Symptom Clusters and Network Structure of Symptom Distress in Middle-Aged and Older Adults Receiving Maintenance Haemodialysis: Associations With Health-Related Quality of Life

Abstract Introduction Patients undergoing maintenance hemodialysis (MHD) frequently experience multiple co-occurring symptoms that adversely affect health-related quality of life (HRQoL). However, the multidimensional structure of symptom burden and its underlying interrelationships remain insufficiently understood. Methods A total of 261 patients aged ≥45 years receiving MHD were included. Symptom burden was assessed using the Modified Dialysis Symptom Index, and HRQoL was measured using the Kidney Disease Quality of Life Short Form (KDQOL-SF™). Exploratory factor analysis was used to identify symptom clusters. A Gaussian graphical model with EBICglasso regularization based on Spearman correlation matrices was used to estimate the symptom network. Associations between symptom clusters, HRQoL, and hematological parameters were examined using Spearman correlation and multivariable regression analyses. Results The most prevalent symptoms were itching (33.3%), fatigue (29.5%), and dry skin (27.6%). Six symptom clusters were identified, explaining 48.9% of the total variance. Network analysis revealed a complex system of interrelated symptoms with acceptable stability (CS = 0.36 for expected influence). Higher symptom cluster scores were associated with poorer HRQoL, particularly in sleep, general health, and disease burden domains. The strongest association was observed between the skin-sleep-fatigue-pain cluster and kidney disease burden (r =−0.344, P< 0.001). In adjusted analyses, one cluster remained independently associated with physical functioning (β = 0.070, P = 0.033) and emotional well-being (β = −0.114, P = 0.019). Associations with hematological parameters were limited. Conclusions Symptom burden in middle-aged and older MHD patients is substantial and structured as interconnected clusters associated with impaired HRQoL. These findings support symptom cluster–based management strategies to improve patient outcomes.