Development and Validation of a Practical Two-Step Prediction Model and Clinical Risk Score for Post-Thrombotic Syndrome
Development and Validation of a Practical Two-Step Prediction Model and Clinical Risk Score for Post-Thrombotic Syndrome
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DOI：
10.1055/s-0038-1655743
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发表时间：
2018-07-01
影响因子：
6.7
通讯作者：
ten Cate-Hoek, Arina J.
中科院分区：
文献类型：
--
作者：
Amin, Elham E.;van Kuijk, Sander M. J.;ten Cate-Hoek, Arina J.
背景血栓后综合征(PTS)是一种常见的深静脉血栓形成的慢性后果，影响生活质量，并与巨大的成本相关。在临床实践中，不可能预测单个患者的风险。方法对荷兰马斯特里赫特大学医学中心血栓形成门诊和意大利帕多瓦大学医院的479例(派生期)和1107例(验证期)连续确诊的腿部深静脉血栓形成患者进行多元回归建模。PTS定义为急性血栓形成后至少6个月的Villalta评分>；=5。结果急性期基线模型中的变量有：年龄、体重指数、性别、静脉曲张、静脉血栓病史、吸烟状况、诱发血栓和血栓位置。对于第二个模型，附加变量是残余静脉阻塞。乐观校正后的受试者工作特征曲线下面积(AUC)基线模型为0.71，二级模型为0.60。校准曲线图显示了经过良好校准的预测。临床风险评分的外部验证是成功的：AUC，0.66(95%可信区间，0.63-0.70)和0.64(95%可信区间，0.60-0.69)。结论根据易于获得的基线临床和人口学特征，可以预测深静脉血栓形成急性期PTS的个体风险。亚急性期的个体风险可以用有限的附加临床特征来预测。
Background Post-thrombotic syndrome (PTS) is a common chronic consequence of deep vein thrombosis that affects the quality of life and is associated with substantial costs. In clinical practice, it is not possible to predict the individual patient risk. We develop and validate a practical two-step prediction tool for PTS in the acute and subacute phase of deep vein thrombosis.Methods Multivariable regression modelling with data from two prospective cohorts in which 479 (derivation) and 1,107 (validation) consecutive patients with objectively confirmed deep vein thrombosis of the leg, from thrombosis outpatient clinic of Maastricht University Medical Centre, the Netherlands (derivation) and Padua University hospital in Italy (validation), were included. PTS was defined as a Villalta score of >= 5 at least 6 months after acute thrombosis.Results Variables in the baseline model in the acute phase were: age, body mass index, sex, varicose veins, history of venous thrombosis, smoking status, provoked thrombosis and thrombus location. For the secondary model, the additional variable was residual vein obstruction. Optimism-corrected area under the receiver operating characteristic curves (AUCs) were 0.71 for the baseline model and 0.60 for the secondary model. Calibration plots showed well-calibrated predictions. External validation of the derived clinical risk scores was successful: AUC, 0.66 (95% confidence interval [CI], 0.63-0.70) and 0.64 (95% CI, 0.60-0.69).Conclusion Individual risk for PTS in the acute phase of deep vein thrombosis can be predicted based on readily accessible baseline clinical and demographic characteristics. The individual risk in the sub-acute phase can be predicted with limited additional clinical characteristics.