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Breast cancer and symptom clusters during radiotherapy
Ellyn E Matthews1, Sarah J Schmiege, Paul F Cook
1College of Nursing, University of Colorado Denver, Aurora, CO 80045, USA. ellyn.matthews@ucdenver.edu
Background:
Symptom clusters assessment shifts the clinical focus from a specific symptom to the patient's experience as a whole. Few studies have examined breast cancer symptom clusters during treatment, and fewer studies have addressed symptom clusters during radiation therapy (RT). The theoretical underpinning of this study is the Symptoms Experience Model. Research is needed to identify antecedents and consequences of cancer-related symptom clusters.
Objective:
The present study was intended to determine the clustering of symptoms during RT in women with breast cancer and significant correlations among the symptoms, individual characteristics, and mood.
Methods:
A secondary data analysis from a descriptive correlational study of 93 women at weeks 3 to 7 of RT from centers in the mid-Atlantic region of the United States, Symptom Distress Scale, the subscales of the Positive and Negative Affect Scale, Life Orientation Test, and Self-transcendence Scale were completed.
Results:
Confirmatory factor analysis revealed symptoms grouped into 3 distinct clusters: pain-insomnia-fatigue, cognitive disturbance-outlook, and gastrointestinal. The pain-insomnia-fatigue and cognitive disturbance-outlook clusters were associated with individual characteristics, optimism, self-transcendence, and positive and negative mood. The gastrointestinal cluster correlated significantly only with positive mood.
Conclusions:
This study provides insight into symptoms that group together and the relationship of symptom clusters to antecedents and mood.
Implications For Practice:
These findings underscore the need to define and standardize the measurement of symptom clusters and understand variability in concurrent symptoms. Attention to symptom clusters shifts the clinical focus from a specific symptom to the patient's experience as a whole and helps identify the most effective interventions.
