Using Early Biomarker Change and Treatment Adherence to Predict Risk of Relapse Among Patients With Chronic Myeloid Leukemia Who Are in Remission.

Abstract

Purpose: There is little guidance for decision making in chronic myeloid leukemia (CML) after patients achieve molecular remission. Our study addresses this gap by developing a risk prediction model for molecular relapse using early longitudinal factors, such as BCR::ABL1 biomarker-level changes and treatment adherence.

Methods: We analyzed electronic health record data of patients with CML diagnosed between 2007 and 2019 from an integrated health system. We used a time-to-event modeling framework using a Cox proportional hazards approach where we evaluated time from molecular remission to molecular relapse. The main predictors were early changes in BCR::ABL1 levels from treatment initiation to the first follow-up measurement (typically around 3 months) and treatment adherence in the first 6 months, categorized as perfect (≥0.98) or less-than-perfect (<0.98). Model performance was assessed through five-fold cross-validation combined with 100 Monte Carlo bootstrapping iterations to ensure robustness and minimize bias.

Results: Patients with early improvement in BCR::ABL1 levels had a 70% lower risk relapse (hazard ratio [HR], 0.30 [95% CI, 0.15 to 0.59]) compared with those without early molecular response. Perfect adherence during this critical early phase of treatment was associated with a 56% lower relapse risk (HR, 0.44 [95% CI, 0.22 to 0.85]). Predictive accuracy was high at 6 months (AUC, 0.90; 95% CI, 0.87 to 0.95) and 1-year postremission (AUC, 0.78; 95% CI, 0.74 to 0.81). Relapse risk was significantly higher among Black, Asian, and Hispanic patients compared with non-Hispanic White patients.

Conclusion: Early biomarker trends and adherence after treatment initiation are critical for accurately predicting relapse among patients who achieve molecular remission. The proposed model addresses a gap in guidance after molecular remission and has the potential to enable personalized monitoring and optimize surveillance strategies, offering transformative potential for CML care.