Correspondence to Lihua Wu; [email protected] ; Wanling Li; [email protected]
STRENGTHS AND LIMITATIONS OF THIS STUDY
The utilisation of a combination of convenience and purposive sampling enhances the reliability and stability of the findings.
Prospective data collection ensures data integrity.
This is a cross-sectional study, and causal inferences between symptoms cannot be made.
The use of self-reported questionnaires may lead to reporting bias.
Introduction
Lymphoma, a type of malignant solid tumour, arises from lymphoid tissues and has emerged as a prevalent form of cancer posing a significant threat to human health. According to the data published by the China National Cancer Center1 and the American Cancer Society,2 there has been a rising trend in the incidence of lymphoma in recent years. The incidence of lymphoma currently ranks eighth among all malignant tumours in China.3 4 Globally, lymphoma ranks as the 8th most common tumour among males and the 10th most common tumour among females.5 While there exist many treatment options for lymphoma patients, chemotherapy remains the primary method of treatment.
Lymphoma patients commonly experience a variety of symptoms, such as nausea, vomiting, loss of appetite, hair loss, fatigue, pain and numbness in the hands and feet, during chemotherapy. In addition to physical discomfort, the complexity, severity and recurrent nature of lymphoma often lead to serious psychological symptoms including anxiety, fear, depression and sadness.6 7 Several studies6 8 9 have reported that most lymphoma patients would experience more than ten symptoms simultaneously or successively during chemotherapy, and these symptoms would produce more negative effects through reinforcement or synergistic effects, thereby seriously affecting the quality of life of the patients. At the same time, the survival period of the patients would be shortened. Hence, the effective management of these symptoms holds particular importance.
Symptom clusters are groups of two or more stable and interrelated symptoms, with stronger correlations observed between symptoms within the same cluster compared with those between different clusters. Each symptom cluster is relatively independent.10 Previous studies have usually managed the symptoms of lymphoma patients in the form of symptom clusters. Researchers have identified 3–7 symptom clusters during chemotherapy in lymphoma patients.7 8 11 However, the number and composition of these clusters vary, and the mechanisms of interaction between symptoms remain unclear. As a consequence, the efficacy and precision of existing symptom management strategies for lymphoma patients are compromised. The existence of multiple symptom clusters in a patient requires the administration of several sets of pharmacological or non-pharmacological interventions, without prioritisation based on the severity or impact of each symptom cluster. In addition, further research on symptom management in cancer patients in recent years has found that cancer symptoms occur in a nested pattern as opposed to distinct clusters.12 13 Therefore, relying solely on symptom clusters for symptom management in lymphoma patients undergoing chemotherapy no longer meets the rigorous standards of modern medicine for efficiency and precision. The optimisation of existing symptom management strategies remains a pressing issue to be solved.
The symptom network analysis involves collectively characterising the disease-related symptoms of a patient and quantitatively studying the network structure, nodes, network indicators, as well as the interrelations between different symptoms using complex network analysis. Contemporaneous symptom networks refer to the symptom networks constructed from the symptoms of the same patient group at a specific measurement time point.14 This method can reflect the mechanisms of interaction and complex relationships among the various symptoms experienced by patients in the real world.15 16 For example, Cramer et al17 constructed a contemporaneous network of 1059 patients with major depressive disorder and generalised anxiety disorder, revealing overlaps in symptoms of sleep disturbances and psychomotor disturbances. Meanwhile, the most influential symptoms in the symptom network can be identified, the core and bridge symptoms can be clarified, and the impact of core symptoms on other symptoms can be discovered, thereby determining specific targets for symptom intervention.18 Targeting core symptoms for intervention treatment can accelerate the deactivation of the network and improve the individualisation and precision of symptom intervention.19 For example, Zhu et al20 constructed a contemporaneous network of 2927 HIV-positive patients and found that the incidence and severity of 16 HIV-related psychological symptoms decreased with the increasing duration of HIV positivity. However, the total connectivity density of the network increased, suggesting that the longer the duration of HIV positivity, the more fragile the patient’s psyche and the easier it would be to activate the other symptoms quickly through one psychological symptom. In a study by Liu et al,21 the anxiety and depression contemporaneous symptom networks of 2016 HIV-infected individuals were analysed, and they found that ‘feeling unhappy’ and ‘feeling fidgety’ were the key bridges connecting the depression and anxiety symptom clusters. This indicates that in managing HIV-infected patients with concurrent anxiety and depression symptoms, attention should be paid to their symptoms of ‘feeling unhappy’ and ‘feeling fidgety’, so as to prevent the anxiety and depression symptom clusters from exacerbating each other. In addition, nurses can predict the prognosis of patients with different characteristics (disease stage, disease type, chemotherapy stage and chemotherapy regimen) based on the density of the symptom network. For instance, a cross-sectional study conducted by Zhu et al22 including 1065 cancer survivors compared the network densities of cancer survivors with different survival times and found that the network density among patients with a survival time of more than 5 years was notably lower compared with those with a survival time of less than 5 years, indicating a more significant difference than observed in symptom severity. A cohort study involving 465 adolescents with depression conducted by Schweren et al23 found that network density could be used as a predictor of longer disease duration. Such information holds important mechanistic implications for devising effective intervention strategies.
