risk score were recorded retrospectively for each patient. Meanwhile, by using of logistic bivariate regression analysis, we have obtained the predicted probability of Caprini and Rogers in combination diagnosis. We have drawn receiver operating characteristic curves (ROC) respectively, and calculated the area under the curve (AUC), then we have done pairwise comparison severally. The Youden index was taken as the cutoff point.

Results

The total incidence of VTE after thoracic surgery was 13.4%. The AUC for the the Caprini model, the Rogers model and the predictive probability model were 0.713±0.043 (P<0.001), 0.577±0.062 (P=0.207) and 0.730±0.041 (P<0.001) respectively. There was no significant difference in AUC between Caprini model and the other two models (P>0.05); while the AUC of the predictive probability model was significantly higher than that of the Rogers model (P=0.015). Caprini model had a sensitivity of 0.923 and a specificity of 0.47 when the Youden index was 0.393. The Rogers model had a sensitivity of 0.385 and a specificity of 0.75 when the Youden index was 0.135. The predictive probability model had a sensitivity of 0.962 and a specificity of 0.48 when the Youden index was 0.444.

Conclusions

The combined use of Caprini and Rogers risk assessment model can improve the accuracy of screening for venous thromboembolism in patients undergoing thoracic surgery.

Key words: Caprini, Rogers, Thoracic surgery, Venous thromboembolism
图1 Caprini、Rogers和预测概率模型的ROC曲线
表1 三种模型的Youden指数及其对应的敏感度和特异度
组别 分界点 Youden指数 敏感度 特异度
Caprini 5.5 0.393 0.923 0.47
Rogers 15.5 0.135 0.385 0.75
预测概率模型 0.1 0.444 0.962 0.48
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