Symptom Clusters in Advanced Cancer Patients: An Empirical Comparison of Statistical Methods and the Impact on Quality of Life

Skye T Dong; Daniel S J Costa; Phyllis N Butow; Melanie R Lovell; Meera Agar; Galina Velikova; Paulos Teckle; Allison Tong; Niall C Tebbutt; Stephen J Clarke; Kim van der Hoek; Madeleine T King; Peter M Fayers

doi:10.1016/j.jpainsymman.2015.07.013

Symptom Clusters in Advanced Cancer Patients: An Empirical Comparison of Statistical Methods and the Impact on Quality of Life

J Pain Symptom Manage. 2016 Jan;51(1):88-98. doi: 10.1016/j.jpainsymman.2015.07.013. Epub 2015 Aug 21.

Authors

Affiliations

¹ School of Psychology, University of Sydney, Sydney, New South Wales, Australia; University of Aberdeen, Aberdeen, UK. Electronic address: skye.dong@sydney.edu.au.
² Psycho-Oncology Co-operative Research Group (PoCoG), University of Sydney, Sydney, New South Wales, Australia; University of Aberdeen, Aberdeen, UK.
³ Department of Palliative Care, Braeside Hospital, HammondCare, Sydney, New South Wales, Australia; University of Aberdeen, Aberdeen, UK.
⁴ Department of Palliative Care, Braeside Hospital, HammondCare, Sydney, New South Wales, Australia; HammondCare, Greenwich Hospital, Sydney, New South Wales, Australia; University of Aberdeen, Aberdeen, UK.
⁵ 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; University of Aberdeen, Aberdeen, UK.
⁶ St James's Hospital, Leeds, UK; University of Aberdeen, Aberdeen, UK.
⁷ 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; University of Aberdeen, Aberdeen, UK.
⁸ Sydney School of Public Health, University of Sydney, Sydney, New South Wales, Australia; University of Aberdeen, Aberdeen, UK.
⁹ Olivia Newton-John Cancer and Wellness Centre, Austin Health, Melbourne, Victoria, Australia; University of Aberdeen, Aberdeen, UK.
¹⁰ Department of Medical Oncology, Royal North Shore Hospital, Sydney, New South Wales, Australia; University of Aberdeen, Aberdeen, UK.
¹¹ Canadian Centre for Applied Research in Cancer Control, BC Cancer Research Centre, Vancouver, Canada; University of Aberdeen, Aberdeen, UK.
¹² Psycho-Oncology Co-operative Research Group (PoCoG), University of Sydney, Sydney, New South Wales, Australia; Central Clinical School, Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia; University of Aberdeen, Aberdeen, UK.
¹³ Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; University of Aberdeen, Aberdeen, UK.

PMID: 26300025
DOI: 10.1016/j.jpainsymman.2015.07.013

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.

Keywords: EORTC QLQ-C30; Symptom clusters; advanced cancer; quality of life; statistical methods.

Publication types

Comparative Study
Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Aged
Aged, 80 and over
Cluster Analysis
Data Interpretation, Statistical*
Factor Analysis, Statistical
Female
Follow-Up Studies
Humans
Male
Middle Aged
Neoplasms / physiopathology*
Observational Studies as Topic
Principal Component Analysis
Quality of Life*
Randomized Controlled Trials as Topic
Regression Analysis
Syndrome
Young Adult

Grants and funding

MR/L01629X/1/MRC_/Medical Research Council/United Kingdom