Patient characteristics associated with conventional schedule vs. dose dense chemotherapy in women with stage I-IIIA breast cancer.
Academic Article
Overview
abstract
INTRODUCTION: Compared to conventional chemotherapy schedules, use of dose-dense chemotherapy, which refers to administration of chemotherapy at standard doses with reduced cycle lengths, is known to improve survival, although may confer greater toxicity risk. We evaluated the patient factors associated with use of conventional vs. dose-dense chemotherapy administration schedules. METHODS: Analyses include 4685 women treated with adjuvant chemotherapy between 2005 and 2019 for Stage I-IIIA breast cancer at Kaiser Permanente Northern California and Kaiser Permanente Washington. Among women treated with drug combinations for which dose-dense administration schedules were observed, we used generalized linear models of the Poisson family with a log-link function to calculate prevalence ratios (PRatios) for the associations between patient factors and use of conventional vs. dose-dense administration schedules. RESULTS: Several factors were associated with receipt of conventional administration schedule, including older age (PRatio75+vs. 18-39: 2.97; 95% CI 2.35-3.75; p-trend < 0.001), renal impairment (PRatio: 1.55; 95% CI 1.11-2.17), and HER2+ status (PRatio: 1.50; 95% CI 1.38-1.62), among others. Factors associated with a lower likelihood of receipt of a conventional regimen schedule include: higher median household income (PRatioQ4 vs. Q1 0.73; 95% CI 0.67-0.80; p-trend < 0.001), diagnosis in later years (PRatio:2012-19 vs. 2005-11 0.44; 95% CI 0.41-0.48), and higher stage (PRatiostage IIIA vs. stage I: 0.51; 95% CI 0.46-0.58; p-trend < 0.001). CONCLUSIONS: Patients receiving conventional schedule vs. dose-dense chemotherapy represent those typically most vulnerable to toxicity or with lower risk disease, but may also represent groups vulnerable to disparities. Further research is needed to establish how to improve the uptake of dose-dense chemotherapy where appropriate.
