None of the 10 evaluated risk assessment models could consistently identify cancer patients with venous thromboembolism at low risk of bleeding, with the Nieto model performing best (C-statistic 0.730).
Observational (n=110)
No
Do existing bleeding risk assessment models accurately predict the risk of major or clinically relevant non-major bleeding in cancer patients treated with anticoagulants for VTE?
Existing bleeding risk assessment models perform suboptimally in cancer patients treated with anticoagulants for VTE, highlighting the need for cancer-specific risk tools.
Effect estimate: C-statistic 0.730 (95% CI 0.619-0.840)
Introduction: Anticoagulant is the cornerstone of the management of VTE at the cost of a non-negligible risk of bleeding. Reliable and validated clinical tools to predict thromboembolic and hemorrhagic events are crucial for individualized decision-making for the type and duration of anticoagulant treatment. We evaluate the available risk models in real life cancer patients with VTE. The objectives of the study were to describe the bleeding of cancer patients with VTE and to evaluate the performance of the different bleeding models to predict the risk of bleeding during a 6-month follow-up. Materials and Methods: VTE-diagnosed patient's demographic and clinical characteristics, treatment regimens and outcomes for up to 6 months were collected. The primary endpoint was the occurrence of a major bleeding (MB) or a clinically relevant non major bleeding (CRNMB) event, categorized according to the ISTH criteria. Results: During the 6-months follow-up period, 26 out of 110 included patients (26.7%) experienced a bleeding event, with 3 recurrences of bleeding. Out of the 29 bleeding events, 19 events were CRNMB and 10 MB. One patient died because of a MB. Bleeding occurred in 27 % of the patients treated with DOACs and 22% of the patients treated with LMWH. Most of the bleedings were gastrointestinal (9 events, 31%); 26.9% of the bleedings occurred in patient with colorectal cancer and 19.6% in patients with lung cancer. In our cohort, none of the 10 RAMs used in our study were able to distinguish cancer patients with a low risk of bleeding, from all bleeding or non-bleeding patients. The Nieto et al. RAM had the best overall performance (C-statistic = 0.730, 95% CI (0.619-0.840)). However, it classified 1 out of 5 patients with major bleeding in the low risk of bleeding group. The rest of the RAMs showed a suboptimal result, with a range of C-statistic between 0.489, 95%CI (0.360-0.617)) and 0.532, 95%CI (0.406-0.658)). Conclusions: The management of CAT patients is challenging due to a higher risk of both recurrent VTE and bleeding events, as compared with non-cancer patients with VTE. None of the existing RAMs was able to consistently identify patients with risk of anticoagulant associated bleeding events.
Poénou et al. (Mon,) conducted a observational in Cancer-associated venous thromboembolism (n=110). Bleeding Risk Assessment Models (RAMs) was evaluated on Occurrence of a major bleeding (MB) or a clinically relevant non major bleeding (CRNMB) event (C-statistic 0.730, 95% CI 0.619-0.840). None of the 10 evaluated risk assessment models could consistently identify cancer patients with venous thromboembolism at low risk of bleeding, with the Nieto model performing best (C-statistic 0.730).