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已发表论文
结直肠癌化疗患者症状网络分析：一项横断面研究
Received 27 August 2025
Accepted for publication 15 November 2025
Published 7 December 2025 Volume 2025:17 Pages 3073—3085
DOI https://doi.org/10.2147/CMAR.S558889
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Bilikere Dwarakanath
Rui Xie,1,* Xiao-Xin Gu,1,2,* Yin Wang1
1School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, People’s Republic of China; 2Department of Health Education, School of International Medical Technology, Shanghai Sanda University, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yin Wang, School of Nursing, Shanghai University of Traditional Chinese Medicine, 1200 CaiLun Road, Pudong New District, Shanghai, 201203, People’s Republic of China, Tel +8618019315493, Email wangyin1977@126.com
Objective: This study aimed to construct a symptom network in patients diagnosed with colorectal cancer (CRC) undergoing chemotherapy, thereby providing a theoretical framework for optimizing symptom management strategies.
Methods: A cross-sectional survey was conducted among 261 patients with CRC receiving chemotherapy across five tertiary care hospitals in Shanghai, selected through convenience sampling. Network analysis was applied to construct the symptom network and to determine centrality indices, including strength centrality. Model accuracy and stability were evaluated using nonparametric bootstrapping techniques.
Results: Symptom prevalence ranged from 5.4% to 85.8%, with severity scores ranging from 0.51± 1.44 to 4.83± 3.23. Fatigue was associated with the highest severity score. Depression (rstrength = 2.188), nausea (rstrength = 1.290), and anorexia (rstrength = 1.223) demonstrated the highest strength centrality values. The constructed network model exhibited high accuracy and stability, as indicated by narrow confidence intervals and Correlation Stability (CS) coefficients exceeding 0.25.
Conclusion: This study is among the first to apply network analysis to chemotherapy-related symptom interactions and potential mechanisms in CRC, highlighting central targets for intervention. These findings may inform more precise and efficient symptom management approaches in oncology care.
Keywords: chemotherapy, colorectal cancer, network analysis, symptom management, symptom network
1School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, People’s Republic of China; 2Department of Health Education, School of International Medical Technology, Shanghai Sanda University, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yin Wang, School of Nursing, Shanghai University of Traditional Chinese Medicine, 1200 CaiLun Road, Pudong New District, Shanghai, 201203, People’s Republic of China, Tel +8618019315493, Email wangyin1977@126.com
Objective: This study aimed to construct a symptom network in patients diagnosed with colorectal cancer (CRC) undergoing chemotherapy, thereby providing a theoretical framework for optimizing symptom management strategies.
Methods: A cross-sectional survey was conducted among 261 patients with CRC receiving chemotherapy across five tertiary care hospitals in Shanghai, selected through convenience sampling. Network analysis was applied to construct the symptom network and to determine centrality indices, including strength centrality. Model accuracy and stability were evaluated using nonparametric bootstrapping techniques.
Results: Symptom prevalence ranged from 5.4% to 85.8%, with severity scores ranging from 0.51± 1.44 to 4.83± 3.23. Fatigue was associated with the highest severity score. Depression (rstrength = 2.188), nausea (rstrength = 1.290), and anorexia (rstrength = 1.223) demonstrated the highest strength centrality values. The constructed network model exhibited high accuracy and stability, as indicated by narrow confidence intervals and Correlation Stability (CS) coefficients exceeding 0.25.
Conclusion: This study is among the first to apply network analysis to chemotherapy-related symptom interactions and potential mechanisms in CRC, highlighting central targets for intervention. These findings may inform more precise and efficient symptom management approaches in oncology care.
Keywords: chemotherapy, colorectal cancer, network analysis, symptom management, symptom network