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Identifying classes of the pain, fatigue, and depression symptom cluster in long-term prostate cancer survivors-results from the multi-regional Prostate Cancer Survivorship Study in Switzerland (PROCAS)


Adam, Salome; Thong, Melissa S Y; Martin-Diener, Eva; Camey, Bertrand; Egger Hayoz, Céline; Konzelmann, Isabelle; Mousavi, Seyed Mohsen; Herrmann, Christian; Rohrmann, Sabine; Wanner, Miriam; Staehelin, Katharina; Strebel, Räto Thomas; Randazzo, Marco; John, Hubert; Schmid, Hans-Peter; Feller, Anita; Arndt, Volker (2021). Identifying classes of the pain, fatigue, and depression symptom cluster in long-term prostate cancer survivors-results from the multi-regional Prostate Cancer Survivorship Study in Switzerland (PROCAS). Supportive Care in Cancer, 29(11):6259-6269.

Abstract

PURPOSE

Aside from urological and sexual problems, long-term (≥5 years after initial diagnosis) prostate cancer (PC) survivors might suffer from pain, fatigue, and depression. These concurrent symptoms can form a cluster. In this study, we aimed to investigate classes of this symptom cluster in long-term PC survivors, to classify PC survivors accordingly, and to explore associations between classes of this cluster and health-related quality of life (HRQoL).

METHODS

Six hundred fifty-three stage T1-T3N0M0 survivors were identified from the Prostate Cancer Survivorship in Switzerland (PROCAS) study. Fatigue was assessed with the EORTC QLQ-FA12, depressive symptoms with the MHI-5, and pain with the EORTC QLQ-C30 questionnaire. Latent class analysis was used to derive cluster classes. Factors associated with the derived classes were determined using multinomial logistic regression analysis.

RESULTS

Three classes were identified: class 1 (61.4%) - "low pain, low physical and emotional fatigue, moderate depressive symptoms"; class 2 (15.1%) - "low physical fatigue and pain, moderate emotional fatigue, high depressive symptoms"; class 3 (23.5%) - high scores for all symptoms. Survivors in classes 2 and 3 were more likely to be physically inactive, report a history of depression or some other specific comorbidity, be treated with radiation therapy, and have worse HRQoL outcomes compared to class 1.

CONCLUSION

Three distinct classes of the pain, fatigue, and depression cluster were identified, which are associated with treatment, comorbidities, lifestyle factors, and HRQoL outcomes. Improving classification of PC survivors according to severity of multiple symptoms could assist in developing interventions tailored to survivors' needs.

Abstract

PURPOSE

Aside from urological and sexual problems, long-term (≥5 years after initial diagnosis) prostate cancer (PC) survivors might suffer from pain, fatigue, and depression. These concurrent symptoms can form a cluster. In this study, we aimed to investigate classes of this symptom cluster in long-term PC survivors, to classify PC survivors accordingly, and to explore associations between classes of this cluster and health-related quality of life (HRQoL).

METHODS

Six hundred fifty-three stage T1-T3N0M0 survivors were identified from the Prostate Cancer Survivorship in Switzerland (PROCAS) study. Fatigue was assessed with the EORTC QLQ-FA12, depressive symptoms with the MHI-5, and pain with the EORTC QLQ-C30 questionnaire. Latent class analysis was used to derive cluster classes. Factors associated with the derived classes were determined using multinomial logistic regression analysis.

RESULTS

Three classes were identified: class 1 (61.4%) - "low pain, low physical and emotional fatigue, moderate depressive symptoms"; class 2 (15.1%) - "low physical fatigue and pain, moderate emotional fatigue, high depressive symptoms"; class 3 (23.5%) - high scores for all symptoms. Survivors in classes 2 and 3 were more likely to be physically inactive, report a history of depression or some other specific comorbidity, be treated with radiation therapy, and have worse HRQoL outcomes compared to class 1.

CONCLUSION

Three distinct classes of the pain, fatigue, and depression cluster were identified, which are associated with treatment, comorbidities, lifestyle factors, and HRQoL outcomes. Improving classification of PC survivors according to severity of multiple symptoms could assist in developing interventions tailored to survivors' needs.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Oncology
Language:English
Date:November 2021
Deposited On:14 Jan 2022 16:08
Last Modified:15 Jan 2022 21:00
Publisher:Springer
ISSN:0941-4355
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1007/s00520-021-06132-w
PubMed ID:33847829

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