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https://ahro.austin.org.au/austinjspui/handle/1/16589| Title: | Symptom clusters in advanced cancer patients: an empirical comparison of statistical methods and the impact on quality of life | Austin Authors: | Dong, Skye T;Costa, Daniel SJ;Butow, Phyllis N;Lovell, Melanie R;Agar, Meera;Velikova, Galina;Teckle, Paulos;Tong, Allison;Tebbutt, Niall C ;Clarke, Stephen J;van der Hoek, Kim;King, Madeleine T;Fayers, Peter M | Affiliation: | School of Psychology, University of Sydney, Sydney, New South Wales, Australia Psycho-Oncology Co-operative Research Group (PoCoG), University of Sydney, Sydney, New South Wales, Australia Department of Palliative Care, Braeside Hospital, HammondCare, Sydney, New South Wales, Australia HammondCare, Greenwich Hospital, Sydney, New South Wales, Australia The University of Sydney Medical School, Sydney, New South Wales, Australia University of NSW, South West Sydney Clinical School, Sydney, New South Wales, Australia Discipline of Palliative and Supportive Services, Flinders University, Adelaide, South Australia, Australia St James's Hospital, Leeds, UK Canadian Centre for Applied Research in Cancer Control, BC Cancer Research Centre, Vancouver, Canada School of Population and Public Health, University of British Columbia, Canada Sydney School of Public Health, University of Sydney, Sydney, New South Wales, Australia Olivia Newton-John Cancer and Wellness Centre, Austin Health, Heidelberg, Victoria, Australia Department of Medical Oncology, Royal North Shore Hospital, Sydney, New South Wales, Australia Central Clinical School, Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia University of Aberdeen, Aberdeen, UK Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway |
Issue Date: | Jan-2016 | Date: | 2015-08-25 | Publication information: | Journal of Pain and Symptom Management 2016; 51(1): 88-98 | Abstract: | CONTEXT: Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. OBJECTIVES: To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. METHODS: Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. RESULTS: Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. CONCLUSIONS: The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential. | URI: | https://ahro.austin.org.au/austinjspui/handle/1/16589 | DOI: | 10.1016/j.jpainsymman.2015.07.013 | Journal: | Journal of Pain and Symptom Management | PubMed URL: | https://pubmed.ncbi.nlm.nih.gov/26300025 | Type: | Journal Article | Subjects: | Symptom clusters EORTC QLQ-C30 Statistical methods Advanced cancer Quality of life |
| Appears in Collections: | Journal articles |
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