Risk of Recurrent Venous Thromboembolism in Patients with Cancer: An Individual Patient Data Meta-analysis and Development of a Prediction Model.

IF 5 2区医学 Q1 HEMATOLOGY Thrombosis and haemostasis Pub Date : 2024-10-16 DOI:10.1055/a-2418-3960

Vincent R Lanting, Toshihiko Takada, Floris T M Bosch, Andrea Marshall, Michael A Grosso, Annie M Young, Agnes Y Y Lee, Marcello Di Nisio, Gary E Raskob, Pieter W Kamphuisen, Harry R Büller, Nick van Es

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Abstract

Background: About 7% of patients with cancer-associated venous thromboembolism (CAT) develop a recurrence during anticoagulant treatment. Identification of high-risk patients may help guide treatment decisions.

Aim: To identify clinical predictors and develop a prediction model for on-treatment recurrent CAT.

Methods: For this individual patient data meta-analysis, we used data from four randomized controlled trials evaluating low-molecular-weight heparin or direct oral anticoagulants (DOACs) for CAT (Hokusai VTE Cancer, SELECT-D, CLOT, and CATCH). The primary outcome was adjudicated on-treatment recurrent CAT during a 6-month follow-up. A clinical prediction model was developed using multivariable logistic regression analysis with backward selection. This model was validated using internal-external cross-validation. Performance was assessed by the c-statistic and a calibration plot.

Results: After excluding patients using vitamin K antagonists, the combined dataset comprised 2,245 patients with cancer and acute CAT who were treated with edoxaban (23%), rivaroxaban (9%), dalteparin (47%), or tinzaparin (20%). Recurrent on-treatment CAT during the 6-month follow-up occurred in 150 (6.7%) patients. Predictors included in the final model were age (restricted cubic spline), breast cancer (odds ratio [OR]: 0.42; 95% confidence interval [CI]: 0.20-0.87), metastatic disease (OR: 1.44; 95% CI: 1.01-2.05), treatment with DOAC (OR: 0.66; 95% CI: 0.44-0.98), and deep vein thrombosis only as an index event (OR: 1.72; 95% CI: 1.31-2.27). The c-statistic of the model was 0.63 (95% CI: 0.54-0.72) after internal-external cross-validation. Calibration varied across studies.

Conclusion: The prediction model for recurrent CAT included five clinical predictors and has only modest discrimination. Prediction of recurrent CAT at the initiation of anticoagulation remains challenging.

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癌症患者复发性静脉血栓栓塞风险：个体患者数据荟萃分析和预测模型的开发。

背景约有 7% 的癌症相关静脉血栓栓塞症（CAT）患者在抗凝治疗期间复发。识别高危患者有助于指导治疗决策。目的识别临床预测因素并建立治疗中复发 CAT 的预测模型。方法在这项患者个体数据（IPD）荟萃分析中，我们使用了四项随机对照试验（Hokusai VTE Cancer、SELECT-D、CLOT 和 CATCH）的数据，这些试验评估了低分子量肝素（LMWH）或直接口服抗凝剂（DOACs）治疗 CAT 的效果。主要结果是随访 6 个月期间判定的治疗后复发 CAT。利用多变量逻辑回归分析和反向选择建立了临床预测模型。该模型通过内部-外部交叉验证进行了验证。通过 c 统计量和校准图评估模型的性能。结果排除使用维生素 K 拮抗剂的患者后，合并数据集包括 2,245 名癌症和急性 CAT 患者，他们分别接受了依多沙班（23%）、利伐沙班（9%）、达肝素（47%）或替扎肝素（20%）治疗。150名患者（6.7%）在6个月的随访期间复发了治疗中的CAT。最终模型中的预测因子包括年龄（受限立方样条曲线）、乳腺癌（OR 0.42；95%-CI 0.20-0.87）、转移性疾病（OR 1.44；95%-CI 1.01-2.05）、DOAC 治疗（OR 0.66；95%-CI 0.44-0.98）以及仅作为指数事件的深静脉血栓（OR 1.72；95%-CI 1.31-2.27）。经过内部-外部交叉验证后，模型的 c 统计量为 0.63（95%-CI 0.54-0.72）。不同研究的校准结果各不相同。结论复发性 CAT 的预测模型包括五个临床预测因子，但辨别度不高。在开始抗凝时预测复发性 CAT 仍具有挑战性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Thrombosis and haemostasis 医学-外周血管病

CiteScore

11.90

自引率

9.00%

发文量

140

审稿时长

1 months

期刊介绍： Thrombosis and Haemostasis publishes reports on basic, translational and clinical research dedicated to novel results and highest quality in any area of thrombosis and haemostasis, vascular biology and medicine, inflammation and infection, platelet and leukocyte biology, from genetic, molecular & cellular studies, diagnostic, therapeutic & preventative studies to high-level translational and clinical research. The journal provides position and guideline papers, state-of-the-art papers, expert analysis and commentaries, and dedicated theme issues covering recent developments and key topics in the field.