Exploring Symptom Clusters and Measurements in Patients with Heart Failure - A Scope Review

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However, the identification and assessment of symptom clusters in HF remain inconsistent, and the measurement tools used vary widely, limiting clinical symptom management and standardized care delivery. Aims : This scoping review aimed to identify the types of symptom clusters in patients with heart failure, evaluate the assessment tools used to measure these clusters, and explore the influencing factors affecting symptom severity, to support more effective clinical management Methods : The review followed the PRISMA-ScR guidelines. A systematic search was conducted across PubMed, Web of Science, and CNKI, including studies published up to January 2025. A total of 12 studies involving 9,328 patients were included. Data extraction and synthesis focused on symptom cluster types, assessment tools, and associated influencing factors. Results : Twelve studies were included, involving 9,328 patients, identifying 25 symptom clusters. Common symptom clusters included respiratory symptoms, fatigue-related symptoms, and psychological/emotional symptoms. The assessment tools primarily used were the MSAS-HF and MLHFQ, but differences in tool usage and inconsistency in the naming and classification of symptom clusters were observed. Conclusion : HF symptom clusters are diverse and inconsistently classified. Existing tools lack standardization. More precise and culturally adaptable assessment frameworks are needed. Implications for practice : Understanding symptom clusters and their assessment tools provides a foundation for developing standardized, culturally appropriate, and nurse-led symptom management strategies. This review offers practical insights to guide nurses in personalized assessment and care planning for heart failure patients. Heart failure symptom clusters assessment tools scoping review quality of life clinical management Figures Figure 1 Introduction Heart failure (HF) is a chronic disease that poses a significant threat to human health, with both incidence and mortality rates steadily increasing in recent years. Due to its complex symptoms and progressive nature, heart failure has become an important global public health burden [ 1 ] . Currently, more than 64 million people worldwide are affected by heart failure [ 2 ] , and its symptom clusters span multiple dimensions, including physiological, psychological, and social factors, exhibiting high heterogeneity. Common symptoms include dyspnea, fatigue, edema, and emotional distress [ 3 ][ 4 ] . The dynamic interactions among these symptom clusters not only increase the disease burden for patients but also significantly reduce their quality of life [ 5 ] . A symptom cluster refers to two or more related symptoms that occur simultaneously [ 6 ] . Early identification and effective management of disease-related symptom clusters in heart failure patients are crucial. Although previous studies have explored symptoms in heart failure patients, differences in symptom cluster identification, analysis, and assessment methods across studies have led to considerable variations in the composition of these clusters [ 7 ] . Therefore, establishing an efficient and comprehensive symptom cluster management system would not only help in accurately identifying patient symptoms and implementing subgroup management but also effectively save healthcare resources and reduce medical costs [ 8 ][ 9 ] . Scoping review, as an evidence-based practice methodology, aims to quickly help researchers understand the research progress in a specific field through systematic searches, integrate existing findings, and provide a basis for solving complex and exploratory problems [ 10 ][ 11 ] . This study follows the scoping review reporting framework to review the types of symptom clusters, assessment tools, and influencing factors in heart failure patients, with the aim of providing a theoretical foundation for further improving symptom cluster management in heart failure patients. Aims This study aims to explore the symptom clusters present in patients with heart failure (HF), to identify the tools used to measure these symptom clusters, and to examine the factors influencing them. It seeks to provide a comprehensive overview that contributes to better management strategies and the development of more effective clinical interventions for heart failure patients. Methods A scoping review methodology was chosen for this study to understand the content and measurement of symptom clusters in patients with heart failure. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) was used [12] . The scoping review followed the five stages proposed in the methodological framework based on Arksey and O'Malley's (2005) [13] and incorporating the most recent guidelines of the framework [14] : (a) identifying the research question, (b) finding relevant studies, (c) selecting studies, (d) charting the data, and (e) collating, summarizing, and reporting the results. The optional sixth stage “consulting with the reference group” was not used [15] . Research Questions The research questions were as follows: Seatch Strategy What symptom clusters are present and characterized in patients with chronic heart failure? What are the tools for measuring symptom clusters in patients with chronic heart failure? What are the factors influencing symptom clusters in patients with chronic heart failure? Search Strategy Two researchers with extensive evidence-based knowledge participated in the systematic review search, covering the following databases: PubMed, Web of Science, Scopus, CINAHL, Embase, Cochrane Library, Sinomed, CNKI, Wanfang, and VIP. The selection of search terms was initially determined through consultation with experts and discussions among the topic group members. Based on the research objectives and content, the following search terms were finalized: “heart failure/CHF/chronic heart failure/cardiac failure*/heart decompensation/right-side heart failure/myocardial failure/congestive heart failure/left-side heart failure” and “symptom cluster/symptom constellation/concurrent symptom/multiple symptom/symptom combination”. The search covered the period from the establishment of the database until January 20, 2025. Only articles in English and Chinese were included. After completing the search, we reviewed the reference lists of each article and included two additional articles. The sample search strategy for PubMed can be found in Appendix 1. Study eligibility criteria The inclusion criteria for our study were: 1) Patients diagnosed with heart failure; 2) Studies addressing symptom clusters or the correlation between ≥2 symptoms. The exclusion criteria were: 1) Duplicate publications; 2) Literature types including reviews and conference abstracts; 3) Non-English and Chinese literature; 4) Literature for which the full text could not be obtained. Study screening and data extraction and analysis The studies retrieved from the databases were imported into EndNote 20 software. After automatic and manual deduplication, two trained researchers (Y L, H W) independently conducted an initial screening by reviewing the titles and abstracts according to the inclusion and exclusion criteria. They then re-screened the full text and briefly recorded the reasons for inclusion or exclusion. In case of any disagreement, a third researcher (L C) was consulted to resolve the issue, and the final decision was made regarding the inclusion of studies. Two researchers (Y L, R W) independently performed data extraction using a standardized form, and the data charts were developed collaboratively by the researchers using Excel software. Any discrepancies during the extraction process were resolved through discussion between the two researchers or adjudicated by a third researcher (H W). The extracted data included the author, publication year, country, study type, study population, sample size, mean age, analysis methods, assessment tools, symptom clusters, and influencing factors. Results Search results A preliminary search of the databases yielded 1469 relevant articles. Two additional articles were included through reference tracing. After removing duplicates, 1307 articles remained. An initial screening by reading the titles and abstracts resulted in the exclusion of 1255 articles, leaving 52 articles. After a full-text review and secondary screening, a total of 12 articles were included [16- 27] . The detailed search process is shown in Figure 1. Study characteristics The 12 included studies involved 9328 heart failure patients, with samples drawn from inpatient, outpatient, and community settings. Only one study specifically recruited patients with advanced heart failure. The study population included 3565 patients with NYHA I/II and 5763 patients with NYHA III/IV. The basic characteristics of the included studies are detailed in Table 1. A total of 25 symptom clusters were identified, covering social, physiological, and psychological aspects. Common symptom clusters included respiratory system symptom clusters [16, 21, 23- 24, 26] , cardiovascular ischemic symptom clusters [21, 24-27] , fatigue-weakness symptom clusters [16, 18, 20, 22-24, 26] , gastrointestinal symptom clusters [18, 21, 23, 25-27] , psychological-emotional symptom clusters [17-19, 24-27] , and edema-congestion symptom clusters [19, 21, 23, 25-27] , among others. Other types of symptom clusters included sleep disturbance symptom clusters [24-25, 27] , autonomic dysfunction symptom clusters [21, 26-27] , pain symptom clusters [17-19] , cognitive dysfunction symptom clusters [20, 22, 25] , and nutritional-metabolic symptom clusters [20, 26-27] . Based on frequency of mention, the respiratory system symptom clusters, fatigue-weakness symptom clusters, and psychological-emotional symptom clusters were the most common. Due to the significant internal structural differences among the symptom clusters in the included studies, the naming and classification of the symptom clusters varied. Assessment Tools for Symptom Clusters in Heart Failure Patients Among the 12 included studies, the tools used to assess symptom clusters included both single-symptom assessment scales and comprehensive assessment scales, with significant heterogeneity in the tools selected across studies. Seven studies [16, 21, 23-27] used the Memorial Symptom Assessment Scale-Heart Failure (MSAS-HF) [28] . This scale was developed by Zambroski et al. in 2004 by adapting the Memorial Symptom Assessment Scale (MSAS) used in oncology patients [29] . The adaptation involved removing five symptoms with a low incidence in heart failure patients and adding five specific heart failure symptoms (chest pain, palpitations, waking up due to shortness of breath at night, worsening of dyspnea when lying flat, and weight gain), resulting in the MSAS-HF. The MSAS-HF assesses symptoms experienced by heart failure patients in the past seven days, consisting of 32 symptom items divided into three dimensions: physiological symptoms (21 items), psychological symptoms (6 items), and heart failure-specific symptoms (5 items). Each symptom item is evaluated across four aspects: incidence, frequency (Likert 