Development of a Machine Learning-Based Risk Score for Predicting Atrial Fibrillation in Treatment-Naive CLL Patients Initiating BTK Inhibitor Therapy

Background: One of the limiting toxicities of BTKi therapy is the development of atrial fibrillation (AF), with an incidence of 3% to 16%. Aim: To identify patients with chronic lymphocytic leukemia (CLL) who are at high risk of developing AF, using a machine learning approach. Methods: The CLL cohort was based on data obtained from electronic medical records from Maccabi, the second-largest healthcare organization in Israel. We evaluated more than 100 variables to develop the scoring schema. The optimal scoring model was determined using the code available at https://github.com/ustunb/risk-slim, implemented in Python 3.5 and CPLEX 12.6. Results: A total of 3964 patients with a CLL diagnosis were available in the database. 208 patients started a BTKi during the study period, 16 of whom developed AF during follow-up. In addition to well-established factors (age, sex, and hypertension) that are used in many existing AF scores that were developed for the general population, the algorithm detected other factors that were associated with a high risk for AF, in particular: type of BTKi used, low eGFR (<30 mL/min/1.73m2), elevated absolute monocytes (>1100/µL), elevated CRP, elevated CK, and elevated B2MG (>2.5 mg/L). Based on the total score, we identified 3 main AF risk groups as following: low (0-6), intermediate (7-11) and high (≥12). The median AFS were 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). The proposed scoring model reached a C-index of 0.744+/-0.082 and it outperformed the score ranking of Shanafelt et al that obtained a C-index of 0.626+/-0.138 on the same data. The improvement was found to be statistically significant (P=0.024). Conclusion: Our novel score has a high concordance index to predict the development of AF in patients with CLL treated with BTKi.