Identifying core symptom clusters based on symptom distress levels in patients with maintenance hemodialysis: a cross-sectional network analysis.

Introduction

Maintenance hemodialysis (MHD) is the most widely used renal replacement therapy for chronic kidney disease [ 1 ]. It is estimated that by 2030, the number of dialysis patients worldwide will reach 5 million [ 2 ]. Patients may experience fatigue, itching, anxiety, insomnia, and other physiological or psychological symptoms during treatment, which seriously impair their quality of life [ 3 , 4 ].

In 2001, Dodd proposed the concept of symptom clusters, pointing out that multiple symptoms originating from the same pathogenesis or underlying causes may appear simultaneously and have synergistic reinforcing effects, and that two or more symptoms can constitute a symptom cluster [ 5 ]. Compared with a single symptom, symptom clusters have a more significant impact on efficacy, prognosis, and quality of life [ 6 ]. In recent years, as the research on symptom clusters has intensified, some scholars have extended it to the field of hemodialysis [ 7–10 ], providing new ideas for symptom management and interventions.

It has been found that symptom clusters may change over time, but the core symptoms remain stable and are closely associated with each symptom [ 11 ]. The core symptoms have the greatest influence and reinforcement among the symptom clusters, and are important for the diagnosis, prognosis and treatment selection of the disease [ 12 , 13 ]. Therefore, core symptoms can be a key entry point for developing more effective symptom ­management interventions.

Network Analysis (NA) [ 14 ] provides a new perspective for in-depth analysis of symptom clusters. By constructing a symptom network, we can accurately identify the core symptoms and bridge symptoms, clearly explain and visualize the trajectory of symptom clusters over time [ 13 ], thus providing a focus for precise intervention and management [ 7 ]. At present, Network Analysis has been extensively applied in areas related to pathology and cancer [ 15–18 ]. However, research on hemodialysis patients is relatively scarce, and much of it focuses on the frequency and severity of symptoms without considering the degree of symptom distress. The level of distress caused by symptoms directly affects patients’ quality of life and mental health, making it clinically significant to explore this aspect in greater depth.

This research extracts symptom clusters based on the degree of distress experienced by MHD patients, constructs a symptom network, and identifies core symptoms. It further explores the core symptom clusters, aiming to provide a reference for enhancing precision in symptom management and reducing patients’ symptom burden.

Methods

Study setting and sampling

We employed convenience sampling to select patients treated in the dialysis centers of three tertiary hospitals in Shandong Province from September to December 2023. According to the sample size requirements for factor analysis, the sample size should be 5 to 10 times the number of items, with a minimum of 100 cases [ 19 ]. The Dialysis Symptom Index used in this study contains 30 symptom items, so the sample size should be at least 150 cases. Considering a 20% invalid response rate, a total of 180 cases were included in the study. This study has been approved by the Ethics Committee of the Second Affiliated Hospital of Shandong First Medical University (Ethical approval number: 2023-H-080).

Inclusion and exclusion criteria

Inclusion criteria: (1) age ≥18 years; (2) hemodialysis treatment for ≥3 months; (3) clear consciousness and the ability to communicate independently, with signed informed consent.

Exclusion criteria: (1) previous or current psychiatric diseases; (2) serious comorbidities, such as malignant tumors, serious infections, and persistent heart failure.

Survey instruments

General information questionnaire for patients

Socio-demographic data (gender, age, religion, marital status, education, occupation, current residence, per capita monthly household income, source of medical expenses, daily self-care ability, and whether or not one has experienced a negative life event in the last year) and disease-related data (comorbidities, dialysis duration, and type of dialysis access).

The dialysis symptom index (DSI)

The scale was adapted and translated into Chinese by Hao Yanhua [ 20 ] based on the original scale developed by Weisbord [ 21 ], to assess the presence, frequency, severity, and distress level of symptoms experienced by MHD patients over the past week. The scale consists of 30 items, including 25 physiological symptoms and 5 psychological symptoms. If a symptom is marked as ‘absent’, it is scored as 0. If ‘present’ is selected, further ratings are made for the symptom’s frequency, severity, and distress level. Frequency and severity are rated using a 4-point Likert scale. Distress level is rated using a 5-point Likert scale. Higher scores indicates a greater symptom burden. Additionally, the scale includes one open-ended item to capture symptoms not covered by the scale but still causing distress to the patient. In this study, the Cronbach’s α coefficient for this scale was 0.906.

The kidney disease questionnaire (KDQ)

The scale was designed by Laupacis [ 22 ] in 1992 for patients undergoing hemodialysis to assess their quality of life over the past two weeks. It was subsequently adapted and translated for use in China by Huang [ 23 ], making it more suitable for Chinese patients. The scale includes five dimensions: physical symptoms, fatigue, depression, interpersonal relationships, and frustration, with a total of 26 items. Each item is scored on a 1-7 scale, yielding a maximum total score of 182 points. Higher scores indicate better quality of life. The Cronbach’s α coefficient for the scale is 0.913.

