Patterns and predictors of oral anticancer agent utilization in diverse metastatic renal cell carcinoma patients.
Wheeler, SB; Spees, L; Jackson, BE; Baggett, C; Wilson, LE; Greiner, MA; George, DJ; Scales, CD; Pritchard, J; Dinan, MA
Published in: Journal of Clinical Oncology
279 Background: Availability of targeted oral anti-cancer agents (OAA) has transformed care delivery for metastatic renal cell carcinoma (RCC) patients. Our objective was to identify patterns and predictors of OAA use within the 12 months after metastatic RCC was detected to understand the extent of real-world adoption of these treatment advances. Methods: We used a novel, North Carolina, registry-linked multi-payer claims data resource to examine patterns of use of sorafenib, sunitinib, pazopanib, everolimus, axitinib, cabozantinib, and levantinib in a cohort of metastatic RCC patients diagnosed over 10 years (2006-2015, with claims through 2016). Patients were required to have 12 months of pre- and post-metastatic-index-date continuous enrollment. Log-Poisson models estimated unadjusted and adjusted risk ratios (RRs) and 95% confidence limits (CLs) for associations between patient characteristics and OAA use. In sensitivity analyses, we used a competing risk framework to estimate adjusted risk differences (RD) in OAA use. Results: Our population-based study of 713 patients demonstrated relatively low (37%) OAA use at any time during the 12 months post-metastatic-index date among publicly and privately insured patients, with shifting patterns of use consistent with regulatory approvals over time. Lower OAA use was observed among patients who were older, frailer, and with greater comorbidity burden. Other patient-level characteristics, such as sex, race, rurality and type of insurance were not significant predictors of OAA use. Conclusions: These data underscore the importance of distinguishing clinically appropriate from potentially poor-quality care and warrant additional studies to understand in more depth the system, provider and patient level drivers of these patterns.