1–4 scale), severity (Likert 1–4 scale), and distress (Likert 0–4 scale). The scale provides a comprehensive assessment of symptoms, but its scoring system is complex and time-consuming for patients to complete. Six studies [19- 21, 24, 26 -27] used the Minnesota Living with Heart Failure Questionnaire (MLHFQ), developed by Rector et al. in 1987 [30] . This tool is a disease-specific measure of quality of life in heart failure patients, consisting of 21 items divided into three dimensions: physical (8 items), emotional (5 items), and other (8 items). Each item is rated on a Likert scale from 0 to 5, with “0” representing “no impact” and “5” representing “severe impact.” The total score ranges from 0 to 105, with higher scores indicating worse quality of life. The Cronbach’s α coefficients for the European, U.S., and Chinese versions of the scale are 0.84, 0.90, and 0.8, respectively [31] . The scale was originally designed to assess the quality of life in heart failure patients, and the symptoms included are those that impact quality of life. As a result, the scale addresses only the dimension of how symptoms affect quality of life, with a limited focus on symptom assessment itself. Methodology for Identifying the Composition of Heart Failure Symptom Clusters All 12 studies included in this research used quantitative analytical methods to identify symptom clusters, including exploratory factor analysis, principal component analysis, latent class analysis, and cluster analysis. The distribution of methods showed that cluster analysis was the most commonly used (n=6) [16, 18, 20, 25-27] , followed by principal component analysis (n=4) [17, 21-22, 24] , and latent class analysis and exploratory factor analysis were each used in one study [19, 23] . Cluster analysis classifies symptoms based on the structural characteristics of the data itself, with the advantage of identifying latent patient subgroups with similar symptom profiles. However, the interpretation of results is subject to some degree of subjectivity [32] . Principal component analysis performs dimensionality reduction of multivariate data through linear transformation, which can effectively simplify heart failure symptom data and build explanatory factor models [33] . However, this method does not account for error in model construction, which may lead to some bias in result interpretation. Latent class analysis, as an emerging method, uses latent class variables to explain the relationships between observed symptom variables. It classifies study subjects by maintaining the principle of local independence [34] . Compared to traditional symptom grouping methods, latent class analysis divides symptom clusters based on individual symptom scale scores and quantifies the proportion of each category. Its innovation lies in: ① establishing a direct link between symptom characteristics and individualized grouping, and ② providing scientific evidence for precision symptom management [35] . Exploratory factor analysis identifies symptom clusters that are potentially related to latent causes based on correlations between symptoms, with the advantage of precise data structure analysis. However, it requires a large sample size for support [32] . The methods differ significantly in their application features: cluster analysis and principal component analysis focus on dimensionality reduction of symptom domains, latent class analysis emphasizes the heterogeneity of study subjects, and exploratory factor analysis focuses on the potential causal relationships between symptoms. Influencing factors of symptom clusters in patients with heart failure Sociodemographic Factors Age is an important factor influencing heart failure symptom clusters. Park et al. [19] found through latent class analysis that older patients are more likely to experience multiple physical symptom clusters, but with a lower perceived severity of psychological symptoms. Salyer et al. [22] observed that elderly patients tend to tolerate symptom clusters better overall, whereas younger patients experience a significant reduction in quality of life due to psychological-emotional symptom clusters (including anxiety, depression, and daytime sleepiness). Two studies [19, 25] pointed out that education level is associated with symptom clusters, with individuals having lower education levels showing weaker symptom management abilities and higher severity of symptom clusters. Gender is also a factor affecting patient recovery. Female patients are more likely to be in a negative emotional state, with higher rates of anxiety, depression, and other negative emotions, which exacerbate physical symptoms such as dyspnea, fatigue, and sleep disturbances [25] . Additionally, factors such as living conditions [20] , quality of life [27] , and perceived control [20] also influence the disease-related symptom clusters in heart failure patients. Disease-Related Factors Three studies [20, 22, 25] consistently indicated that the NYHA functional classification is positively correlated with the severity of symptom clusters. Patients with NYHA functional classes III or IV experience greater discomfort during daily activities due to symptoms. In terms of comorbidities, two studies [19, 22] confirmed that hypertension, atrial fibrillation, and diabetes exacerbate the severity of various symptoms. Additionally, sleep disturbances, anemia, and obesity may intensify fatigue, pain, and gastrointestinal symptoms [22] . Among laboratory and functional indicators, elevated NT-proBNP levels, reduced 6-minute walking distance, and lower ejection fraction have been confirmed as objective predictors [25][27] . Psychosocial Factors Studies have shown [4] that anxiety can amplify the perception of physical symptoms, creating a "psychological-physiological vicious cycle" that worsens the complexity of symptom clusters. Huang et al. [20] found that psychological distress is significantly correlated with the severity of physiological symptoms such as dyspnea and fatigue. Furthermore, Salyer et al. [22] further pointed out that "negative emotional symptom clusters" (including anxiety, depression, cognitive impairment, etc.) have a significant negative impact on patients' quality of life. Discussion This study systematically reviewed the content, measurement tools, influencing factors, and related research methods of symptom clusters in heart failure patients. The results reveal the multidimensionality, complexity, and clinical significance of heart failure symptom clusters, providing important references for future research and practice. Diversity and Complexity of Symptom Clusters This study found that the symptom clusters in heart failure patients exhibit high diversity, encompassing multiple dimensions including physiological, psychological, and social functions. A total of 25 symptom clusters were identified, including respiratory system symptoms, cardiovascular ischemic symptom clusters, fatigue-weakness symptom clusters, gastrointestinal symptom clusters, psychological-emotional symptom clusters, and edema-congestion symptom clusters, among others. However, there is significant heterogeneity in the naming and classification of these symptom clusters. For example, dyspnea was classified as a respiratory system symptom in some studies [21, 23-24] , while in others it was categorized as an uncomfortable symptom [17-18, 22] or a congestion symptom [25-26] . Additionally, the composition of psychological-emotional symptom clusters was inconsistent; some studies classified them as anxiety-depression-dominant "negative emotional symptom clusters" [23-27] , while others included cognitive impairment or sleep issues, forming a "psychological-cognitive symptom cluster" [17, 22] . This heterogeneity may stem from differences in study design, sample characteristics, and statistical methods. For example, cluster analysis focuses on symptom co-occurrence patterns, while principal component analysis is more concerned with underlying causal relationships. Future studies need to further clarify the core symptoms of symptom clusters, validate the intrinsic connections between symptoms in conjunction with biological mechanisms, and promote the standardization of naming and classification to support precision symptom management. Heterogeneity and Optimization Directions of Heart Failure Symptom Cluster Assessment Tools There is significant heterogeneity in the selection of symptom cluster assessment tools. Seven studies [16, 21, 23-27] used the MSAS-HF scale [28] , which has the advantage of comprehensively covering physiological, psychological, and heart failure-specific symptoms. However, its large number of items (32) may increase the burden on patients. In contrast, the MLHFQ scale (used in 6 studies) focuses on the impact of symptoms on quality of life, but due to its limited number of items (21), it may underestimate symptom diversity. Additionally, some studies used statistical methods such as exploratory factor analysis or latent class analysis to identify symptom clusters from a data-driven perspective, but the interpretation of results remains subject to subjective limitations. The current tools have several limitations, including: (1) a lack of cross-cultural adaptability, such as insufficient psychometric validation for the Chinese version of the scales; (2) inadequate integration of objective indicators (e.g., NT-proBNP, 6-minute walking distance) with subjective symptoms; and (3) limited capacity for assessing dynamic symptom evolution and interactions. Future research should focus on developing multidimensional tools that incorporate biomarkers and patient-reported outcomes (PROs) and leverage machine learning techniques to optimize the dynamic monitoring and subgroup analysis of symptom clusters, providing a basis for personalized interventions. Influencing Factors of Symptom Clusters and Clinical Implications Symptom clusters in heart failure patients are specific, and identifying the factors that influence these clusters not only aids in disease management but may also help healthcare providers identify high-risk populations and develop personalized symptom management plans [36] . Currently, there is a lack of research specifically focusing on the factors that influence symptom clusters in heart failure patients, and existing studies are mostly concentrated on sociodemographic, disease-related, and psychological factors. This study found that the severity and manifestation of heart failure symptom clusters are influenced by multidimensional factors: Sociodemographic Factors Studies by Salyer et al. [22] and Cai et al. [25] showed that gender and age are influencing factors for the severity of symptom clusters in heart failure patients. The reason for this may be that, compared to younger patients, older patients are more likely to have multiple chronic comorbidities. Older patients tend to experience multiple physical symptom clusters but have a lower perception of psychological symptoms, which may be related to differences in disease adaptation abilities and pain thresholds [37] . Middle-aged and elderly female patients, especially those post-menopausal, are more prone to psychological problems due to hormonal changes and neuroendocrine dysfunction. Furthermore, female