ArticleJCO clinical cancer informatics2025
Using Early Biomarker Change and Treatment Adherence to Predict Risk of Relapse Among Patients With Chronic Myeloid Leukemia Who Are in Remission.
Article in JCO clinical cancer informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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5 authors.
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Abstract
purposeThere 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.
methodsWe 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.
resultsPatients 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.
conclusionEarly 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.
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