Predictive model of aromatase inhibitor non-adherence using patient-reported outcomes in women with breast cancer (SWOG S1105).

Abstract

12019 Background: Non-adherence to aromatase inhibitors (AIs) for breast cancer is common and increases risk of recurrence. Few prospective studies have systematically evaluated factors associated with non-adherence. We analyzed baseline sociodemographic, prescription, and patient reported outcome (PRO) symptoms and quality-of-life to identify factors associated with non-adherence prospectively over 3-years. Methods: Patients enrolled in SWOG S1105 were required to have been on an AI for ≥30 days. Patients were assessed for non-adherence to AIs every 3 months for 36 months, with non-adherence defined as urine AI metabolite assay results satisfying any of the following: < 10 ng/mL, undetectable, specimen submitted outside of the ± 21 day follow-up appointment window, or no submitted specimen. At baseline patients were asked about insurance, pill number dispensed and medication cost, and they completed PROs focused on pain and endocrine symptoms (BPI (Brief Pain Inventory), FACT-ES (Endocrine Symptoms)), as well as their beliefs about medications (TSQM (Treatment Satisfaction Questionnaire for Medicine) and BMQ (Brief Medication Questionnaire)). PRO scales were split at the median creating high vs low binary predictors. We determined the association of baseline factors and non-adherence at 36 months. We also evaluated an adverse risk model for AI non-adherence by summing the number of statistically significant adverse factors associated with non-adherence. Logistic regression was used. Results: In total, 724 patients were registered from 40 institutions between May, 2012 and September, 2013. The median age was 60.9 years, and 64.5% were on AI < 12 months prior to registration. Overall, 35.9% were non-adherent at 36 months. Younger patients ( < 65 years) were less adherent (39% vs. 29% non-adherence, OR = 1.51, p = 0.02). Baseline scores on the BPI, FACT-ES, BMQ and TSQM were each statistically significantly associated with AI adherence. Non-adherence was significantly higher among patients scoring poorly on all 4 PRO instruments (65%) compared to those scoring poorly 0 or 1 PRO instruments (27%; OR, 4.68 [2.84-7.73], p < .0001). For each increase in the number of adverse risk PRO scores, the risk of non-adherence increased by 45% (OR = 1.45, p < .0001). Similar results were found when age was included in the score. Conclusions: Presence of multiple baseline risk factors identified through PRO instruments increases non-adherence to AI’s 4-fold. Use of PROs can identify patients for targeted interventions to improve adherence.