patients experience higher rates of anxiety and depression due to social role pressures and other factors [38] , which further exacerbate physical symptoms such as dyspnea and fatigue. Education level is also an important factor influencing symptom burden in patients. Patients with higher education levels are generally better able to access medical information and understand the disease progression, resulting in a lighter symptom burden. In contrast, patients with lower education levels may lack health literacy and have weaker symptom management abilities, leading to an increased severity of symptom clusters [39] . Additionally, research by Hackett et al. [40] found that good social support can effectively reduce the occurrence of psychological symptom clusters. Patients who live alone or lack social support are more likely to develop "psychological symptom clusters" due to the lack of caregiving resources. Clinically, it is important to develop stratified management strategies for high-risk populations (such as women and those with lower education levels), enhance health education and social support, and alleviate the discomfort caused by disease symptoms to reduce or alleviate patients' disease-related uncertainty. Disease-Related Factors Multiple studies have shown that NYHA classification and comorbidities significantly impact the severity of symptom clusters in heart failure patients. Huang et al. [20] indicated that patients with NYHA class III/IV experience a significantly increased symptom burden, which may be related to reduced cardiac output, inadequate peripheral tissue perfusion, and excessive activation of the neuroendocrine system (e.g., the renin-angiotensin-aldosterone system). These patients often experience a combination of symptoms such as dyspnea, edema, and fatigue due to hemodynamic deterioration. Additionally, comorbidities like hypertension and diabetes exacerbate symptom interactions through the release of inflammatory factors (e.g., IL-6, TNF-α) [41] . For example, insulin resistance in diabetic patients may worsen energy metabolism disorders, leading to the synergistic deterioration of fatigue and gastrointestinal symptoms (e.g., nausea, appetite loss). Laboratory indicators such as elevated NT-proBNP levels (>1,800 pg/mL) and decreased left ventricular ejection fraction (LVEF < 40%) have been shown to exacerbate the severity of symptom clusters [42-43] . Future studies should further integrate multimodal data (e.g., biomarkers, imaging parameters, and patient-reported symptoms) to construct dynamic predictive models for early identification and precision intervention of high-risk patients. In clinical practice, it is recommended to include NYHA classification, comorbidity types, and NT-proBNP levels in the symptom management assessment system to provide a basis for developing personalized treatment strategies. Psychosocial Factors Psychosocial factors play a crucial role in the formation and evolution of symptom clusters in heart failure. Studies have shown that anxiety and depression not only constitute independent psychological symptom clusters but may also amplify the perception of physical symptoms through neuroendocrine mechanisms (e.g., hypothalamic-pituitary-adrenal axis dysregulation), creating a "psychological-physiological vicious cycle" [44][45] . For example, in an anxious state, increased sympathetic nervous activity in patients may exacerbate palpitations and dyspnea, while elevated cortisol levels due to chronic stress may further worsen fatigue and metabolic disturbances. Additionally, disease uncertainty and low self-efficacy may impair patients' ability to manage symptoms. Arnold et al. [46] found that patients with higher disease uncertainty are more sensitive to symptom clusters and have significantly lower quality of life scores. Patients with low self-efficacy, due to a lack of confidence in controlling their symptoms, are more likely to fall into passive coping patterns (e.g., avoidance behavior), leading to an increased symptom burden. Therefore, clinical interventions should integrate a biopsychosocial model, using cognitive-behavioral therapy or mindfulness training to break the vicious cycle of symptoms, and strengthen doctor-patient communication to reduce disease uncertainty, thereby effectively improving symptom management Limitations This study has several limitations: (1) The included literature mainly consists of cross-sectional studies, which makes it difficult to reveal the dynamic evolution of symptom clusters; (2) The differences in measurement tools between Chinese and English-language studies may introduce cultural bias; (3) The statistical definitions of some symptom clusters lack clinical validation. Future studies should focus on longitudinal designs and combine mixed methods (quantitative + qualitative) to explore the biological mechanisms and sociocultural contexts of symptom clusters in greater depth. Furthermore, interdisciplinary collaboration should be promoted to develop standardized assessment frameworks, enabling precise and personalized symptom management for heart failure. Additionally, the reviewer encountered challenges related to language barriers, which led to the consideration of only published studies in Chinese or English. This may have introduced some degree of selection bias. The reviewer did not contact the researchers involved in the study to obtain further relevant information, which could have resulted in missing data. Furthermore, the methodological quality of the included studies was not systematically assessed. Conclusion This study, through a scoping review, identified multiple symptom clusters in patients with heart failure, with the most common clusters being related to respiratory, cardiovascular, and fatigue symptoms. These symptom clusters are influenced by factors such as gender, age, educational level, and NYHA classification. The existing literature lacks a unified standard for the naming and classification of symptom clusters in heart failure patients, and the assessment tools for these symptom clusters are highly heterogeneous. As a result, there are significant differences in the internal composition of symptom clusters across different studies. Future research should further explore the evolution trajectories and influencing factors of symptom clusters in heart failure patients, develop more specific assessment tools for symptom clusters, and optimize the standards for symptom cluster identification and naming. This will provide more reliable reference points for the development of precise and personalized symptom management strategies for heart failure. Declarations Consent to Participate: Not applicable, as this study is a scoping review of previously published studies and does not involve direct participation of human subjects. Ethics Approval: Not applicable, as this study is a scoping review of existing literature and did not involve any new data collection or human subjects. Funding: This study was supported by the National Natural Science Foundation of China (No.72464022) and the Scientific Research Project approved by the Chinese Nursing Association in 2023 (ZHKY202321). Clinical trial number: Not applicable. Clinical trial registration: Not applicable. Human Ethics and Consent to Participate: Not applicable, as this study is a scoping review of existing studies, and does not involve human participants or original data collection. Experiments on Humans and Animals: Not applicable, as this study is a scoping review of existing studies and did not involve any experimental work. References Writing Committee Members; ACC/AHA Joint Committee Members. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure. J Card Fail . 2022;28(5):e1-e167. doi:10.1016/j.cardfail.2022.02.010 Benjamin EJ, Blaha MJ, Chiuve SE, et al. Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation . 2017;135(10):e146-e603. doi:10.1161/CIR.0000000000000485 Qiu C, Yu DS, Song D, Wang X. The prognostic impact of symptom clusters in patients with heart failure: A systematic review and meta-analysis. J Adv Nurs . 2022;78(9):2713-2730. doi:10.1111/jan.15302 Yu DS, Li PW, Chong SO. Symptom cluster among patients with advanced heart failure: a review of its manifestations and impacts on health outcomes. Curr Opin Support Palliat Care . 2018;12(1):16-24. doi:10.1097/SPC.0000000000000316 Miaskowski C, Barsevick A, Berger A, et al. Advancing Symptom Science Through Symptom Cluster Research: Expert Panel Proceedings and Recommendations. J Natl Cancer Inst . 2017;109(4):djw253. doi:10.1093/jnci/djw253 Kim HJ, McGuire DB, Tulman L, Barsevick AM. Symptom clusters: concept analysis and clinical implications for cancer nursin. Cancer Nurs . 2005;28(4):270-284. doi:10.1097/00002820-200507000-00005 Qiu C, Yu DS, Song D, Wang X. The prognostic impact of symptom clusters in patients with heart failure: A systematic review and meta-analysis. J Adv Nurs . 2022;78(9):2713-2730. doi:10.1111/jan.15302 Geiger AM. Symptom Management: War Problems, Moonshot Solutions?. J Natl Cancer Inst . 2017;109(4):djw254. doi:10.1093/jnci/djw254 Heo S, Moser DK, Pressler SJ, et al. Dose-dependent relationship of physical and depressive symptoms with health-related quality of life in patients with heart failure. Eur J Cardiovasc Nurs . 2013;12(5):454-460. doi:10.1177/1474515112470996 Davis K, Drey N, Gould D. What are scoping studies? A review of the nursing literature. Int J Nurs Stud . 2009;46(10):1386-1400. doi:10.1016/j.ijnurstu.2009.02.010 Kastner M, Antony J, Soobiah C, Straus SE, Tricco AC. Conceptual recommendations for selecting the most appropriate knowledge synthesis method to answer research questions related to complex evidence. J Clin Epidemiol . 2016;73:43-49. doi:10.1016/j.jclinepi.2015.11.022 McGowan J, Straus S, Moher D, et al. Reporting scoping reviews-PRISMA ScR extension. J Clin Epidemiol . 2020;123:177-179. doi:10.1016/j.jclinepi.2020.03.016 Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8:19–32. doi: 10.1080/1364557032000119616. Peters MDJ, Marnie C, Tricco AC, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Implement . 2021;19(1):3-10. doi:10.1097/XEB.0000000000000277 Karlsson K, Olsson C, Erlandsson A, Ahlberg KM, Larsson M. Exploring Symptom Clusters and Their Measurements in Patients With Lung Cancer: A Scoping Review for Practice and Research. Oncol Nurs Forum . 2023;50(6):783-815. doi:10.1188/23.ONF.783-815 Song EK, Moser DK, Rayens MK, Lennie TA. Symptom clusters predict event-free survival in patients with heart failure. J Cardiovasc Nurs. 2010;25(4):284-291. doi:10.1097/JCN.0b013e3181cfbcbb Herr JK, Salyer J, Flattery M, et al. Heart failure symptom clusters and functional status—a cross-sectional study. J Adv Nurs . 2015;71(6):1274-1287. doi:10.1111/jan.12596 Yu DS, Chan HY, Leung DY, Hui E, Sit JW. Symptom clusters and quality of life among patients with advanced heart failure. J Geriatr Cardiol . 2016;13(5):408-414. doi:10.11909/j.issn.1671-5411.2016.05.014 Park J, Moser DK, Griffith K, Harring JR, Johantgen M. Exploring symptom clusters in people with heart failure. Clin Nurs Res. 2019;28(2):165-181. doi:10.1177/1054773817729606 Huang TY, Moser DK, Hwang SL. Identification, associated factors, and prognosis of symptom clusters in Taiwanese patients with heart failure. J Nurs Res. 2018;26(1):60-67. doi:10.1097/JNR.0000000000000199 Yi Y, Yuan Y, Song H, Ji H, Tian M, Liu F, Zhang L. The correlation between symptom clusters and quality of life in heart failure patients. J Nurs Sci . 