Data collection

Before the survey, two graduate students have been trained uniformly, the purpose of the survey was explained to the patients in detail and informed consent was obtained, and the questions were answered through the Wen Juan Xing. For patients who could not complete the questionnaire by themselves, a questionnaire was filled out by the questioner according to his or her answers. To ensure the accuracy of the data, the results of the questionnaires were entered in pairs. Finally, a total of 354 eligible questionnaires were analyzed.

Data analysis

Descriptive statistics for general information

SPSS IBM 26.0, R 4.0.4 were applied for statistical analysis. The test level was set at bilateral α = 0.05, p < 0.05 indicating a statistically significant difference. Count data were described using frequencies and percentages; normally distributed data were described using mean ± standard deviation (MD ± SD), skewed data were described using median and interquartile range (P25, P75); statistical differences among variables were discussed using parametric and non-parametric tests.

Extraction of symptom clusters

Using exploratory factor analysis combined with maximum variance rotation, the symptoms with eigenvalues > 1 and factor loadings ≥ 0.4 were extracted to form symptom clusters, and if the loadings≥ 0.4 in more than one factor, the symptom was deleted. The Cronbach’s α coefficient needs to be greater than 0.6.

Constructing a symptoms network diagram

The Qgraph package in R software was used to construct a symptom network diagram based on the EBIC glasso function and Spearman correlation. In this network, nodes represent symptoms, and the lines between nodes reflect the strength of the association between symptoms, and the thicker the line, the stronger the correlation. Five centrality indicators, namely Strength, Closeness, Betweenness, Bridge Strength and Expected Influence(EI), were used to quantify the characteristics of the nodes. Strength is the sum of the absolute values of the weights of the edge correlation coefficients. Closeness reflects how close a symptom is to the other symptoms. Betweenness measures how often a node occurs in the shortest path between any two other nodes. Bridge strength indicates the total connectivity of a node to other nodes to assess the symptom’s bridging role in the network [ 24 ]. EI reveals the role a node may play in the activation, persistence, and remission of the network as a measure of the node’s potential impact on other symptoms [ 25 ]. The accuracy of network connections was tested by estimating 95% confidence intervals (CIs) for edge weights. The centrality index stability was assessed using the correlation stability (CS) coefficient, which represents the proportion of cases that can be removed from the analysis while maintaining a correlation of at least r = 0.70 within the 95% confidence interval. A CS coefficient greater than 0.25 is acceptable, with values preferably above 0.5 [ 15 , 26 , 27 ,].

Additionally, hierarchical regression analysis was conducted to examine the symptom clusters that significantly affect quality of life.

Results

General information

The study enrolled 354 patients, 207 men and 147 women. Ages ranged from 19 to 77 (53.06 ± 10.80) years. The mean duration of dialysis was 4.96 ± 5.10 years. Autologous arteriovenous fistulae were used as main dialysis method in 322 patients. The general information of the patients is detailed in S1.

Symptom occurrence and symptom cluster extraction

The most common and severe symptoms in patients with MHD were fatigue, dry skin, and itching, and the symptoms with the highest degree of distress were fatigue, itching, and trouble falling asleep. The 18 symptoms with an incidence of >20% [ 28 , 29 ] were selected, and symptom clusters were extracted using the degree of distress to the patient, with KMO (Kaiser-Meyer-Olkin Measure of Sampling Adequacy) of 0.842 and Bartlett’s spherical test of p < 0.001, making them suitable for factor analysis.

Five factors were ultimately extracted, with a cumulative variance contribution of 45.5% after eliminating factors with low reliability. According to the characteristics of the factors, the retained factors were named as psychological symptom cluster, sleep disorder symptom cluster, uremia-related symptom cluster, and neurological symptom cluster, as shown in Table 1 .

The symptom cluster with the highest score was the uremia-related symptom cluster (1.35 ± 0.89), and the lowest score was the neurological symptom cluster (0.43 ± 0.72), as shown in Table 2 .

Symptom network analysis

According to the edge thickness in the symptom network in Figure 1 , it can be seen that there is a significant positive correlation between trouble falling asleep and trouble staying asleep ( r = 0.44), worry and anxiety ( r = 0.43), and headache and dizziness ( r = 0.30).

The centrality indicators ( Figure 2 , Figure 3 ) indicated that worry has the highest strength ( r s = 1.0) and expected influence (EI = 1.0), trouble falling asleep has the highest closeness( r c = 0.01), and fatigue has the highest betweenness ( r b =30) and bridge strength ( r bs = 0.53). The lower 95% confidence interval for the edge weights ( Figure 4 ) suggests greater accuracy in the network.

