Patient Demographics, Clinical Characteristics, and Medication Adherence to Treatment Among Patients on Oral Tyrosine Kinase Inhibitors in a Retail Pharmacy Setting

INTRODUCTION

Previous studies have demonstrated the importance of adherence to oral tyrosine kinase inhibitors in improving outcomes, including achieving a complete cytogenic response. Patients with chronic myeloid leukemia (CML) that had a suboptimal response were more likely to be non-adherent. Early identification and intervention based on predictors of non-adherence may lead to improved outcomes for patients in the non-trial setting. This research aimed to determine the rate of adherence and persistence to oral tyrosine kinase inhibitors (TKI) and to assess associated effect of patient characteristics using real world data from a retail pharmacy setting.

METHODS

This retrospective analysis of administrative pharmacy claims data included a random sample of 5000 patients who filled at least one TKI medication (bosutinib, dasatinib, imatinib, nilotinib) from national retail pharmacy chain in the study period of May 1, 2018 to April 30, 2021. Data elements included prescription fill attributes, patient-level demographics, medication adherence by therapeutic class (TKI, antidiabetics, antihypertensives and antihyperlipidemics), as well as patient health conditions and diagnoses. Patient adherence barrier data were also analyzed for a subset of patients who received select clinical interventions. This research was reviewed and approved by Advarra IRB as exempt (Pro00044844).

Medication adherence was measured using the proportion of days covered (PDC) metric. For each therapeutic class, PDC was measured from first fill date for that class from May 01 2018 to April 30 2021, followed for maximum of 365 days, and calculated as the ratio of the number of days of medication available and the measurement period. A cut-off to indicate suboptimum adherence of <85% was used for TKI and <80% for other classes. Length of therapy was measured as number of days a patient had underline medication coverage from the index date to the start date of medication gap that was>45 days.

PDC and length of therapy and their influential factors were assessed using generalized linear models. Persistency rates were calculated descriptively and using Kaplan-Meier analyses. Associations among PDCs in TKI and common chronic medications were assessed using multivariate correlation statistics. All statistics were conducted using SAS 9.4.

RESULTS

The random sample of patients had a mean age of 61.7 years (median=65.6, IQR= =51, 75) and 49.9% were male. TKI use in our sample was predominantly imatinib (2,857, 57.1%) and dasatinib (1,428, 28.6%) with fewer patients on nilotinib (556, 11.1%) or bosutinib (159, 3.2%). Among those patients with adherence barrier data, the average number of barriers was 1.7. Among TKI users, 38.3% also had hypertension, 13.6% had diabetes and 15.6% had hyperlipidemia. Percent of TKI users who had common chronic conditions and who were taking corresponding therapies were 38.2%, 33.9%, and 33.3% for hypertension, hyperlipidemia, and diabetes, correspondingly. Mean TKI PDC was 0.797 (95% CL 0.789 to 0.804) with a median of 0.889. Over half of patients (55% patients) had a PDC>=.85. Mean TKI length of therapy was 18.3 months with a median of 15 months with differences by therapy.

Correlation of TKI PDC to three common chronic therapy PDC were all low: 0.095 for antidiabetics; 0.032 for antihypertensives; 0.083 for antilipidemics) and not statistically significant. Age was a significant predicator of PDC, with every 10-year increase in age associated with a 2% increase in PDC. When a patient had previously stated adherence barriers, PDC was estimated to decrease by 1.7% for each barrier faced by the patient. Only a small portion (33%-38%) of TKI patients, who had a diagnosis for a common chronic condition, had a claim for the corresponding therapeutic class.

CONCLUSIONS

Adherence to TKI was influenced by non-modifiable risk such as age and modifiable risks such as the number of adherence barriers. Many patients on TKI who also had a common chronic condition were not taking medications for their chronic condition, noting a discordance in care. Ongoing capture of barrier data beyond specialty medications will help predict patient adherence behavior and identify targeted interventions.