2018;25(1):14-17. doi:10.16460/j.issn1008-9969.2018.01.010 Salyer J, Flattery M, Lyon DE. Heart failure symptom clusters and quality of life. Heart Lung. 2019;48(5):366-372. doi:10.1016/j.hrtlng.2019.04.006 Hu Y, Jiang J, Xu L, Wang C, Wang P, Yang B, Tao M. Symptom clusters and quality of life among patients with chronic heart failure: A cross-sectional study. Jpn J Nurs Sci . 2021;18(1):e12366. doi:10.1111/jjns.12366 Hu Y, Jiang J, Xu L, Wang C, Wang P, Yang B, Tao M. Symptom clusters and quality of life among patients with chronic heart failure: A cross-sectional study. Jpn J Nurs Sci. 2021;18(1):e12366. doi:10.1111/jjns.12366 Cai J, Tian H, Yang Q. Analysis of symptom clusters and their influencing factors in patients with chronic heart failure. Chin J Mod Nurs . 2021;27(25):6. doi:10.3760/cma.j.cn115682-20201218-06757 Liu Y, Hao R, Chen H, Tian H, Yang Q. A multicenter cross-sectional survey on the relationship between symptom clusters and quality of life in patients with chronic heart failure. Chin J Chronic Dis Prev Control . 2022;30(7):507-511+516. doi:10.16386/j.cjpccd.issn.1004-6194.2022.07.006 Li Z, Fu D, Qiu X, et al. Study on the symptom clusters in patients with chronic heart failure and their relationship with quality of life. South China J Prev Med . 2023;49(1):97-100. Zambroski CH, Lennie TA, Chung ML, Heo S, Smoot T, Ziegler C. Use of the Memorial Symptom Assessment Scale-Heart Failure in heart failure patients. Circulation. 2004;110(Supplement III):17. Chang VT, Hwang SS, Feuerman M, Kasimis BS, Thaler HT. The memorial symptom assessment scale short form (MSAS-SF). Cancer . 2000;89(5):1162-1171. doi:10.1002/1097-0142(20000901)89:53.0.co;2-y Rector TS, Kubo SH, Cohn YN. Patients’ self-assessment of their congestive heart failure. Part 2: Content, reliability and validity of a new measure, the Minnesota Living with Heart Failure Questionnaire. Heart Fail . 1987;3(5):198-209. doi:10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a2843 Moser DK, Lee KS, Wu JR, Mudd-Martin G, Jaarsma T, Huang TY, Fan XZ, Strömberg A, Lennie TA, Riegel B. Identification of symptom clusters among patients with heart failure: An international observational study. Int J Nurs Stud . 2014;51(10):1366-1372. doi:10.1016/j.ijnurstu.2014.02.004 Kim HJ, Abraham IL. Statistical approaches to modeling symptom clusters in cancer patients. Cancer Nurs . 2008;31(5):E1-E10. doi:10.1097/01.NCC.0000305757.58615.c8 Tabachnick B, Fidell L. Using Multivariate Statistics . 6th ed. Pearson International; 2013. https://elibrary.pearson.de/book/99.150005/9781292034546 Hagenaars JA, McCutcheon AL, eds. Applied Latent Class Analysis . Cambridge University Press; 2002. doi:10.1017/CBO9780511499531 Kaufman L, Rousseeuw PJ. Finding Groups in Data: An Introduction to Cluster Analysis . Wiley; 2005. doi:10.1002/9780470316801 Jurgens CY, Lee CS, Riegel B. Psychometric Analysis of the Heart Failure Somatic Perception Scale as a Measure of Patient Symptom Perception. J Cardiovasc Nurs. 2017;32(2):140-147. doi:10.1097/JCN.0000000000000320 Zhi Y, Zhang Y, Zhang Y, Zhang M, Kong Y. Age-associated changes in multimodal pain perception. Age Ageing . 2024;53(5):afae107. doi:10.1093/ageing/afae107 Kuehner C. Why is depression more common among women than among men?. Lancet Psychiatry . 2017;4(2):146-158. doi:10.1016/S2215-0366(16)30263-2 Shi D, Li Z, Yang J, Liu BZ, Xia H. Symptom experience and symptom burden of patients following first-ever stroke within 1 year: a cross-sectional study. Neural Regen Res . 2018;13(11):1907-1912. doi:10.4103/1673-5374.23944 Hackett ML, Anderson CS. Predictors of depression after stroke: a systematic review of observational studies. Stroke . 2005;36(10):2296-2301. doi:10.1161/01.STR.0000183622.75135.a4 Paulus WJ, Tschöpe C. A novel paradigm for heart failure with preserved ejection fraction: comorbidities drive myocardial dysfunction and remodeling through coronary microvascular endothelial inflammation. J Am Coll Cardiol . 2013;62(4):263-271. doi:10.1016/j.jacc.2013.02.092 McDonagh TA, Metra M, Adamo M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure [published correction appears in Eur Heart J. 2021 Dec 21;42(48):4901. doi: 10.1093/eurheartj/ehab670.]. Eur Heart J . 2021;42(36):3599-3726. doi:10.1093/eurheartj/ehab368 Januzzi JL, van Kimmenade R, Lainchbury J, et al. NT-proBNP testing for diagnosis and short-term prognosis in acute destabilized heart failure: an international pooled analysis of 1256 patients: the International Collaborative of NT-proBNP Study. Eur Heart J . 2006;27(3):330-337. doi:10.1093/eurheartj/ehi631 Celano CM, Villegas AC, Albanese AM, Gaggin HK, Huffman JC. Depression and Anxiety in Heart Failure: A Review. Harv Rev Psychiatry . 2018;26(4):175-184. doi:10.1097/HRP.0000000000000162 Jiang W, Alexander J, Christopher E, et al. Relationship of depression to increased risk of mortality and rehospitalization in patients with congestive heart failure. Arch Intern Med . 2001;161(15):1849-1856. doi:10.1001/archinte.161.15.1849 Arnold R, Ranchor AV, DeJongste MJ, et al. The relationship between self-efficacy and self-reported physical functioning in chronic obstructive pulmonary disease and chronic heart failure. Behav Med . 2005;31(3):107-115. doi:10.3200/BMED.31.3.107-115 Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table 1. Summary of symptom cluster studies for heart failure patients Appendix1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 18 Jun, 2025 Editor invited by journal 23 May, 2025 Editor assigned by journal 22 May, 2025 Submission checks completed at journal 22 May, 2025 First submitted to journal 16 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6684234","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473450947,"identity":"43b57dc7-627f-44f2-954c-54956fc31453","order_by":0,"name":"Yingjie Li","email":"","orcid":"","institution":"School of Nursing, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Yingjie","middleName":"","lastName":"Li","suffix":""},{"id":473450948,"identity":"85eee8c3-8811-4e88-9b7f-4981400dc704","order_by":1,"name":"Huiwen Wang","email":"","orcid":"","institution":"the second affiliated hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Huiwen","middleName":"","lastName":"Wang","suffix":""},{"id":473450949,"identity":"018f7f89-1cc3-4100-9f8d-f0fa047d4784","order_by":2,"name":"Lu Chen","email":"","orcid":"","institution":"the second affiliated hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Chen","suffix":""},{"id":473450950,"identity":"e08452d8-c6fa-416c-bfd5-6ca94a158353","order_by":3,"name":"Rui Wu","email":"","orcid":"","institution":"the second affiliated hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Wu","suffix":""},{"id":473450951,"identity":"88c488ec-1e99-40b0-9e1c-93520b633664","order_by":4,"name":"Mengdiu Liu","email":"","orcid":"","institution":"School of Nursing, Jiangxi Medical College, Nanchang 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03:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6684234/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6684234/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85175871,"identity":"cdb83369-21a0-4ac4-93b6-2e9c0c6a9417","added_by":"auto","created_at":"2025-06-23 06:30:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122301,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow diagram for article selection\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6684234/v1/0037f5eaec393f4c4e0018ea.png"},{"id":85176468,"identity":"be052004-5cdb-451b-a699-a70155af8ffc","added_by":"auto","created_at":"2025-06-23 06:38:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":609012,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6684234/v1/f8a89028-91d9-4108-bf57-d6b51ec2505c.pdf"},{"id":85175872,"identity":"1dfc0be1-3a41-4f4b-aa71-25f92d9f992a","added_by":"auto","created_at":"2025-06-23 06:30:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33273,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1. Summary of symptom cluster studies for heart failure patients\u003c/p\u003e","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6684234/v1/3470ce5be147a5fd2811a743.docx"},{"id":85176440,"identity":"d954aedd-9c19-418a-8cb3-7563412f2b1e","added_by":"auto","created_at":"2025-06-23 06:38:11","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14086,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6684234/v1/ea674a040c1cae3fff229df8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Symptom Clusters and Measurements in Patients with Heart Failure - A Scope Review","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart failure (HF) is a chronic disease that poses a significant threat to human health, with both incidence and mortality rates steadily increasing in recent years. Due to its complex symptoms and progressive nature, heart failure has become an important global public health burden\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Currently, more than 64\u0026nbsp;million people worldwide are affected by heart failure\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, and its symptom clusters span multiple dimensions, including physiological, psychological, and social factors, exhibiting high heterogeneity. Common symptoms include dyspnea, fatigue, edema, and emotional distress\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The dynamic interactions among these symptom clusters not only increase the disease burden for patients but also significantly reduce their quality of life\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. A symptom cluster refers to two or more related symptoms that occur simultaneously\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Early identification and effective management of disease-related symptom clusters in heart failure patients are crucial. Although previous studies have explored symptoms in heart failure patients, differences in symptom cluster identification, analysis, and assessment methods across studies have led to considerable variations in the composition of these clusters\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Therefore, establishing an efficient and comprehensive symptom cluster management system would not only help in accurately identifying patient symptoms and implementing subgroup management but also effectively save healthcare resources and reduce medical costs\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eScoping review, as an evidence-based practice methodology, aims to quickly help researchers understand the research progress in a specific field through systematic searches, integrate existing findings, and provide a basis for solving complex and exploratory problems\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. This study follows the scoping review reporting framework to review the types of symptom clusters, assessment tools, and influencing factors in heart failure patients, with the aim of providing a theoretical foundation for further improving symptom cluster management in heart failure patients.