A clinical prediction model for recurrent cancer-associated thrombosis incorporating age, breast cancer, metastatic disease, DOAC treatment, and DVT only demonstrated modest discrimination with a c-statistic of 0.63.
Meta-Analysis (n=2,245)
Open-label
Yes
Can a clinical prediction model accurately identify patients with cancer-associated venous thromboembolism at high risk for recurrence during anticoagulant treatment?
A clinical prediction model for recurrent cancer-associated thrombosis demonstrated only modest discrimination (c-statistic 0.63), highlighting that predicting recurrence at the initiation of anticoagulation remains challenging.
Effect estimate: c-statistic 0.63 (95% CI 0.54-0.72)
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.To identify clinical predictors and develop a prediction model for on-treatment recurrent CAT.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.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.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.
Lanting et al. (Thu,) conducted a meta-analysis in Cancer-associated venous thromboembolism (n=2,245). Clinical prediction model was evaluated on Adjudicated on-treatment recurrent cancer-associated thrombosis (c-statistic 0.63, 95% CI 0.54-0.72). A clinical prediction model for recurrent cancer-associated thrombosis incorporating age, breast cancer, metastatic disease, DOAC treatment, and DVT only demonstrated modest discrimination with a c-statistic of 0.63.