UCSF

Symptom clusters research is an emerging field in oncology nursing, and little is known about symptom clusters among women with breast cancer undergoing treatment. The aims of the current study were to identify symptom clusters present in women with breast cancer undergoing chemotherapy using different symptom dimensions (i.e., frequency, severity, distress); identify which personal, health and illness, and treatment-related variables can predict severity of the symptom clusters; and evaluate how symptom clusters (clustered by severity dimension) change over time. A secondary analysis of a sample of 219 women with breast cancer undergoing chemotherapy was conducted. Ten symptoms were assessed using the symptom experience scale (SES) and the hospital anxiety and depression scale (HADS). Exploratory factor analysis and simple and multiple regressions were used to identify symptom clusters and predictors of severity of symptom clusters. Two symptom clusters were identified and stayed approximately constant across different symptom dimensions. The first cluster consisted of nausea, loss of appetite, ± sleep disturbance. The second cluster consisted of pain, fatigue, bowel pattern, concentration, appearance, ± sleep disturbance, anxiety, and depression. However, the symptom clusters seemed to be dynamic over time. Among 16 variables that were assessed, baseline age, hemoglobin level, symptoms severity, and the mental component summary score were significant predictors of the severity of first symptom cluster. Employment status and baseline Karnofsky performance status, mental component summary, physical component summary, and symptom severity scores were significant predictors of the severity of the second symptom cluster. Symptom clusters may change over time even in a homogeneous sample. This may be related to the dynamic nature of symptoms and complex interactions among the symptoms within one cluster or across different clusters. Future research should further investigate symptom clusters trajectories over time.