External validation and updating of prediction models of bleeding risk in patients with cancer receiving anticoagulants

Abstract

ObjectivePatients 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.MethodsWe 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.ResultsThe 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.ConclusionsExisting 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.

Development and Validation of Machine Learning Algorithms to Predict 1-Year Ischemic Stroke and Bleeding Events in Patients with Atrial Fibrillation and Cancer

“…In fact, genetic factors and disease severity were not available in SEER-Medicare data and dynamic features (i.e., cancer progression, new diagnosis of diseases) were not included in the models due to complexities. Similar to previous risk scores, we found that bleeding history was an important factor in prediction of subsequent major bleeding [ 17 , 55 ]. Second, we excluded patients who have already initiated OACs before AFib diagnosis and those who initiated AFib during follow-up because OACs may increase risk of bleeding.…”

“…Such poor performance suggested complex interactions between patients’ characteristics and outcomes in the presence of cancer. First, although we obtained additional cancer characteristics compared with traditional risk scores, the performance was not improved [ 55 ]. This may suggest that our models failed to capture important features in prediction of major bleeding.…”