Symptoms Predictive of Overall Quality of Life Using the Edmonton Symptom Assessment Scale in Breast Cancer Patients Receiving Radiotherapy.


Journal

Clinical breast cancer
ISSN: 1938-0666
Titre abrégé: Clin Breast Cancer
Pays: United States
ID NLM: 100898731

Informations de publication

Date de publication:
12 2019
Historique:
received: 21 01 2019
revised: 25 03 2019
accepted: 07 05 2019
pubmed: 12 6 2019
medline: 9 9 2020
entrez: 12 6 2019
Statut: ppublish

Résumé

Breast cancer patients often experience multiple symptoms that negatively affect quality of life (QOL). Patient-reported scores on symptom screening tools are used by health care professionals to manage QOL. We aimed to examine which symptoms from the Edmonton Symptom Assessment Scale (ESAS) were most predictive of overall well-being (QOL) in breast cancer patients over the course of radiotherapy (RT). ESAS results completed before, at the end of, and after RT were obtained from all nonmetastatic breast cancer patients. Univariate and multivariable (backward stepwise selection) linear regression analyses were applied to select the most significant ESAS symptoms or treatment variables related to overall QOL at all 3 time points. A total of 1224 patients were included in the study. Before RT, multivariable analysis identified 5 symptoms that were significantly associated with overall QOL: pain, tiredness, anxiety, depression, and loss of appetite. At the end of RT, pain, tiredness, and anxiety were the most significant predictors of QOL. After RT, 6 symptoms were found to have the strongest correlation with QOL: pain, tiredness, anxiety, depression, loss of appetite, and drowsiness. At each time point, patients with higher scores for the identified significant symptoms were likely to have a worse overall QOL. Of the ESAS symptoms identified as significant predictors of QOL, pain, tiredness, and anxiety correlated with overall well-being at all time points. Special attention should be paid to manage symptoms that are most predictive of overall QOL in order to ensure optimal symptom management in breast cancer patients receiving RT.

Sections du résumé

BACKGROUND
Breast cancer patients often experience multiple symptoms that negatively affect quality of life (QOL). Patient-reported scores on symptom screening tools are used by health care professionals to manage QOL. We aimed to examine which symptoms from the Edmonton Symptom Assessment Scale (ESAS) were most predictive of overall well-being (QOL) in breast cancer patients over the course of radiotherapy (RT).
PATIENTS AND METHODS
ESAS results completed before, at the end of, and after RT were obtained from all nonmetastatic breast cancer patients. Univariate and multivariable (backward stepwise selection) linear regression analyses were applied to select the most significant ESAS symptoms or treatment variables related to overall QOL at all 3 time points.
RESULTS
A total of 1224 patients were included in the study. Before RT, multivariable analysis identified 5 symptoms that were significantly associated with overall QOL: pain, tiredness, anxiety, depression, and loss of appetite. At the end of RT, pain, tiredness, and anxiety were the most significant predictors of QOL. After RT, 6 symptoms were found to have the strongest correlation with QOL: pain, tiredness, anxiety, depression, loss of appetite, and drowsiness. At each time point, patients with higher scores for the identified significant symptoms were likely to have a worse overall QOL.
CONCLUSION
Of the ESAS symptoms identified as significant predictors of QOL, pain, tiredness, and anxiety correlated with overall well-being at all time points. Special attention should be paid to manage symptoms that are most predictive of overall QOL in order to ensure optimal symptom management in breast cancer patients receiving RT.

Identifiants

pubmed: 31182402
pii: S1526-8209(19)30055-2
doi: 10.1016/j.clbc.2019.05.007
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

405-410

Informations de copyright

Copyright © 2019 Elsevier Inc. All rights reserved.

Auteurs

Selina Chow (S)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Bo Angela Wan (BA)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

William Pidduck (W)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Liying Zhang (L)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Carlo DeAngelis (C)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Stephanie Chan (S)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Caitlin Yee (C)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Leah Drost (L)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Eric Leung (E)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Philomena Sousa (P)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Donna Lewis (D)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Henry Lam (H)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Ronald Chow (R)

London Regional Cancer Program, London Health Sciences Centre, University of Western Ontario, London, Ontario, Canada.

Michael Lock (M)

London Regional Cancer Program, London Health Sciences Centre, University of Western Ontario, London, Ontario, Canada.

Edward Chow (E)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada. Electronic address: edward.chow@sunnybrook.ca.

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Classifications MeSH