Department of Vascular Surgery, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences and Tongji Shanxi Hospital, Tongji Medical College of HUST, Taiyuan 030032, China.
School of Public Health, Shanxi Medical University, Taiyuan 030001, China.
Thromb Res. 2022 Jun;214:68-75. doi: 10.1016/j.thromres.2022.04.003. Epub 2022 Apr 13.
To identify independent prediction factors for post thrombotic syndrome (PTS) following acute deep vein thrombosis (DVT) and develop a clinical prediction model assessing the risk of PTS in individual patient.
We prospectively recruited consecutive adult patients with acute DVT who were managed at Shanxi Bethune Hospital, China between June 2014 and December 2016. Investigator assessed PTS using the Villalta scale at 1, 6, 12, 18 and 24 months following diagnosis of DVT. Variable selection was performed by applying the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. Based on these data, we established a clinical prediction model for the development of PTS following DVT. The Bootstrap method was used for internal validation. During the process of model development, we re-collected the information of DVT patients from 2016 to 2017 for a temporal validation. The performance of the prediction model included discrimination and calibration, and clinical utility of prediction model was also evaluated using a decision curve analysis.
A total of 808 consecutive patients with acute DVT were enrolled in the training and validation datasets, of which 540 patients were included in the training dataset for the development of prediction model and the other 268 patients were in the other dataset for temporal validation. Seventy-six patients in training dataset developed PTS. The prediction factors associated with PTS were ilio-femoral DVT (OR = 4.835, 95% CI: 2.471-9.463), active cancer (OR = 3.006, 95% CI: 1.404-6.435), history of chronic venous insufficiency (OR = 7.464, 95% CI: 3.568-15.616), previous venous thromboembolism (OR = 6.326, 95% CI: 2.872-13.932), and chronic kidney disease (OR = 9.916, 95% CI: 2.238-43.937), duration of compression therapy <6 months (OR = 2.894, 95% CI: 1.595-5.251). The c index of the prediction model was 0.825 (0.774-0.877), and the c index of internal validation and temporal verification were 0.816 and 0.773 (95% CI: 0.699-0.848), indicated that the prediction model had a good discrimination in predicting PTS risk following DVT. All the calibration curve showed the model had a good calibration. The decision curve analysis showed a better net benefit of prediction model predicting PTS risk within threshold probability ranged from 0% to 72% and 86% to 98% in training dataset, and 0% to 58% in the validation datasets.
Our prediction model can accurately estimate the likelihood of PTS risk and identify high-risk patients who may develop PTS following DVT based on individual characteristics, but further external validation is still required.
确定急性深静脉血栓形成（DVT）后血栓后综合征（PTS）的独立预测因素，并开发一种评估个体患者 PTS 风险的临床预测模型。
我们前瞻性招募了 2014 年 6 月至 2016 年 12 月期间在中国山西白求恩医院接受治疗的急性 DVT 成年患者。研究者使用 Villalta 量表在 DVT 诊断后 1、6、12、18 和 24 个月评估 PTS。通过应用 10 折交叉验证的最小绝对收缩和选择算子（LASSO）进行变量选择。基于这些数据，我们建立了一个用于评估 DVT 后 PTS 发生的临床预测模型。使用 Bootstrap 方法进行内部验证。在模型开发过程中，我们重新收集了 2016 年至 2017 年的 DVT 患者信息，进行了时间验证。预测模型的性能包括区分度和校准度，并使用决策曲线分析评估了预测模型的临床实用性。
共有 808 例连续急性 DVT 患者纳入了训练和验证数据集，其中 540 例患者被纳入训练数据集用于预测模型的开发，其余 268 例患者纳入另一数据集用于时间验证。训练数据集中有 76 例患者发展为 PTS。与 PTS 相关的预测因素包括髂股静脉血栓形成（OR=4.835，95%CI：2.471-9.463）、活动性癌症（OR=3.006，95%CI：1.404-6.435）、慢性静脉功能不全史（OR=7.464，95%CI：3.568-15.616）、既往静脉血栓栓塞症（OR=6.326，95%CI：2.872-13.932）和慢性肾脏病（OR=9.916，95%CI：2.238-43.937）、压迫治疗持续时间<6 个月（OR=2.894，95%CI：1.595-5.251）。预测模型的 c 指数为 0.825（0.774-0.877），内部验证和时间验证的 c 指数分别为 0.816 和 0.773（95%CI：0.699-0.848），表明该预测模型在预测 DVT 后 PTS 风险方面具有良好的区分度。所有校准曲线均表明该模型具有良好的校准度。决策曲线分析表明，在训练数据集的阈值概率为 0%至 72%和 86%至 98%以及验证数据集的 0%至 58%范围内，预测模型预测 PTS 风险的净获益更好。
我们的预测模型可以基于个体特征准确估计 PTS 风险的可能性，并识别出可能发生 DVT 后 PTS 的高危患者，但仍需要进一步的外部验证。