\u003c/p\u003e"},{"header":"Aims","content":"\u003cp\u003eThis study aims to explore the symptom clusters present in patients with heart failure (HF), to identify the tools used to measure these symptom clusters, and to examine the factors influencing them. It seeks to provide a comprehensive overview that contributes to better management strategies and the development of more effective clinical interventions for heart failure patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA scoping review methodology was chosen for this study to understand the content and measurement of symptom clusters in patients with heart failure. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) was used\u003csup\u003e[12]\u003c/sup\u003e. The scoping review followed the five stages proposed in the methodological framework based on Arksey and O\u0026apos;Malley\u0026apos;s (2005)\u003csup\u003e[13]\u003c/sup\u003e and incorporating the most recent guidelines of the framework\u003csup\u003e[14]\u003c/sup\u003e: (a) identifying the research question, (b) finding relevant studies, (c) selecting studies, (d) charting the data, and (e) collating, summarizing, and reporting the results. The optional sixth stage \u0026ldquo;consulting with the reference group\u0026rdquo; was not used\u003csup\u003e[15]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eResearch Questions\u003c/p\u003e\n\u003cp\u003eThe research questions were as follows:\u003c/p\u003e\n\u003cp\u003eSeatch Strategy\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eWhat symptom clusters are present and characterized in patients with chronic heart failure?\u003c/li\u003e\n \u003cli\u003eWhat are the tools for measuring symptom clusters in patients with chronic heart failure?\u003c/li\u003e\n \u003cli\u003eWhat are the factors influencing symptom clusters in patients with chronic heart failure?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSearch Strategy\u003c/p\u003e\n\u003cp\u003eTwo researchers with extensive evidence-based knowledge participated in the systematic review search, covering the following databases: PubMed, Web of Science, Scopus, CINAHL, Embase, Cochrane Library, Sinomed, CNKI, Wanfang, and VIP. The selection of search terms was initially determined through consultation with experts and discussions among the topic group members. Based on the research objectives and content, the following search terms were finalized: \u0026ldquo;heart failure/CHF/chronic heart failure/cardiac failure*/heart decompensation/right-side heart failure/myocardial failure/congestive heart failure/left-side heart failure\u0026rdquo; and \u0026ldquo;symptom cluster/symptom constellation/concurrent symptom/multiple symptom/symptom combination\u0026rdquo;. The search covered the period from the establishment of the database until January 20, 2025. Only articles in English and Chinese were included. After completing the search, we reviewed the reference lists of each article and included two additional articles. The sample search strategy for PubMed can be found in Appendix 1.\u003c/p\u003e\n\u003cp\u003eStudy eligibility criteria\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria for our study were: 1) Patients diagnosed with heart failure; 2) Studies addressing symptom clusters or the correlation between \u0026ge;2 symptoms. The exclusion criteria were: 1) Duplicate publications; 2) Literature types including reviews and conference abstracts; 3) Non-English and Chinese literature; 4) Literature for which the full text could not be obtained.\u003c/p\u003e\n\u003cp\u003eStudy screening and data extraction and analysis\u003c/p\u003e\n\u003cp\u003eThe studies retrieved from the databases were imported into EndNote 20 software. After automatic and manual deduplication, two trained researchers (Y L, H W) independently conducted an initial screening by reviewing the titles and abstracts according to the inclusion and exclusion criteria. They then re-screened the full text and briefly recorded the reasons for inclusion or exclusion. In case of any disagreement, a third researcher (L C) was consulted to resolve the issue, and the final decision was made regarding the inclusion of studies. Two researchers (Y L, R W) independently performed data extraction using a standardized form, and the data charts were developed collaboratively by the researchers using Excel software. Any discrepancies during the extraction process were resolved through discussion between the two researchers or adjudicated by a third researcher (H W). The extracted data included the author, publication year, country, study type, study population, sample size, mean age, analysis methods, assessment tools, symptom clusters, and influencing factors.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSearch results\u003c/p\u003e\n\u003cp\u003eA preliminary search of the databases yielded 1469 relevant articles. Two additional articles were included through reference tracing. After removing duplicates, 1307 articles remained. An initial screening by reading the titles and abstracts resulted in the exclusion of 1255 articles, leaving 52 articles. After a full-text review and secondary screening, a total of 12 articles were included \u003csup\u003e[16-\u003c/sup\u003e\u003csup\u003e27]\u003c/sup\u003e. The detailed search process is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003eStudy characteristics\u003c/p\u003e\n\u003cp\u003eThe 12 included studies involved 9328 heart failure patients, with samples drawn from inpatient, outpatient, and community settings. Only one study specifically recruited patients with advanced heart failure. The study population included 3565 patients with NYHA I/II and 5763 patients with NYHA III/IV. The basic characteristics of the included studies are detailed in Table 1. A total of 25 symptom clusters were identified, covering social, physiological, and psychological aspects. Common symptom clusters included respiratory system symptom clusters\u003csup\u003e[16,\u003c/sup\u003e\u003csup\u003e21,\u003c/sup\u003e\u003csup\u003e23-\u003c/sup\u003e\u003csup\u003e24,\u0026nbsp;\u003c/sup\u003e\u003csup\u003e26]\u003c/sup\u003e, cardiovascular ischemic symptom clusters\u003csup\u003e[21, 24-27]\u003c/sup\u003e, fatigue-weakness symptom clusters\u003csup\u003e[16, 18, 20, 22-24, 26]\u003c/sup\u003e, gastrointestinal symptom clusters\u003csup\u003e[18, 21, 23, 25-27]\u003c/sup\u003e, psychological-emotional symptom clusters\u003csup\u003e[17-19, 24-27]\u003c/sup\u003e, and edema-congestion symptom clusters\u003csup\u003e[19, 21, 23, 25-27]\u003c/sup\u003e, among others. Other types of symptom clusters included sleep disturbance symptom clusters\u003csup\u003e[24-25, 27]\u003c/sup\u003e, autonomic dysfunction symptom clusters\u003csup\u003e[21, 26-27]\u003c/sup\u003e, pain symptom clusters\u003csup\u003e[17-19]\u003c/sup\u003e, cognitive dysfunction symptom clusters\u003csup\u003e[20, 22, 25]\u003c/sup\u003e, and nutritional-metabolic symptom clusters\u003csup\u003e[20, 26-27]\u003c/sup\u003e. Based on frequency of mention, the respiratory system symptom clusters, fatigue-weakness symptom clusters, and psychological-emotional symptom clusters were the most common. Due to the significant internal structural differences among the symptom clusters in the included studies, the naming and classification of the symptom clusters varied.\u003c/p\u003e\n\u003cp\u003eAssessment Tools for Symptom Clusters in Heart Failure Patients\u003c/p\u003e\n\u003cp\u003eAmong the 12 included studies, the tools used to assess symptom clusters included both single-symptom assessment scales and comprehensive assessment scales, with significant heterogeneity in the tools selected across studies. Seven studies\u003csup\u003e[16, 21, 23-27]\u003c/sup\u003e used the Memorial Symptom Assessment Scale-Heart Failure (MSAS-HF)\u003csup\u003e[28]\u003c/sup\u003e. This scale was developed by Zambroski et al. in 2004 by adapting the Memorial Symptom Assessment Scale (MSAS) used in oncology patients\u003csup\u003e[29]\u003c/sup\u003e. The adaptation involved removing five symptoms with a low incidence in heart failure patients and adding five specific heart failure symptoms (chest pain, palpitations, waking up due to shortness of breath at night, worsening of dyspnea when lying flat, and weight gain), resulting in the MSAS-HF. The MSAS-HF assesses symptoms experienced by heart failure patients in the past seven days, consisting of 32 symptom items divided into three dimensions: physiological symptoms (21 items), psychological symptoms (6 items), and heart failure-specific symptoms (5 items). Each symptom item is evaluated across four aspects: incidence, frequency (Likert 1–4 scale), severity (Likert 1–4 scale), and distress (Likert 0–4 scale). The scale provides a comprehensive assessment of symptoms, but its scoring system is complex and time-consuming for patients to complete.\u003c/p\u003e\n\u003cp\u003eSix studies\u003csup\u003e[19-\u003c/sup\u003e\u003csup\u003e21,\u003c/sup\u003e\u003csup\u003e24,\u003c/sup\u003e\u003csup\u003e26\u003c/sup\u003e\u003csup\u003e-27]\u003c/sup\u003e used the Minnesota Living with Heart Failure Questionnaire (MLHFQ), developed by Rector et al. in 1987\u003csup\u003e[30]\u003c/sup\u003e. This tool is a disease-specific measure of quality of life in heart failure patients, consisting of 21 items divided into three dimensions: physical (8 items), emotional (5 items), and other (8 items). Each item is rated on a Likert scale from 0 to 5, with “0” representing “no impact” and “5” representing “severe impact.” The total score ranges from 0 to 105, with higher scores indicating worse quality of life. The Cronbach’s α coefficients for the European, U.S., and Chinese versions of the scale are 0.84, 0.90, and 0.8, respectively\u003csup\u003e[31]\u003c/sup\u003e. The scale was originally designed to assess the quality of life in heart failure patients, and the symptoms included are those that impact quality of life. As a result, the scale addresses only the dimension of how symptoms affect quality of life, with a limited focus on symptom assessment itself.