Existing bleeding risk models poorly predicted bleeding in anticoagulated cancer patients (c-statistics ~0.56), while an updated model achieved a c-statistic of 0.61 (95% CI 0.54-0.70).
Cohort (n=1,304)
Do existing or updated bleeding risk models accurately predict the composite of major and clinically relevant non-major bleeding in patients with cancer receiving anticoagulants?
Existing bleeding risk models perform poorly in cancer patients receiving anticoagulants, and an updated model incorporating age and bleeding history provides only modest improvement.
Effect estimate: c-statistic 0.61 (95% CI 0.54-0.70)
OBJECTIVE: Patients with cancer are at increased bleeding risk, and anticoagulants increase this risk even more. Yet, validated bleeding risk models for prediction of bleeding risk in patients with cancer are lacking. The aim of this study is to predict bleeding risk in anticoagulated patients with cancer. METHODS: We performed a study using the routine healthcare database of the Julius General Practitioners' Network. Five bleeding risk models were selected for external validation. Patients with a new cancer episode during anticoagulant treatment or those initiating anticoagulation during active cancer were included. The outcome was the composite of major bleeding and clinically relevant non-major (CRNM) bleeding. Next, we internally validated an updated bleeding risk model accounting for the competing risk of death. RESULTS: The validation cohort consisted of 1304 patients with cancer, mean age 74.0±10.9 years, 52.2% males. In total 215 (16.5%) patients developed a first major or CRNM bleeding during a mean follow-up of 1.5 years (incidence rate; 11.0 per 100 person-years (95% CI 9.6 to 12.5)). The c-statistics of all selected bleeding risk models were low, around 0.56. Internal validation of an updated model accounting for death as competing risk showed a slightly improved c-statistic of 0.61 (95% CI 0.54 to 0.70). On updating, only age and a history of bleeding appeared to contribute to the prediction of bleeding risk. CONCLUSIONS: Existing bleeding risk models cannot accurately differentiate bleeding risk between patients. Future studies may use our updated model as a starting point for further development of bleeding risk models in patients with cancer.
Trinks-Roerdink et al. (Sat,) conducted a cohort in Cancer in patients receiving anticoagulants (n=1,304). Bleeding risk models was evaluated on Composite of major bleeding and clinically relevant non-major (CRNM) bleeding (c-statistic 0.61, 95% CI 0.54-0.70). Existing bleeding risk models poorly predicted bleeding in anticoagulated cancer patients (c-statistics ~0.56), while an updated model achieved a c-statistic of 0.61 (95% CI 0.54-0.70).