Exploration of symptom clusters during hemodialysis and symptom network analysis of older maintenance hemodialysis patients: a cross-sectional study

Abstract

Background Symptom networks can provide empirical evidence for the development of personalized and precise symptom management strategies. However, few studies have established networks of symptoms experienced by older patients on maintenance hemodialysis. Our goal was to examine the type of symptom clusters of older maintenance hemodialysis patients during dialysis and construct a symptom network to understand the symptom characteristics of this population. Methods The modified Dialysis Symptom Index was used for a cross-sectional survey. Network analysis was used to analyze the symptom network and node characteristics, and factor analysis was used to examine symptom clusters. Results A total of 167 participants were included in this study. The participants included 111 men and 56 women with a mean age of 70.05 ± 7.40. The symptom burdens with the highest scores were dry skin, dry mouth, itching, and trouble staying asleep. Five symptom clusters were obtained from exploratory factor analysis, of which the clusters with the most severe symptom burdens were the gastrointestinal discomfort symptom cluster, sleep disorder symptom cluster, skin discomfort symptom cluster, and mood symptom cluster. Based on centrality markers, it could be seen that feeling nervous and trouble staying asleep had the highest strength, and feeling nervous and feeling irritable had the highest closeness and betweenness. Conclusions Hemodialysis patients have a severe symptom burden and multiple symptom clusters. Dry skin, itching, and dry mouth are sentinel symptoms in the network model; feeling nervous and trouble staying asleep are core symptoms of patients; feeling nervous and feeling irritable are bridge symptoms in this symptom network model. Clinical staff can formulate precise and efficient symptom management protocols for patients by using the synergistic effects of symptoms in the symptom clusters based on sentinel symptoms, core symptoms, and bridge symptoms.

The relationship between fatigue, pruritus, and thirst distress with quality of life among patients receiving hemodialysis: a mediator model to test concept of treatment adherence

“…By understanding the interconnected nature of symptoms and their impact on patient's well-being, healthcare providers can develop personalized interventions to effectively manage these symptoms. The theory underscores the significance of tailored and precise symptom management strategies to enhance patient outcomes 67 – 69 .…”

“…Through a systematic approach that takes into account the synergy between symptoms, providers can effectively classify and manage symptom clusters. This shift in focus from individual symptoms to interconnected symptom clusters allows for a more targeted and comprehensive management strategy 69 , 70 .…”

The Association Between Intradialytic Symptom Clusters and Recovery Time in Patients Undergoing Maintenance Hemodialysis: An Exploratory Analysis

“…While previous studies have explored symptom clusters in advanced kidney disease, they have largely focused on the overall symptom burden in this population, thus symptoms assessed may be rooted in causes other than dialysis treatment. 5 - 9 , 11 - 13 Identifying symptom clusters that occur during dialysis treatment specifically is important as individuals with greater intradialytic symptom burden require more time to recover from feeling unwell after treatment. 14 - 16 Consequentially, longer post-dialysis recovery time (>12 hours compared with 2-6 hours) is associated with hospitalization and mortality.…”

Exploring symptom clusters in Chinese patients with peritoneal dialysis: a network analysis

“…Factor analysis reveals clusters based on symptom co-occurrence [ 29 ], while network analysis provides insights into how individual symptoms within clusters interact and influence each other, identifying key symptoms for targeted management. This integrated approach offers a comprehensive view of symptom dynamics, enhancing our understanding and intervention strategies in clinical practice [ 30 ]. Drawing from research on patients with mental health issues or cancer [ 31 , 32 ], this method aids healthcare professionals and researchers in pinpointing core, bridging, and sentinel symptoms.…”