Construction of Symptom Network in Maintenance Hemodialysis Patients
Conditions: Symptom Cluster, Symptoms and Signs, Hemodialysis Complication, Hemodialysis-Associated Pruritus
Sponsor: RenJi Hospital

 
Study Protocol and Statistical 
Analysis Plan 
（Version: 2.0  Date：2023.3.28） 
 
 
 
Name:  Construction of Symptom Network in Maintenance Hemodialysis Patients  
Applicant:  Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine   
Principal investigator:              Liu Tingting                   
 

Objective 
Through a cross-sectional questionnaire survey, a general information 
questionnaire and Dialysis Frequency, Severity, and Symptom Burden Index were 
used to investigate the frequency, severity, and degree of symptom distress in MHD 
patients, and to analyze the differences in symptom distress status between long and 
short dialysis age MHD patients. Using the R 4.2.2 software qgraph package, 
construct symptom networks for MHD patients of long and short dialysis age through 
network analysis. Analyze network centrality indicators, including intensity, closeness 
centrality, and mediation centrality, to identify core symptoms and compare if there 
are any differences between the two groups, aiming to lay the foundation for precise 
and efficient phased symptom management. 
Background 
End stage renal disease (ESRD) is caused by various kidney diseases, leading to 
irreversible decline in renal function. The main renal replacement therapy (RRT) for 
ESRD is hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation. 
Due to the impact of kidney supply on kidney transplantation, it is limited in clinical 
practice, and hemodialysis has become the most common method of RRT in most 
countries. According to the registration system of the Chinese Research Data Services 
(CNRDS), as of the end of 2021, there were 751098 dialysis patients and 155738 new 
patients in China, with an average dialysis age of 50.9 months. The proportion of 
patients with dialysis age greater than 5 years was 31.5%. There are a large number of 
hemodialysis patients with a longer dialysis age. 
With the continuous development of dialysis technology, the survival rate of 
maintenance hemodialysis (MHD) patients is becoming higher and higher. However, 
MHD patients may experience various symptoms during long-term treatment, which 
is the main cause of discomfort and physical and mental health for patients. The 
physical and mental pain or torment experienced by patients due to the disease itself 
and/or the symptoms caused by treatment is called symptom distress. Multiple studies 
have shown that the incidence of symptom distress in MHD patients ranges from 

13.75% to 91.5%, which is at a high level. Symptom distress is negatively correlated 
with the quality of life of MHD patients. Excessive symptom distress increases the 
cost of treatment and exacerbates the psychological burden on patients. Persistent 
symptom burden can significantly affect the quality of life of MHD patients and 
increase their mortality rate. MHD patients suffer from multiple symptoms at the 
same time. Li Jiuhong et al.'s research results show that MHD patients experience a 
total of 3-25 (13.12 ± 5.27) symptoms, which is similar to Weisbord et al.'s average of 
9 symptoms. MHD patients experience a wide variety of symptoms, and how to 
accurately and effectively manage symptoms is the future research direction. 
With the prolongation of dialysis treatment time for patients, some urea toxin 
that cannot be completely cleared by dialysis accumulates in the patient's body, which 
is closely related to discomfort symptoms such as itching, joint pain, and difficulty 
falling asleep. Ran Yuli et al. found that the dialysis age of MHD patients is 
negatively correlated with their physical function and role. With the prolongation of 
dialysis age, the physiological function of MHD patients will decrease to varying 
degrees, and various disease-related complications will also occur. The focus of 
symptom management will shift accordingly. Conducting grouping studies based on 
dialysis age for MHD patients has certain clinical significance. At present, domestic 
scholars use a 5-year hemodialysis period as the boundary, dividing dialysis age>5 
years into long dialysis age, and dialysis age ≤ 5 years into short dialysis age. Hu Qian 
and others set up symptom management consultants to develop symptom management 
pathways and guide responsible nurses to implement symptom management for 
patients according to the pathway table. The results indicate that setting up symptom 
management consultants can reduce the incidence of symptoms in MHD patients, 
improve their quality of life, and improve patient satisfaction. A meta-analysis showed 
that aerobic exercise can improve several symptoms related to hemodialysis in adults 
receiving MHD, including restless leg syndrome, depressive symptoms, muscle 
spasms, and fatigue. Zhao Ying et al. used air wave pressure therapy to prevent 
dialysis related muscle spasms, Zuo et al. managed symptoms of fatigue through 
nurse led non drug multidisciplinary holistic care, Beerappa et al. improved sleep 

