AI Article Synopsis
- - The study aimed to identify symptom clusters in advanced cancer patients using the Edmonton Symptom Assessment System (ESAS) in a palliative radiotherapy setting.
- - Various statistical methods (PCA, EFA, and HCA) were employed to analyze ESAS data, revealing consistent clusters that included depression and anxiety, nausea and appetite loss, and tiredness and drowsiness.
- - The findings highlight the importance of understanding these symptom clusters in order to improve patient management and inform future research on symptom interactions in advanced cancer scenarios.
Article Abstract
Background: Cancer patients often present with several concurrent symptoms. There is evidence to suggest that related symptoms can cluster together in stable groups. The present study sought to identify symptom clusters in advanced cancer patients using the Edmonton Symptom Assessment System (ESAS) in a palliative outpatient radiotherapy clinic.
Methods: Principal component analysis (PCA), exploratory factor analysis (EFA), and hierarchical cluster analysis (HCA) were used to identify symptom clusters among the 9 ESAS items using ESAS scores from each patient's first visit.
Results: PCA identified three symptom clusters (cluster 1: depression, anxiety; cluster 2: nausea, dyspnea, loss of appetite; cluster 3: pain, well-being, tiredness, drowsiness). EFA identified two clusters (cluster 1: tiredness, drowsiness, loss of appetite, well-being, pain, nausea, dyspnea; cluster 2: depression, anxiety). HCA identified three symptom clusters (cluster 1: depression, anxiety, pain, well-being; cluster 2: tiredness, drowsiness, dyspnea; cluster 3: nausea, loss of appetite).
Conclusions: Symptom clusters were identified using three analytical methods. The following items were always in the same cluster: depression and anxiety; nausea and appetite loss; well-being and pain; tiredness and drowsiness. Further research in symptom clusters is necessary to advance our understanding of the complex symptom interactions in advanced cancer patients and to determine the most clinically relevant symptom clusters.
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| http://dx.doi.org/10.21037/apm.2018.08.06 | DOI Listing |
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