Predictors of tyrosine kinase inhibitor adherence trajectories in patients with newly diagnosed chronic myeloid leukemia.

Abstract

Although consistent use of tyrosine kinase inhibitors (TKIs) confers significant improvements in long-term survival for individuals with chronic myeloid leukemia (CML), only 70% of CML patients are adherent to TKIs. Understanding the factors that contribute to non-adherence and establishing dynamic adherence patterns in this population are essential aspects of targeted drug monitoring and intervention strategies. Newly diagnosed CML patients were identified in the MarketScan database and relevant covariate values extracted. Proportion of days covered (PDC) per 30-day interval was used to calculate adherence over a 12-month follow-up period. We conducted a latent profile analysis (LPA) on these PDC estimates to identify distinct, dynamic patterns of TKI adherence. Identified trajectories were grouped into four clinically relevant categories and predictors of membership in these categories were determined via multinomial logistic regression. Four broad adherence categories were identified from the LPA: never adherent, initially non-adherent becoming adherent, initially adherent becoming non-adherent, and stable adherent. Results from the subsequent multinomial logistic regression indicated that younger age, female sex, greater monthly financial burden, fewer comorbidities, fewer concomitant medications, year of diagnosis, higher starting dose, TKI type, and a longer duration from diagnosis to treatment were significantly associated with membership in at least one of the three non-stable adherent groups. Select sociodemographic and clinical characteristics were found to predict membership in clinically meaningful groups of longitudinal TKI adherence. These findings could have major implications for informing personalized monitoring and intervention strategies for individuals who are likely to be non-adherent.