Abstract B18: Pain, depression, and fatigue symptom cluster as a predictor of physical quality of life in advanced and recurrent cancer Free

B18

Pain, depression, and fatigue are common symptoms affecting quality of life (QoL) in cancer patients and survivors. Recent research has suggested the existence of a pain, depression, and fatigue (PDF) symptom cluster. The present study examined whether the PDF symptom cluster predicts physical QoL in cancer patients with advanced disease, an underrepresented group in this area of research. Method: Patients (N=121) with newly diagnosed advanced breast cancer (i.e. metastatic or recurrent disease) were accrued. Women were consecutive cases at a university-affiliated National Cancer Institute-designated Comprehensive Cancer Center or self- and physician-referred cases from the community. The Brief Pain Questionnaire (BPQ), Center for Epidemiological Studies Depression Scale (CES-D), and Fatigue Symptom Inventory (FSI) were used to measure pain, depression, and fatigue, respectively. The Karnofsky Performance Status Scale (KPS) assessed functional status, which was used as a proxy for QoL. Patients were assessed at baseline, and 4, 8, and 12 month follow-ups. A composite score for pain, depression, and fatigue was calculated for each participant by adding the individual scores from each symptom scale. Hierarchical linear regressions were conducted to examine whether PDF predicted physical functioning over time, controlling for relevant disease and demographic variables. Results: Correlation analyses revealed strong positive intercorrelations between pain, depression, and fatigue at all time points. Analyses revealed that a lower PDF composite score at 4 months predicted greater physical functioning both at 8 months and 12 months (p<0.05). Discussion: Results suggest that there may be a longitudinal effect of PDF symptom clustering on quality of life in these patients. Symptom management interventions may need to treat all three symptoms to be maximally effective. Future research could investigate mechanisms for the relationships among symptoms, or generate effective treatment strategies.

Citation Information: Cancer Prev Res 2008;1(7 Suppl):B18.