Identifying CLL patients at high risk of atrial fibrillation on treatment using machine learning

An increased risk of developing atrial fibrillation (AF) has been observed in patients with chronic lymphocytic leukemia (CLL) who were treated with ibrutinib and other BTK inhibitors. Previous studies have explored the prevalence of AF in CLL and the risk of developing AF at time of diagnosis. However, the interaction between treatment type with other risk factors on risk of developing atrial fibrillation at the time of treatment initiation has not been investigated. This becomes particularly crucial in CLL, as there is often a substantial time gap between diagnosis and treatment, unlike many other cancers. We propose a treatment-aware approach using predictive modeling to identify the risk factors associated with AF at time of treatment initiation. Moreover, the model provides treatment-dependent risk factors by including the interaction between the treatment types and other risk factors. The results demonstrated that the treatment-aware modeling including interactions outperformed currentrisk scores.