Evidence map›Paper›PMID 42801075›Full record

ArticlePeerJ2026

Symptom network analysis in maintenance hemodialysis patients: a multicenter study.

Xiaorong Liu, Huifen Zhao, Ziqing Hong, Yumei Peng, Jianqing Zheng, Suzhen Xie, Shuifeng Chen

Abstract readMulticenter Study
In one paragraph

Article in PeerJ, 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xiaorong LiuDepartment of Hemodialysis, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Huifen ZhaoClinical Nursing Teaching and Research Department, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Ziqing HongFujian Medical University, Fuzhou, Fujian, China.
Yumei PengThe First Affiliated Hospital of Xiamen University, Xiamen, China.
Jianqing ZhengDepartment of Radiation Oncology, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Suzhen XieDepartment of Hemodialysis, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Shuifeng ChenDepartment of Hemodialysis, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To identify symptom clusters and construct a symptom network in multicenter maintenance hemodialysis (MHD) patients, with the aim of elucidating core symptoms and informing evidence-based symptom management strategies. Methods: A cross-sectional study was conducted using convenience sampling to recruit 502 MHD patients from 10 hemodialysis centers in Quanzhou, Fujian Province, China. Symptom burden was assessed using the Dialysis Symptom Index. Exploratory factor analysis was employed to identify symptom clusters. Network analysis was performed using R software (version 4.3.1), with the qgraph, bootnet, and network tools packages utilized for network visualization, stability analysis, and centrality estimation. Network centrality indices were calculated, and strength centrality-which demonstrated adequate stability-was used as the primary index to identify core symptoms. Results: Exploratory factor analysis revealed five distinct symptom clusters: emotional symptoms, fluid and electrolyte imbalance symptoms, gastrointestinal symptoms, uremic toxins symptoms, and sleep disturbance symptoms. Network analysis demonstrated that muscle cramps exhibited the highest strength centrality (1.758), followed by nervousness (1.332) and worrying (1.238), identifying muscle cramps as the most central symptom within the network structure. The correlation stability (CS) coefficient of strength centrality was 0.749, indicating adequate stability, whereas closeness and betweenness centralities were unstable (CS-coefficient = 0.00) and were therefore not interpreted. Conclusion: MHD patients experience multiple co-occurring symptom clusters, with muscle cramps identified as the most central symptom in the symptom network and fatigue representing the most severe and second most prevalent symptom. These findings highlight the importance of prioritizing interventions targeting muscle cramps, fatigue, and other high-strength symptoms. Healthcare professionals should develop tailored, symptom-specific management strategies to effectively alleviate symptom burden and enhance clinical outcomes in MHD patients.

Indexed as

Renal DialysisAdultAgedChinaCross-Sectional StudiesFactor Analysis, StatisticalFemaleHumansMaleMiddle AgedMuscle CrampSymptom BurdenMaintenance hemodialysisStudySymptom groupSymptom network

Identifiers

PMID42801075
PMCPMC13615734

What Socratic holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.