Currently, contemporaneous symptom network studies have focused on patients with AIDS,24 psychiatric and psychological disorders,25 26 digestive system tumours,27 multiple myeloma,28 breast cancer,29 as well as head and neck cancer.30 Contemporaneous symptom network during chemotherapy in lymphoma patients remains to be further explored. Therefore, the General Data Questionnaire and Lymphoma Symptom Assessment scale will be hereby employed to conduct a cross-sectional survey of symptoms (incidence, severity, frequency and distress) in lymphoma patients during chemotherapy. The study will be primarily conducted to construct a contemporaneous symptom network in lymphoma patients during chemotherapy, analyse the network centrality indexes, explore the core symptoms within the network and delve into the relationship between symptoms. Overall, these attempts could forge a scientific and reliable theoretical basis for constructing a precise symptom management plan and reducing the symptom burden in lymphoma patients during chemotherapy.
Methods and analysis
Study design and setting
This is a protocol of single-centre, prospective and cross-sectional study with the following objectives: to construct a contemporaneous symptom network in lymphoma patients during chemotherapy; to analyse the network centrality indexes and explore the core symptoms within the network and to explore the relationship between symptoms.
In order to enhance the stability and representativeness of the study results, while also considering the limited availability of patient resources and time constraints, a combination of convenience sampling and maximum difference sampling will be used to select a maximum difference sample based on the patient’s age, gender, cancer stage, chemotherapy regimen and stage of chemotherapy. It is expected that 315 lymphoma patients hospitalised in the Lymphoma Department of Shanxi Bethune Hospital will be selected as the study subjects. Participant recruitment will begin on 1 June 2024 and is expected to be completed on 1 June 2025.
Participants
The inclusion criteria are as follows:
Patients diagnosed with lymphoma through pathology and aware of their diagnosis.
Those treated with only chemotherapy.
Those aged ≥18 years.
Those who could consciously carry out daily verbal communication and exchange.
Those providing written informed consent and voluntarily participating in the survey.
The exclusion criteria are as follows:
Patients suffering from current or previous mental illness or cognitive dysfunction (determined by reviewing the patient’s medical records and interviewing the caregiver).
Those with comorbidities of severe cardiac, cerebral or pulmonary failure or other serious complications.
Sample size
The Lymphoma Symptom Assessment Scale involves a survey of 40 symptoms. In this study, the top 10 symptoms will be selected to be included in the network analysis according to the incidence of symptom from high to low.27 Therefore, the threshold parameters to be estimated to construct the network model are 10, and pairwise correlation parameters are 45 [10×(10−1)/2].31 The total parameters involve 55 cases. In addition, the sample size is calculated using 3–5 cases of each parameter to ensure the model’s reliability. Furthermore, considering the 20% shedding rate, the required sample size involves 198–330 cases.
Instruments
The questionnaire includes the General Data Questionnaire and the Lymphoma Symptom Assessment Scale. For details, please refer to online supplemental file.
General Data Questionnaire
Patients’ sociodemographic and disease-related characteristics will be collected using General Data Questionnaire designed by the researcher. The sociodemographic information includes age, gender, ethnicity, place of residence and education level. The disease-related information includes the type of disease, time of diagnosis, stage of chemotherapy and chemotherapy regimen.
Lymphoma Symptom Assessment Scale
The frequency, severity and distress of symptoms in lymphoma patients during chemotherapy will be investigated using Lymphoma Symptom Assessment Scale revised by Chinese scholar Shen32 based on the Chinese version of the Memory Symptoms Scale by combining the characteristics of lymphoma patients and through literature review, semistructured interviews, brainstorming and expert consultation. The scale, recognised for its strong reliability and validity, consists of 40 items that can comprehensively assess the occurrence and severity of 40 symptoms, including pain, fatigue, cough, nausea, dysphagia, etc, experienced by lymphoma patients over the last 7 days.
