Symptom clusters in newly diagnosed glioma patients: which symptom clusters are independently associated with functioning and global health status?

Marijke B Coomans, Linda Dirven, Neil K Aaronson, Brigitta G Baumert, Martin Van Den Bent, Andrew Bottomley, Alba A Brandes, Olivier Chinot, Corneel Coens, Thierry Gorlia, Ulrich Herrlinger, Florence Keime-Guibert, Annika Malmström, Francesca Martinelli, Roger Stupp, Andrea Talacchi, Michael Weller, Wolfgang Wick, Jaap C Reijneveld, Martin J B Taphoorn, Marijke B Coomans, Linda Dirven, Neil K Aaronson, Brigitta G Baumert, Martin Van Den Bent, Andrew Bottomley, Alba A Brandes, Olivier Chinot, Corneel Coens, Thierry Gorlia, Ulrich Herrlinger, Florence Keime-Guibert, Annika Malmström, Francesca Martinelli, Roger Stupp, Andrea Talacchi, Michael Weller, Wolfgang Wick, Jaap C Reijneveld, Martin J B Taphoorn

Abstract

Background: Symptom management in glioma patients remains challenging, as patients suffer from various concurrently occurring symptoms. This study aimed to identify symptom clusters and examine the association between these symptom clusters and patients' functioning.

Methods: Data of the CODAGLIO project was used, including individual patient data from previously published international randomized controlled trials (RCTs) in glioma patients. Symptom prevalence and level of functioning were assessed with European Organisation for Research and Treatment of Cancer (EORTC) quality of life QLQ-C30 and QLQ-BN20 self-report questionnaires. Associations between symptoms were examined with Spearman correlation coefficients and partial correlation networks. Hierarchical cluster analyses were performed to identify symptom clusters. Multivariable regression analyses were performed to determine independent associations between the symptom clusters and functioning, adjusted for possible confounders.

Results: Included in the analysis were 4307 newly diagnosed glioma patients from 11 RCTs who completed the EORTC questionnaires before randomization. Many patients (44%) suffered from 5-10 symptoms simultaneously. Four symptom clusters were identified: a motor cluster, a fatigue cluster, a pain cluster, and a gastrointestinal/seizures/bladder control cluster. Having symptoms in the motor cluster was associated with decreased (≥10 points difference) physical, role, and social functioning (betas ranged from -11.3 to -15.9, all P < 0.001), independent of other factors. Similarly, having symptoms in the fatigue cluster was found to negatively influence role functioning (beta of -12.3, P < 0.001), independent of other factors.

Conclusions: Two symptom clusters, the fatigue and motor cluster, were frequently affected in glioma patients and were found to independently have a negative association with certain aspects of patients' functioning as measured with a self-report questionnaire.

Keywords: EORTC QLQ-C30; QLQ-BN20; glioma; health-related quality of life; symptom; symptom cluster.

© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Figures

Fig. 1
Fig. 1
Severity of symptoms for the selected symptoms scales/items measured with the EORTC QLQ-C30 and QLQ-BN20 questionnaires. A darker color indicates more severe symptoms. The single items (dyspnea, insomnia, appetite loss, constipation, diarrhea, headache, seizures, drowsiness, hair loss, itchy skin, weakness of the legs, and bladder control) were rated as: no, mild, moderate, and severe. For the symptom scales (fatigue, visual disorder, motor dysfunction, communication deficit, nausea and vomiting, and pain), the symptoms consisted of multiple items. The Fig. represents the severity on a 0–100 scale, where 0 (white) indicates no symptoms and 100 (black) indicates severe symptoms.
Fig. 2
Fig. 2
Spearman correlation matrix of selected symptoms measured with the EORTC QLQ-C30 and QLQ-BN20 questionnaires. Thicker and darker lines represent stronger partial correlations. Continued lines represent positive partial correlations, dotted lines represent negative partial correlations. The position of the variables represent the closeness, node strength, and betweenness of the variables. Central variables with more connections and thicker lines are most strongly correlated with other variables.
Fig. 3
Fig. 3
Dendrogram illustrating the results of the hierarchical cluster analysis (HCA). The distance at which the branches join indicates the similarity between the symptoms (shorter branches represent greater similarity). Symptoms with greater similarity were clustered first, presented on the left side of the figure. This cluster analysis shows that nausea and vomiting were clustered as a first step, followed by seizures (step 2). Next, pain and headache (step 3) and motor dysfunction and weakness of the legs were clustered (step 4), and so on. The optimal number of clusters was determined at step 6, resulting in the 4 clusters indicated in this Fig. (indicated in gray).

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