disorders in MHD patients through aromatherapy, Guo Junjun et al. improved fatigue 
symptoms and alleviated sleep disorders in MHD patients through acupoint massage. 
When conducting symptom management for MHD patients, existing studies have not 
distinguished the age of dialysis, and currently, symptom management for MHD 
patients mainly focuses on a single symptom or symptom group, ignoring the 
interaction between multiple symptoms in the real world. In real clinical situations, 
patients often have multiple symptoms at the same time, and it is urgent to identify the 
core symptoms of patients to improve the accuracy and efficiency of symptom 
management. 
With the development of information technology, big data analysis technology, 
and the innovation of complex scientific research concepts in the real world, the 
concept of symptom networks is gradually replacing the previous research paradigm 
that focuses on individual symptoms and must control other variables. Symptom 
network is a collective representation of symptoms related to a patient's disease, and 
based on complex network analysis to quantitatively study the correlation between 
different symptoms. It studies the network structure, nodes, and network indicators 
composed of individual symptoms to reflect the interaction mechanism of symptoms 
in the real world and provide precise intervention targets. Discovering core symptom 
targets through symptom networks can improve the management efficiency and 
intervention accuracy of symptom management, evolving from the working mode of 
"having multiple symptoms and providing multiple sets of intervention measures" to 
"identifying intervention targets and providing intervention measures targeting target 
symptoms". Since Fried et al. first proposed the concept of symptom networks in 
2015, the research paradigm of symptom networks has been applied in 
psychopathology, chronic disease symptom management, long-term follow-up of 
tumors, and patient self-management. Lin et al. constructed a self-reported 
psychological and neurological symptom network for head and neck cancer patients 
through network analysis. The results showed that network analysis clarified the 
interrelationships between five psychological and neurological symptoms, and 
identified depression as the core symptom. It is recommended to develop symptom 

management measures for depression in clinical practice. Han et al. constructed 
symptom networks for autism and schizophrenia. Yang Hongli et al. applied symptom 
networks to HIV infected individuals and identified fatigue as the core symptom. 
They suggested that when HIV infected individuals have multiple symptoms at the 
same time, high-intensity and precise intervention on fatigue can reduce the 
transmission of fatigue as a mediating bridge in the symptom network, reduce the 
severity of fatigue symptoms, and also alleviate the severity of other symptoms. As 
mentioned above, symptom network analysis can identify core symptoms, and 
changing these core symptoms may be an important entry point for changing the 
entire network. In practical clinical work, implementing key interventions by nurses 
on core symptoms can more effectively improve other related symptoms and improve 
intervention efficiency. Applying symptom networks to the field of symptom 
management is a promising and precise technology in real-world clinical settings. 
This study has developed a symptom distress questionnaire for maintenance 
hemodialysis patients. Previous literature on symptom distress and symptom 
management in MHD patients has been reviewed. Whether there are differences in 
symptom distress among MHD patients of different dialysis ages, and how to identify 
the core symptoms of MHD patients of different dialysis ages through symptom 
networks to improve the accuracy and effectiveness of intervention are all directions 
that need to be studied in the future. The research hypotheses of this study are: ① 
There are differences in the frequency, severity, and degree of symptom distress 
between long and short dialysis age MHD patients; ② By using network analysis 
methods to construct symptom networks for long and short dialysis age MHD patients, 
identify the core symptoms of different dialysis age MHD patients. Based on 
literature review and clinical experience, it is assumed that the core symptoms of long 
dialysis age MHD patients are itching or dry skin, while the core symptoms of short 
dialysis age MHD patients are fatigue or dry mouth. 
Contents 
1. Current survey of symptom burden in maintenance hemodialysis patients 

① Understanding the current status of symptom distress in maintenance 
hemodialysis patients through cross-sectional questionnaire survey 
② Comparative analysis of symptom distress in maintenance hemodialysis patients 
with long and short dialysis age 
2. Construction of symptom network and core symptom recognition for maintenance 
hemodialysis patients 
① A multicenter study on the current status of symptom distress in maintenance 
hemodialysis patients 
② Construct symptom networks for long-term, short-term, and all maintenance 
hemodialysis patients using network analysis based on cross-sectional survey data 
from multiple centers 
③ Identify and analyze the core symptoms of long dialysis age, short dialysis age, 
and all maintenance hemodialysis patients based on network centrality parameters 
Methods 
Participants 
Convenient sampling method was used to select MHD patients from six blood 
purification centers, including the East, West, South, Baoshan Branch, Ningbo 
Hangzhou Bay Branch, and Punan Hospital of Renji Hospital Affiliated to Shanghai 
Jiao Tong University School of Medicine. 
(1) Inclusion criteria: ① Age not less than 18 years old; ② Regular hemodialysis 
time greater than or equal to 3 months; ③ The dialysis plan did not change within 
one month; ④ Informed consent and voluntary participation in this study. 
(2) Exclusion criteria: ① Previous or current mental illness; ② Cognitive 
impairment; ③ Acute cardiovascular and cerebrovascular disease; ④ Acute 
infection period; ⑤ Complications such as severe cerebrovascular sequelae and 
tumors. 
(3) Sample size and calculation method: The Dialysis Frequency, Severity, and 
Symptom Burden Index for dialysis patients used in this study includes a total of 30 
symptoms. When constructing a symptom network model, the threshold parameters 

