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 ove...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21140-4 |
| _version_ | 1811238744564432896 |
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| author | Elaheh Kalantari Samaneh Kouchaki Christine Miaskowski Kord Kober Payam Barnaghi |
| author_facet | Elaheh Kalantari Samaneh Kouchaki Christine Miaskowski Kord Kober Payam Barnaghi |
| author_sort | Elaheh Kalantari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T12:47:35Z |
| format | Article |
| id | doaj.art-bbc2d49cc7cf471ba9a4d01c076f82f6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T12:47:35Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-bbc2d49cc7cf471ba9a4d01c076f82f62022-12-22T03:32:35ZengNature PortfolioScientific Reports2045-23222022-10-0112111910.1038/s41598-022-21140-4Network analysis to identify symptoms clusters and temporal interconnections in oncology patientsElaheh Kalantari0Samaneh Kouchaki1Christine Miaskowski2Kord Kober3Payam Barnaghi4Centre for Vision, Speech and Signal Processing (CVSSP), University of SurreyCentre for Vision, Speech and Signal Processing (CVSSP), University of SurreyDepartment of Physiological Nursing, University of California San FranciscoDepartment of Physiological Nursing, University of California San FranciscoDepartment of Brain Sciences, Imperial College LondonAbstract 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.https://doi.org/10.1038/s41598-022-21140-4 |
| spellingShingle | Elaheh Kalantari Samaneh Kouchaki Christine Miaskowski Kord Kober Payam Barnaghi Network analysis to identify symptoms clusters and temporal interconnections in oncology patients Scientific Reports |
| title | Network analysis to identify symptoms clusters and temporal interconnections in oncology patients |
| title_full | Network analysis to identify symptoms clusters and temporal interconnections in oncology patients |
| title_fullStr | Network analysis to identify symptoms clusters and temporal interconnections in oncology patients |
| title_full_unstemmed | Network analysis to identify symptoms clusters and temporal interconnections in oncology patients |
| title_short | Network analysis to identify symptoms clusters and temporal interconnections in oncology patients |
| title_sort | network analysis to identify symptoms clusters and temporal interconnections in oncology patients |
| url | https://doi.org/10.1038/s41598-022-21140-4 |
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