Diversity in changes of HRQoL over a 1-year period after radiotherapy in Norwegian breast cancer patients: results of cluster analyses

Abstract

Purpose The diversity in long-term changes in health-related quality of life (HRQoL) among breast cancer (BC) survivors is poorly understood. The aim of this study was to identify clusters of trajectories (subgroups of patients with similar patterns of changes) of selected HRQoL domains over a 1-year period after radiotherapy (RT) in BC patients. Methods The group consisted of 250 BC patients referred for postoperative RT. Global quality of life (QoL), functions, and cancer-specific symptoms were assessed using the European Organisation for Research and Treatment of Cancer (EORTC) core Quality of Life Questionnaire (QLQ-C30) before starting RT, at completion of RT and 3, 6, and 12 months after RT. A hierarchical cluster analysis was used to identify possible trajectories of HRQoL domains. Results Three distinct types of clusters of trajectories were identified for all outcome variables: Type 1 clusters encompassing the rather time-stable high-global QoL cluster, high-functioning clusters, and low-symptom clusters (44–98% of patients), Type 2 clusters with medium levels of HRQoL domains (8–49%), Type 3 clusters encompassing low-global QoL, low-functioning, and high-symptoms clusters (2–51%). Conclusions Our results demonstrated a noticeable heterogeneity of changes in HRQoL domains after BC treatment. The findings support the importance of an accurate patient-reported HRQoL assessment as a routine element of BC survivors’ care. The pre-RT assessment of HRQoL alone allows to predict the course of HRQoL changes over the 1-year period after RT and the risk of “falling into” low functioning clusters. Electronic supplementary material The online version of this article (10.1007/s11136-019-02127-7) contains supplementary material, which is available to authorized users.

The importance of using ordinal scores for patient classification based on health‐related quality of life trajectories

“…Thus, anxious patients at baseline can be suggested programs based on art therapy to prevent this deterioration. The results support a recent study in patients with breast cancer that also found three clusters of trajectories for the five HRQoL dimensions after treatment 33 . Over a period of 1 year, they identified three subgroups of patients for the global functioning variable.…”

“…Although we chose a two‐trajectory model for GHS, this choice was motivated only by the small number of patients in the third trajectory ( n = 2); our first analyses had led us to the same conclusion, identifying three patient profiles. Lazarewicz et al 33 reported that a majority of patients belonged to the “high” (44%) or “medium” (43%) GHS group. This supports our finding that 55% of our sample belonged to the “improvement” trajectory.…”

“…The results support a recent study in patients with breast cancer that also found three clusters of trajectories for the five HRQoL dimensions after treatment. 33 Over a period of 1 year, they identified three subgroups of patients for the global functioning variable. Although we chose a two-trajectory model for GHS, this choice was motivated only by the small number of patients in the third trajectory (n = 2); our first analyses had led us to the same conclusion, identifying three patient profiles.…”

Patient-reported outcomes in postmenopausal breast cancer survivors – comparisons with normative data

“…It could therefore be appropriate to move beyond investigating mean scores and identify how symptoms and late effects may develop and change over time in subgroups of patients and survivors. A recent study measuring responses to the QLQ-C30 in 250 BC patients over a 1-year period after completion of radiotherapy thus found three distinct types of clusters of trajectories for all outcome variables, i.e., stable trajectories of high, medium, and low HRQoL [45], suggesting that identifying the predictors of the trajectories could identify those with special rehabilitation needs.…”

Decision Tree Analyses for Prediction of QoL over a One-Year Period in Breast Cancer Patients: An Added Value of Patient-Reported Outcomes

“…This issue is particularly relevant to breast cancer (BC) patients who, in most developed countries, receive proper healthcare and constitute the largest group of cancer survivors [8]. Thus, tertiary prevention encompassing restoration and maintenance of good QoL after treatment becomes the key priority [9][10][11][12][13]. This positive trend was confirmed in longitudinal studies estimating QoL on the basis of average-level data [9][10][11][13][14][15] or in subgroups defined by treatment modalities [16,17].…”