Network analysis to identify symptoms clusters and temporal interconnections in oncology patients

Abstract

Oncology patients experience numerous co-occurring symptoms during their treatment. The identification of sentinel/core symptoms is a vital prerequisite for therapeutic interventions. In this study, using Network Analysis, we investigated the inter-relationships among 38 common symptoms over time (i.e., a total of six time points over two cycles of chemotherapy) in 987 oncology patients with four different types of cancer (i.e., breast, gastrointestinal, gynaecological, and lung). In addition, we evaluated the associations between and among symptoms and symptoms clusters and examined the strength of these interactions over time. Eight unique symptom clusters were identified within the networks. Findings from this research suggest that changes occur in the relationships and interconnections between and among co-occurring symptoms and symptoms clusters that depend on the time point in the chemotherapy cycle and the type of cancer. The evaluation of the centrality measures provides new insights into the relative importance of individual symptoms within various networks that can be considered as potential targets for symptom management interventions.

Figure 1

The estimated networks for 38 cancer symptoms with the identified symptom clusters and centrality indices during cycle 1 of CTX using ratings of symptom occurrence. Patients with four types of cancer (i.e., breast, gastrointestinal, gynaecological, and lung) with complete data across the six assessments (n = 987) were included in these analyses. Nodes represent symptoms and edges represent pairwise correlations between the symptoms, after conditioning on all of the other nodes in the network. Symptom clusters are depicted with different colours. Centrality indices were ordered by strength values. (a) Estimated network of 38 cancer symptoms with the identified clusters for time-point 1: prior to the second or third cycle of CTX administration. (b) Centrality indices (betweenness, closeness, and strength) for the estimated network shown in (a). Symptom(s) with no closeness coefficient appear separated from the rest of the network. (c) Estimated network for 38 cancer symptoms with the identified clusters for time-point 2: approximately 1 week after CTX administration. (d) Centrality indices (betweenness, closeness, and strength) for the estimated network shown in (c). (e) Estimated network for 38 cancer symptoms with the identified clusters for time-point 3: approximately 2 weeks after CTX administration. (f) Centrality indices (betweenness, closeness, and strength) for the estimated network shown in (e).

Figure 2

The estimated networks for 38 cancer symptoms with the identified symptom clusters and centrality indices during cycle 2 of CTX using ratings of symptom occurrence. Patients with four types of cancer (i.e., breast, gastrointestinal, gynaecological, and lung) with complete data across the six assessments (n = 987) were included in these analyses. Nodes represent symptoms and edges represent pairwise correlations between the symptoms, after conditioning on all of the other nodes in the network. Symptom clusters are depicted with different colours. Centrality indices were ordered by strength values. (g) Estimated network of 38 cancer symptoms with the identified clusters for time-point 4: prior to the third or fourth cycle of CTX administration. (h) Centrality indices (betweenness, closeness, and strength) for the estimated network shown in (g). (i) Estimated network for 38 cancer symptoms with the identified clusters for time-point 5: approximately 1 week after CTX administration. (j) Centrality indices (betweenness, closeness, and strength) for the estimated network shown in (i). (k) Estimated network for 38 cancer symptoms with the identified clusters for time-point 6: approximately 2 weeks after CTX administration. (l) Centrality indices (betweenness, closeness, strength) for the estimated network shown in (k).

Figure 3

The estimated networks for 38 cancer symptoms with the identified symptom clusters and centrality indices during cycle 1 of CTX using ratings of symptom occurrence. Patients with “breast” cancer with complete data across the six assessments (n = 408) were included in these analyses. Nodes represent symptoms and edges represent pairwise correlations between the symptoms, after conditioning on all of the other nodes in the network. Symptom clusters are depicted with different colours. Centrality indices were ordered by strength values. Symptom(s) with no closeness coefficient appeared separated from the rest of the network. (a) Estimated network for 38 cancer symptoms with the identified clusters for time-point 1: prior to the second or third cycle of CTX administration. (b) Centrality indices (betweenness, closeness, and strength) for the estimated network shown in (a). (c) Estimated network for 38 cancer symptoms with the identified clusters for time-point 2: approximately 1 week after CTX administration. (d) Centrality indices (betweenness, closeness, and strength) for the estimated network shown in (c). (e) Estimated network for 38 cancer symptoms with the identified clusters for time-point 3: approximately 2 weeks after CTX administration. (f) Centrality indices (betweenness, closeness, and strength) for the estimated network shown in (e).

Figure 4

Comparison of centrality indices (betweenness, closeness, strength). Centrality indices ordered by strength values. Symptom(s) with no closeness coefficient appeared separated from the rest of the network. (a) Centrality indices (betweenness, closeness, and strength) for the estimated network shown in Fig. 1a. (b) Centrality indices (betweenness, closeness, and strength) for the estimated network at time-point 1 after removing difficulty breathing node.