An investigation of population heterogeneity of cancer-related fatigue in breast cancer patients

Abstract

Background: Fatigue is one of the most prevalent and significant symptoms experienced by breast cancer patients. Cancer-related fatigue can result in difficulties for the patients in maintaining their prior level of physical functioning, thus affecting their daily life and work performance. The aim of the present study was to examine potential population heterogeneity in fatigue symptoms of breast cancer patients via mixture modeling. Methods: Participants were 197 Chinese female breast cancer patients (mean age = 49.4 years, SD = 8.0; average cancer duration = 23.1 months, SD = 7.5). They completed self-report measures on fatigue and cancer-related psychopathological states, namely, perceived stress, anxiety, depression, pain, sleep disturbance, and quality of life. Latent profile analysis and factor mixture analysis were carried out using Mplus 7 and the optimal number of latent classes was selected based on the Bayesian information criterion (BIC). The identified classes were validated by comparing their demographic, clinical, and symptomatic characteristics using a stepwise distal outcome approach. Results: The two-class, two-factor model fitted significantly better than the one-class, two-factor model (p < .05) and provided the best fit to the data in terms of a good BIC and high classification accuracy (entropy = .90). The exhausted class (N = 88, 44.7%) showed high levels of fatigue severity and interference. The restored class (N = 109, 55.3%) exhibited moderate severity and low interference. Compared to the restored class, the exhausted class reported significantly higher levels of perceived stress, anxiety, depression, pain, and sleep disturbance, and lower quality of life. Discussions: The present findings suggest the existence of two clinically distinct fatigue classes. The population heterogeneity in cancer-related fatigue and the psychopathological correlates provide important information in fostering quality care for different subgroups of patients. Acknowledgement: This study was supported by the General Research Fund, Research Grants Council (GRF/HKU745110H). We would like to thank Hong Kong Cancer Fund, Queen Mary Hospital, Pamela Youde Nethersole Eastern Hospital, and Dr. M.Y. Luk for their help in patient recruitment and all the patients who participated in the study.