P11.01 Symptom networks in glioma: a novel approach to study multidimensional symptomatology in glioma patients

Abstract

\n \n \n Glioma patients experience a high symptom burden contributing to poorer quality of life. Symptoms include depression, cognitive impairment, and fatigue and vary throughout the disease. These symptoms are rarely studied from a comprehensive perspective, while their interdependence may be relevant for their development, perpetuation, and ultimately successful treatment. The emerging field of symptom network analysis uncovers the multidimensional symptom space. Nodes are the symptoms, and edges are operationalized as the full conditional association, or partial correlation, between two symptom severity scores across patients. Highly connected nodes are considered central and may be particularly relevant targets for treatment as disruption of these central nodes impact the entire network. We visualized the overall glioma symptom network, compared multidimensional results to known literature, and statistically compared networks between relevant patient subgroups.\n \n \n \n A dataset comprised of 355 observations of 180 glioma patients at different disease phases was analysed. Cognitive testing and questionnaires regarding health-related and glioma-specific quality of life, fatigue, depression, and cognition resulted in the definition of 30 symptom nodes. Symptom clusters were visually explored in the resulting networks, as were node strength, betweenness, and closeness centrality measures for each node. Networks were statistically compared between preoperative patients and patients during stable disease, as well as patients with low versus high-grade gliomas. Networks of patients with normal versus severe levels of fatigue were also compared as cancer-/glioma-related fatigue has a strong impact on quality of life and can correlate with other common symptoms such as pain, depression, and/or sleep disturbance.\n \n \n \n Symptom clusters existed between: 1) bodily pain, headache and physical functioning; 2) concentration and motivation; and 3) fatigue and drowsiness. Fatigue and mental health were the most central nodes in the networks. Furthermore, the overall connectivity between symptoms was significantly higher in the severely fatigued patients than in patients with normal fatigue. No network differences were found between low versus high-grade, and preoperative versus stable disease networks.\n \n \n \n Fatigue is a central node in glioma patients’ burden of disease, and symptoms are more tightly intercorrelated in patients experiencing severe fatigue. From our data, we hypothesize that fatigue co-exists with or perpetuates other symptoms. Thus, although these results are preliminary, the network approach may innovate hypothesis generation in symptom management.\n