ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2023
Exploring core symptoms and interrelationships among symptoms in children with acute leukemia during chemotherapy: A network analysis.
Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring Symptom Cluster Patterns in Adult Cancer Patients Undergoing Chemotherapy: A Systematic Review.Journal of Korean Academy of Nursing · 2024Pooled it
- Reconsidering the interpretation of network analysis in symptom research.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Exploring core and sentinel symptoms in elderly patients with type 2 diabetes through network analysis and the Apriori algorithm.Frontiers in medicine · 2026Article
- Symptom dynamics through multiphase treatment in acute myeloid leukemia: a cross-lagged panel network analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Longitudinal symptom network patterns in cancer patients during the end of life.Scientific reports · 2025Article
- Estimating personal temporal symptom networks for childhood cancer survivors.Communications medicine · 2025Article
- Symptom distress among Chinese children with cancer: what factors influence parent-child concordance?European journal of pediatrics · 2025Article
- Core symptoms in patients with colorectal cancer receiving chemotherapy: a network analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Core Symptoms and Dynamic Interactions of Depressive Symptoms in Older Chinese Adults: A Longitudinal Network Analysis.Depression and anxiety · 2025Article
- Chemotherapy-related symptoms in children with leukemia: application of latent profile analysis and network analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Article
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Abstract
purposeChildren with acute leukemia have suffered from a considerable symptom burden during chemotherapy. However, few studies have focused on exploring the mechanisms among symptoms in children with acute leukemia. Our study aims to explore core symptoms and describe the interrelationships among symptoms in children with acute leukemia during chemotherapy.
methodsFrom January 2021 to March 2023, 469 children with acute leukemia were recruited from 20 Chinese cities. The Memorial Symptom Assessment Scale 10-18 (MSAS 10-18) was used to evaluate the prevalence and severity of symptoms during chemotherapy. A network analysis was performed by the R software based on 31 symptoms. Centrality indices and density were used to explore core symptoms and describe interrelationships among symptoms in the network during chemotherapy.
resultsWorrying and feeling irritable were the central symptoms across the three centrality indices, including strength, closeness, and betweenness. Lack of energy was the most prevalent symptom; however, it was less central than other symptoms. The density of the "induction and remission" network significantly differed from other cycles' counterparts (p < 0.001). Global strength was greater in the " ≥ 8 years group " network than the " < 8 years group " network (p = 0.023).
conclusionNetwork analysis provides a novel approach to identifying the core symptoms and understanding the interrelationships among symptoms. Our study indicates the need to assess emotional symptoms in children with acute leukemia during chemotherapy, especially during the induction and remission phases, as well as in older children. Future research is imperative to construct trajectories of dynamic symptom networks and centrality indices in longitudinal data to investigate the causal relationships among symptoms.
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