邝允勋, 李佳玉, 何海龙, 罗小云. Caprini风险评估模型预测恶性肿瘤住院患者深静脉血栓形成的确证性研究[J]. 中国肿瘤临床, 2019, 46(13): 682-685. DOI: 10.3969/j.issn.1000-8179.2019.13.374
引用本文: 邝允勋, 李佳玉, 何海龙, 罗小云. Caprini风险评估模型预测恶性肿瘤住院患者深静脉血栓形成的确证性研究[J]. 中国肿瘤临床, 2019, 46(13): 682-685. DOI: 10.3969/j.issn.1000-8179.2019.13.374
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Kuang Yunxun, Li Jiayu, He Hailong, Luo Xiaoyun. Validation of the Caprini risk model for predicting deep venous thrombosis in hospitalized patients with malignant tumors[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2019, 46(13): 682-685. DOI: 10.3969/j.issn.1000-8179.2019.13.374
Citation: Kuang Yunxun, Li Jiayu, He Hailong, Luo Xiaoyun. Validation of the Caprini risk model for predicting deep venous thrombosis in hospitalized patients with malignant tumors[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2019, 46(13): 682-685. DOI: 10.3969/j.issn.1000-8179.2019.13.374
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Caprini风险评估模型预测恶性肿瘤住院患者深静脉血栓形成的确证性研究

Validation of the Caprini risk model for predicting deep venous thrombosis in hospitalized patients with malignant tumors

    摘要
  • 摘要:
      目的  评估Caprini风险评估模型在恶性肿瘤患者中预测深静脉血栓形成的有效性。
      方法  对2015年1月至2017年1月在首都医科大学附属北京世纪坛医院住院的504例恶性肿瘤患者进行深静脉血栓筛查，分析Caprini血栓风险模型评分和危险分级，并与Khorana模型进行比较。
      结果  深静脉血栓形成患者的Caprini得分中位数为6（4~8）分，高于无深静脉血栓组的5（4~7）分（P=0.004）。低中危、高危、极高危组的深静脉血栓形成发生率的差异具有统计学意义（Z=-1.933，P=0.053）。Caprini评分的接收者操作特征曲线下面积（area under ROC curve，AUC）为0.611（95% CI：0.54~0.69，P=0.004），截断值为6分时，约登指数最大。Khorana模型的AUC为0.65（95% CI：0.57~0.72，P < 0.001），与Caprini模型的差异无统计学意义（Z=0.674，P=0.50）。在恶性肿瘤手术患者中，Caprini模型的AUC为0.85（95% CI：0.66~0.96，P < 0.01），Khorana模型为0.68（95% CI：0.47~0.84，P=0.18）；对于非手术接受化疗患者，Khorana模型的AUC为0.72（95% CI：0.61~0.82，P=0.01），Caprini模型为0.55（95% CI：0.44~0.67，P=0.54）。
      结论  Caprini和Khorana风险评估模型均具有一定的预测价值，但区分度不高。在接受手术的肿瘤患者中Caprini具有更好的预测性。Khorana模型适合非手术化疗患者。Caprini模型在恶性肿瘤患者中的应用有待进一步深入研究。

     

    Abstract:
      Objective  To evaluate the effectiveness of the Caprini risk assessment model in predicting deep venous thrombosis in hospitalized patients with malignant tumors.
      Methods  Deep venous thrombosis screening was performed in 504 patients with malignant tumors who were hospitalized in Beijing Shijitan Hospital between January 2015 and January 2017. Their Caprini thrombosis risk model scores and risk classifications were analyzed and compared with those of the Khorana risk model.
      Results  The median Caprini score of patients with deep venous thrombosis was 6 (range 4-8), which was higher than the score of 5 (range 4-7) in the group without deep venous thrombosis (Z=10.033, P=0.004). Statistically significant differences in the incidence of deep venous thrombosis were found among the low-medium, high-, and extremely high-risk groups (Z=-1.933, P=0.053). The area under the receiver-operating characteristic curve (AUC) of the Caprini scores was 0.61195% confidence interval (CI):0.54-0.69, P=0.004, and the cutoff value was 6 points, with the largest Youden index. The AUC of the Khorana model was 0.65 (95% CI:0.57-0.72, P < 0.001), and the difference between the Khorana and Caprini models was not statistically significant (Z=0.674, P=0.500). The AUC of the Caprini model was 0.85 (95% CI:0.66-0.96, P < 0.01) and that of the Khorana model was 0.68 (95% CI:0.47-0.84, P=0.18) in the patients who underwent malignant tumor surgery. The AUC of the Khorana model was 0.72 (95% CI:0.61-0.82, P=0.01) and that of the Caprini model was 0.55 (95% CI:0.44-0.67, P=0.54) in the non-operative patients who received chemotherapy.
      Conclusions  The Caprini and Khorana risk assessment models have certain predictive values, but the discrimination is not good. The Caprini model is providing better predictability in patients with malignant tumors treated with surgery. The Khorana model is suitable for non-operative patients who received chemotherapy. Further studies on the application of the Caprini risk assessment model in patients with malignant tumors are needed.

     

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