Development and validation of a predictive model of aromatase inhibitor-induced arthralgia among patients with early breast cancer.

Abstract

Aromatase inhibitor-induced arthralgia (AIA) is common in early-stage breast cancer patients on aromatase inhibitors (AI), potentially compromising adherence and outcomes. We developed and validated prediction models for short- and longer-term AIA. Postmenopausal patients with stage I-III breast cancer on adjuvant AI in CANTO (NCT01993498) were included. Primary outcome was AIA (any grade articular/muscular pain, Common Terminology Criteria for Adverse Events [CTCAE] v4.0) at year 1 (Y-1) and 4 (Y-4) cohort visits. Baseline clinical, behavioral, treatment-related, patient-reported (EORTC QLQ-C30, HADS) variables were modeled using multivariable logistic regression and bootstrap in development and temporal validation cohorts. Baseline inflammatory markers were examined in a sub-cohort. The Y-1 and Y-4 development cohorts included 3,065 and 2,390 patients (mean age 64.0 [SD 7.2] and 63.7 years [6.9]), respectively. AIA occurred in 61.3% (Y-1) and 66.1% (Y-4) patients. Validation cohorts included 1,313 (Y-1) and 1,009 (Y-4) patients with similar characteristics. Across models, higher BMI, greater baseline fatigue, prior articular/muscular disease, chemotherapy exposure, pre-existing pain were associated with subsequent AIA; anastrozole use was associated with AIA at Y-4. AUC was 0.62-0.65 in development cohorts; 0.61-0.67 in validation cohorts. In the biomarker sub-cohort (n = 637), higher baseline IL-8 was associated with lower Y-1 AIA odds (OR 0.74, 95% CI 0.56-0.98). Using baseline clinical and patient-reported data, we generated models identifying patients at increased risk of early and persistent AIA. While performance was modest, risk stratification could help trigger stepped-care pathways to optimize AI adherence and outcomes. IL-8 association requires independent replication.