Evidence map›Paper›PMID 42909813›Full record

ArticleAnnals of surgical oncology2026

Symptom Structure, Heterogeneity, and Network Features on Postoperative Day 3 After Oesophagectomy: A Multisite, Cross-sectional, Multimethod Analysis.

Shaoxue Li, Yiting Luo, Siying Wu, Yuen-Shan Ho, Naomi Takemura, Janelle Yorke, Jiaying Li, Shuwen Li, Zengjie Ye

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Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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9 authors.

Shaoxue LiSchool of Nursing, Guangzhou Medical University, Guangzhou, China.
Yiting LuoSchool of Nursing, Guangzhou Medical University, Guangzhou, China.
Siying WuSchool of Nursing, Anhui Medical University, Hefei, China.
Yuen-Shan HoSchool of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China.
Naomi TakemuraSchool of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China.
Janelle YorkeSchool of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China. janelle.yorke@polyu.edu.hk.
Jiaying LiThe Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China. jiayingli1@cuhk.edu.hk.
Shuwen LiSchool of Nursing, The First Affiliated Hospital, Anhui Medical University, Hefei, China. lishuwen@ahmu.edu.cn.
Zengjie YeSchool of Nursing, Guangzhou Medical University, Guangzhou, China. zengjieye@qq.com.ORCID http://orcid.org/0000-0002-4437-3947

Funding

Guangdong Philosophy and Social Science Foundation No.GD25YSH17National Natural Science Foundation of China 71904033National Natural Science Foundation of China 72274043Sanming Project of Medicine in Shenzhen SZZYSM202206014Young Elite Scientists Sponsorship Program by CACM No. 2021-QNRC2-B08
6 · The paper itself

Abstract

backgroundThe acute inpatient symptom state following oesophagectomy is multidimensional and heterogeneous. However, the structure, between-patient heterogeneity, and network features of symptoms remain incompletely characterised.

methodWe analysed 1047 postoperative patients with oesophageal cancer from four hospitals in Anhui Province, China, in the "Be Resilient to Oesophageal Cancer" program. We applied principal component analysis to summarise symptom dimensions and latent profile analysis to derive patient groups of symptom burden. Symptom networks were estimated and centrality computed to identify candidate targets. We then performed in silico node-perturbation simulations to estimate potential system-level effects of alleviating versus aggravating individual symptoms.

resultsFour symptom dimensions emerged from the principal component analysis: somatic discomfort and fatigue, psychological distress, social functioning impairment, and gastrointestinal/appetite-related symptoms. A low-burden group (80.2%) and a high-burden group (19.8%) were identified. The overall network's most central symptom was "reduced social interaction" (r = 2.52). Within groups, "worry" was most central in the low-burden group (r = 2.08), whereas "incision pain" was most central in the high-burden group (r=2.56). Computer simulations revealed that, within alleviating interventions, somatic symptoms accounted for the highest proportion across all symptom types (incision pain: -20.68% to -9.17%). Conversely, within aggravating interventions, psychosocial symptoms constituted the largest share in both the overall symptom network and the low-burden group (helplessness: 24.13% to 30.27%), whereas somatic symptoms dominated the high-burden group (incision pain: 9.17%).

conclusionA multimethod pipeline identified reduced social interaction, worry, and incision pain as profile-specific, structurally prominent symptoms within the postoperative day 3 symptom networks. These findings highlight distinct psychosocial and somatic features across symptom-burden profiles and provide hypotheses for subsequent longitudinal and interventional studies.

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Computer simulationLatent profile analysisNetwork analysisOesophageal cancerPrincipal component analysisSymptom burden

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