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Exploring the dynamics of perioperative symptom networks in colorectal cancer patients: a cross-lagged panel network analysis

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Abstract

Background

Colorectal cancer incidence is on the rise, necessitating precise symptom management. However, causal relationships among symptoms have been challenging to establish due to reliance on cross-sectional data. Cross-lagged panel network (CLPN) analysis offers a solution, leveraging longitudinal data for insight.

Objective

We employed CLPN analysis to construct symptom networks in colorectal cancer patients at three perioperative time points, aiming to identify predictive relationships and intervention opportunities.

Methods

We evaluated the prevalence and severity of symptoms throughout the perioperative period, encompassing T1 the first day of admission, T2 2–3 days postoperatively, and T3 discharge, utilizing the M. D. Anderson Symptom Inventory Gastrointestinal Cancer Module (MDASI-GI). To identify crucial nodes in the network and explore predictive and interactive effects among symptoms, CLPNs were constructed from longitudinal data in R.

Results

The analysis revealed a stable network, with disturbed sleep exhibiting the highest out-EI (outgoing expected influence) during T1. Distress had a sustained impact throughout the perioperative. Disturbed sleep at T1 predicted T2 bloating, fatigue, distress, and pain. T1 distress predicted T2 sadness severity. T2 distress primarily predicted T3 fatigue, disturbed sleep, changes in taste, and bloating. T2 shortness of breath predicted T3 changes in taste and loss of appetite. Furthermore, biochemical markers like RBC and ALB had notable influence on symptom clusters during T1→T2 and T2→T3, respectively.

Conclusion

Prioritizing disturbed sleep during T1 and addressing distress throughout the perioperative phase is recommended. Effective symptom management not only breaks the chain of symptom progression, enhancing healthcare impact, but also eases patient symptom burdens.

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Data availability

The dataset for this study will not be made publicly available due to ethical restrictions. The dataset will be personally available if there is reasonable request. Readers and all interested researchers may contact Shang Bin (Sevenage007@163.com).

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Acknowledgements

The authors would like to appreciate all the staff who helped us complete this project.

Funding

This study was supported by the “QingLan Project” of universities in Jiangsu Province. Funders were not involved in this study.

Qinglan Project of Jiangsu Province of China,Su Teacher's Letter (2023) No. 51

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Author notes

  1. Bin Shang and Zekun Bian contributed equally to the article and shared the first authorship.

Authors and Affiliations

  1. School of Medicine, Jiangsu University, No. 301 Xuefu Road, Jingkou District, Zhenjiang City, Jiangsu Province, China

    Bin Shang, Zekun Bian, Caifeng Luo & Jing Wu

  2. Department of Nursing, Jiangsu University Jingjiang College, Zhenjiang, China

    Fei Lv

  3. Gastrointestinal Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China

    Shuhong Lv & Qing Wei

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  1. Bin Shang
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Contributions

Bin SHANG: Conceptualization; Data curation; Investigation; Methodology; Writing – original draft; Writing – review & editing.

Zekun BIAN: Investigation; Methodology; Writing – review & editing.

Caifeng LUO: Conceptualization; Supervision; Writing – review & editing.

Fei LV: Writing – review & editing; Funding acquisition.

Jing WU: Supervision; Investigation.

Shuhong LV: Investigation.

Qing WEI: Investigation.

Corresponding author

Correspondence to Caifeng Luo.

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The authors declare no competing interests.

Ethics approval and consent to participate

This study was conducted in compliance with the principles outlined in the Declaration of Helsinki. The research protocol was approved by the Medical Ethics Committee of Jiangsu University (No. 20221019-1). Prior to their participation, written informed consent was obtained from all subjects involved in the study.

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Shang, B., Bian, Z., Luo, C. et al. Exploring the dynamics of perioperative symptom networks in colorectal cancer patients: a cross-lagged panel network analysis. Support Care Cancer 32, 62 (2024). https://doi.org/10.1007/s00520-023-08288-z

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