The predictive value of patient-reported outcomes on the impact of breast cancer treatment-related quality of life

PurposePatient-reported outcomes (PROs) have been widely used to measure breast cancer (BC) treatment outcomes. However, evidence is still limited on using routinely PROs to personalize treatment decision-making, including or not chemotherapy, targeted therapy, and radiotherapy. Using patient baseline PRO scores, we aimed to use PROs before treatment initiation to predict improvement or decline in health-related quality of life (HRQoL) due to treatment that they receive.MethodsIn two French cancer sites, women with non-metastatic BC completed the EORTC QLQ-C30 and QLQ-BR23 and BREAST-Q questionnaires to assess their PROs at baseline and again at 6 months. The outcome measured was post-operative change in PROs with minimal important difference for QLQ-C30 domains. We performed multivariate ordinal logistic regression to estimate the incremental probability of post-operative PRO improvements and deteriorations depending upon treatment options and baseline HRQoL.ResultsOne hundred twenty-seven women completed questionnaires. Chemotherapy had significant negative impacts on Global health status (GHS) and on physical and social functioning. Chemotherapy and radiotherapy increased patient fatigue scores after adjusting for clinical factors (p< 0.01 and p< 0.05, respectively). The incremental probability of GHS deteriorations for chemotherapy was +0.3, +0.5, and +0.34 for patients with baseline GHS scores of 40, 70, and 100, respectively. This showed that different pre-treatment PROs might predict differential effects of chemotherapy on women change in HRQoL.ConclusionPatients with different baseline PRO scores may experience dissimilar impacts from BC treatments on post-operative PROs in terms of improvements and deteriorations. Oncologists might decide to adapt the treatment option based on a given level of the negative impact. Future studies should concentrate on incorporating this information into routine clinical decision-making strategies to optimize the treatment benefit for patients.