Latent class analysis reveals distinct subgroups of patients based on symptom occurrence and demographic and clinical characteristics

Miaskowski, Christine, Dunn, Laura, Ritchie, Christine, Paul, Steven M., Cooper, Bruce, Aouizerat, Bradley E., Alexander, Kimberly, Skerman, Helen, & Yates, Patsy (2015) Latent class analysis reveals distinct subgroups of patients based on symptom occurrence and demographic and clinical characteristics. Journal of Pain and Symptom Management, 50(1), pp. 28-37.

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Abstract

Context

Cancer patients experience a broad range of physical and psychological symptoms as a result of their disease and its treatment. On average, these patients report ten unrelieved and co-occurring symptoms.

Objectives

To determine if subgroups of oncology outpatients receiving active treatment (n=582) could be identified based on their distinct experience with thirteen commonly occurring symptoms; to determine whether these subgroups differed on select demographic, and clinical characteristics; and to determine if these subgroups differed on quality of life (QOL) outcomes.

Methods

Demographic, clinical, and symptom data from one Australian and two U.S. studies were combined. Latent class analysis (LCA) was used to identify patient subgroups with distinct symptom experiences based on self-report data on symptom occurrence using the Memorial Symptom Assessment Scale (MSAS).

Results

Four distinct latent classes were identified (i.e., All Low (28.0%), Moderate Physical and Lower Psych (26.3%), Moderate Physical and Higher Psych (25.4%), All High (20.3%)). Age, gender, education, cancer diagnosis, and presence of metastatic disease differentiated among the latent classes. Patients in the All High class had the worst QOL scores.

Conclusion

Findings from this study confirm the large amount of interindividual variability in the symptom experience of oncology patients. The identification of demographic and clinical characteristics that place patients are risk for a higher symptom burden can be used to guide more aggressive and individualized symptom management interventions.

Impact and interest:

10 citations in Scopus
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7 citations in Web of Science®

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ID Code: 81902
Item Type: Journal Article
Refereed: Yes
Keywords: symptom clusters, latent class analysis, symptom profiles, age differences, gender differences
DOI: 10.1016/j.jpainsymman.2014.12.011
ISSN: 0885-3924
Subjects: Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > NURSING (111000) > Nursing not elsewhere classified (111099)
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Nursing
Copyright Owner: Copyright 2015 Elsevier
Copyright Statement: This is the author’s version of a work that was accepted for publication in Journal of Pain and Symptom Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Pain and Symptom Management, [Vol 50, Issue 1, (2015)] DOI: 10.1016/j.jpainsymman.2014.12.011
Deposited On: 19 Feb 2015 02:29
Last Modified: 06 Aug 2016 12:42

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