Symptom network analysis in maintenance hemodialysis patients: a multicenter study

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.

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.

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.

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.