that 
need 
to 
be 
estimated 
are 
30, 
paired 
association 
parameters 
are
, and the total parameters are
. Therefore, the 
sample size needs to be at least 465. Considering 10% of invalid samples, the sample 
size of this study is at least 
. 
Instruments 
General Information Questionnaire: Designed by the researchers themselves, 
including laboratory examination indicators such as gender, age, dialysis age, marital 
status, educational level, work status, residential status, medical expense payment 
method, economic burden, primary disease, comorbidities, recent medication, 
hemoglobin, etc. 
Dialysis Frequency, Severity, and Symptom Burden Index( DFSSBI ) : DFSSBI 
is an adaptation of the Dialysis Symptom Index (DSI) developed by Weisbord et al., 
using the symptom management theory model as the theoretical framework, by 
American scholars Danquah et al., which increases the frequency of symptoms and 
the degree of distress in two dimensions. This scale has been proven to have good 
reliability and validity in foreign countries. Cronbach's α The coefficient is 0.923, the 
content validity coefficient is 0.939, and the retest reliability is 0.83. It has good 
internal consistency, stability reliability, and structural validity. The scale consists of 
30 items, including 25 physiological symptoms such as fatigue, dry skin, itching, etc; 
Psychological symptoms include sadness, irritability, tension, etc., totaling 5 
symptoms. The appearance of symptoms is obtained in the form of "having" and "not 
having". The frequency and severity of occurrence are evaluated using a Likert 4 level 
score, with values ranging from "occasional/mild" to "always/very" ranging from 1-4 
points. The degree of distress is evaluated using a Likert 5 level score, with values 
ranging from 0-4 points from "no impact" to "very serious" ranging from 0-360 points. 
The higher the score, the heavier the individual's burden of symptoms. Zhou Xiaojuan 
et al. used the Chinese version of DFSSBI to investigate the symptom burden of 
hemodialysis patients, and their Cronbach's α The coefficient is 0.89, and the retest 
reliability is 0.91, indicating that the scale is suitable for application in domestic 

hemodialysis patients. 
Data Collection 
Before the investigation, obtain the consent of the hospital ethics committee and 
departments, explain and communicate with the survey subjects. On dialysis day, 
researchers will use unified guidelines to explain the research purpose and explain the 
answer methods and precautions for each question to the survey subjects. The 
questionnaire is filled out by the patient themselves. For survey subjects who are 
unable to independently complete the questionnaire, the researcher can collect data 
through one-on-one Q&A. Collect and check for any missing items on the spot, and 
promptly supplement and complete them. 
Statistical Analysis Plan 
Enter the collected information into the database established by Epidata 3.1, and 
after verification, import it into SPSS 21.0 software for data analysis. The patient's 
gender, marital status, education level, work status, and other counting data are 
described using frequency and percentage. Age, hemoglobin, blood phosphorus, and 
other measuring data that meet normality are represented by mean and standard 
deviation, while those that do not meet normality are represented by median and 
interquartile spacing. The symptom distress scores of MHD patients with different 
characteristics were tested for normality and homogeneity of variance. If they obey 
the normal distribution and have the same variance, t-test and analysis of variance 
were used. Otherwise, nonparametric rank sum test was used. Use R4.2.2 software 
qgraph package for network analysis to generate complex network descriptions of the 
relationship between symptoms. Use network centrality indicators such as strength, 
tight centrality, and mediation centrality to represent the degree of interconnection of 
symptom networks. The intensity reflects the total direct connection between 
symptoms and other symptoms, that is, the ability of symptoms to affect other 
symptoms; The closeness centrality reflects the reciprocal of the distance between 
symptoms and other symptoms, that is, the core position of symptoms in the network; 
Mediation centrality reflects the number of shortest paths through the symptom, 
which serves as a bridge between the symptoms in the network.