\u003c/p\u003e\n\u003cp\u003eMethodology for Identifying the Composition of Heart Failure Symptom Clusters\u003c/p\u003e\n\u003cp\u003eAll 12 studies included in this research used quantitative analytical methods to identify symptom clusters, including exploratory factor analysis, principal component analysis, latent class analysis, and cluster analysis. The distribution of methods showed that cluster analysis was the most commonly used (n=6)\u003csup\u003e[16, 18, 20, 25-27]\u003c/sup\u003e, followed by principal component analysis (n=4)\u003csup\u003e[17, 21-22, 24]\u003c/sup\u003e, and latent class analysis and exploratory factor analysis were each used in one study\u003csup\u003e[19, 23]\u003c/sup\u003e. Cluster analysis classifies symptoms based on the structural characteristics of the data itself, with the advantage of identifying latent patient subgroups with similar symptom profiles. However, the interpretation of results is subject to some degree of subjectivity\u003csup\u003e[32]\u003c/sup\u003e. Principal component analysis performs dimensionality reduction of multivariate data through linear transformation, which can effectively simplify heart failure symptom data and build explanatory factor models\u003csup\u003e[33]\u003c/sup\u003e. However, this method does not account for error in model construction, which may lead to some bias in result interpretation. Latent class analysis, as an emerging method, uses latent class variables to explain the relationships between observed symptom variables. It classifies study subjects by maintaining the principle of local independence\u003csup\u003e[34]\u003c/sup\u003e. Compared to traditional symptom grouping methods, latent class analysis divides symptom clusters based on individual symptom scale scores and quantifies the proportion of each category. Its innovation lies in:\u0026nbsp;①\u0026nbsp;establishing a direct link between symptom characteristics and individualized grouping, and\u0026nbsp;②\u0026nbsp;providing scientific evidence for precision symptom management\u003csup\u003e[35]\u003c/sup\u003e. Exploratory factor analysis identifies symptom clusters that are potentially related to latent causes based on correlations between symptoms, with the advantage of precise data structure analysis. However, it requires a large sample size for support\u003csup\u003e[32]\u003c/sup\u003e. The methods differ significantly in their application features: cluster analysis and principal component analysis focus on dimensionality reduction of symptom domains, latent class analysis emphasizes the heterogeneity of study subjects, and exploratory factor analysis focuses on the potential causal relationships between symptoms.\u003c/p\u003e\n\u003cp\u003eInfluencing factors of symptom clusters in patients with heart failure\u003c/p\u003e\n\u003cp\u003eSociodemographic Factors\u003c/p\u003e\n\u003cp\u003eAge is an important factor influencing heart failure symptom clusters. Park et al.\u003csup\u003e[19]\u003c/sup\u003e found through latent class analysis that older patients are more likely to experience multiple physical symptom clusters, but with a lower perceived severity of psychological symptoms. Salyer et al.\u003csup\u003e[22]\u003c/sup\u003e observed that elderly patients tend to tolerate symptom clusters better overall, whereas younger patients experience a significant reduction in quality of life due to psychological-emotional symptom clusters (including anxiety, depression, and daytime sleepiness). Two studies\u003csup\u003e[19, 25]\u003c/sup\u003e pointed out that education level is associated with symptom clusters, with individuals having lower education levels showing weaker symptom management abilities and higher severity of symptom clusters. Gender is also a factor affecting patient recovery. Female patients are more likely to be in a negative emotional state, with higher rates of anxiety, depression, and other negative emotions, which exacerbate physical symptoms such as dyspnea, fatigue, and sleep disturbances\u003csup\u003e[25]\u003c/sup\u003e. Additionally, factors such as living conditions\u003csup\u003e[20]\u003c/sup\u003e, quality of life\u003csup\u003e[27]\u003c/sup\u003e, and perceived control\u003csup\u003e[20]\u003c/sup\u003e also influence the disease-related symptom clusters in heart failure patients.\u003c/p\u003e\n\u003cp\u003eDisease-Related Factors\u003c/p\u003e\n\u003cp\u003eThree studies\u003csup\u003e[20, 22, 25]\u003c/sup\u003e consistently indicated that the NYHA functional classification is positively correlated with the severity of symptom clusters. Patients with NYHA functional classes III or IV experience greater discomfort during daily activities due to symptoms. In terms of comorbidities, two studies\u003csup\u003e[19, 22]\u003c/sup\u003e confirmed that hypertension, atrial fibrillation, and diabetes exacerbate the severity of various symptoms. Additionally, sleep disturbances, anemia, and obesity may intensify fatigue, pain, and gastrointestinal symptoms\u003csup\u003e[22]\u003c/sup\u003e. Among laboratory and functional indicators, elevated NT-proBNP levels, reduced 6-minute walking distance, and lower ejection fraction have been confirmed as objective predictors\u003csup\u003e[25][27]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePsychosocial Factors\u003c/p\u003e\n\u003cp\u003eStudies have shown\u003csup\u003e[4]\u003c/sup\u003e that anxiety can amplify the perception of physical symptoms, creating a \"psychological-physiological vicious cycle\" that worsens the complexity of symptom clusters. Huang et al.\u003csup\u003e[20]\u003c/sup\u003e found that psychological distress is significantly correlated with the severity of physiological symptoms such as dyspnea and fatigue. Furthermore, Salyer et al.\u003csup\u003e[22]\u003c/sup\u003e further pointed out that \"negative emotional symptom clusters\" (including anxiety, depression, cognitive impairment, etc.) have a significant negative impact on patients' quality of life.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically reviewed the content, measurement tools, influencing factors, and related research methods of symptom clusters in heart failure patients. The results reveal the multidimensionality, complexity, and clinical significance of heart failure symptom clusters, providing important references for future research and practice.\u003c/p\u003e\n\u003cp\u003eDiversity and Complexity of Symptom Clusters\u003c/p\u003e\n\u003cp\u003eThis study found that the symptom clusters in heart failure patients exhibit high diversity, encompassing multiple dimensions including physiological, psychological, and social functions. A total of 25 symptom clusters were identified, including respiratory system symptoms, cardiovascular ischemic symptom clusters, fatigue-weakness symptom clusters, gastrointestinal symptom clusters, psychological-emotional symptom clusters, and edema-congestion symptom clusters, among others. However, there is significant heterogeneity in the naming and classification of these symptom clusters. For example, dyspnea was classified as a respiratory system symptom in some studies\u003csup\u003e[21, 23-24]\u003c/sup\u003e, while in others it was categorized as an uncomfortable symptom\u003csup\u003e[17-18, 22]\u003c/sup\u003e or a congestion symptom\u003csup\u003e[25-26]\u003c/sup\u003e. Additionally, the composition of psychological-emotional symptom clusters was inconsistent; some studies classified them as anxiety-depression-dominant \"negative emotional symptom clusters\"\u003csup\u003e[23-27]\u003c/sup\u003e, while others included cognitive impairment or sleep issues, forming a \"psychological-cognitive symptom cluster\"\u003csup\u003e[17, 22]\u003c/sup\u003e. This heterogeneity may stem from differences in study design, sample characteristics, and statistical methods. For example, cluster analysis focuses on symptom co-occurrence patterns, while principal component analysis is more concerned with underlying causal relationships. Future studies need to further clarify the core symptoms of symptom clusters, validate the intrinsic connections between symptoms in conjunction with biological mechanisms, and promote the standardization of naming and classification to support precision symptom management.\u003c/p\u003e\n\u003cp\u003eHeterogeneity and Optimization Directions of Heart Failure Symptom Cluster Assessment Tools\u003c/p\u003e\n\u003cp\u003eThere is significant heterogeneity in the selection of symptom cluster assessment tools. Seven studies\u003csup\u003e[16, 21, 23-27]\u003c/sup\u003e used the MSAS-HF scale\u003csup\u003e[28]\u003c/sup\u003e, which has the advantage of comprehensively covering physiological, psychological, and heart failure-specific symptoms. However, its large number of items (32) may increase the burden on patients. In contrast, the MLHFQ scale (used in 6 studies) focuses on the impact of symptoms on quality of life, but due to its limited number of items (21), it may underestimate symptom diversity. Additionally, some studies used statistical methods such as exploratory factor analysis or latent class analysis to identify symptom clusters from a data-driven perspective, but the interpretation of results remains subject to subjective limitations. The current tools have several limitations, including: (1) a lack of cross-cultural adaptability, such as insufficient psychometric validation for the Chinese version of the scales; (2) inadequate integration of objective indicators (e.g., NT-proBNP, 6-minute walking distance) with subjective symptoms; and (3) limited capacity for assessing dynamic symptom evolution and interactions. Future research should focus on developing multidimensional tools that incorporate biomarkers and patient-reported outcomes (PROs) and leverage machine learning techniques to optimize the dynamic monitoring and subgroup analysis of symptom clusters, providing a basis for personalized interventions.\u003c/p\u003e\n\u003cp\u003eInfluencing Factors of Symptom Clusters and Clinical Implications\u003c/p\u003e\n\u003cp\u003eSymptom clusters in heart failure patients are specific, and identifying the factors that influence these clusters not only aids in disease management but may also help healthcare providers identify high-risk populations and develop personalized symptom management plans\u003csup\u003e[36]\u003c/sup\u003e. Currently, there is a lack of research specifically focusing on the factors that influence symptom clusters in heart failure patients, and existing studies are mostly concentrated on sociodemographic, disease-related, and psychological factors. This study found that the severity and manifestation of heart failure symptom clusters are influenced by multidimensional factors:\u003c/p\u003e\n\u003cp\u003eSociodemographic Factors\u003c/p\u003e\n\u003cp\u003eStudies by Salyer et al.\u003csup\u003e[22]\u003c/sup\u003e and Cai et al.\u003csup\u003e[25]\u003c/sup\u003e showed that gender and age are influencing factors for the severity of symptom clusters in heart failure patients. The reason for this may be that, compared to younger patients, older patients are more likely to have multiple chronic comorbidities. Older patients tend to experience multiple physical symptom clusters but have a lower perception of psychological symptoms, which may be related to differences in disease adaptation abilities and pain thresholds\u003csup\u003e[37]\u003c/sup\u003e. Middle-aged and elderly female patients, especially those post-menopausal, are more prone to psychological problems due to hormonal changes and neuroendocrine dysfunction. Furthermore, female patients experience higher rates of anxiety and depression due to social role pressures and other factors\u003csup\u003e[38]\u003c/sup\u003e, which further exacerbate physical symptoms such as dyspnea and fatigue.\u003c/p\u003e\n\u003cp\u003eEducation level is also an important factor influencing symptom burden in patients. Patients with higher education levels are generally better able to access medical information and understand the disease progression, resulting in a lighter symptom burden. In contrast, patients with lower education levels may lack health literacy and have weaker symptom management abilities, leading to an increased severity of symptom clusters\u003csup\u003e[39]\u003c/sup\u003e. Additionally, research by Hackett et al.