Data collection
Data will be collected using a printed paper questionnaire, which consists of two components, that is, General Data Questionnaire and Lymphoma Symptom Assessment Scale. Before starting the questionnaire survey, the investigators will be uniformly and rigorously trained on the survey content and methods. During the survey, standard language will be used for explanations, so as to avoid bias caused by the investigators. To examine the readability and comprehensibility of the General Data Questionnaire and Lymphoma Symptom Assessment scale among lymphoma patients, a pilot survey will be first conducted on 20 lymphoma patients. Throughout both the pilot and the formal survey phases, the investigators will introduce the study objectives to the patients. Following this, they will distribute questionnaires after obtaining written informed consent from the patients. Additionally, the investigators will provide uniform instructions to guide patients in completing the questionnaires. Completed questionnaires will be collected immediately, and investigators will promptly check for any omissions, errors or multiple selections. Figure 1 depicts the pathways for participant recruitment and data collection.
Statistical analysis
Statistical analysis will be performed by using SPSS V.26.0 and R software V.4.1.2. The reliability of the sample data will be tested by calculating Cronbach’s α coefficient. Frequencies, percentages, means and SD will be used to describe sociodemographic characteristics, disease-related characteristics and symptom severity. Furthermore, a contemporaneous network containing the top 10 symptoms will be visualised using the R-package qgraph. In this network, each node represents a symptom while the edges indicate the relationships between two symptoms. The thickness of edges reflects the strength of the correlation between two symptoms, with thicker edges indicating stronger correlations. At the same time, solid lines represent positive correlations while dashed lines indicate negative correlations. In addition, the relationships between symptoms will be analysed based on EBICglasso function and Spearman correlation, and the spring layout will be used to place the node with the strongest correlation in the centre of the network. R-package bootnet will be used to assess the accuracy and stability of the network. Network centrality will be assessed by calculating 95% CIs for the edge weight values, and the network stability will be assessed by calculating correlation stability coefficients for the node effects using a case-dropping subset bootstrap. The correlation stability coefficient should ideally exceed 0.5 but should be at least 0.25. Strength, closeness and betweenness will serve as indices of network centrality. Strength, that is, the sum of absolute values of the edge weights’ correlation coefficients, signifies the significance of a symptom within the network, with a higher value indicating a greater influence of the symptom on other symptoms. Meanwhile, closeness is the reciprocal of the distance from the symptom to other symptoms. A higher closeness value suggests that the symptom is more likely to be a core symptom. In addition, betweenness is defined as the number of times a node appears on paths between two other nodes within the network. A higher betweenness value indicates that the symptom is more likely to be a bridge symptom. ∑s will be used as an indicator of network density, and R-package mgm will be used to determine the predictability of each node. High predictability in symptoms indicates that managing adjacent nodes could effectively control the symptom while low predictability may necessitate direct intervention on the symptom or the identification of markers outside the network. In this study, bootstrap difference tests for edge weights and centrality indices will be performed to determine whether the estimates of network connectivity and centrality differ across various variables.
Patient and public involvement
Patients or the public were not involved in the trial design or planning of the study.
Ethics and dissemination
This study confirms to the principles of the Declaration of Helsinki and relevant ethical guidelines. Ethical approval has been obtained from Shanxi Bethune Hospital Ethics Committee (approval number:YXLL-2023-186). The final outcomes will be published in a peer-reviewed journal and disseminated through a conference.
The authors thank the future participants.
Ethics statements
Patient consent for publication
Not applicable.
Contributors XC and LW contributed to study conception and design. XC drafted the original manuscript. LW and WaL did critical revision of the manuscript for important intellectual content. LizL, WeL and LinL contributed to sampling method and data analysis method. LW and WaL acted as the study guarantor. LW and WaL contributed equally and are co-corresponding authors. All authors have read and approved the final manuscript.
Funding This work was supported by Science Fundation of Shanxi Bethune Hospital (grant number: 2023YH02).
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
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Details
; Liu, Lizhen 1 ; Li, Wenxin 1 ; Lei, Lingling 1 ; Li, Wanling 2
; Wu, Lihua 1 1 Department of Lymphatic Oncology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China
2 Department of Nursing, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China; Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China