Figure 5 showed that the stability of betweenness decreases sharply, while the stability of strength and closeness were better. The strength, closeness, betweenness, bridge strength, and EI were 0.36, 0.282, 0.127, 0.282, and 0.36, respectively. Good performance on all indicators except betweenness.

The results of bootstrap difference test for edge weights in Figure 6 shows S1 (trouble falling asleep) to S2 (trouble staying asleep), E1 (worry) to E2 (anxiety), and N1 (headache) to N2 (dizziness) were significantly different from other edge weights in the network.

Most of the symptoms showed little variability in strength in Figure 7 . However, the symptoms E1 (worry), S1 (trouble falling asleep), U5 (fatigue), and S2 (trouble staying asleep) displayed the strongest variability.

Effect of symptom clusters on quality of life

Quality of life scores were used as the dependent variable, and the variables that were statistically significant after univariate analysis of the general data (S2) were selected as the first-level control variables and the scores of the psychological symptom cluster, sleep disorder symptom cluster, uremia-related symptom cluster, and neurological symptom cluster were sequentially included in the stratified regression analyses. The final variables included in the regression equation were occupation, source of medical expenses, daily self-care ability, renal bone disease, psychological symptom cluster, sleep disorder symptom cluster, and uremia-related symptom cluster.

The results ( Tables 3 and 4 ) showed that Model 5 explained 59% of the total variance in quality of life, with the psychological symptom cluster explaining the largest proportion (29%).

Discussion

Fatigue is the most severe symptom and the bridge symptom in the symptom network

Fatigue often occurs or worsens before or after dialysis, and is the most common symptom during dialysis [ 30 , 31 ]. In this study, fatigue exhibited the highest incidence, severity, and distress levels. Several studies have shown that fatigue significantly reduces the ability to perform daily activities and self-care, and has long been recognized as a major cause of functional decline and reduced quality of life [ 32 , 33 ]. Meanwhile, fatigue is the bridge symptom in this network, so it is an important node for symptom management interventions. The results are consistent with Zhai [ 34 ]. Jones [ 35 ] found that eliminating nodes with high bridge strength can effectively limit the propagation of symptoms from other nodes through data simulation of network structure, highlighting the role of bridge symptoms in blocking the development of related symptoms. Therefore, the connection between symptoms can be disrupted by precisely intervening in fatigue, thereby reducing the symptom burden on patients.

In clinical practice, healthcare professionals can instruct patients in exercise, massage, acupressure, meditation, and aromatherapy [ 36 ], and timely improve the scheme according to individual differences shown by patients. At present, there is a lack of comprehensive understanding of its etiology and fundamental solutions, which remains an area for further investigation by researchers.

Worry is the core symptom in the symptom network and has the greatest potential impact on other symptoms

Patients with MHD adhere to a strict dialysis schedule and are subject to dietary restrictions [ 37 ]. When patients face uncomfortable symptoms and lack adequate information about their condition, their physical and psychological burdens increase, making them more vulnerable to negative emotions like worry and anxiety. In this study, worry exhibited the highest strength and EI, indicating that it is the core symptom with the greatest potential impact on other symptoms. This is consistent with Zeng’s research [ 12 ] on the core symptoms of patients with multiple myeloma undergoing chemotherapy. Zhou [ 7 ] concluded that nervousness is the core symptom of elderly patients with MHD, which may be related to the age difference of the population included in this study, but it also showed that psychological symptoms were the key node for intervention. Bower [ 38 ] found that worry was a strong predictor of fatigue onset and may cause insomnia, which in turn contributes to fatigue [ 39 ]. Negative emotions, such as worry, can also lead to gastrointestinal dysfunction, contributing to symptoms like loss of appetite [ 40 ].

Therefore, healthcare professionals should prioritize worry as a target for intervention. Through further interviews, the underlying causes of patients’ worry should be explored, with an emphasis on health education to correct misconceptions and strengthen patients’ self-care beliefs. Additionally, encouraging patients to actively engage in social activities and broaden their social support network [ 41 ] can help divert attention from the illness, reduce psychological stress, and ultimately lower the incidence of anxiety, thereby enhancing the overall effectiveness of interventions within the symptom network.

