Abstract
BACKGROUND: One of the limiting toxicities of BTKi is the development of atrial fibrillation (AF), with an incidence of 3%-16%. AIM: This study aimed to identify patients with chronic lymphocytic leukemia (CLL) starting both first- and second-generation BTKis who are at high risk of developing AF using a machine learning approach. METHODS: The CLL cohort is based on data obtained from electronic medical records from Maccabi, the second-largest healthcare organization in Israel. The optimal scoring model was determined using the Risk-calibrated Supersparse Linear Integer Model (RiskSLIM) algorithm. RESULTS: A total of 3964 patients with a CLL diagnosis were available in the database. Of these, 208 patients started BTKi during the study period (125 on ibrutinib and 83 on acalabrutinib), and 16 patients developed AF during follow-up. In addition to well-established factors such as age, sex, and hypertension, the algorithm detected other factors associated with a high risk for AF: type of BTKi used, low eGFR, elevated absolute monocytes (> 1100/μL), elevated CRP, elevated CK, and elevated B2MG (> 2.5 mg/L). Based on the total AF-free survival (AFS), we identified three main risk groups: low (0-6), intermediate (7-11) and high (≥ 12). The median AF-free survival (AFS) was 28 and 56 months for the high-risk and intermediate-risk groups, respectively, and was not reached for the low-risk group. The difference between the groups was statistically significant (p = 0.0013). CONCLUSION: Our novel score has a high concordance index for predicting the development of AF in patients with CLL treated with first- and second-generation BTKis. It combines age, sex, medical history, and laboratory tests associated with inflammatory status and disease burden and can be applied in clinical settings worldwide.