Abstract
PURPOSE: Supportive care resources are limited, requiring a strategic, tailored approach, especially with the growing population of breast cancer patients. We examined whether routinely collected patient-reported outcome measures (PROMs) can identify meaningful health-related quality of life (HRQoL) trajectories and help guide resource allocation of supportive care. METHODS: As part of routine care at the Netherlands Cancer Institute, 2181 early-stage breast cancer patients completed the EORTC QLQ-C30 and BR23 before treatment and six months later. Through Latent Class and Latent Transition Analyses, we identified HRQoL subgroups and transitions between subgroups over time. Multinomial regression examined sociodemographic and clinical correlates. Network analysis detected key domains and inter-domain connections within subgroups. RESULTS: Three HRQoL subgroups were identified at baseline: Excellent HRQoL (53%), Good HRQoL with Psychosocial concerns (33%), and Poor HRQoL with severe functional limitations (13%). Multiple comorbidities and history of depression were independently and strongly associated with membership to less favorable subgroups. Subgroup transitions were rare (1–5%). Network analysis showed subgroup-specific key domains: emotional functioning in the Psychosocial concerns-subgroup, and menopausal symptoms, social, and role functioning in the Poor HRQoL-subgroup; the latter subgroup showing the highest interconnectivity between HRQoL-domains. CONCLUSION: Baseline HRQoL and sociodemographic factors, independent of treatment, is associated with HRQoL trajectories and can guide efficient re-allocation of resources to those patients with complex needs. Most patients maintain excellent HRQoL and may benefit from low-intensity or self-management support, whereas targeted, multidisciplinary interventions should be prioritized for those with complex needs. Different key domains and connectivity suggest different types of supportive care are needed for different subgroups.