Uremia-related symptom cluster is the symptom cluster with the greatest degree of patient distress

The uremia-related symptom cluster holds the highest score and is most distressing for patients. This cluster includes symptoms such as itching, dry mouth, dry skin, bone or joint pain, and fatigue, which is consistent with the findings of domestic scholars such as Zhai [ 34 ] and Hao [ 20 ]. However, it differs from the symptom profiles reported by other countries’ researchers. Ng [ 42 ] listed dizziness, numbness in the feet, fatigue, joint aches, and muscle soreness as part of the uremia symptom profile, whereas Amro [ 43 ] identified nausea, loss of appetite, a feeling of constriction, and shortness of breath as the primary symptoms of the uremia symptom cluster, and found that individuals with this cluster were at a higher risk of mortality. This may stem from variations in the study populations between China and abroad, disparities in medical care and treatments, and differences in the assessment tools, symptom definitions, and classification criteria employed in these studies. Despite the controversial nature of including symptoms within the cluster, several studies have discovered that the symptoms typically encompassed are closely associated with the accumulation of urotoxins within the body [ 8 , 44 , 45 ]. Reducing urotoxin levels in the body may be a key way to effectively improve the discomfort of symptoms within this symptom cluster [ 20 , 46 ]. Furthermore, dialysis patients frequently coexist with other chronic conditions, such as cardiovascular disease, diabetes, and chronic liver disease, which may cause or exacerbate symptoms within the cluster either by themselves or by the medications used to treat them [ 47 ]. Therefore control of the primary disease should be one of the intervention priorities.

In this study, itching and dry skin were second only to fatigue in terms of severity and distress, and both were often concomitant, with a significant impact on patients’ quality of life. Notably, Arzhan [ 44 ] confirmed that dry skin is an important etiological factor in itching, and combined treatment could improve overall functional status. In clinical practice, it is essential to enhance disease education and provide targeted drug guidance to improve patients’ knowledge and self-management capabilities.

Psychological symptom cluster is the core symptom cluster of patients with MHD and has the greatest impact on quality of life

Through stratified regression analyses of the four symptom clusters, we found that the psychological symptom cluster had the greatest impact on patients’ quality of life, while the core symptom worry was also in this cluster, which can be established as the core symptom cluster. This symptom cluster also contains anxiety and irritability. Ye [ 11 ] referred to a similar cluster as the psychoemotional symptom cluster, positing that it constitutes the core of cancer patients’ symptoms, emphasizing the importance of psychological symptoms in the symptom network. Fei [ 48 ] found in a study on symptom clusters in COPD that the psychological symptom cluster is significantly associated with poorer quality of life. Patients with psychological symptom clusters tend to have lower self-efficacy and disease adherence, emphasizing that healthcare providers should pay greater attention to the psychological symptom cluster when developing symptom management plans. Furthermore, research has shown that the psychological symptom cluster is the only one that can predict well-being. Screening and treating psychological symptoms can improve patients’ mental health [ 49 ]. Early identification and management of the psychological symptom cluster are particularly important for MHD patients. During routine clinical practice, it is essential to closely monitor patients’ emotional states, behavioral signs, and verbal feedback, and to conduct regular psychological screening and assessments to detect potential psychological symptom clusters early. Additionally, guiding patients in aerobic exercises, relaxation techniques, and other interventions [ 50–53 ] can enhance their psychological adaptation [ 54 ], prevent or reduce the occurrence and exacerbation of other related symptoms or clusters, and improve the overall symptom burden across the network.

Study limitations

This study was conducted only in three hemodialysis centers in Shandong Province, with a relatively small sample size, which may limit the generalizability of the results. Expanding the sample size in future research could enhance the representativeness and stability of the symptom network’s centrality indices. Additionally, as an exploratory cross-sectional study, it cannot provide insights into the temporal progression of symptoms or establish causal relationships. Future research could adopt longitudinal follow-up or experimental designs to build upon this study

Conclusions

This study constructs a symptom network based on symptom distress, identifying fatigue and worry as bridge and core symptoms, respectively. The psychological symptom cluster is the central group in the network and should be prioritized in interventions. Uremia-related symptoms cause the greatest distress and warrant significant attention. The symptom network offers a valuable foundation for clinical research, enabling more targeted observation and evaluation of symptom interactions. Future studies should focus on core symptoms as intervention targets and develop comprehensive strategies that prioritize psychological symptoms to improve patients’ self-care beliefs, self-management, and ultimately reduce symptom burden.

Supplementary Material

supplement.docx

Acknowledgments

We appreciate all the study participants and those who contributed their time and effort to this study.

Ethical considerations

This study was approved by the Academic Ethics Committee of the Second Affiliated Hospital of Shandong First Medical University (approval number:2023-H-080;approval date: July 28,2023).

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Authors’ contributions

Yaxin Chang: conceptualization, methodology, data curation, formal analysis, and, writing - original draft. Ke Wang: conceptualization, formal analysis, and supervision. Mengjia Liu: investigation, and data analysis. Zhifang Zhang: investigation. Huiwen Ma: investigation. Xinping Gao: investigation. Zhaoxia Yang: writing -review & editing.

Disclosure statement

There is no conflict of interest declared.

Data availability statement

The raw data has not been released in public data repositories due to privacy concerns. However, detailed descriptions of the dataset and analytical methods are included within this article. For peer reviewers or other researchers with a legitimate need for access to the original data, please contact the first author [Yaxin Chang] for further information and possible data access arrangements.

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