\u003csup\u003e[40]\u003c/sup\u003e found that good social support can effectively reduce the occurrence of psychological symptom clusters. Patients who live alone or lack social support are more likely to develop \"psychological symptom clusters\" due to the lack of caregiving resources. Clinically, it is important to develop stratified management strategies for high-risk populations (such as women and those with lower education levels), enhance health education and social support, and alleviate the discomfort caused by disease symptoms to reduce or alleviate patients' disease-related uncertainty.\u003c/p\u003e\n\u003cp\u003eDisease-Related Factors\u003c/p\u003e\n\u003cp\u003eMultiple studies have shown that NYHA classification and comorbidities significantly impact the severity of symptom clusters in heart failure patients. Huang et al.\u003csup\u003e[20]\u003c/sup\u003e indicated that patients with NYHA class III/IV experience a significantly increased symptom burden, which may be related to reduced cardiac output, inadequate peripheral tissue perfusion, and excessive activation of the neuroendocrine system (e.g., the renin-angiotensin-aldosterone system). These patients often experience a combination of symptoms such as dyspnea, edema, and fatigue due to hemodynamic deterioration. Additionally, comorbidities like hypertension and diabetes exacerbate symptom interactions through the release of inflammatory factors (e.g., IL-6, TNF-α)\u003csup\u003e[41]\u003c/sup\u003e. For example, insulin resistance in diabetic patients may worsen energy metabolism disorders, leading to the synergistic deterioration of fatigue and gastrointestinal symptoms (e.g., nausea, appetite loss).\u003c/p\u003e\n\u003cp\u003eLaboratory indicators such as elevated NT-proBNP levels (\u0026gt;1,800 pg/mL) and decreased left ventricular ejection fraction (LVEF \u0026lt; 40%) have been shown to exacerbate the severity of symptom clusters\u003csup\u003e[42-43]\u003c/sup\u003e. Future studies should further integrate multimodal data (e.g., biomarkers, imaging parameters, and patient-reported symptoms) to construct dynamic predictive models for early identification and precision intervention of high-risk patients. In clinical practice, it is recommended to include NYHA classification, comorbidity types, and NT-proBNP levels in the symptom management assessment system to provide a basis for developing personalized treatment strategies.\u003c/p\u003e\n\u003cp\u003ePsychosocial Factors\u003c/p\u003e\n\u003cp\u003ePsychosocial factors play a crucial role in the formation and evolution of symptom clusters in heart failure. Studies have shown that anxiety and depression not only constitute independent psychological symptom clusters but may also amplify the perception of physical symptoms through neuroendocrine mechanisms (e.g., hypothalamic-pituitary-adrenal axis dysregulation), creating a \"psychological-physiological vicious cycle\"\u003csup\u003e[44][45]\u003c/sup\u003e. For example, in an anxious state, increased sympathetic nervous activity in patients may exacerbate palpitations and dyspnea, while elevated cortisol levels due to chronic stress may further worsen fatigue and metabolic disturbances.\u003c/p\u003e\n\u003cp\u003eAdditionally, disease uncertainty and low self-efficacy may impair patients' ability to manage symptoms. Arnold et al.\u003csup\u003e[46]\u003c/sup\u003e found that patients with higher disease uncertainty are more sensitive to symptom clusters and have significantly lower quality of life scores. Patients with low self-efficacy, due to a lack of confidence in controlling their symptoms, are more likely to fall into passive coping patterns (e.g., avoidance behavior), leading to an increased symptom burden. Therefore, clinical interventions should integrate a biopsychosocial model, using cognitive-behavioral therapy or mindfulness training to break the vicious cycle of symptoms, and strengthen doctor-patient communication to reduce disease uncertainty, thereby effectively improving symptom management\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations: (1) The included literature mainly consists of cross-sectional studies, which makes it difficult to reveal the dynamic evolution of symptom clusters; (2) The differences in measurement tools between Chinese and English-language studies may introduce cultural bias; (3) The statistical definitions of some symptom clusters lack clinical validation. Future studies should focus on longitudinal designs and combine mixed methods (quantitative + qualitative) to explore the biological mechanisms and sociocultural contexts of symptom clusters in greater depth. Furthermore, interdisciplinary collaboration should be promoted to develop standardized assessment frameworks, enabling precise and personalized symptom management for heart failure.\u003c/p\u003e\n\u003cp\u003eAdditionally, the reviewer encountered challenges related to language barriers, which led to the consideration of only published studies in Chinese or English. This may have introduced some degree of selection bias. The reviewer did not contact the researchers involved in the study to obtain further relevant information, which could have resulted in missing data. Furthermore, the methodological quality of the included studies was not systematically assessed.\u003c/p\u003e\n\n\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study, through a scoping review, identified multiple symptom clusters in patients with heart failure, with the most common clusters being related to respiratory, cardiovascular, and fatigue symptoms. These symptom clusters are influenced by factors such as gender, age, educational level, and NYHA classification. The existing literature lacks a unified standard for the naming and classification of symptom clusters in heart failure patients, and the assessment tools for these symptom clusters are highly heterogeneous. As a result, there are significant differences in the internal composition of symptom clusters across different studies. Future research should further explore the evolution trajectories and influencing factors of symptom clusters in heart failure patients, develop more specific assessment tools for symptom clusters, and optimize the standards for symptom cluster identification and naming. This will provide more reliable reference points for the development of precise and personalized symptom management strategies for heart failure.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e Not applicable, as this study is a scoping review of previously published studies and does not involve direct participation of human subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval:\u003c/strong\u003e Not applicable, as this study is a scoping review of existing literature and did not involve any new data collection or human subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by the National Natural Science Foundation of China (No.72464022) and the Scientific Research Project approved by the Chinese Nursing Association in 2023 (ZHKY202321).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate:\u003c/strong\u003e Not applicable, as this study is a scoping review of existing studies, and does not involve human participants or original data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperiments on Humans and Animals:\u003c/strong\u003e Not applicable, as this study is a scoping review of existing studies and did not involve any experimental work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWriting Committee Members; ACC/AHA Joint Committee Members. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure. \u003cem\u003eJ Card Fail\u003c/em\u003e. 2022;28(5):e1-e167. doi:10.1016/j.cardfail.2022.02.010\u003c/li\u003e\n\u003cli\u003eBenjamin EJ, Blaha MJ, Chiuve SE, et al. Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. \u003cem\u003eCirculation\u003c/em\u003e. 2017;135(10):e146-e603. doi:10.1161/CIR.0000000000000485\u003c/li\u003e\n\u003cli\u003eQiu C, Yu DS, Song D, Wang X. The prognostic impact of symptom clusters in patients with heart failure: A systematic review and meta-analysis. \u003cem\u003eJ Adv Nurs\u003c/em\u003e. 2022;78(9):2713-2730. doi:10.1111/jan.15302\u003c/li\u003e\n\u003cli\u003eYu DS, Li PW, Chong SO. Symptom cluster among patients with advanced heart failure: a review of its manifestations and impacts on health outcomes. \u003cem\u003eCurr Opin Support Palliat Care\u003c/em\u003e. 2018;12(1):16-24. doi:10.1097/SPC.0000000000000316\u003c/li\u003e\n\u003cli\u003eMiaskowski C, Barsevick A, Berger A, et al. Advancing Symptom Science Through Symptom Cluster Research: Expert Panel Proceedings and Recommendations. \u003cem\u003eJ Natl Cancer Inst\u003c/em\u003e. 2017;109(4):djw253. doi:10.1093/jnci/djw253\u003c/li\u003e\n\u003cli\u003eKim HJ, McGuire DB, Tulman L, Barsevick AM. Symptom clusters: concept analysis and clinical implications for cancer nursin. \u003cem\u003eCancer Nurs\u003c/em\u003e. 2005;28(4):270-284. doi:10.1097/00002820-200507000-00005\u003c/li\u003e\n\u003cli\u003eQiu C, Yu DS, Song D, Wang X. The prognostic impact of symptom clusters in patients with heart failure: A systematic review and meta-analysis. \u003cem\u003eJ Adv Nurs\u003c/em\u003e. 2022;78(9):2713-2730. doi:10.1111/jan.15302\u003c/li\u003e\n\u003cli\u003eGeiger AM. Symptom Management: War Problems, Moonshot Solutions?. \u003cem\u003eJ Natl Cancer Inst\u003c/em\u003e. 2017;109(4):djw254. doi:10.1093/jnci/djw254\u003c/li\u003e\n\u003cli\u003eHeo S, Moser DK, Pressler SJ, et al. Dose-dependent relationship of physical and depressive symptoms with health-related quality of life in patients with heart failure. \u003cem\u003eEur J Cardiovasc Nurs\u003c/em\u003e. 2013;12(5):454-460. doi:10.1177/1474515112470996\u003c/li\u003e\n\u003cli\u003eDavis K, Drey N, Gould D. What are scoping studies? A review of the nursing literature. \u003cem\u003eInt J Nurs Stud\u003c/em\u003e. 2009;46(10):1386-1400. doi:10.1016/j.ijnurstu.2009.02.010\u003c/li\u003e\n\u003cli\u003eKastner M, Antony J, Soobiah C, Straus SE, Tricco AC. Conceptual recommendations for selecting the most appropriate knowledge synthesis method to answer research questions related to complex evidence. \u003cem\u003eJ Clin Epidemiol\u003c/em\u003e. 2016;73:43-49. doi:10.1016/j.jclinepi.2015.11.022\u003c/li\u003e\n\u003cli\u003eMcGowan J, Straus S, Moher D, et al. Reporting scoping reviews-PRISMA ScR extension. \u003cem\u003eJ Clin Epidemiol\u003c/em\u003e. 2020;123:177-179. doi:10.1016/j.jclinepi.2020.03.016\u003c/li\u003e\n\u003cli\u003eArksey H, O\u0026apos;Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8:19\u0026ndash;32. doi: 10.1080/1364557032000119616.