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

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.