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Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: A network analysis
BACKGROUND: Research into the clustering of symptoms may improve the understanding of the underlying mechanisms that affect survivors' symptom burden. This study applied network analyses in a balanced sample of cancer survivors to 1) explore the clustering of symptoms and 2) assess differences...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9291877/ https://www.ncbi.nlm.nih.gov/pubmed/34387856 http://dx.doi.org/10.1002/cncr.33852 |
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author | de Rooij, Belle H. Oerlemans, Simone van Deun, Katrijn Mols, Floortje de Ligt, Kelly M. Husson, Olga Ezendam, Nicole P. M. Hoedjes, Meeke van de Poll‐Franse, Lonneke V. Schoormans, Dounya |
author_facet | de Rooij, Belle H. Oerlemans, Simone van Deun, Katrijn Mols, Floortje de Ligt, Kelly M. Husson, Olga Ezendam, Nicole P. M. Hoedjes, Meeke van de Poll‐Franse, Lonneke V. Schoormans, Dounya |
author_sort | de Rooij, Belle H. |
collection | PubMed |
description | BACKGROUND: Research into the clustering of symptoms may improve the understanding of the underlying mechanisms that affect survivors' symptom burden. This study applied network analyses in a balanced sample of cancer survivors to 1) explore the clustering of symptoms and 2) assess differences in symptom clustering between cancer types, treatment regimens, and short‐term and long‐term survivors. METHODS: This study used cross‐sectional survey data, collected between 2008 and 2018, from the population‐based Patient Reported Outcomes Following Initial Treatment and Long Term Evaluation of Survivorship registry, which included survivors of 7 cancer types (colorectal cancer, breast cancer, ovarian cancer, thyroid cancer, chronic lymphocytic leukemia, Hodgkin lymphoma, and non‐Hodgkin lymphoma). Regularized partial correlation network analysis was used to explore and visualize the associations between self‐reported symptoms (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire) and the centrality of these symptoms in the network (ie, how strongly a symptom was connected to other symptoms) for the total sample and for subgroups separately. RESULTS: In the total sample (n = 1330), fatigue was the most central symptom in the network with moderate direct relationships with emotional symptoms, cognitive symptoms, appetite loss, dyspnea, and pain. These relationships persisted after adjustments for sociodemographic and clinical characteristics. Connections between fatigue and emotional symptoms, appetite loss, dyspnea, and pain were consistently found across all cancer types (190 for each), treatment regimens, and short‐term and long‐term survivors. CONCLUSIONS: In a heterogenous sample of cancer survivors, fatigue was consistently the most central symptom in all networks. Although longitudinal data are needed to build a case for the causal nature of these symptoms, cancer survivorship rehabilitation programs could focus on fatigue to reduce the overall symptom burden. |
format | Online Article Text |
id | pubmed-9291877 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92918772022-07-20 Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: A network analysis de Rooij, Belle H. Oerlemans, Simone van Deun, Katrijn Mols, Floortje de Ligt, Kelly M. Husson, Olga Ezendam, Nicole P. M. Hoedjes, Meeke van de Poll‐Franse, Lonneke V. Schoormans, Dounya Cancer Original Articles BACKGROUND: Research into the clustering of symptoms may improve the understanding of the underlying mechanisms that affect survivors' symptom burden. This study applied network analyses in a balanced sample of cancer survivors to 1) explore the clustering of symptoms and 2) assess differences in symptom clustering between cancer types, treatment regimens, and short‐term and long‐term survivors. METHODS: This study used cross‐sectional survey data, collected between 2008 and 2018, from the population‐based Patient Reported Outcomes Following Initial Treatment and Long Term Evaluation of Survivorship registry, which included survivors of 7 cancer types (colorectal cancer, breast cancer, ovarian cancer, thyroid cancer, chronic lymphocytic leukemia, Hodgkin lymphoma, and non‐Hodgkin lymphoma). Regularized partial correlation network analysis was used to explore and visualize the associations between self‐reported symptoms (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire) and the centrality of these symptoms in the network (ie, how strongly a symptom was connected to other symptoms) for the total sample and for subgroups separately. RESULTS: In the total sample (n = 1330), fatigue was the most central symptom in the network with moderate direct relationships with emotional symptoms, cognitive symptoms, appetite loss, dyspnea, and pain. These relationships persisted after adjustments for sociodemographic and clinical characteristics. Connections between fatigue and emotional symptoms, appetite loss, dyspnea, and pain were consistently found across all cancer types (190 for each), treatment regimens, and short‐term and long‐term survivors. CONCLUSIONS: In a heterogenous sample of cancer survivors, fatigue was consistently the most central symptom in all networks. Although longitudinal data are needed to build a case for the causal nature of these symptoms, cancer survivorship rehabilitation programs could focus on fatigue to reduce the overall symptom burden. John Wiley and Sons Inc. 2021-08-13 2021-12-15 /pmc/articles/PMC9291877/ /pubmed/34387856 http://dx.doi.org/10.1002/cncr.33852 Text en © 2021 The Authors. Cancer published by Wiley Periodicals LLC on behalf of American Cancer Society. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles de Rooij, Belle H. Oerlemans, Simone van Deun, Katrijn Mols, Floortje de Ligt, Kelly M. Husson, Olga Ezendam, Nicole P. M. Hoedjes, Meeke van de Poll‐Franse, Lonneke V. Schoormans, Dounya Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: A network analysis |
title | Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: A network analysis |
title_full | Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: A network analysis |
title_fullStr | Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: A network analysis |
title_full_unstemmed | Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: A network analysis |
title_short | Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: A network analysis |
title_sort | symptom clusters in 1330 survivors of 7 cancer types from the profiles registry: a network analysis |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9291877/ https://www.ncbi.nlm.nih.gov/pubmed/34387856 http://dx.doi.org/10.1002/cncr.33852 |
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