\u003c/li\u003e\n\u003cli\u003ePeters MDJ, Marnie C, Tricco AC, et al. Updated methodological guidance for the conduct of scoping reviews. \u003cem\u003eJBI Evid Implement\u003c/em\u003e. 2021;19(1):3-10. doi:10.1097/XEB.0000000000000277\u003c/li\u003e\n\u003cli\u003eKarlsson K, Olsson C, Erlandsson A, Ahlberg KM, Larsson M. Exploring Symptom Clusters and Their Measurements in Patients With Lung Cancer: A Scoping Review for Practice and Research. \u003cem\u003eOncol Nurs Forum\u003c/em\u003e. 2023;50(6):783-815. doi:10.1188/23.ONF.783-815\u003c/li\u003e\n\u003cli\u003eSong EK, Moser DK, Rayens MK, Lennie TA. Symptom clusters predict event-free survival in patients with heart failure. J Cardiovasc Nurs. 2010;25(4):284-291. doi:10.1097/JCN.0b013e3181cfbcbb\u003c/li\u003e\n\u003cli\u003eHerr JK, Salyer J, Flattery M, et al. Heart failure symptom clusters and functional status\u0026mdash;a cross-sectional study. \u003cem\u003eJ Adv Nurs\u003c/em\u003e. 2015;71(6):1274-1287. doi:10.1111/jan.12596\u003c/li\u003e\n\u003cli\u003eYu DS, Chan HY, Leung DY, Hui E, Sit JW. Symptom clusters and quality of life among patients with advanced heart failure. \u003cem\u003eJ Geriatr Cardiol\u003c/em\u003e. 2016;13(5):408-414. doi:10.11909/j.issn.1671-5411.2016.05.014\u003c/li\u003e\n\u003cli\u003ePark J, Moser DK, Griffith K, Harring JR, Johantgen M. Exploring symptom clusters in people with heart failure. Clin Nurs Res. 2019;28(2):165-181. doi:10.1177/1054773817729606\u003c/li\u003e\n\u003cli\u003eHuang TY, Moser DK, Hwang SL. Identification, associated factors, and prognosis of symptom clusters in Taiwanese patients with heart failure. J Nurs Res. 2018;26(1):60-67. doi:10.1097/JNR.0000000000000199\u003c/li\u003e\n\u003cli\u003eYi Y, Yuan Y, Song H, Ji H, Tian M, Liu F, Zhang L. The correlation between symptom clusters and quality of life in heart failure patients. \u003cem\u003eJ Nurs Sci\u003c/em\u003e. 2018;25(1):14-17. doi:10.16460/j.issn1008-9969.2018.01.010\u003c/li\u003e\n\u003cli\u003eSalyer J, Flattery M, Lyon DE. Heart failure symptom clusters and quality of life. Heart Lung. 2019;48(5):366-372. doi:10.1016/j.hrtlng.2019.04.006\u003c/li\u003e\n\u003cli\u003eHu Y, Jiang J, Xu L, Wang C, Wang P, Yang B, Tao M. Symptom clusters and quality of life among patients with chronic heart failure: A cross-sectional study. \u003cem\u003eJpn J Nurs Sci\u003c/em\u003e. 2021;18(1):e12366. doi:10.1111/jjns.12366\u003c/li\u003e\n\u003cli\u003eHu Y, Jiang J, Xu L, Wang C, Wang P, Yang B, Tao M. Symptom clusters and quality of life among patients with chronic heart failure: A cross-sectional study. Jpn J Nurs Sci. 2021;18(1):e12366. doi:10.1111/jjns.12366\u003c/li\u003e\n\u003cli\u003eCai J, Tian H, Yang Q. Analysis of symptom clusters and their influencing factors in patients with chronic heart failure. \u003cem\u003eChin J Mod Nurs\u003c/em\u003e. 2021;27(25):6. doi:10.3760/cma.j.cn115682-20201218-06757\u003c/li\u003e\n\u003cli\u003eLiu Y, Hao R, Chen H, Tian H, Yang Q. A multicenter cross-sectional survey on the relationship between symptom clusters and quality of life in patients with chronic heart failure. \u003cem\u003eChin J Chronic Dis Prev Control\u003c/em\u003e. 2022;30(7):507-511+516. doi:10.16386/j.cjpccd.issn.1004-6194.2022.07.006\u003c/li\u003e\n\u003cli\u003eLi Z, Fu D, Qiu X, et al. Study on the symptom clusters in patients with chronic heart failure and their relationship with quality of life. \u003cem\u003eSouth China J Prev Med\u003c/em\u003e. 2023;49(1):97-100.\u003c/li\u003e\n\u003cli\u003eZambroski CH, Lennie TA, Chung ML, Heo S, Smoot T, Ziegler C. Use of the Memorial Symptom Assessment Scale-Heart Failure in heart failure patients. Circulation. 2004;110(Supplement III):17.\u003c/li\u003e\n\u003cli\u003eChang VT, Hwang SS, Feuerman M, Kasimis BS, Thaler HT. The memorial symptom assessment scale short form (MSAS-SF). \u003cem\u003eCancer\u003c/em\u003e. 2000;89(5):1162-1171. doi:10.1002/1097-0142(20000901)89:5\u0026lt;1162::aid-cncr26\u0026gt;3.0.co;2-y\u003c/li\u003e\n\u003cli\u003eRector TS, Kubo SH, Cohn YN. Patients\u0026rsquo; self-assessment of their congestive heart failure. Part 2: Content, reliability and validity of a new measure, the Minnesota Living with Heart Failure Questionnaire. \u003cem\u003eHeart Fail\u003c/em\u003e. 1987;3(5):198-209. doi:10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a2843\u003c/li\u003e\n\u003cli\u003eMoser DK, Lee KS, Wu JR, Mudd-Martin G, Jaarsma T, Huang TY, Fan XZ, Str\u0026ouml;mberg A, Lennie TA, Riegel B. Identification of symptom clusters among patients with heart failure: An international observational study. \u003cem\u003eInt J Nurs Stud\u003c/em\u003e. 2014;51(10):1366-1372. doi:10.1016/j.ijnurstu.2014.02.004\u003c/li\u003e\n\u003cli\u003eKim HJ, Abraham IL. Statistical approaches to modeling symptom clusters in cancer patients. \u003cem\u003eCancer Nurs\u003c/em\u003e. 2008;31(5):E1-E10. doi:10.1097/01.NCC.0000305757.58615.c8\u003c/li\u003e\n\u003cli\u003eTabachnick B, Fidell L. \u003cem\u003eUsing Multivariate Statistics\u003c/em\u003e. 6th ed. Pearson International; 2013. https://elibrary.pearson.de/book/99.150005/9781292034546\u003c/li\u003e\n\u003cli\u003eHagenaars JA, McCutcheon AL, eds. \u003cem\u003eApplied Latent Class Analysis\u003c/em\u003e. Cambridge University Press; 2002. doi:10.1017/CBO9780511499531\u003c/li\u003e\n\u003cli\u003eKaufman L, Rousseeuw PJ. \u003cem\u003eFinding Groups in Data: An Introduction to Cluster Analysis\u003c/em\u003e. Wiley; 2005. doi:10.1002/9780470316801\u003c/li\u003e\n\u003cli\u003eJurgens CY, Lee CS, Riegel B. Psychometric Analysis of the Heart Failure Somatic Perception Scale as a Measure of Patient Symptom Perception. J Cardiovasc Nurs. 2017;32(2):140-147. doi:10.1097/JCN.0000000000000320\u003c/li\u003e\n\u003cli\u003eZhi Y, Zhang Y, Zhang Y, Zhang M, Kong Y. Age-associated changes in multimodal pain perception. \u003cem\u003eAge Ageing\u003c/em\u003e. 2024;53(5):afae107. doi:10.1093/ageing/afae107\u003c/li\u003e\n\u003cli\u003eKuehner C. Why is depression more common among women than among men?. \u003cem\u003eLancet Psychiatry\u003c/em\u003e. 2017;4(2):146-158. doi:10.1016/S2215-0366(16)30263-2\u003c/li\u003e\n\u003cli\u003eShi D, Li Z, Yang J, Liu BZ, Xia H. Symptom experience and symptom burden of patients following first-ever stroke within 1 year: a cross-sectional study. \u003cem\u003eNeural Regen Res\u003c/em\u003e. 2018;13(11):1907-1912. doi:10.4103/1673-5374.23944\u003c/li\u003e\n\u003cli\u003eHackett ML, Anderson CS. Predictors of depression after stroke: a systematic review of observational studies. \u003cem\u003eStroke\u003c/em\u003e. 2005;36(10):2296-2301. doi:10.1161/01.STR.0000183622.75135.a4\u003c/li\u003e\n\u003cli\u003ePaulus WJ, Tsch\u0026ouml;pe C. A novel paradigm for heart failure with preserved ejection fraction: comorbidities drive myocardial dysfunction and remodeling through coronary microvascular endothelial inflammation. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. 2013;62(4):263-271. doi:10.1016/j.jacc.2013.02.092\u003c/li\u003e\n\u003cli\u003eMcDonagh TA, Metra M, Adamo M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure [published correction appears in Eur Heart J. 2021 Dec 21;42(48):4901. doi: 10.1093/eurheartj/ehab670.]. \u003cem\u003eEur Heart J\u003c/em\u003e. 2021;42(36):3599-3726. doi:10.1093/eurheartj/ehab368\u003c/li\u003e\n\u003cli\u003eJanuzzi JL, van Kimmenade R, Lainchbury J, et al. NT-proBNP testing for diagnosis and short-term prognosis in acute destabilized heart failure: an international pooled analysis of 1256 patients: the International Collaborative of NT-proBNP Study. \u003cem\u003eEur Heart J\u003c/em\u003e. 2006;27(3):330-337. doi:10.1093/eurheartj/ehi631\u003c/li\u003e\n\u003cli\u003eCelano CM, Villegas AC, Albanese AM, Gaggin HK, Huffman JC. Depression and Anxiety in Heart Failure: A Review. \u003cem\u003eHarv Rev Psychiatry\u003c/em\u003e. 2018;26(4):175-184. doi:10.1097/HRP.0000000000000162\u003c/li\u003e\n\u003cli\u003eJiang W, Alexander J, Christopher E, et al. Relationship of depression to increased risk of mortality and rehospitalization in patients with congestive heart failure. \u003cem\u003eArch Intern Med\u003c/em\u003e. 2001;161(15):1849-1856. doi:10.1001/archinte.161.15.1849\u003c/li\u003e\n\u003cli\u003eArnold R, Ranchor AV, DeJongste MJ, et al. The relationship between self-efficacy and self-reported physical functioning in chronic obstructive pulmonary disease and chronic heart failure. \u003cem\u003eBehav Med\u003c/em\u003e. 2005;31(3):107-115. doi:10.3200/BMED.31.3.107-115\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Heart failure, symptom clusters, assessment tools, scoping review, quality of life, clinical management","lastPublishedDoi":"10.21203/rs.3.rs-6684234/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6684234/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Heart failure (HF) is a complex chronic condition characterized by diverse and overlapping symptom clusters across physiological, psychological, and social dimensions. However, the identification and assessment of symptom clusters in HF remain inconsistent, and the measurement tools used vary widely, limiting clinical symptom management and standardized care delivery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims\u003c/strong\u003e: This scoping review aimed to identify the types of symptom clusters in patients with heart failure, evaluate the assessment tools used to measure these clusters, and explore the influencing factors affecting symptom severity, to support more effective clinical management\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: The review followed the PRISMA-ScR guidelines. A systematic search was conducted across PubMed, Web of Science, and CNKI, including studies published up to January 2025. A total of 12 studies involving 9,328 patients were included. Data extraction and synthesis focused on symptom cluster types, assessment tools, and associated influencing factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Twelve studies were included, involving 9,328 patients, identifying 25 symptom clusters. Common symptom clusters included respiratory symptoms, fatigue-related symptoms, and psychological/emotional symptoms. The assessment tools primarily used were the MSAS-HF and MLHFQ, but differences in tool usage and inconsistency in the naming and classification of symptom clusters were observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: HF symptom clusters are diverse and inconsistently classified. Existing tools lack standardization. More precise and culturally adaptable assessment frameworks are needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for practice\u003c/strong\u003e: Understanding symptom clusters and their assessment tools provides a foundation for developing standardized, culturally appropriate, and nurse-led symptom management strategies. This review offers practical insights to guide nurses in personalized assessment and care planning for heart failure patients.\u003c/p\u003e","manuscriptTitle":"Exploring Symptom Clusters and Measurements in Patients with Heart Failure - A Scope Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 06:30:06","doi":"10.21203/rs.3.rs-6684234/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-06-18T14:59:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-23T17:20:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-22T08:53:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-22T08:49:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-05-17T03:26:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee5ca946-1168-43c7-80dd-8b764a72d751","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T06:30:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-23 06:30:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6684234","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6684234","identity":"rs-6684234","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}