Risk of recurrent venous thromboembolism in patients with cancer: an individual patient data meta-analysis and development of a prediction model.

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 (IPD) meta-analysis, we used data from four randomized controlled trials evaluating low-molecular-weight heparin (LMWH) 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 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 6-month follow-up occurred in 150 (6.7%) patients. Predictors included in the final model were age (restricted cubic spline), breast cancer (OR 0.42; 95%-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 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. Conclusions 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.